From 32fb3cdae3d2731917ac5e99c3cdf93e5f501719 Mon Sep 17 00:00:00 2001 From: ksyint <82583462+ksyint@users.noreply.github.com> Date: Tue, 21 May 2024 22:41:16 +0900 Subject: [PATCH 1/2] Delete text_diffuser directory --- text_diffuser/__init__.py | 0 text_diffuser/any_text.py | 519 ------ text_diffuser/assets/font/Arial.ttf | Bin 1036584 -> 0 bytes text_diffuser/assets/mask_1.png | Bin 1559 -> 0 bytes text_diffuser/assets/mask_2.png | Bin 2214 -> 0 bytes text_diffuser/assets/original_input.jpeg | Bin 5026 -> 0 bytes text_diffuser/assets/original_input_2.jpeg | Bin 4096 -> 0 bytes text_diffuser/evaluate.py | 597 ------- text_diffuser/inference.py | 1070 ------------ text_diffuser/model/layout_generator.py | 290 ---- text_diffuser/model/layout_transformer.py | 153 -- text_diffuser/model/text_segmenter/unet.py | 54 - .../model/text_segmenter/unet_parts.py | 81 - text_diffuser/pipeline_demo.ipynb | 359 ---- text_diffuser/pipeline_text_diffuser_sd15.py | 1368 --------------- text_diffuser/t_diffusers/__init__.py | 0 text_diffuser/t_diffusers/callbacks.py | 193 --- text_diffuser/t_diffusers/modeling_utils.py | 1021 ----------- text_diffuser/t_diffusers/scheduling_ddpm.py | 627 ------- .../t_diffusers/unet_2d_condition.py | 1527 ----------------- text_diffuser/train.py | 1237 ------------- text_diffuser/util.py | 387 ----- 22 files changed, 9483 deletions(-) delete mode 100644 text_diffuser/__init__.py delete mode 100644 text_diffuser/any_text.py delete mode 100644 text_diffuser/assets/font/Arial.ttf delete mode 100644 text_diffuser/assets/mask_1.png delete mode 100644 text_diffuser/assets/mask_2.png delete mode 100644 text_diffuser/assets/original_input.jpeg delete mode 100644 text_diffuser/assets/original_input_2.jpeg delete mode 100644 text_diffuser/evaluate.py delete mode 100644 text_diffuser/inference.py delete mode 100644 text_diffuser/model/layout_generator.py delete mode 100644 text_diffuser/model/layout_transformer.py delete mode 100644 text_diffuser/model/text_segmenter/unet.py delete mode 100644 text_diffuser/model/text_segmenter/unet_parts.py delete mode 100644 text_diffuser/pipeline_demo.ipynb delete mode 100644 text_diffuser/pipeline_text_diffuser_sd15.py delete mode 100644 text_diffuser/t_diffusers/__init__.py delete mode 100644 text_diffuser/t_diffusers/callbacks.py delete mode 100644 text_diffuser/t_diffusers/modeling_utils.py delete mode 100644 text_diffuser/t_diffusers/scheduling_ddpm.py delete mode 100644 text_diffuser/t_diffusers/unet_2d_condition.py delete mode 100644 text_diffuser/train.py delete mode 100644 text_diffuser/util.py diff --git a/text_diffuser/__init__.py b/text_diffuser/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/text_diffuser/any_text.py b/text_diffuser/any_text.py deleted file mode 100644 index 96af0b5..0000000 --- a/text_diffuser/any_text.py +++ /dev/null @@ -1,519 +0,0 @@ -# type: ignore - -""" -AnyText: Multilingual Visual Text Generation And Editing -Paper: https://arxiv.org/abs/2311.03054 -Code: https://github.com/tyxsspa/AnyText -Copyright (c) Alibaba, Inc. and its affiliates. -""" - -# ruff: noqa -import os -import random -import re -import time - -import cv2 -import einops -import numpy as np -import torch -from bert_tokenizer import BasicTokenizer -from cldm.ddim_hacked import DDIMSampler -from cldm.model import create_model, load_state_dict -from modelscope.hub.snapshot_download import snapshot_download -from modelscope.models.base import TorchModel -from modelscope.models.builder import MODELS -from modelscope.pipelines import pipeline -from modelscope.pipelines.base import Model, Pipeline -from modelscope.pipelines.builder import PIPELINES -from modelscope.preprocessors.base import Preprocessor -from modelscope.preprocessors.builder import PREPROCESSORS -from modelscope.utils.config import Config -from modelscope.utils.constant import Tasks -from PIL import ImageFont -from pytorch_lightning import seed_everything -from t3_dataset import draw_glyph, draw_glyph2 -from util import check_channels, resize_image, save_images - - -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" - -checker = BasicTokenizer() -BBOX_MAX_NUM = 8 -PLACE_HOLDER = "*" -max_chars = 20 - - -@MODELS.register_module("my-anytext-task", module_name="anytext-model") -class AnyTextModel(TorchModel): - - def __init__(self, model_dir, *args, **kwargs): - super().__init__(model_dir, *args, **kwargs) - self.use_fp16 = kwargs.get("use_fp16", True) - self.use_translator = kwargs.get("use_translator", True) - self.init_model(**kwargs) - - """ - return: - result: list of images in numpy.ndarray format - rst_code: 0: normal -1: error 1:warning - str_warning: string of error or warning - debug_info: string for debug, only valid if show_debug=True - """ - - def forward(self, input_tensor, **forward_params): - tic = time.time() - str_warning = "" - # get inputs - seed = input_tensor.get("seed", -1) - if seed == -1: - seed = random.randint(0, 99999999) - seed_everything(seed) - prompt = input_tensor.get("prompt") - draw_pos = input_tensor.get("draw_pos") - ori_image = input_tensor.get("ori_image") - - mode = forward_params.get("mode") - sort_priority = forward_params.get("sort_priority", "↕") - show_debug = forward_params.get("show_debug", False) - revise_pos = forward_params.get("revise_pos", False) - img_count = forward_params.get("image_count", 4) - ddim_steps = forward_params.get("ddim_steps", 20) - w = forward_params.get("image_width", 512) - h = forward_params.get("image_height", 512) - strength = forward_params.get("strength", 1.0) - cfg_scale = forward_params.get("cfg_scale", 9.0) - eta = forward_params.get("eta", 0.0) - a_prompt = forward_params.get( - "a_prompt", - "best quality, extremely detailed,4k, HD, supper legible text, clear text edges, clear strokes, neat writing, no watermarks", - ) - n_prompt = forward_params.get( - "n_prompt", - "low-res, bad anatomy, extra digit, fewer digits, cropped, worst quality, low quality, watermark, unreadable text, messy words, distorted text, disorganized writing, advertising picture", - ) - - prompt, texts = self.modify_prompt(prompt) - if prompt is None and texts is None: - return ( - None, - -1, - "You have input Chinese prompt but the translator is not loaded!", - "", - ) - n_lines = len(texts) - if mode in ["text-generation", "gen"]: - edit_image = np.ones((h, w, 3)) * 127.5 # empty mask image - elif mode in ["text-editing", "edit"]: - if draw_pos is None or ori_image is None: - return ( - None, - -1, - "Reference image and position image are needed for text editing!", - "", - ) - if isinstance(ori_image, str): - ori_image = cv2.imread(ori_image)[..., ::-1] - assert ( - ori_image is not None - ), f"Can't read ori_image image from{ori_image}!" - elif isinstance(ori_image, torch.Tensor): - ori_image = ori_image.cpu().numpy() - else: - assert isinstance( - ori_image, np.ndarray - ), f"Unknown format of ori_image: {type(ori_image)}" - edit_image = ori_image.clip(1, 255) # for mask reason - edit_image = check_channels(edit_image) - edit_image = resize_image( - edit_image, max_length=768 - ) # make w h multiple of 64, resize if w or h > max_length - h, w = edit_image.shape[:2] # change h, w by input ref_img - # preprocess pos_imgs(if numpy, make sure it's white pos in black bg) - if draw_pos is None: - pos_imgs = np.zeros((w, h, 1)) - if isinstance(draw_pos, str): - draw_pos = cv2.imread(draw_pos)[..., ::-1] - assert draw_pos is not None, f"Can't read draw_pos image from{draw_pos}!" - pos_imgs = 255 - draw_pos - elif isinstance(draw_pos, torch.Tensor): - pos_imgs = draw_pos.cpu().numpy() - else: - assert isinstance( - draw_pos, np.ndarray - ), f"Unknown format of draw_pos: {type(draw_pos)}" - pos_imgs = pos_imgs[..., 0:1] - pos_imgs = cv2.convertScaleAbs(pos_imgs) - _, pos_imgs = cv2.threshold(pos_imgs, 254, 255, cv2.THRESH_BINARY) - # seprate pos_imgs - pos_imgs = self.separate_pos_imgs(pos_imgs, sort_priority) - if len(pos_imgs) == 0: - pos_imgs = [np.zeros((h, w, 1))] - if len(pos_imgs) < n_lines: - if n_lines == 1 and texts[0] == " ": - pass # text-to-image without text - else: - return ( - None, - -1, - f"Found {len(pos_imgs)} positions that < needed {n_lines} from prompt, check and try again!", - "", - ) - elif len(pos_imgs) > n_lines: - str_warning = f"Warning: found {len(pos_imgs)} positions that > needed {n_lines} from prompt." - # get pre_pos, poly_list, hint that needed for anytext - pre_pos = [] - poly_list = [] - for input_pos in pos_imgs: - if input_pos.mean() != 0: - input_pos = ( - input_pos[..., np.newaxis] - if len(input_pos.shape) == 2 - else input_pos - ) - poly, pos_img = self.find_polygon(input_pos) - pre_pos += [pos_img / 255.0] - poly_list += [poly] - else: - pre_pos += [np.zeros((h, w, 1))] - poly_list += [None] - np_hint = np.sum(pre_pos, axis=0).clip(0, 1) - # prepare info dict - info = {} - info["glyphs"] = [] - info["gly_line"] = [] - info["positions"] = [] - info["n_lines"] = [len(texts)] * img_count - gly_pos_imgs = [] - for i in range(len(texts)): - text = texts[i] - if len(text) > max_chars: - str_warning = ( - f'"{text}" length > max_chars: {max_chars}, will be cut off...' - ) - text = text[:max_chars] - gly_scale = 2 - if pre_pos[i].mean() != 0: - gly_line = draw_glyph(self.font, text) - glyphs = draw_glyph2( - self.font, - text, - poly_list[i], - scale=gly_scale, - width=w, - height=h, - add_space=False, - ) - gly_pos_img = cv2.drawContours( - glyphs * 255, [poly_list[i] * gly_scale], 0, (255, 255, 255), 1 - ) - if revise_pos: - resize_gly = cv2.resize( - glyphs, (pre_pos[i].shape[1], pre_pos[i].shape[0]) - ) - new_pos = cv2.morphologyEx( - (resize_gly * 255).astype(np.uint8), - cv2.MORPH_CLOSE, - kernel=np.ones( - (resize_gly.shape[0] // 10, resize_gly.shape[1] // 10), - dtype=np.uint8, - ), - iterations=1, - ) - new_pos = ( - new_pos[..., np.newaxis] if len(new_pos.shape) == 2 else new_pos - ) - contours, _ = cv2.findContours( - new_pos, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE - ) - if len(contours) != 1: - str_warning = f"Fail to revise position {i} to bounding rect, remain position unchanged..." - else: - rect = cv2.minAreaRect(contours[0]) - poly = np.int0(cv2.boxPoints(rect)) - pre_pos[i] = ( - cv2.drawContours(new_pos, [poly], -1, 255, -1) / 255.0 - ) - gly_pos_img = cv2.drawContours( - glyphs * 255, [poly * gly_scale], 0, (255, 255, 255), 1 - ) - gly_pos_imgs += [gly_pos_img] # for show - else: - glyphs = np.zeros((h * gly_scale, w * gly_scale, 1)) - gly_line = np.zeros((80, 512, 1)) - gly_pos_imgs += [ - np.zeros((h * gly_scale, w * gly_scale, 1)) - ] # for show - pos = pre_pos[i] - info["glyphs"] += [self.arr2tensor(glyphs, img_count)] - info["gly_line"] += [self.arr2tensor(gly_line, img_count)] - info["positions"] += [self.arr2tensor(pos, img_count)] - # get masked_x - masked_img = ((edit_image.astype(np.float32) / 127.5) - 1.0) * (1 - np_hint) - masked_img = np.transpose(masked_img, (2, 0, 1)) - masked_img = torch.from_numpy(masked_img.copy()).float().cuda() - if self.use_fp16: - masked_img = masked_img.half() - encoder_posterior = self.model.encode_first_stage(masked_img[None, ...]) - masked_x = self.model.get_first_stage_encoding(encoder_posterior).detach() - if self.use_fp16: - masked_x = masked_x.half() - info["masked_x"] = torch.cat([masked_x for _ in range(img_count)], dim=0) - - hint = self.arr2tensor(np_hint, img_count) - cond = self.model.get_learned_conditioning( - dict( - c_concat=[hint], - c_crossattn=[[prompt + " , " + a_prompt] * img_count], - text_info=info, - ) - ) - un_cond = self.model.get_learned_conditioning( - dict(c_concat=[hint], c_crossattn=[[n_prompt] * img_count], text_info=info) - ) - shape = (4, h // 8, w // 8) - self.model.control_scales = [strength] * 13 - samples, intermediates = self.ddim_sampler.sample( - ddim_steps, - img_count, - shape, - cond, - verbose=False, - eta=eta, - unconditional_guidance_scale=cfg_scale, - unconditional_conditioning=un_cond, - ) - if self.use_fp16: - samples = samples.half() - x_samples = self.model.decode_first_stage(samples) - x_samples = ( - (einops.rearrange(x_samples, "b c h w -> b h w c") * 127.5 + 127.5) - .cpu() - .numpy() - .clip(0, 255) - .astype(np.uint8) - ) - results = [x_samples[i] for i in range(img_count)] - if ( - mode == "edit" and False - ): # replace backgound in text editing but not ideal yet - results = [r * np_hint + edit_image * (1 - np_hint) for r in results] - results = [r.clip(0, 255).astype(np.uint8) for r in results] - if len(gly_pos_imgs) > 0 and show_debug: - glyph_bs = np.stack(gly_pos_imgs, axis=2) - glyph_img = np.sum(glyph_bs, axis=2) * 255 - glyph_img = glyph_img.clip(0, 255).astype(np.uint8) - results += [np.repeat(glyph_img, 3, axis=2)] - input_prompt = prompt - for t in texts: - input_prompt = input_prompt.replace("*", f'"{t}"', 1) - print(f"Prompt: {input_prompt}") - # debug_info - if not show_debug: - debug_info = "" - else: - debug_info = f'Prompt: {input_prompt}
\ - Size: {w}x{h}
\ - Image Count: {img_count}
\ - Seed: {seed}
\ - Use FP16: {self.use_fp16}
\ - Cost Time: {(time.time()-tic):.2f}s' - rst_code = 1 if str_warning else 0 - return results, rst_code, str_warning, debug_info - - def init_model(self, **kwargs): - font_path = kwargs.get("font_path", "font/Arial_Unicode.ttf") - self.font = ImageFont.truetype(font_path, size=60) - cfg_path = kwargs.get("cfg_path", "models_yaml/anytext_sd15.yaml") - ckpt_path = kwargs.get( - "model_path", os.path.join(self.model_dir, "anytext_v1.1.ckpt") - ) - clip_path = os.path.join(self.model_dir, "clip-vit-large-patch14") - self.model = create_model( - cfg_path, cond_stage_path=clip_path, use_fp16=self.use_fp16 - ) - if self.use_fp16: - self.model = self.model.half() - self.model.load_state_dict( - load_state_dict(ckpt_path, location="cuda"), strict=False - ) - self.model.eval() - self.ddim_sampler = DDIMSampler(self.model) - if self.use_translator: - self.trans_pipe = pipeline( - task=Tasks.translation, - model=os.path.join(self.model_dir, "nlp_csanmt_translation_zh2en"), - ) - else: - self.trans_pipe = None - - def modify_prompt(self, prompt): - prompt = prompt.replace("“", '"') - prompt = prompt.replace("”", '"') - p = '"(.*?)"' - strs = re.findall(p, prompt) - if len(strs) == 0: - strs = [" "] - else: - for s in strs: - prompt = prompt.replace(f'"{s}"', f" {PLACE_HOLDER} ", 1) - if self.is_chinese(prompt): - if self.trans_pipe is None: - return None, None - old_prompt = prompt - prompt = self.trans_pipe(input=prompt + " .")["translation"][:-1] - print(f"Translate: {old_prompt} --> {prompt}") - return prompt, strs - - def is_chinese(self, text): - text = checker._clean_text(text) - for char in text: - cp = ord(char) - if checker._is_chinese_char(cp): - return True - return False - - def separate_pos_imgs(self, img, sort_priority, gap=102): - num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img) - components = [] - for label in range(1, num_labels): - component = np.zeros_like(img) - component[labels == label] = 255 - components.append((component, centroids[label])) - if sort_priority == "↕": - fir, sec = 1, 0 # top-down first - elif sort_priority == "↔": - fir, sec = 0, 1 # left-right first - components.sort(key=lambda c: (c[1][fir] // gap, c[1][sec] // gap)) - sorted_components = [c[0] for c in components] - return sorted_components - - def find_polygon(self, image, min_rect=False): - contours, hierarchy = cv2.findContours( - image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE - ) - max_contour = max(contours, key=cv2.contourArea) # get contour with max area - if min_rect: - # get minimum enclosing rectangle - rect = cv2.minAreaRect(max_contour) - poly = np.int0(cv2.boxPoints(rect)) - else: - # get approximate polygon - epsilon = 0.01 * cv2.arcLength(max_contour, True) - poly = cv2.approxPolyDP(max_contour, epsilon, True) - n, _, xy = poly.shape - poly = poly.reshape(n, xy) - cv2.drawContours(image, [poly], -1, 255, -1) - return poly, image - - def arr2tensor(self, arr, bs): - arr = np.transpose(arr, (2, 0, 1)) - _arr = torch.from_numpy(arr.copy()).float().cuda() - if self.use_fp16: - _arr = _arr.half() - _arr = torch.stack([_arr for _ in range(bs)], dim=0) - return _arr - - -@PREPROCESSORS.register_module("my-anytext-task", module_name="anytext-preprocessor") -class AnyTextPreprocessor(Preprocessor): - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.trainsforms = self.init_preprocessor(**kwargs) - - def __call__(self, results): - return self.trainsforms(results) - - def init_preprocessor(self, **kwarg): - """Provide default implementation based on preprocess_cfg and user can reimplement it. - if nothing to do, then return lambda x: x - """ - return lambda x: x - - -@PIPELINES.register_module("my-anytext-task", module_name="anytext-pipeline") -class AnyTextPipeline(Pipeline): - - def __init__(self, model, preprocessor=None, **kwargs): - assert isinstance(model, str) or isinstance( - model, Model - ), "model must be a single str or Model" - if isinstance(model, str): - if not os.path.exists(model): - model = snapshot_download(model) - pipe_model = AnyTextModel(model_dir=model, **kwargs) - elif isinstance(model, Model): - pipe_model = model - else: - raise NotImplementedError - pipe_model.eval() - if preprocessor is None: - preprocessor = AnyTextPreprocessor() - super().__init__(model=pipe_model, preprocessor=preprocessor, **kwargs) - - def _sanitize_parameters(self, **pipeline_parameters): - return {}, pipeline_parameters, {} - - def _check_input(self, inputs): - pass - - def _check_output(self, outputs): - pass - - def forward(self, inputs, **forward_params): - return super().forward(inputs, **forward_params) - - def postprocess(self, inputs): - return inputs - - -usr_config_path = "models" -config = Config( - { - "framework": "pytorch", - "task": "my-anytext-task", - "model": {"type": "anytext-model"}, - "pipeline": {"type": "anytext-pipeline"}, - "allow_remote": True, - } -) -# config.dump('models/' + 'configuration.json') - -if __name__ == "__main__": - img_save_folder = "SaveImages" - inference = pipeline( - "my-anytext-task", model=usr_config_path, use_fp16=True, use_translator=False - ) - params = { - "show_debug": True, - "image_count": 2, - "ddim_steps": 20, - } - - # 1. text generation - mode = "text-generation" - input_data = { - "prompt": 'photo of caramel macchiato coffee on the table, top-down perspective, with "Any" "Text" written on it using cream', - "seed": 66273235, - "draw_pos": "example_images/gen9.png", - } - results, rtn_code, rtn_warning, debug_info = inference( - input_data, mode=mode, **params - ) - if rtn_code >= 0: - save_images(results, img_save_folder) - # 2. text editing - mode = "text-editing" - input_data = { - "prompt": 'A cake with colorful characters that reads "EVERYDAY"', - "seed": 8943410, - "draw_pos": "example_images/edit7.png", - "ori_image": "example_images/ref7.jpg", - } - results, rtn_code, rtn_warning, debug_info = inference( - input_data, mode=mode, **params - ) - if rtn_code >= 0: - save_images(results, img_save_folder) - print(f"Done, result images are saved in: {img_save_folder}") diff --git a/text_diffuser/assets/font/Arial.ttf b/text_diffuser/assets/font/Arial.ttf deleted file mode 100644 index 8682d94623e575a4ecc9586a35fc909dff37fb2c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1036584 zcmeFa37lO;mB(Lix%cgBcW0wX0%T9o(9uzF19Jc0->F-#U%yT#@E@H2XZ|1SrK)b#t>sjm zQ|FvIRk!0Z&bf&Md3XNO6?^V}?RR(F+tvDRc5dsNcVGI7WrfM*yIpO;an9uq*nPiO zu2|R_|5aCe!IxZiLjUe7R_^xcGgp7XHTM3tb7%Z;`HBOVt(kLt>>A&`mR2Y4ec*~c zuKVJl4>-5S_nm89^2!zS7xpiI^_h;!ygy>Um+yVxGjCh+a{7Ba=@%Zfbe}`^`{3)J zbndW|DgU9P)*N~Ak}qC;qsz8$aBk+mANA%_rhf9apP%Ql2kh)zeBv=DAG_v7SC5

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z?XQOWjX%w{@NIRslqmB19Yl@ADQ`8wtXg)&VLGsA`mf~U36pGNEx~SK; z>!u-PnebU`L|FG@B5%i{xEQ@PW2Fvs*~ZKjJxn+c@M%XRf-k!_`X=KQBiBQ1Of^n Tzb=U6$eO{aUC-3hIs5iMN_C>I literal 0 HcmV?d00001 diff --git a/text_diffuser/clipscore/LICENSE b/text_diffuser/clipscore/LICENSE new file mode 100644 index 0000000..4045a87 --- /dev/null +++ b/text_diffuser/clipscore/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, Yejin Choi + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/text_diffuser/clipscore/README.md b/text_diffuser/clipscore/README.md new file mode 100644 index 0000000..2a07756 --- /dev/null +++ b/text_diffuser/clipscore/README.md @@ -0,0 +1,175 @@ +# What's in here? + +This repo contains the code for our EMNLP 2021 paper: [CLIPScore: A +Reference-free Evaluation Metric for Image +Captioning](https://arxiv.org/abs/2104.08718). CLIPScore is a metric +that you can use to evaluate the quality of an automatic image +captioning system. In our paper, we show that CLIPScore achieves high +correlation with human judgment on literal image captioning +tasks. However, unlike BLEU or CIDEr, CLIPScore doesn't require +reference captions. + +If you find the paper or this code useful, please consider citing: + +``` +@inproceedings{hessel2021clipscore, + title={{CLIPScore:} A Reference-free Evaluation Metric for Image Captioning}, + author={Hessel, Jack and Holtzman, Ari and Forbes, Maxwell and Bras, Ronan Le and Choi, Yejin}, + booktitle={EMNLP}, + year={2021} +} +``` + +# How do I run the code? + +## Command Line + +Example usage +``` +> python clipscore.py example/good_captions.json example/images/ +... +CLIPScore: 0.8584 +``` + +If you include optionally some references, you will see RefCLIPScore, alongside a usual set of +caption generation evaluation metrics. The references are optional. + +``` +> python clipscore.py example/good_captions.json example/images/ --references_json example/refs.json +... +BLEU-1: 0.6667 +BLEU-2: 0.4899 +BLEU-3: 0.3469 +BLEU-4: 0.0000 +METEOR: 0.3444 +ROUGE: 0.4280 +CIDER: 0.5637 +SPICE: 0.4000 +CLIPScore: 0.8584 +RefCLIPScore: 0.8450 +``` + +Worse captions should get lower scores: +``` +> python clipscore.py example/bad_captions.json example/images/ --references_json example/refs.json +... +BLEU-1: 0.4815 +BLEU-2: 0.2404 +BLEU-3: 0.1359 +BLEU-4: 0.0000 +METEOR: 0.1861 +ROUGE: 0.3121 +CIDER: 0.2790 +SPICE: 0.1500 +CLIPScore: 0.7153 +RefCLIPScore: 0.7253 +``` + +You can treat/report CLIPScore and RefCLIPScore similarly to the other +evaluation metrics. See the paper for more details about CLIPScore and +RefCLIPScore. Full usage options can be given by `python clipscore.py +-h`. An example set of inputs, including a candidate json, image +directory, and references json is given this repo under `example/` + +The input files are formatted as follows: + +The candidates json should be a dictionary that maps from +`{"string_image_identifier": "candidate"}`, e.g., + +``` +{'image1': 'an orange cat and a grey cat are lying together.', + 'image2': 'a black dog looks at the camera.' + ...} +``` + +The image directory should be a directory containing the images that +act as the keys in the candidates json, e.g., + +``` +images/ +├── image1.jpg +└── image2.jpg +``` + +and, finally, the references json should be a dictionary that maps from +`{"string_image_identifier": ["list", "of", "references"]}`, e.g., + +``` +{"image1": ["two cats are sleeping next to each other.", + "a grey cat is cuddling with an orange cat on a blanket.", + "the orange cat is happy that the black cat is close to it."], + "image2": ["a dog is wearing ear muffs as it lies on a carpet.", + "a black dog and an orange cat are looking at the photographer.", + "headphones are placed on a dogs ears."]} +``` + +## MSCOCO dataset in pycocoevalcap + +If you're running on the MSCOCO dataset and using the standard +evaluation toolkit, you can use our version of +[pycocoevalcap](https://github.com/jmhessel/pycocoevalcap) to evaluate. +You won't even need to download the original MSCOCO images, thanks to +a bit of magic :-) + +To use `pycocoevalcap` on the MSCOCO dataset in the MSCOCO format, you +can simply use: + +``` +pip install git+https://github.com/jmhessel/pycocoevalcap.git +``` + +there is an example evaluation in that repo under +`examples/eval.py`. After pip installing, if you clone the +`pycocoeval` repo and run + +``` +python eval.py +``` + +after a bit of time, the output should be: +``` +Bleu_1: 0.579 +Bleu_2: 0.404 +Bleu_3: 0.279 +Bleu_4: 0.191 +METEOR: 0.195 +ROUGE_L: 0.396 +CIDEr: 0.600 +SPICE: 0.133 +CLIPScore: 0.528 +RefCLIPScore: 0.605 +``` + +## Reproducibility notes: + +- CLIPScore can run either on CPU or GPU. But, there are slight + differences due to floating point precision. As discussed + [here](https://github.com/openai/CLIP/issues/30#issuecomment-771099118), + on CPU, all operations run in `float32`, but on GPU, some operations + run in `float16`. The differences are generally small (e.g., for the + example run above, with `example/good_captions.json` captions and + `example/images/` images, on CPU, the output is `CLIPScore: 0.8585`, + but on GPU, the output is `CLIPScore: 0.8584`.) *All experiments in the + paper were run on GPU, and this code will raise a warning if you're not + using a GPU.* + +- Because CLIPScore depends on the images to compute, resizing, + compressing, etc. can all cause slight differences in computing the + CLIPScore. Even saving a jpg twice can result in different + compression, because that format is lossy! To this end, we release + the checksums of the images we used for the paper. see `checksums/` + for more info. For the pycocoevalcap repo, we have also included the + checksums for MSCOCO --- see + [here](https://github.com/jmhessel/pycocoevalcap/tree/master/clipscore) + for more info. + +- The prompt we used for the text side of CLIP, as mentioned in the + paper is ``A photo depicts" This is hard-coded into this repo. Other + prompts will result in slightly different results, and we don't + recommend them for the sake of reproducibility. + + ## Acknowledgment + + The authors would like to thank Jungo Kasai for being the first to use + this repo. Thanks to Jungo, we fixed a few issues, and added some + information about reproducibility that was missing before. diff --git a/text_diffuser/clipscore/checksums/README.md b/text_diffuser/clipscore/checksums/README.md new file mode 100644 index 0000000..0cda963 --- /dev/null +++ b/text_diffuser/clipscore/checksums/README.md @@ -0,0 +1,8 @@ +### Whats in here? + +We include the checksums of the images we used to compute CLIPScore +for the paper. Because the evaluation metric depends on the image +itself, things like jpg compression, resizing, etc. can impact +reproducability. We downloaded the datasets from the original sources +and didn't modify them. But if you're having trouble matching these +checksums, definitely get in touch! \ No newline at end of file diff --git a/text_diffuser/clipscore/checksums/composite_flickr30k_checksum.txt b/text_diffuser/clipscore/checksums/composite_flickr30k_checksum.txt new file mode 100644 index 0000000..a8ee071 --- /dev/null +++ b/text_diffuser/clipscore/checksums/composite_flickr30k_checksum.txt @@ -0,0 +1,991 @@ +6b179f5916011a7ec70442ba2b346c15 flickr30k_images/1007129816.jpg +d263fd204c316d9a6a96146053e68fca flickr30k_images/1009434119.jpg +41b2c91f0a10427c618630370f6b7ff0 flickr30k_images/101362133.jpg +154415a25a1fc36298414e58ddcbaf2b flickr30k_images/102617084.jpg +4a71189749054a4872deb1830cedbe0b flickr30k_images/10287332.jpg +51f01c33d56495a53c190df669a7b3aa flickr30k_images/1039637574.jpg +7c7a1dad75b5eceffddb9abb18e2cc22 flickr30k_images/1043819504.jpg +7a496b974604e8d90452c798e6c0f91b flickr30k_images/1043910339.jpg +ad6265a87f07aeccbe2d8cc961e1c45f flickr30k_images/1044798682.jpg 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--image_dir example/images \ No newline at end of file diff --git a/text_diffuser/clipscore/clipscore.py b/text_diffuser/clipscore/clipscore.py new file mode 100644 index 0000000..c5f4327 --- /dev/null +++ b/text_diffuser/clipscore/clipscore.py @@ -0,0 +1,241 @@ +''' +Code for CLIPScore (https://arxiv.org/abs/2104.08718) +@inproceedings{hessel2021clipscore, + title={{CLIPScore:} A Reference-free Evaluation Metric for Image Captioning}, + author={Hessel, Jack and Holtzman, Ari and Forbes, Maxwell and Bras, Ronan Le and Choi, Yejin}, + booktitle={EMNLP}, + year={2021} +} +''' +import argparse +import clip +import torch +from PIL import Image +from sklearn.preprocessing import normalize +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +import torch +import tqdm +import numpy as np +import sklearn.preprocessing +import collections +import os +import pathlib +import json +import generation_eval_utils +import pprint +import warnings +from packaging import version + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--candidates_json', + type=str, + help='Candidates json mapping from image_id --> candidate.') + + parser.add_argument( + '--image_dir', + type=str, + help='Directory of images, with the filenames as image ids.') + + parser.add_argument( + '--references_json', + default=None, + help='Optional references json mapping from image_id --> [list of references]') + + parser.add_argument( + '--compute_other_ref_metrics', + default=1, + type=int, + help='If references is specified, should we compute standard reference-based metrics?') + + parser.add_argument( + '--save_per_instance', + default=None, + help='if set, we will save per instance clipscores to this file') + + args = parser.parse_args() + + if isinstance(args.save_per_instance, str) and not args.save_per_instance.endswith('.json'): + print('if you\'re saving per-instance, please make sure the filepath ends in json.') + quit() + return args + + +class CLIPCapDataset(torch.utils.data.Dataset): + def __init__(self, data, prefix='A photo depicts'): + self.data = data + self.prefix = prefix + if self.prefix[-1] != ' ': + self.prefix += ' ' + + def __getitem__(self, idx): + c_data = self.data[idx] + c_data = clip.tokenize(self.prefix + c_data, truncate=True).squeeze() + return {'caption': c_data} + + def __len__(self): + return len(self.data) + + +class CLIPImageDataset(torch.utils.data.Dataset): + def __init__(self, data): + self.data = data + # only 224x224 ViT-B/32 supported for now + self.preprocess = self._transform_test(224) + + def _transform_test(self, n_px): + return Compose([ + Resize(n_px, interpolation=Image.BICUBIC), + CenterCrop(n_px), + lambda image: image.convert("RGB"), + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + #self.data : numpy array (b, w, h, channel) + def __getitem__(self, idx): + c_data = self.data[idx] + image = Image.fromarray(c_data) + #위 이미지는 pillow array + image = self.preprocess(image) + return {'image':image} + + def __len__(self): + return len(self.data) + + +def extract_all_captions(captions, model, device, batch_size=256, num_workers=8): + data = torch.utils.data.DataLoader( + CLIPCapDataset(captions), + batch_size=batch_size, num_workers=num_workers, shuffle=False) + all_text_features = [] + with torch.no_grad(): + for b in tqdm.tqdm(data): + b = b['caption'].to(device) + all_text_features.append(model.encode_text(b).cpu().numpy()) + all_text_features = np.vstack(all_text_features) + return all_text_features + + +def extract_all_images(images, model, device, batch_size=64, num_workers=8): + #images: numpy array (b,w,h,c) + data = torch.utils.data.DataLoader( + CLIPImageDataset(images), + batch_size=batch_size, num_workers=num_workers, shuffle=False) + all_image_features = [] + with torch.no_grad(): + for b in tqdm.tqdm(data): + b = b['image'].to(device) + if device == 'cuda': + b = b.to(torch.float16) + all_image_features.append(model.encode_image(b).cpu().numpy()) + all_image_features = np.vstack(all_image_features) + return all_image_features + + +def get_clip_score(model, images, candidates, device, w=2.5): + ''' + get standard image-text clipscore. + images can either be: + - a list of strings specifying filepaths for images + - a precomputed, ordered matrix of image features + ''' + if isinstance(images, list): + # need to extract image features + images = extract_all_images(images, model, device) + + candidates = extract_all_captions(candidates, model, device) + + #as of numpy 1.21, normalize doesn't work properly for float16 + if version.parse(np.__version__) < version.parse('1.21'): + images = sklearn.preprocessing.normalize(images, axis=1) + candidates = sklearn.preprocessing.normalize(candidates, axis=1) + else: + warnings.warn( + 'due to a numerical instability, new numpy normalization is slightly different than paper results. ' + 'to exactly replicate paper results, please use numpy version less than 1.21, e.g., 1.20.3.') + images = images / np.sqrt(np.sum(images**2, axis=1, keepdims=True)) + candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True)) + + per = w*np.clip(np.sum(images * candidates, axis=1), 0, None) + return np.mean(per), per, candidates + + +def get_refonlyclipscore(model, references, candidates, device): + ''' + The text only side for refclipscore + ''' + if isinstance(candidates, list): + candidates = extract_all_captions(candidates, model, device) + + flattened_refs = [] + flattened_refs_idxs = [] + for idx, refs in enumerate(references): + flattened_refs.extend(refs) + flattened_refs_idxs.extend([idx for _ in refs]) + + flattened_refs = extract_all_captions(flattened_refs, model, device) + + if version.parse(np.__version__) < version.parse('1.21'): + candidates = sklearn.preprocessing.normalize(candidates, axis=1) + flattened_refs = sklearn.preprocessing.normalize(flattened_refs, axis=1) + else: + warnings.warn( + 'due to a numerical instability, new numpy normalization is slightly different than paper results. ' + 'to exactly replicate paper results, please use numpy version less than 1.21, e.g., 1.20.3.') + + candidates = candidates / np.sqrt(np.sum(candidates**2, axis=1, keepdims=True)) + flattened_refs = flattened_refs / np.sqrt(np.sum(flattened_refs**2, axis=1, keepdims=True)) + + cand_idx2refs = collections.defaultdict(list) + for ref_feats, cand_idx in zip(flattened_refs, flattened_refs_idxs): + cand_idx2refs[cand_idx].append(ref_feats) + + assert len(cand_idx2refs) == len(candidates) + + cand_idx2refs = {k: np.vstack(v) for k, v in cand_idx2refs.items()} + + per = [] + for c_idx, cand in tqdm.tqdm(enumerate(candidates)): + cur_refs = cand_idx2refs[c_idx] + all_sims = cand.dot(cur_refs.transpose()) + per.append(np.max(all_sims)) + + return np.mean(per), per + + +def cal_clip_score(img_list,caption_list): + + + image_paths = img_list + #image_paths : numpy array (b,w,h,c) + + candidates =caption_list + + device = "cuda" + + model, transform = clip.load("ViT-B/32", device=device, jit=False) + model.eval() + #image_paths : numpy array (b,w,h,c) + image_feats = extract_all_images( + image_paths, model, device, batch_size=64, num_workers=8) + + _, per_instance_image_text, candidate_feats = get_clip_score(model, image_feats, candidates, device) + + + scores = {f"image{index+1}": {'CLIPScore': float(clipscore)} + for index,clipscore in enumerate(per_instance_image_text)} + #print(scores) + final_score=np.mean([s['CLIPScore'] for s in scores.values()]) + + return final_score + + + + +if __name__ == '__main__': + list1=["example/images/image1.jpg","example/images/image2.jpg"] + list2=["an orange cat and a grey cat are lying together.","a black dog wearing headphones looks at the camera as an orange cat walks in the background."] + s=cal_clip_score(list1,list2) + print(s) diff --git a/text_diffuser/clipscore/example/bad_captions.json b/text_diffuser/clipscore/example/bad_captions.json new file mode 100644 index 0000000..f1c48b0 --- /dev/null +++ b/text_diffuser/clipscore/example/bad_captions.json @@ -0,0 +1 @@ +{"image1": "a calico cat and a white cat are lying together.", "image2": "a black dog wearing a hat looks at the camera as a tabby walks in the background."} \ No newline at end of file diff --git a/text_diffuser/clipscore/example/good_captions.json b/text_diffuser/clipscore/example/good_captions.json new file mode 100644 index 0000000..d2d054b --- /dev/null +++ b/text_diffuser/clipscore/example/good_captions.json @@ -0,0 +1 @@ +{"image1": "an orange cat and a grey cat are lying together.", "image2": "a black dog wearing headphones looks at the camera as an orange cat walks in the background."} \ No newline at end of file diff --git a/text_diffuser/clipscore/example/images/image1.jpg b/text_diffuser/clipscore/example/images/image1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ac4855c602c1ac21edaa5bf68446d1a041b883dd GIT binary patch literal 133587 zcmbT6Wl$Wj+u)%|`6Ug096;3EI+L83-NLPkUWFG2dBg8b^=8egMhV7|fnrvQ78 z^a>dTx)HEIcAU;dde=DLExICpRy@ps=X8q^7nGR^QOr)ZEqG)7#fSFgP?dJu^Euzp%Kp zxwXBsySIOEc!apPyt=-*y}N(-4;L~L%70`1YyTVU|KP&?$Mp&o6$KUjKU~PKJpWY` zTvRk#?$>xy8tCTE_;fr!F$kpNva35W>3KE(5Pox+d_%;*x5>1 z;X6AxN=vh1dJ>fYAZ5^flK;2YviHB4D}X!7k|fGZWl4I~P&t+^n;f{Od4_c(@ZgO8xNt_!VT$M{t00Xy<_*LYq z@Ny&idX0zvnC8t%+QN*>RPHe3l@8ANlrxl7@jE@f5%r9c#SJFP1YZc5E*wi?U88L>Eul6Gv{hHvFFb8`;6b1+LF z-79&gdIcPWKmQOYhdyVaAeX&vYl(50_LEVJv7VF<`}UpnqnTU=pe2m=o^v3PxTqhX z{O7hme9PIV(9W681Xc6E|M2YuvJOV6@t%uw^yHwg*j;e`F;G0!w5;*FhDm*M@Db9f zX?UTDTmvWBAgP-Pf?m?_zH-~T>-tYplqKSI=$>u;33edR79z<8Ee)E{OS7ZmMYLsj z0*W{q89*pM(iE`8$ti(T`m%;%6V?RPjy$`m5=Dr$lz^h+X0@F9fx$QJ=drGtXK{z8 zhD2-3wGo$(bSyo@4JhwRDe%b81vgDe^Ij$%mOg)NnO-x%)KuXh5f=BN->ty!U;BU% zpKCJT5;Yf3zX9BG6O!7box2`=EZ(qd4<}^PrmruYt&c1=FdQ})&0p&Bzd46PGYu!F zo{W<`rlb!{_5II;D$`^7n8~J1%lo$-ZZK zj=EPg^Xw{ohou?c%hhtLkLpu1W`{e!N76fyf0q+3e`}^1zncS^1&OA`{C)<;%(%0b zVwN-2Co(of->b}<7aqs18M2nfheqN4G`97`lrQ_Va1zg6{j&}LjS@eCfeRcRW8$-E zFFiIxg-r{}SJsym3n98ZWj@Yz8>t<0PLkEf4%?>_ZXzE7MIA zbY!P<*{}NPPITi1I`3Q2TweI^0{$XleyM4kW9$=RU43!ZzuY8XgSm6bVP_@@=iY%4{V$fsnaGAy~(AJ;5@sf;JI2?R6VFu z0*PWa&ky3_j_d7<3f8TFZc&_C#en<|_3Ug`0qmaj8(bLU_Rd$8reF>p59*^z_#YTscxsP~P<@$p(QmY0X2K1MM@WWh4X2IZ1k$rFJ`NhjT8~*Tb z%Ye-{$@Z0wpaK~eS{sM8!L7^mL4MdV#`c?au8;4GeS5$--r#LRpTKOtEt-CMpNhZ#i_~$*@F>brXkWik*Pzk6)0Ms7z%m;b^4xu> zUD)oUkaJPy+g@uJJvaZhnY`HSZnpi$qZVz%izsLdDzEQWq|#~i+*5zH`r6Fay|N$* zZxYIuKgwFOSL*5P%(s+UlINN@z1_;Z^D(pIcvd1Ojc-D@2^MLcBHC}m*ZAXW2lJS@ z>pY?E(j-c!tEV>u_u`;Cqn%T{ zA(xZfszGK4-@(nlNO%T!f?H0%URObGMYQK?E0-)(vmKRd?VVSZ<7aItq?md#dy(+How=j@ZT2OY70W4c1_;s9ldl(k)?&; 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z?XQOWjX%w{@NIRslqmB19Yl@ADQ`8wtXg)&VLGsA`mf~U36pGNEx~SK; z>!u-PnebU`L|FG@B5%i{xEQ@PW2Fvs*~ZKjJxn+c@M%XRf-k!_`X=KQBiBQ1Of^n Tzb=U6$eO{aUC-3hIs5iMN_C>I diff --git a/text_diffuser/evaluate.py b/text_diffuser/evaluate.py deleted file mode 100644 index 6ff5a60..0000000 --- a/text_diffuser/evaluate.py +++ /dev/null @@ -1,597 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file provides the inference script. -# ------------------------------------------ - -import argparse -import logging -import os -import random -from pathlib import Path -from typing import Optional - -import accelerate -import datasets -import numpy as np -import torch -import torch.utils.checkpoint -import transformers -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from datasets import disable_caching -from huggingface_hub import HfFolder, Repository, create_repo, whoami -from model.layout_generator import get_layout_from_prompt -from model.text_segmenter.unet import UNet -from packaging import version -from PIL import ( - Image, -) - -# import for visualization -from termcolor import colored -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer -from util import ( - filter_segmentation_mask, - make_caption_pil, - segmentation_mask_visualization, -) - -import diffusers -from diffusers import ( - AutoencoderKL, - DDPMScheduler, - UNet2DConditionModel, -) -from diffusers.utils import check_min_version -from diffusers.utils.import_utils import is_xformers_available - - -disable_caching() -check_min_version("0.15.0.dev0") -logger = get_logger(__name__, log_level="INFO") - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default="runwayml/stable-diffusion-v1-5", # no need to modify this - help="Path to pretrained model or model identifier from huggingface.co/models. Please do not modify this.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--mode", - type=str, - default=None, - required=True, - choices=["text-to-image", "text-to-image-with-template", "text-inpainting"], - help="Three modes can be used.", - ) - parser.add_argument( - "--prompt", - type=str, - default="", - required=False, - help="The text prompts provided by users.", - ) - parser.add_argument( - "--prompt_list", - type=str, - default="", - required=True, - help="The list of prompts.", - ) - parser.add_argument( - "--template_image", - type=str, - default="", - help="The template image should be given when using 【text-to-image-with-template】 mode.", - ) - parser.add_argument( - "--original_image", - type=str, - default="", - help="The original image should be given when using 【text-inpainting】 mode.", - ) - parser.add_argument( - "--text_mask", - type=str, - default="", - help="The text mask should be given when using 【text-inpainting】 mode.", - ) - parser.add_argument( - "--output_dir", - type=str, - default="output", - help="The path of the generation directory.", - ) - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="The directory where the downloaded models and datasets will be stored.", - ) - parser.add_argument( - "--seed", - type=int, - default=0, # set to 0 during evaluation - help="A seed for reproducible training.", - ) - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--classifier_free_scale", - type=float, - default=7.5, # following stable diffusion (https://github.com/CompVis/stable-diffusion) - help="Classifier free scale following https://arxiv.org/abs/2207.12598.", - ) - parser.add_argument( - "--drop_caption", - action="store_true", - help="Whether to drop captions during training following https://arxiv.org/abs/2207.12598..", - ) - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=0, - help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", - ) - parser.add_argument( - "--push_to_hub", - action="store_true", - help="Whether or not to push the model to the Hub.", - ) - parser.add_argument( - "--hub_token", - type=str, - default=None, - help="The token to use to push to the Model Hub.", - ) - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="fp16", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), - ) - parser.add_argument( - "--report_to", - type=str, - default="tensorboard", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' - ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' - ), - ) - parser.add_argument( - "--local_rank", - type=int, - default=-1, - help="For distributed training: local_rank", - ) - parser.add_argument( - "--checkpointing_steps", - type=int, - default=500, - help=( - "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" - " training using `--resume_from_checkpoint`." - ), - ) - parser.add_argument( - "--checkpoints_total_limit", - type=int, - default=5, - help=( - "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." - " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" - " for more docs" - ), - ) - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default=None, # should be specified during inference - help=( - "Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), - ) - parser.add_argument( - "--enable_xformers_memory_efficient_attention", - action="store_true", - help="Whether or not to use xformers.", - ) - parser.add_argument( - "--font_path", - type=str, - default="Arial.ttf", - help="The path of font for visualization.", - ) - parser.add_argument( - "--sample_steps", - type=int, - default=50, # following stable diffusion (https://github.com/CompVis/stable-diffusion) - help="Diffusion steps for sampling.", - ) - parser.add_argument( - "--vis_num", - type=int, - default=9, # please decreases the number if out-of-memory error occurs - help="Number of images to be sample. Please decrease it when encountering out of memory error.", - ) - parser.add_argument( - "--binarization", - action="store_true", - help="Whether to binarize the template image.", - ) - parser.add_argument( - "--use_pillow_segmentation_mask", - type=bool, - default=True, - help="In the 【text-to-image】 mode, please specify whether to use the segmentation masks provided by PILLOW", - ) - parser.add_argument( - "--character_segmenter_path", - type=str, - default="textdiffuser-ckpt/text_segmenter.pth", - help="checkpoint of character-level segmenter", - ) - args = parser.parse_args() - - print(f'{colored("[√]", "green")} Arguments are loaded.') - print(args) - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - return args - - -def get_full_repo_name( - model_id: str, organization: Optional[str] = None, token: Optional[str] = None -): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - - -# @torchsnooper.snoop() -def main(): - args = parse_args() - # If passed along, set the training seed now. - seed = args.seed if args.seed is not None else random.randint(0, 1000000) - set_seed(seed) - print(f'{colored("[√]", "green")} Seed is set to {seed}.') - - logging_dir = os.path.join(args.output_dir, args.logging_dir) - # sub_output_dir = f"{args.prompt}_[{args.mode.upper()}]_[SEED-{seed}]" - - print(f'{colored("[√]", "green")} Logging dir is set to {logging_dir}.') - - accelerator_project_config = ProjectConfiguration( - total_limit=args.checkpoints_total_limit - ) - - accelerator = Accelerator( - gradient_accumulation_steps=1, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - logging_dir=logging_dir, - project_config=accelerator_project_config, - ) - - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state, main_process_only=False) - if accelerator.is_local_main_process: - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_warning() - diffusers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - diffusers.utils.logging.set_verbosity_error() - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name( - Path(args.output_dir).name, token=args.hub_token - ) - else: - repo_name = args.hub_model_id - create_repo(repo_name, exist_ok=True, token=args.hub_token) - repo = Repository( - args.output_dir, clone_from=repo_name, token=args.hub_token - ) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - print(args.output_dir) - - # Load scheduler, tokenizer and models. - tokenizer = CLIPTokenizer.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer", - revision=args.revision, - ) - text_encoder = CLIPTextModel.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="text_encoder", - revision=args.revision, - ) - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision - ).cuda() - unet = UNet2DConditionModel.from_pretrained( - args.resume_from_checkpoint, subfolder="unet", revision=None - ).cuda() - - # Freeze vae and text_encoder - vae.requires_grad_(False) - text_encoder.requires_grad_(False) - - if args.enable_xformers_memory_efficient_attention: - if is_xformers_available(): - import xformers - - xformers_version = version.parse(xformers.__version__) - if xformers_version == version.parse("0.0.16"): - logger.warn( - "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." - ) - unet.enable_xformers_memory_efficient_attention() - else: - raise ValueError( - "xformers is not available. Make sure it is installed correctly" - ) - - # `accelerate` 0.16.0 will have better support for customized saving - if version.parse(accelerate.__version__) >= version.parse("0.16.0"): - # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format - def save_model_hook(models, weights, output_dir): - - for i, model in enumerate(models): - model.save_pretrained(os.path.join(output_dir, "unet")) - - # make sure to pop weight so that corresponding model is not saved again - weights.pop() - - def load_model_hook(models, input_dir): - - for i in range(len(models)): - # pop models so that they are not loaded again - model = models.pop() - - # load diffusers style into model - load_model = UNet2DConditionModel.from_pretrained( - input_dir, subfolder="unet" - ) - model.register_to_config(**load_model.config) - - model.load_state_dict(load_model.state_dict()) - del load_model - - accelerator.register_save_state_pre_hook(save_model_hook) - accelerator.register_load_state_pre_hook(load_model_hook) - - with open(args.prompt_list) as fr: - prompts = fr.readlines() - prompts = [_.strip() for _ in prompts] - - for idx in range(args.vis_num): - os.makedirs( - os.path.join(args.output_dir, "textdiffuser", "images_" + str(idx)), - exist_ok=True, - ) - - for prompt_index, prompt in enumerate(prompts): - - args.prompt = prompt - - # setup schedulers - scheduler = DDPMScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder="scheduler" - ) - scheduler.set_timesteps(args.sample_steps) - sample_num = args.vis_num - noise = torch.randn((sample_num, 4, 64, 64)).to("cuda") # (b, 4, 64, 64) - input = noise # (b, 4, 64, 64) - - captions = [args.prompt] * sample_num - captions_nocond = [""] * sample_num - print(f'{colored("[√]", "green")} Prompt is loaded: {args.prompt}.') - - # encode text prompts - inputs = tokenizer( - captions, - max_length=tokenizer.model_max_length, - padding="max_length", - truncation=True, - return_tensors="pt", - ).input_ids # (b, 77) - encoder_hidden_states = text_encoder(inputs)[0].cuda() # (b, 77, 768) - print( - f'{colored("[√]", "green")} encoder_hidden_states: {encoder_hidden_states.shape}.' - ) - - inputs_nocond = tokenizer( - captions_nocond, - max_length=tokenizer.model_max_length, - padding="max_length", - truncation=True, - return_tensors="pt", - ).input_ids # (b, 77) - encoder_hidden_states_nocond = text_encoder(inputs_nocond)[ - 0 - ].cuda() # (b, 77, 768) - print( - f'{colored("[√]", "green")} encoder_hidden_states_nocond: {encoder_hidden_states_nocond.shape}.' - ) - - # load character-level segmenter - segmenter = UNet(3, 96, True).cuda() - segmenter = torch.nn.DataParallel(segmenter) - segmenter.load_state_dict(torch.load(args.character_segmenter_path)) - segmenter.eval() - print(f'{colored("[√]", "green")} Text segmenter is successfully loaded.') - - #### text-to-image #### - if args.mode == "text-to-image": - render_image, segmentation_mask_from_pillow = get_layout_from_prompt(args) - - if args.use_pillow_segmentation_mask: - segmentation_mask = torch.Tensor( - np.array(segmentation_mask_from_pillow) - ).cuda() # (512, 512) - else: - to_tensor = transforms.ToTensor() - image_tensor = ( - to_tensor(render_image).unsqueeze(0).cuda().sub_(0.5).div_(0.5) - ) - with torch.no_grad(): - segmentation_mask = segmenter(image_tensor) - segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) - - segmentation_mask = filter_segmentation_mask(segmentation_mask) - segmentation_mask = torch.nn.functional.interpolate( - segmentation_mask.unsqueeze(0).unsqueeze(0).float(), - size=(256, 256), - mode="nearest", - ) - segmentation_mask = ( - segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to("cuda") - ) # (1, 1, 256, 256) - print( - f'{colored("[√]", "green")} character-level segmentation_mask: {segmentation_mask.shape}.' - ) - - feature_mask = torch.ones(sample_num, 1, 64, 64).to( - "cuda" - ) # (b, 1, 64, 64) - masked_image = torch.zeros(sample_num, 3, 512, 512).to( - "cuda" - ) # (b, 3, 512, 512) - masked_feature = vae.encode( - masked_image - ).latent_dist.sample() # (b, 4, 64, 64) - masked_feature = masked_feature * vae.config.scaling_factor - print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.') - print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.') - - # diffusion process - intermediate_images = [] - for t in tqdm(scheduler.timesteps): - with torch.no_grad(): - noise_pred_cond = unet( - sample=input, - timestep=t, - encoder_hidden_states=encoder_hidden_states, - segmentation_mask=segmentation_mask, - feature_mask=feature_mask, - masked_feature=masked_feature, - ).sample # b, 4, 64, 64 - noise_pred_uncond = unet( - sample=input, - timestep=t, - encoder_hidden_states=encoder_hidden_states_nocond, - segmentation_mask=segmentation_mask, - feature_mask=feature_mask, - masked_feature=masked_feature, - ).sample # b, 4, 64, 64 - noisy_residual = noise_pred_uncond + args.classifier_free_scale * ( - noise_pred_cond - noise_pred_uncond - ) # b, 4, 64, 64 - prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample - input = prev_noisy_sample - intermediate_images.append(prev_noisy_sample) - - # decode and visualization - input = 1 / vae.config.scaling_factor * input - sample_images = vae.decode(input.float(), return_dict=False)[ - 0 - ] # (b, 3, 512, 512) - - image_pil = render_image.resize((512, 512)) - segmentation_mask = segmentation_mask[0].squeeze().cpu().numpy() - character_mask_pil = Image.fromarray( - ((segmentation_mask != 0) * 255).astype("uint8") - ).resize((512, 512)) - character_mask_highlight_pil = segmentation_mask_visualization( - args.font_path, segmentation_mask - ) - caption_pil = make_caption_pil(args.font_path, captions) - - # save pred_img - pred_image_list = [] - for image in sample_images.float(): - image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) - image = image.cpu().permute(0, 2, 3, 1).numpy()[0] - image = Image.fromarray((image * 255).round().astype("uint8")).convert( - "RGB" - ) - pred_image_list.append(image) - - for image_index, image in enumerate(pred_image_list): - image.save( - f"{args.output_dir}/textdiffuser/images_{image_index}/{prompt_index}_{image_index}.jpg" - ) - - -if __name__ == "__main__": - main() diff --git a/text_diffuser/inference.py b/text_diffuser/inference.py deleted file mode 100644 index 5af365e..0000000 --- a/text_diffuser/inference.py +++ /dev/null @@ -1,1070 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file provides the inference script. -# ------------------------------------------ - -import argparse -import logging -import os -import random -from pathlib import Path -from typing import Dict, List, Optional, Union - -import accelerate -import cv2 -import datasets -import numpy as np -import torch -import torch.utils.checkpoint -import transformers -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from datasets import disable_caching -from huggingface_hub import HfFolder, Repository, create_repo, hf_hub_download, whoami -from model.layout_generator import get_layout_from_prompt -from model.text_segmenter.unet import UNet -from packaging import version -from PIL import ( - Image, - ImageEnhance, - ImageOps, -) -from safetensors import safe_open -from t_diffusers.scheduling_ddpm import DDPMScheduler -from t_diffusers.unet_2d_condition import UNet2DConditionModel - -# import for visualization -from termcolor import colored -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import ( - CLIPImageProcessor, - CLIPTextModel, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) -from util import ( - combine_image, - filter_segmentation_mask, - make_caption_pil, - segmentation_mask_visualization, - transform_mask, -) - -import diffusers -from diffusers import AutoencoderKL -from diffusers.models import ImageProjection -from diffusers.models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, _get_model_file -from diffusers.utils import check_min_version - - -""" -from diffusers.utils.import_utils import is_xformers_available -""" - -disable_caching() -check_min_version("0.15.0.dev0") -logger = get_logger(__name__, log_level="INFO") - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default="runwayml/stable-diffusion-v1-5", # no need to modify this - help="Path to pretrained model or model identifier from huggingface.co/models. Please do not modify this.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--mode", - type=str, - default=None, - required=True, - choices=["text-to-image", "text-to-image-with-template", "text-inpainting"], - help="Three modes can be used.", - ) - parser.add_argument( - "--prompt", - type=str, - default="", - required=True, - help="The text prompts provided by users.", - ) - parser.add_argument( - "--template_image", - type=str, - default="", - help="The template image should be given when using 【text-to-image-with-template】 mode.", - ) - parser.add_argument( - "--original_image", - type=str, - default="", - help="The original image should be given when using 【text-inpainting】 mode.", - ) - parser.add_argument( - "--text_mask", - type=str, - default="", - help="The text mask should be given when using 【text-inpainting】 mode.", - ) - parser.add_argument( - "--output_dir", - type=str, - default="output", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="The directory where the downloaded models and datasets will be stored.", - ) - parser.add_argument( - "--seed", type=int, default=None, help="A seed for reproducible training." - ) - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--classifier_free_scale", - type=float, - default=7.5, # following stable diffusion (https://github.com/CompVis/stable-diffusion) - help="Classifier free scale following https://arxiv.org/abs/2207.12598.", - ) - parser.add_argument( - "--drop_caption", - action="store_true", - help="Whether to drop captions during training following https://arxiv.org/abs/2207.12598..", - ) - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=0, - help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", - ) - parser.add_argument( - "--push_to_hub", - action="store_true", - help="Whether or not to push the model to the Hub.", - ) - parser.add_argument( - "--hub_token", - type=str, - default=None, - help="The token to use to push to the Model Hub.", - ) - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="fp16", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), - ) - parser.add_argument( - "--report_to", - type=str, - default="tensorboard", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' - ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' - ), - ) - parser.add_argument( - "--local_rank", - type=int, - default=-1, - help="For distributed training: local_rank", - ) - parser.add_argument( - "--checkpointing_steps", - type=int, - default=500, - help=( - "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" - " training using `--resume_from_checkpoint`." - ), - ) - parser.add_argument( - "--checkpoints_total_limit", - type=int, - default=5, - help=( - "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." - " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" - " for more docs" - ), - ) - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default="GoGiants1/td-unet15", # should be specified during inference - help=( - "Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), - ) - parser.add_argument( - "--enable_xformers_memory_efficient_attention", - action="store_true", - help="Whether or not to use xformers.", - ) - parser.add_argument( - "--font_path", - type=str, - default="assets/font/Arial.ttf", - help="The path of font for visualization.", - ) - parser.add_argument( - "--sample_steps", - type=int, - default=50, # following stable diffusion (https://github.com/CompVis/stable-diffusion) - help="Diffusion steps for sampling.", - ) - parser.add_argument( - "--vis_num", - type=int, - default=9, # please decreases the number if out-of-memory error occurs - help="Number of images to be sample. Please decrease it when encountering out of memory error.", - ) - parser.add_argument( - "--binarization", - action="store_true", - help="Whether to binarize the template image.", - ) - parser.add_argument( - "--use_pillow_segmentation_mask", - type=bool, - default=True, - help="In the 【text-to-image】 mode, please specify whether to use the segmentation masks provided by PILLOW", - ) - parser.add_argument( - "--character_segmenter_path", - type=str, - default="textdiffuser-ckpt/text_segmenter.pth", - help="checkpoint of character-level segmenter", - ) - args = parser.parse_args() - - print(f'{colored("[√]", "green")} Arguments are loaded.') - print(args) - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - return args - - -def get_full_repo_name( - model_id: str, organization: Optional[str] = None, token: Optional[str] = None -): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - - -def load_ip_adapter( - pretrained_model_name_or_path_or_dict: Union[ - str, List[str], Dict[str, torch.Tensor] - ], - subfolder: Union[str, List[str]], - weight_name: Union[str, List[str]], - image_encoder_folder: Optional[str] = "image_encoder", - **kwargs, -): - """ - Parameters: - pretrained_model_name_or_path_or_dict (`str` or `List[str]` or `os.PathLike` or `List[os.PathLike]` or `dict` or `List[dict]`): - Can be either: - - - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on - the Hub. - - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved - with [`ModelMixin.save_pretrained`]. - - A [torch state - dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). - subfolder (`str` or `List[str]`): - The subfolder location of a model file within a larger model repository on the Hub or locally. - If a list is passed, it should have the same length as `weight_name`. - weight_name (`str` or `List[str]`): - The name of the weight file to load. If a list is passed, it should have the same length as - `weight_name`. - image_encoder_folder (`str`, *optional*, defaults to `image_encoder`): - The subfolder location of the image encoder within a larger model repository on the Hub or locally. - Pass `None` to not load the image encoder. If the image encoder is located in a folder inside `subfolder`, - you only need to pass the name of the folder that contains image encoder weights, e.g. `image_encoder_folder="image_encoder"`. - If the image encoder is located in a folder other than `subfolder`, you should pass the path to the folder that contains image encoder weights, - for example, `image_encoder_folder="different_subfolder/image_encoder"`. - """ - - # handle the list inputs for multiple IP Adapters - if not isinstance(weight_name, list): - weight_name = [weight_name] - - if not isinstance(pretrained_model_name_or_path_or_dict, list): - pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict] - if len(pretrained_model_name_or_path_or_dict) == 1: - pretrained_model_name_or_path_or_dict = ( - pretrained_model_name_or_path_or_dict * len(weight_name) - ) - - if not isinstance(subfolder, list): - subfolder = [subfolder] - if len(subfolder) == 1: - subfolder = subfolder * len(weight_name) - - if len(weight_name) != len(pretrained_model_name_or_path_or_dict): - raise ValueError( - "`weight_name` and `pretrained_model_name_or_path_or_dict` must have the same length." - ) - - if len(weight_name) != len(subfolder): - raise ValueError("`weight_name` and `subfolder` must have the same length.") - - # Load the main state dict first. - cache_dir = kwargs.pop("cache_dir", None) - force_download = kwargs.pop("force_download", False) - resume_download = kwargs.pop("resume_download", False) - proxies = kwargs.pop("proxies", None) - local_files_only = kwargs.pop("local_files_only", None) - token = kwargs.pop("token", None) - revision = kwargs.pop("revision", None) - low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) - - user_agent = { - "file_type": "attn_procs_weights", - "framework": "pytorch", - } - state_dicts = [] - for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip( - pretrained_model_name_or_path_or_dict, weight_name, subfolder - ): - if not isinstance(pretrained_model_name_or_path_or_dict, dict): - model_file = _get_model_file( - pretrained_model_name_or_path_or_dict, - weights_name=weight_name, - cache_dir=cache_dir, - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - token=token, - revision=revision, - subfolder=subfolder, - user_agent=user_agent, - ) - if weight_name.endswith(".safetensors"): - state_dict = {"image_proj": {}, "ip_adapter": {}} - with safe_open(model_file, framework="pt", device="cpu") as f: - for key in f.keys(): - if key.startswith("image_proj."): - state_dict["image_proj"][key.replace("image_proj.", "")] = ( - f.get_tensor(key) - ) - elif key.startswith("ip_adapter."): - state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = ( - f.get_tensor(key) - ) - else: - state_dict = torch.load(model_file, map_location="cpu") - else: - state_dict = pretrained_model_name_or_path_or_dict - - keys = list(state_dict.keys()) - if keys != ["image_proj", "ip_adapter"]: - raise ValueError( - "Required keys are (`image_proj` and `ip_adapter`) missing from the state dict." - ) - - state_dicts.append(state_dict) - - # load CLIP image encoder here if it has not been registered to the pipeline yet - - if image_encoder_folder is not None: - if not isinstance(pretrained_model_name_or_path_or_dict, dict): - logger.info( - f"loading image_encoder from {pretrained_model_name_or_path_or_dict}" - ) - if image_encoder_folder.count("/") == 0: - image_encoder_subfolder = Path( - subfolder, image_encoder_folder - ).as_posix() - else: - image_encoder_subfolder = Path(image_encoder_folder).as_posix() - - image_encoder = CLIPVisionModelWithProjection.from_pretrained( - pretrained_model_name_or_path_or_dict, - subfolder=image_encoder_subfolder, - low_cpu_mem_usage=low_cpu_mem_usage, - ) - else: - raise ValueError( - "`image_encoder` cannot be loaded because `pretrained_model_name_or_path_or_dict` is a state dict." - ) - else: - logger.warning( - "image_encoder is not loaded since `image_encoder_folder=None` passed. You will not be able to use `ip_adapter_image` when calling the pipeline with IP-Adapter." - "Use `ip_adapter_image_embeds` to pass pre-generated image embedding instead." - ) - - # create feature extractor if it has not been registered to the pipeline yet - - feature_extractor = CLIPImageProcessor() - - # load ip-adapter into unet - # unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet - # unet._load_ip_adapter_weights(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage) - - return state_dicts, feature_extractor, image_encoder - - -def encode_image( - image_encoder: CLIPVisionModelWithProjection, # clip encoder - feature_extractor: CLIPImageProcessor, - image, - device, - num_images_per_prompt, - output_hidden_states=None, -): - dtype = next(image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = feature_extractor( - image, return_tensors="pt" - ).pixel_values # torch가 아니면 torch로 변환 - - image = image.to(device=device, dtype=dtype) - if output_hidden_states: - image_enc_hidden_states = image_encoder( - image, output_hidden_states=True - ).hidden_states[-2] - image_enc_hidden_states = image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - uncond_image_enc_hidden_states = image_encoder( - torch.zeros_like(image), output_hidden_states=True - ).hidden_states[-2] - uncond_image_enc_hidden_states = ( - uncond_image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - ) - return image_enc_hidden_states, uncond_image_enc_hidden_states - else: - image_embeds = image_encoder(image).image_embeds - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_embeds = torch.zeros_like(image_embeds) - - return image_embeds, uncond_image_embeds - - -def prepare_ip_adapter_image_embeds( - unet, - image_encoder, - feature_extractor, - ip_adapter_image, # masked_image - ip_adapter_image_embeds, - device, - num_images_per_prompt, - do_classifier_free_guidance, -): - if ip_adapter_image_embeds is None: - if not isinstance(ip_adapter_image, list): - ip_adapter_image = [ip_adapter_image] - - if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers): - raise ValueError( - f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - ) - - image_embeds = [] - for single_ip_adapter_image, image_proj_layer in zip( - ip_adapter_image, unet.encoder_hid_proj.image_projection_layers - ): - output_hidden_state = not isinstance(image_proj_layer, ImageProjection) - single_image_embeds, single_negative_image_embeds = encode_image( - image_encoder, - feature_extractor, - single_ip_adapter_image, - device, - 1, - output_hidden_state, - ) - single_image_embeds = torch.stack( - [single_image_embeds] * num_images_per_prompt, dim=0 - ) - single_negative_image_embeds = torch.stack( - [single_negative_image_embeds] * num_images_per_prompt, dim=0 - ) - - if do_classifier_free_guidance: - single_image_embeds = torch.cat( - [single_negative_image_embeds, single_image_embeds] - ) - single_image_embeds = single_image_embeds.to(device) - - image_embeds.append(single_image_embeds) - else: - repeat_dims = [1] - image_embeds = [] - for single_image_embeds in ip_adapter_image_embeds: - if do_classifier_free_guidance: - single_negative_image_embeds, single_image_embeds = ( - single_image_embeds.chunk(2) - ) - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, - *(repeat_dims * len(single_image_embeds.shape[1:])), - ) - single_negative_image_embeds = single_negative_image_embeds.repeat( - num_images_per_prompt, - *(repeat_dims * len(single_negative_image_embeds.shape[1:])), - ) - single_image_embeds = torch.cat( - [single_negative_image_embeds, single_image_embeds] - ) - else: - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, - *(repeat_dims * len(single_image_embeds.shape[1:])), - ) - image_embeds.append(single_image_embeds) - - return image_embeds - - -# @torchsnooper.snoop() -def main(): - args = parse_args() - # If passed along, set the training seed now. - seed = args.seed if args.seed is not None else random.randint(0, 1000000) - set_seed(seed) - print(f'{colored("[√]", "green")} Seed is set to {seed}.') - - logging_dir = os.path.join(args.output_dir, args.logging_dir) - sub_output_dir = f"{args.prompt}_[{args.mode.upper()}]_[SEED-{seed}]" - - print(f'{colored("[√]", "green")} Logging dir is set to {logging_dir}.') - - accelerator_project_config = ProjectConfiguration( - total_limit=args.checkpoints_total_limit, logging_dir=logging_dir - ) - - accelerator = Accelerator( - gradient_accumulation_steps=1, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - project_config=accelerator_project_config, - ) - - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state, main_process_only=False) - if accelerator.is_local_main_process: - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_warning() - diffusers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - diffusers.utils.logging.set_verbosity_error() - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name( - Path(args.output_dir).name, token=args.hub_token - ) - else: - repo_name = args.hub_model_id - create_repo(repo_name, exist_ok=True, token=args.hub_token) - _ = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - print(args.output_dir) - - # Load scheduler, tokenizer and models. - tokenizer = CLIPTokenizer.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer", - revision=args.revision, - ) - text_encoder = CLIPTextModel.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="text_encoder", - revision=args.revision, - ) - - # TODO: AnyText Ideas! -> use multi-lingual BERT - # from transformers import BertTokenizer, BertModel - # tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') - # model = BertModel.from_pretrained("bert-base-multilingual-cased") - # text = "Replace me by any text you'd like." - # encoded_input = tokenizer(text, return_tensors='pt') - # output = model(**encoded_input) - - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision - ).cuda() - unet: UNet2DConditionModel = UNet2DConditionModel.from_pretrained( - args.resume_from_checkpoint, subfolder="unet", revision=None - ).cuda() - - # load ip-adapter - # from https://huggingface.co/h94/IP-Adapter/tree/main/models - - state_dicts, feature_extractor, image_encoder = load_ip_adapter( - "h94/IP-Adapter", - subfolder=[ - "models", - ], - weight_name=[ - "ip-adapter_sd15.safetensors", - ], - image_encoder_folder="image_encoder", - ) - - unet._load_ip_adapter_weights( - state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT - ) - print(f'{colored("[√]", "green")} load ip_adapter into unet') - # Freeze vae and text_encoder - vae.requires_grad_(False) - text_encoder.requires_grad_(False) - - ### Newly Added - # feature_extractor.requires_grad_(False) - # image_encoder.requires_grad_(False) - # feature_extractor - image_encoder.to("cuda", torch.float32) - ### - - # `accelerate` 0.16.0 will have better support for customized saving - if version.parse(accelerate.__version__) >= version.parse("0.16.0"): - # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format - def save_model_hook(models, weights, output_dir): - for i, model in enumerate(models): - model.save_pretrained(os.path.join(output_dir, "unet")) - # make sure to pop weight so that corresponding model is not saved again - weights.pop() - - def load_model_hook(models, input_dir): - - for i in range(len(models)): - # pop models so that they are not loaded again - model = models.pop() - - # load diffusers style into model - load_model = UNet2DConditionModel.from_pretrained( - input_dir, subfolder="unet" - ) - model.register_to_config(**load_model.config) - - model.load_state_dict(load_model.state_dict()) - del load_model - - accelerator.register_save_state_pre_hook(save_model_hook) - accelerator.register_load_state_pre_hook(load_model_hook) - - # setup schedulers - scheduler = DDPMScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder="scheduler" - ) - scheduler.set_timesteps(args.sample_steps) - sample_num = args.vis_num - noise = torch.randn((sample_num, 4, 64, 64)).to("cuda") # (b, 4, 64, 64) - input = noise # (b, 4, 64, 64) - scene_caption = args.prompt.split("'")[0]+args.prompt.split("'")[2] - captions = [scene_caption] * sample_num - captions_nocond = [""] * sample_num - print(f'{colored("[√]", "green")} Prompt is loaded: {args.prompt}.') - - # encode text prompts - inputs = tokenizer( - captions, - max_length=tokenizer.model_max_length, - padding="max_length", - truncation=True, - return_tensors="pt", - ).input_ids # (b, 77) id로 token을 숫자화 - encoder_hidden_states = text_encoder(inputs)[0].cuda() # (b, 77, 768) - print( - f'{colored("[√]", "green")} encoder_hidden_states: {encoder_hidden_states.shape}.' - ) - - inputs_nocond = tokenizer( - captions_nocond, - max_length=tokenizer.model_max_length, - padding="max_length", - truncation=True, - return_tensors="pt", - ).input_ids # (b, 77) - encoder_hidden_states_nocond = text_encoder(inputs_nocond)[ - 0 - ].cuda() # (b, 77, 768) 1024size로 나중에 rescale? - print( - f'{colored("[√]", "green")} encoder_hidden_states_nocond: {encoder_hidden_states_nocond.shape}.' - ) - - character_segmenter_path = hf_hub_download( - "GoGiants1/td-unet15", "text_segmenter.pth", repo_type="model" - ) - print(f'{colored("[√]", "green")} character_segmenter_path downloaded.') - - # load character-level segmenter - segmenter = UNet(3, 96, True).cuda() - segmenter = torch.nn.DataParallel(segmenter) - segmenter.load_state_dict(torch.load(character_segmenter_path)) - segmenter.eval() - print(f'{colored("[√]", "green")} Text segmenter is successfully loaded.') - image = Image.open(args.original_image).convert("RGB").resize((512, 512)) - print(image, image.size) - ip_adapter_image = image - - batch_size = args.vis_num - num_images_per_prompt = 1 - - image_embeds = prepare_ip_adapter_image_embeds( - unet, - image_encoder, - feature_extractor, - ip_adapter_image, - None, - "cuda", - batch_size * num_images_per_prompt, - True, - ) - - added_cond_kwargs_cond = ( - {"image_embeds": image_embeds} if (ip_adapter_image is not None) else None - ) - - - - #### text-to-image #### - if args.mode == "text-to-image": - render_image, segmentation_mask_from_pillow = get_layout_from_prompt(args) - - if args.use_pillow_segmentation_mask: - segmentation_mask = torch.Tensor( - np.array(segmentation_mask_from_pillow) - ).cuda() # (512, 512) - else: - to_tensor = transforms.ToTensor() - image_tensor = ( - to_tensor(render_image).unsqueeze(0).cuda().sub_(0.5).div_(0.5) - ) - with torch.no_grad(): - segmentation_mask = segmenter(image_tensor) - segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) - - segmentation_mask = filter_segmentation_mask(segmentation_mask) - segmentation_mask = torch.nn.functional.interpolate( - segmentation_mask.unsqueeze(0).unsqueeze(0).float(), - size=(256, 256), - mode="nearest", - ) - segmentation_mask = ( - segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to("cuda") - ) # (1, 1, 256, 256) - print( - f'{colored("[√]", "green")} character-level segmentation_mask: {segmentation_mask.shape}.' - ) - - feature_mask = torch.ones(sample_num, 1, 64, 64).to("cuda") # (b, 1, 64, 64) - masked_image = torch.zeros(sample_num, 3, 512, 512).to( - "cuda" - ) # (b, 3, 512, 512) - masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64) - masked_feature = masked_feature * vae.config.scaling_factor - print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.') - print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.') - - #### text-to-image-with-template #### - if args.mode == "text-to-image-with-template": - template_image = ( - Image.open(args.template_image).resize((256, 256)).convert("RGB") - ) - - # whether binarization is needed - print( - f'{colored("[Warning]", "red")} args.binarization is set to {args.binarization}. You may need it when using handwritten images as templates.' - ) - if args.binarization: - gray = ImageOps.grayscale(template_image) - binary = gray.point(lambda x: 255 if x > 96 else 0, "1") - template_image = binary.convert("RGB") - - to_tensor = transforms.ToTensor() - image_tensor = ( - to_tensor(template_image).unsqueeze(0).cuda().sub_(0.5).div_(0.5) - ) # (b, 3, 256, 256) - - with torch.no_grad(): - segmentation_mask = segmenter(image_tensor) # (b, 96, 256, 256) - segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) # (256, 256) - segmentation_mask = filter_segmentation_mask(segmentation_mask) # (256, 256) - segmentation_mask_pil = Image.fromarray( - segmentation_mask.type(torch.uint8).cpu().numpy() - ).convert("RGB") - - segmentation_mask = torch.nn.functional.interpolate( - segmentation_mask.unsqueeze(0).unsqueeze(0).float(), - size=(256, 256), - mode="nearest", - ) # (b, 1, 256, 256) - segmentation_mask = ( - segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to("cuda") - ) # (b, 1, 256, 256) - print( - f'{colored("[√]", "green")} Character-level segmentation_mask: {segmentation_mask.shape}.' - ) - - feature_mask = torch.ones(sample_num, 1, 64, 64).to("cuda") # (b, 1, 64, 64) - masked_image = torch.zeros(sample_num, 3, 512, 512).to( - "cuda" - ) # (b, 3, 512, 512) - masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64) - masked_feature = masked_feature * vae.config.scaling_factor # (b, 4, 64, 64) - print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.') - print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.') - - render_image = template_image # for visualization - - #### text-inpainting #### - if args.mode == "text-inpainting": - text_mask = cv2.imread(args.text_mask) - threshold = 50 - _, text_mask = cv2.threshold(text_mask, threshold, 255, cv2.THRESH_BINARY) - text_mask = Image.fromarray(text_mask).convert("RGB").resize((256, 256)) - text_mask_tensor = ( - transforms.ToTensor()(text_mask).unsqueeze(0).cuda().sub_(0.5).div_(0.5) - ) - with torch.no_grad(): - segmentation_mask = segmenter(text_mask_tensor) - - segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) - segmentation_mask = filter_segmentation_mask(segmentation_mask) - segmentation_mask = torch.nn.functional.interpolate( - segmentation_mask.unsqueeze(0).unsqueeze(0).float(), - size=(256, 256), - mode="nearest", - ) - - image_mask = transform_mask(args.text_mask) - image_mask = torch.from_numpy(image_mask).cuda().unsqueeze(0).unsqueeze(0) - - image = Image.open(args.original_image).convert("RGB").resize((512, 512)) - image_tensor = ( - transforms.ToTensor()(image).unsqueeze(0).cuda().sub_(0.5).div_(0.5) - ) - # masked_image = image_tensor * (1 - image_mask) - # masked_feature = ( - # vae.encode(masked_image).latent_dist.sample().repeat(sample_num, 1, 1, 1) - # ) - # masked_feature = masked_feature * vae.config.scaling_factor - masked_image = torch.zeros(sample_num, 3, 512, 512).to( - "cuda" - ) # (b, 3, 512, 512) - masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64) - masked_feature = masked_feature * vae.config.scaling_factor - - image_mask = torch.nn.functional.interpolate( - image_mask, size=(256, 256), mode="nearest" - ).repeat(sample_num, 1, 1, 1) - segmentation_mask = segmentation_mask * image_mask - # feature_mask = torch.nn.functional.interpolate( - # image_mask, size=(64, 64), mode="nearest" - # ) - feature_mask = torch.ones(sample_num, 1, 64, 64).to("cuda") # (b, 1, 64, 64) - - print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.') - print( - f'{colored("[√]", "green")} segmentation_mask: {segmentation_mask.shape}.' - ) - print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.') - - render_image = Image.open(args.original_image) - - # diffusion process - intermediate_images = [] - - feature_mask = torch.cat([feature_mask]*2) - masked_feature = torch.cat([masked_feature]*2) - segmentation_mask = torch.cat([segmentation_mask]*2) - encoder_hidden_states = torch.cat([encoder_hidden_states,encoder_hidden_states_nocond]) - for t in tqdm(scheduler.timesteps): - with torch.no_grad(): - latent_model_input = torch.cat([input]*2) - latent_model_input = scheduler.scale_model_input( - latent_model_input, t - ) - noise_pred = unet( - sample=latent_model_input, - timestep=t, - encoder_hidden_states=encoder_hidden_states, - segmentation_mask=segmentation_mask, - feature_mask=feature_mask, - masked_feature=masked_feature, - added_cond_kwargs=added_cond_kwargs_cond, # Added for IP-Adapter - ).sample # b, 4, 64, 64 - - noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) - noisy_residual = noise_pred_uncond + args.classifier_free_scale * ( - noise_pred_cond - noise_pred_uncond - ) # b, 4, 64, 64 - input = scheduler.step(noisy_residual, t, input, return_dict=False)[0] - intermediate_images.append(input) - - # decode and visualization - input = 1 / vae.config.scaling_factor * input - sample_images = vae.decode(input.float(), return_dict=False)[0] # (b, 3, 512, 512) - - image_pil = render_image.resize((512, 512)) - segmentation_mask = segmentation_mask[0].squeeze().cpu().numpy() - character_mask_pil = Image.fromarray( - ((segmentation_mask != 0) * 255).astype("uint8") - ).resize((512, 512)) - character_mask_highlight_pil = segmentation_mask_visualization( - args.font_path, segmentation_mask - ) - caption_pil = make_caption_pil(args.font_path, captions) - - # save pred_img - pred_image_list = [] - for image in sample_images.float(): - image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) - image = image.cpu().permute(0, 2, 3, 1).numpy()[0] - image = Image.fromarray((image * 255).round().astype("uint8")).convert("RGB") - pred_image_list.append(image) - - os.makedirs(f"{args.output_dir}/{sub_output_dir}", exist_ok=True) - - # save additional info - if args.mode == "text-to-image": - image_pil.save( - os.path.join(args.output_dir, sub_output_dir, "render_text_image.png") - ) - enhancer = ImageEnhance.Brightness(segmentation_mask_from_pillow) - im_brightness = enhancer.enhance(5) - im_brightness.save( - os.path.join( - args.output_dir, sub_output_dir, "segmentation_mask_from_pillow.png" - ) - ) - if args.mode == "text-to-image-with-template": - template_image.save( - os.path.join(args.output_dir, sub_output_dir, "template.png") - ) - enhancer = ImageEnhance.Brightness(segmentation_mask_pil) - im_brightness = enhancer.enhance(5) - im_brightness.save( - os.path.join( - args.output_dir, sub_output_dir, "segmentation_mask_from_template.png" - ) - ) - if args.mode == "text-inpainting": - character_mask_highlight_pil = character_mask_pil - # background - background = Image.open(args.original_image).resize((512, 512)) - alpha = Image.new("L", background.size, int(255 * 0.2)) - background.putalpha(alpha) - # foreground - foreground = Image.open(args.text_mask).convert("L").resize((512, 512)) - threshold = 200 - alpha = foreground.point(lambda x: 0 if x > threshold else 255, "1") - foreground.putalpha(alpha) - character_mask_pil = Image.alpha_composite( - foreground.convert("RGBA"), background.convert("RGBA") - ).convert("RGB") - # merge - pred_image_list_new = [] - for pred_image in pred_image_list: - ''' - pred_image = inpainting_merge_image( - Image.open(args.original_image), - Image.open(args.text_mask).convert("L"), - pred_image, - ) - ''' - pred_image_list_new.append(pred_image) - pred_image_list = pred_image_list_new - - combine_image( - args, - sub_output_dir, - pred_image_list, - image_pil, - character_mask_pil, - character_mask_highlight_pil, - caption_pil, - ) - - # create a soft link - if os.path.exists(os.path.join(args.output_dir, "latest")): - os.unlink(os.path.join(args.output_dir, "latest")) - os.symlink( - os.path.abspath(os.path.join(args.output_dir, sub_output_dir)), - os.path.abspath(os.path.join(args.output_dir, "latest/")), - ) - - color_sub_output_dir = colored(sub_output_dir, "green") - print( - f'{colored("[√]", "green")} Save successfully. Please check the output at {color_sub_output_dir} OR the latest folder' - ) - - -if __name__ == "__main__": - main() diff --git a/text_diffuser/model/layout_generator.py b/text_diffuser/model/layout_generator.py deleted file mode 100644 index 3934899..0000000 --- a/text_diffuser/model/layout_generator.py +++ /dev/null @@ -1,290 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file aims to predict the layout of keywords in user prompts. -# ------------------------------------------ - -import re -import warnings - -import numpy as np -import torch -from huggingface_hub import hf_hub_download -from model.layout_transformer import LayoutTransformer, TextConditioner -from PIL import Image, ImageDraw, ImageFont -from termcolor import colored -from transformers import CLIPTokenizer -from util import ( - adjust_font_size, - adjust_overlap_box, - alphabet_dic, - get_key_words, - get_width, - shrink_box, -) - - -warnings.filterwarnings("ignore", category=DeprecationWarning) - - -# import layout transformer -model = LayoutTransformer().cuda().eval() - -layout_transformer_path = hf_hub_download( - repo_id="GoGiants1/td-unet15", - filename="layout_transformer.pth", - repo_type="model", -) - -model.load_state_dict(torch.load(layout_transformer_path)) - -# import text encoder and tokenizer -text_encoder = TextConditioner().cuda().eval() -tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - - -def process_caption(font_path, caption, keywords): - # remove punctuations. please remove this statement if you want to paint punctuations - caption = re.sub("([^\u0041-\u005a\u0061-\u007a\u0030-\u0039])", " ", caption) - - # tokenize it into ids and get length - caption_words = tokenizer( - [caption], - truncation=True, - max_length=77, - return_length=True, - return_overflowing_tokens=False, - padding="max_length", - return_tensors="pt", - ) - caption_words_ids = caption_words["input_ids"] # (1, 77) - length = caption_words["length"] # (1, ) - - # convert id to words - words = tokenizer.convert_ids_to_tokens(caption_words_ids.view(-1).tolist()) - words = [i.replace("", "") for i in words] - words_valid = words[: int(length)] - - # store the box coordinates and state of each token - info_array = np.zeros((77, 5)) # (77, 5) - - # split the caption into words and convert them into lower case - caption_split = caption.split() - caption_split = [i.lower() for i in caption_split] - - start_dic = {} # get the start index of each word - state_list = [] # 0: start, 1: middle, 2: special token - word_match_list = [] # the index of the word in the caption - current_caption_index = 0 - current_match = "" - for i in range(length): - - # the first and last token are special tokens - if i == 0 or i == length - 1: - state_list.append(2) - word_match_list.append(127) - continue - - if current_match == "": - state_list.append(0) - start_dic[current_caption_index] = i - else: - state_list.append(1) - - current_match += words_valid[i] - word_match_list.append(current_caption_index) - if current_match == caption_split[current_caption_index]: - current_match = "" - current_caption_index += 1 - - while len(state_list) < 77: - state_list.append(127) - while len(word_match_list) < 77: - word_match_list.append(127) - - length_list = [] - width_list = [] - for i in range(len(word_match_list)): - if word_match_list[i] == 127: - length_list.append(0) - width_list.append(0) - else: - length_list.append(len(caption.split()[word_match_list[i]])) - width_list.append(get_width(font_path, caption.split()[word_match_list[i]])) - - while len(length_list) < 77: - length_list.append(127) - width_list.append(0) - - length_list = torch.Tensor(length_list).long() # (77, ) - width_list = torch.Tensor(width_list).long() # (77, ) - - boxes = [] - duplicate_dict = {} # some words may appear more than once - for keyword in keywords: - keyword = keyword.lower() - if keyword in caption_split: - if keyword not in duplicate_dict: - duplicate_dict[keyword] = caption_split.index(keyword) - index = caption_split.index(keyword) - else: - if ( - duplicate_dict[keyword] + 1 < len(caption_split) - and keyword in caption_split[duplicate_dict[keyword] + 1 :] - ): - index = duplicate_dict[keyword] + caption_split[ - duplicate_dict[keyword] + 1 : - ].index(keyword) - duplicate_dict[keyword] = index - else: - continue - - index = caption_split.index(keyword) - index = start_dic[index] - info_array[index][0] = 1 - - box = [0, 0, 0, 0] - boxes.append(list(box)) - info_array[index][1:] = box - - boxes_length = len(boxes) - if boxes_length > 8: - boxes = boxes[:8] - while len(boxes) < 8: - boxes.append([0, 0, 0, 0]) - - return ( - caption, - length_list, - width_list, - torch.from_numpy(info_array), - words, - torch.Tensor(state_list).long(), - torch.Tensor(word_match_list).long(), - torch.Tensor(boxes), - boxes_length, - ) - - -def get_layout_from_prompt(args): - - # prompt = args.prompt - font_path = args.font_path - keywords = get_key_words(args.prompt) - - print( - f'{colored("[!]", "red")} Detected keywords: {keywords} from prompt {args.prompt}' - ) - - text_embedding, mask = text_encoder(args.prompt) # (1, 77 768) / (1, 77) - - # process all relevant info - ( - caption, - length_list, - width_list, - target, - words, - state_list, - word_match_list, - boxes, - boxes_length, - ) = process_caption(font_path, args.prompt, keywords) - target = target.cuda().unsqueeze(0) # (77, 5) - width_list = width_list.cuda().unsqueeze(0) # (77, ) - length_list = length_list.cuda().unsqueeze(0) # (77, ) - state_list = state_list.cuda().unsqueeze(0) # (77, ) - word_match_list = word_match_list.cuda().unsqueeze(0) # (77, ) - - padding = torch.zeros(1, 1, 4).cuda() - boxes = boxes.unsqueeze(0).cuda() - right_shifted_boxes = torch.cat([padding, boxes[:, 0:-1, :]], 1) # (1, 8, 4) - - def getsize(font, text): - left, top, right, bottom = font.getbbox(text) - return right - left, bottom - top - - # inference - return_boxes = [] - with torch.no_grad(): - for box_index in range(boxes_length): - - if box_index == 0: - encoder_embedding = None - - output, encoder_embedding = model( - text_embedding, - length_list, - width_list, - mask, - state_list, - word_match_list, - target, - right_shifted_boxes, - train=False, - encoder_embedding=encoder_embedding, - ) - output = torch.clamp(output, min=0, max=1) # (1, 8, 4) - - # add overlap detection - output = adjust_overlap_box(output, box_index) # (1, 8, 4) - - right_shifted_boxes[:, box_index + 1, :] = output[:, box_index, :] - xmin, ymin, xmax, ymax = output[0, box_index, :].tolist() - return_boxes.append([xmin, ymin, xmax, ymax]) - - # print the location of keywords - print("index\tkeyword\tx_min\ty_min\tx_max\ty_max") - for index, keyword in enumerate(keywords): - x_min = int(return_boxes[index][0] * 512) - y_min = int(return_boxes[index][1] * 512) - x_max = int(return_boxes[index][2] * 512) - y_max = int(return_boxes[index][3] * 512) - print(f"{index}\t{keyword}\t{x_min}\t{y_min}\t{x_max}\t{y_max}") - - # paint the layout - render_image = Image.new("RGB", (512, 512), (255, 255, 255)) - draw = ImageDraw.Draw(render_image) - segmentation_mask = Image.new("L", (512, 512), 0) - segmentation_mask_draw = ImageDraw.Draw(segmentation_mask) - - for index, box in enumerate(return_boxes): - box = [int(i * 512) for i in box] - xmin, ymin, xmax, ymax = box - - width = xmax - xmin - height = ymax - ymin - text = keywords[index] - - font_size = adjust_font_size(args, width, height, draw, text) - font = ImageFont.truetype(args.font_path, font_size) - - # draw.rectangle([xmin, ymin, xmax,ymax], outline=(255,0,0)) - draw.text((xmin, ymin), text, font=font, fill=(0, 0, 0)) - - boxes = [] - for i, char in enumerate(text): - - # paint character-level segmentation masks - # https://github.com/python-pillow/Pillow/issues/3921 - _, bottom_1 = getsize(font, text[i]) - right, bottom_2 = getsize(font, text[: i + 1]) - bottom = bottom_1 if bottom_1 < bottom_2 else bottom_2 - width, height = font.getmask(char).size - right += xmin - bottom += ymin - top = bottom - height - left = right - width - - char_box = (left, top, right, bottom) - boxes.append(char_box) - - char_index = alphabet_dic[char] - segmentation_mask_draw.rectangle( - shrink_box(char_box, scale_factor=0.9), fill=char_index - ) - - print(f'{colored("[√]", "green")} Layout is successfully generated') - return render_image, segmentation_mask diff --git a/text_diffuser/model/layout_transformer.py b/text_diffuser/model/layout_transformer.py deleted file mode 100644 index 84e242c..0000000 --- a/text_diffuser/model/layout_transformer.py +++ /dev/null @@ -1,153 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file define the Layout Transformer for predicting the layout of keywords. -# ------------------------------------------ - -import torch -import torch.nn as nn -from transformers import CLIPTextModel, CLIPTokenizer - - -class TextConditioner(nn.Module): - - def __init__(self): - super(TextConditioner, self).__init__() - self.transformer = CLIPTextModel.from_pretrained( - "openai/clip-vit-large-patch14" - ) - self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - - # fix - self.transformer.eval() - for param in self.transformer.parameters(): - param.requires_grad = False - - def forward(self, prompt_list): - batch_encoding = self.tokenizer( - prompt_list, - truncation=True, - max_length=77, - return_length=True, - return_overflowing_tokens=False, - padding="max_length", - return_tensors="pt", - ) - text_embedding = self.transformer(batch_encoding["input_ids"].cuda()) - return ( - text_embedding.last_hidden_state.cuda(), - batch_encoding["attention_mask"].cuda(), - ) # 1, 77, 768 / 1, 768 - - -class LayoutTransformer(nn.Module): - - def __init__(self, layer_number=2): - super(LayoutTransformer, self).__init__() - - self.encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) - self.transformer = torch.nn.TransformerEncoder( - self.encoder_layer, num_layers=layer_number - ) - - self.decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) - self.decoder_transformer = torch.nn.TransformerDecoder( - self.decoder_layer, num_layers=layer_number - ) - - self.mask_embedding = nn.Embedding(2, 512) - self.length_embedding = nn.Embedding(256, 512) - self.width_embedding = nn.Embedding(256, 512) - self.position_embedding = nn.Embedding(256, 512) - self.state_embedding = nn.Embedding(256, 512) - self.match_embedding = nn.Embedding(256, 512) - - self.x_embedding = nn.Embedding(512, 512) - self.y_embedding = nn.Embedding(512, 512) - self.w_embedding = nn.Embedding(512, 512) - self.h_embedding = nn.Embedding(512, 512) - - self.encoder_target_embedding = nn.Embedding(256, 512) - - self.input_layer = nn.Sequential( - nn.Linear(768, 512), - nn.ReLU(), - nn.Linear(512, 512), - ) - - self.output_layer = nn.Sequential( - nn.Linear(512, 128), - nn.ReLU(), - nn.Linear(128, 4), - ) - - def forward( - self, - x, - length, - width, - mask, - state, - match, - target, - right_shifted_boxes, - train=False, - encoder_embedding=None, - ): - - # detect whether the encoder_embedding is cached - if encoder_embedding is None: - # augmentation - if train: - width = ( - width - + torch.randint(-3, 3, (width.shape[0], width.shape[1])).cuda() - ) - - x = self.input_layer(x) # (1, 77, 512) - width_embedding = self.width_embedding( - torch.clamp(width, 0, 255).long() - ) # (1, 77, 512) - encoder_target_embedding = self.encoder_target_embedding( - target[:, :, 0].long() - ) # (1, 77, 512) - pe_embedding = self.position_embedding(torch.arange(77).cuda()).unsqueeze( - 0 - ) # (1, 77, 512) - total_embedding = ( - x + width_embedding + pe_embedding + encoder_target_embedding - ) # combine all the embeddings (1, 77, 512) - total_embedding = total_embedding.permute(1, 0, 2) # (77, 1, 512) - encoder_embedding = self.transformer(total_embedding) # (77, 1, 512) - - right_shifted_boxes_resize = (right_shifted_boxes * 512).long() # (1, 8, 4) - right_shifted_boxes_resize = torch.clamp( - right_shifted_boxes_resize, 0, 511 - ) # (1, 8, 4) - - # decoder pe - pe_decoder = torch.arange(8).cuda() # (8, ) - pe_embedding_decoder = self.position_embedding(pe_decoder).unsqueeze( - 0 - ) # (1, 8, 512) - decoder_input = ( - pe_embedding_decoder - + self.x_embedding(right_shifted_boxes_resize[:, :, 0]) - + self.y_embedding(right_shifted_boxes_resize[:, :, 1]) - + self.w_embedding(right_shifted_boxes_resize[:, :, 2]) - + self.h_embedding(right_shifted_boxes_resize[:, :, 3]) - ) # (1, 8, 512) - decoder_input = decoder_input.permute(1, 0, 2) # (8, 1, 512) - - # generate triangular mask - mask = nn.Transformer.generate_square_subsequent_mask(8) # (8, 8) - mask = mask.cuda() # (8, 8) - decoder_result = self.decoder_transformer( - decoder_input, encoder_embedding, tgt_mask=mask - ) # (8, 1, 512) - decoder_result = decoder_result.permute(1, 0, 2) # (1, 8, 512) - - box_prediction = self.output_layer(decoder_result) # (1, 8, 4) - return box_prediction, encoder_embedding diff --git a/text_diffuser/model/text_segmenter/unet.py b/text_diffuser/model/text_segmenter/unet.py deleted file mode 100644 index 903ecbd..0000000 --- a/text_diffuser/model/text_segmenter/unet.py +++ /dev/null @@ -1,54 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file define the architecture of unet. -# ------------------------------------------ -import torch -import torch.nn as nn -from model.text_segmenter.unet_parts import DoubleConv, Down, OutConv, Up - - -class UNet(nn.Module): - def __init__(self, n_channels, n_classes, bilinear=True): - super(UNet, self).__init__() - self.n_channels = n_channels - self.n_classes = n_classes - self.bilinear = bilinear - - self.inc = DoubleConv(n_channels, 64) - self.down1 = Down(64, 128) - self.down2 = Down(128, 256) - self.down3 = Down(256, 512) - factor = 2 if bilinear else 1 - self.down4 = Down(512, 1024 // factor) - self.up1 = Up(1024, 512 // factor, bilinear) - self.up2 = Up(512, 256 // factor, bilinear) - self.up3 = Up(256, 128 // factor, bilinear) - self.up4 = Up(128, 64, bilinear) - self.outc = OutConv(64, n_classes) - - def forward(self, x): - x1 = self.inc(x) - x2 = self.down1(x1) - x3 = self.down2(x2) - x4 = self.down3(x3) - x5 = self.down4(x4) - x = self.up1(x5, x4) - x = self.up2(x, x3) - x = self.up3(x, x2) - x = self.up4(x, x1) - logits = self.outc(x) - # logits = torch.sigmoid(logits) - return logits - - -if __name__ == "__main__": - net = UNet(39, 39, True) - - net = net.cuda() - - image = torch.Tensor(32, 39, 64, 64).cuda() - result = net(image) - print(result.shape) diff --git a/text_diffuser/model/text_segmenter/unet_parts.py b/text_diffuser/model/text_segmenter/unet_parts.py deleted file mode 100644 index 5a45621..0000000 --- a/text_diffuser/model/text_segmenter/unet_parts.py +++ /dev/null @@ -1,81 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file define the architecture of unet. -# ------------------------------------------ - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class DoubleConv(nn.Module): - """(convolution => [BN] => ReLU) * 2""" - - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - if not mid_channels: - mid_channels = out_channels - self.double_conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(mid_channels), - nn.ReLU(inplace=True), - nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(out_channels), - nn.ReLU(inplace=True), - ) - - def forward(self, x): - return self.double_conv(x) - - -class Down(nn.Module): - """Downscaling with maxpool then double conv""" - - def __init__(self, in_channels, out_channels): - super().__init__() - self.maxpool_conv = nn.Sequential( - nn.MaxPool2d(2), DoubleConv(in_channels, out_channels) - ) - - def forward(self, x): - return self.maxpool_conv(x) - - -class Up(nn.Module): - """Upscaling then double conv""" - - def __init__(self, in_channels, out_channels, bilinear=True): - super().__init__() - - # if bilinear, use the normal convolutions to reduce the number of channels - if bilinear: - self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True) - self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) - else: - self.up = nn.ConvTranspose2d( - in_channels, in_channels // 2, kernel_size=2, stride=2 - ) - self.conv = DoubleConv(in_channels, out_channels) - - def forward(self, x1, x2): - x1 = self.up(x1) - # input is CHW - diffY = x2.size()[2] - x1.size()[2] - diffX = x2.size()[3] - x1.size()[3] - - x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) - - x = torch.cat([x2, x1], dim=1) - return self.conv(x) - - -class OutConv(nn.Module): - def __init__(self, in_channels, out_channels): - super(OutConv, self).__init__() - self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) - - def forward(self, x): - return self.conv(x) diff --git a/text_diffuser/pipeline_demo.ipynb b/text_diffuser/pipeline_demo.ipynb deleted file mode 100644 index 0afdd27..0000000 --- a/text_diffuser/pipeline_demo.ipynb +++ /dev/null @@ -1,359 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "edeb0c16aeeb47b98dd8f52e9019b83b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Loading pipeline components...: 0%| | 0/7 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(output)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/text_diffuser/pipeline_text_diffuser_sd15.py b/text_diffuser/pipeline_text_diffuser_sd15.py deleted file mode 100644 index cf7dde2..0000000 --- a/text_diffuser/pipeline_text_diffuser_sd15.py +++ /dev/null @@ -1,1368 +0,0 @@ -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import inspect -from typing import Any, Callable, Dict, List, Optional, Union - -import cv2 -import numpy as np -import torch -from huggingface_hub import hf_hub_download -from model.text_segmenter.unet import UNet -from packaging import version -from t_diffusers.callbacks import ( - MultiPipelineCallbacks, - PipelineCallback, -) -from t_diffusers.unet_2d_condition import ( - UNet2DConditionModel, -) - -# use our own UNet2DConditionModel -from transformers import ( - CLIPImageProcessor, - CLIPTextModel, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) -from util import filter_segmentation_mask - -from diffusers.configuration_utils import FrozenDict -from diffusers.image_processor import PipelineImageInput, VaeImageProcessor -from diffusers.loaders import ( - FromSingleFileMixin, - IPAdapterMixin, - LoraLoaderMixin, - TextualInversionLoaderMixin, -) -from diffusers.models import AutoencoderKL, ImageProjection -from diffusers.models.lora import adjust_lora_scale_text_encoder -from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin -from diffusers.pipelines.stable_diffusion.pipeline_output import ( - StableDiffusionPipelineOutput, -) -from diffusers.pipelines.stable_diffusion.safety_checker import ( - StableDiffusionSafetyChecker, -) -from diffusers.schedulers import KarrasDiffusionSchedulers -from diffusers.utils import ( - USE_PEFT_BACKEND, - deprecate, - logging, - replace_example_docstring, - scale_lora_layers, - unscale_lora_layers, -) -from diffusers.utils.torch_utils import randn_tensor - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import StableDiffusionPipeline - - >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) - >>> pipe = pipe.to("cuda") - - >>> prompt = "a photo of an astronaut riding a horse on mars" - >>> image = pipe(prompt).images[0] - ``` -""" - - -def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): - """ - Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 - """ - std_text = noise_pred_text.std( - dim=list(range(1, noise_pred_text.ndim)), keepdim=True - ) - std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) - # rescale the results from guidance (fixes overexposure) - noise_pred_rescaled = noise_cfg * (std_text / std_cfg) - # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images - noise_cfg = ( - guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg - ) - return noise_cfg - - -def retrieve_timesteps( - scheduler, - num_inference_steps: Optional[int] = None, - device: Optional[Union[str, torch.device]] = None, - timesteps: Optional[List[int]] = None, - sigmas: Optional[List[float]] = None, - **kwargs, -): - """ - Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles - custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. - - Args: - scheduler (`SchedulerMixin`): - The scheduler to get timesteps from. - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` - must be `None`. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - timesteps (`List[int]`, *optional*): - Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, - `num_inference_steps` and `sigmas` must be `None`. - sigmas (`List[float]`, *optional*): - Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, - `num_inference_steps` and `timesteps` must be `None`. - - Returns: - `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the - second element is the number of inference steps. - """ - if timesteps is not None and sigmas is not None: - raise ValueError( - "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" - ) - if timesteps is not None: - accepts_timesteps = "timesteps" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys() - ) - if not accepts_timesteps: - raise ValueError( - f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) - scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) - timesteps = scheduler.timesteps - num_inference_steps = len(timesteps) - elif sigmas is not None: - accept_sigmas = "sigmas" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys() - ) - if not accept_sigmas: - raise ValueError( - f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" sigmas schedules. Please check whether you are using the correct scheduler." - ) - scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) - timesteps = scheduler.timesteps - num_inference_steps = len(timesteps) - else: - scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) - timesteps = scheduler.timesteps - return timesteps, num_inference_steps - - -class StableDiffusionPipeline( - DiffusionPipeline, - StableDiffusionMixin, - TextualInversionLoaderMixin, - LoraLoaderMixin, - IPAdapterMixin, - FromSingleFileMixin, -): - r""" - Pipeline for text-to-image generation using Stable Diffusion. - - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods - implemented for all pipelines (downloading, saving, running on a particular device, etc.). - - The pipeline also inherits the following loading methods: - - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. - text_encoder ([`~transformers.CLIPTextModel`]): - Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). - tokenizer ([`~transformers.CLIPTokenizer`]): - A `CLIPTokenizer` to tokenize text. - unet ([`UNet2DConditionModel`]): - A `UNet2DConditionModel` to denoise the encoded image latents. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - safety_checker ([`StableDiffusionSafetyChecker`]): - Classification module that estimates whether generated images could be considered offensive or harmful. - Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. - feature_extractor ([`~transformers.CLIPImageProcessor`]): - A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. - """ - - model_cpu_offload_seq = "segmenter->text_encoder->image_encoder->unet->vae" - _optional_components = [ - "safety_checker", - "feature_extractor", - "image_encoder", - "segmenter", - ] - _exclude_from_cpu_offload = ["safety_checker"] - _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: KarrasDiffusionSchedulers, - safety_checker: StableDiffusionSafetyChecker, - feature_extractor: CLIPImageProcessor, - image_encoder: CLIPVisionModelWithProjection = None, - requires_safety_checker: bool = True, - ): - super().__init__() - - if ( - hasattr(scheduler.config, "steps_offset") - and scheduler.config.steps_offset != 1 - ): - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" - f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " - "to update the config accordingly as leaving `steps_offset` might led to incorrect results" - " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," - " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" - " file" - ) - deprecate( - "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False - ) - new_config = dict(scheduler.config) - new_config["steps_offset"] = 1 - scheduler._internal_dict = FrozenDict(new_config) - - if ( - hasattr(scheduler.config, "clip_sample") - and scheduler.config.clip_sample is True - ): - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." - " `clip_sample` should be set to False in the configuration file. Please make sure to update the" - " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" - " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" - " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" - ) - deprecate( - "clip_sample not set", "1.0.0", deprecation_message, standard_warn=False - ) - new_config = dict(scheduler.config) - new_config["clip_sample"] = False - scheduler._internal_dict = FrozenDict(new_config) - - if safety_checker is None and requires_safety_checker: - logger.warning( - f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" - " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" - " results in services or applications open to the public. Both the diffusers team and Hugging Face" - " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" - " it only for use-cases that involve analyzing network behavior or auditing its results. For more" - " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." - ) - - if safety_checker is not None and feature_extractor is None: - raise ValueError( - "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" - " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." - ) - - is_unet_version_less_0_9_0 = hasattr( - unet.config, "_diffusers_version" - ) and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse( - "0.9.0.dev0" - ) - is_unet_sample_size_less_64 = ( - hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - ) - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate( - "sample_size<64", "1.0.0", deprecation_message, standard_warn=False - ) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - - print("Downloading text_segmenter.pth...") - character_segmenter_path = hf_hub_download( - "GoGiants1/td-unet15", "text_segmenter.pth", repo_type="model" - ) - segmenter = UNet(3, 96, True).cuda() - self.segmenter = torch.nn.DataParallel(segmenter) - self.segmenter.load_state_dict(torch.load(character_segmenter_path)) - self.segmenter.eval() - self.register_modules( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - safety_checker=safety_checker, - feature_extractor=feature_extractor, - image_encoder=image_encoder, - ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) - self.mask_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor, - do_normalize=False, - do_binarize=True, - do_convert_grayscale=True, - ) - self.register_to_config(requires_safety_checker=requires_safety_checker) - - def _encode_prompt( - self, - prompt, - device, - num_images_per_prompt, - do_classifier_free_guidance, - negative_prompt=None, - prompt_embeds: Optional[torch.Tensor] = None, - negative_prompt_embeds: Optional[torch.Tensor] = None, - lora_scale: Optional[float] = None, - **kwargs, - ): - deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." - deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) - - prompt_embeds_tuple = self.encode_prompt( - prompt=prompt, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=do_classifier_free_guidance, - negative_prompt=negative_prompt, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - lora_scale=lora_scale, - **kwargs, - ) - - # concatenate for backwards comp - prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) - - return prompt_embeds - - def encode_prompt( - self, - prompt, - device, - num_images_per_prompt, - do_classifier_free_guidance, - negative_prompt=None, - prompt_embeds: Optional[torch.Tensor] = None, - negative_prompt_embeds: Optional[torch.Tensor] = None, - lora_scale: Optional[float] = None, - clip_skip: Optional[int] = None, - ): - r""" - Encodes the prompt into text encoder hidden states. - - Args: - prompt (`str` or `List[str]`, *optional*): - prompt to be encoded - device: (`torch.device`): - torch device - num_images_per_prompt (`int`): - number of images that should be generated per prompt - do_classifier_free_guidance (`bool`): - whether to use classifier free guidance or not - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is - less than `1`). - prompt_embeds (`torch.Tensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.Tensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - lora_scale (`float`, *optional*): - A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. - clip_skip (`int`, *optional*): - Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that - the output of the pre-final layer will be used for computing the prompt embeddings. - """ - # set lora scale so that monkey patched LoRA - # function of text encoder can correctly access it - if lora_scale is not None and isinstance(self, LoraLoaderMixin): - self._lora_scale = lora_scale - - # dynamically adjust the LoRA scale - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) - else: - scale_lora_layers(self.text_encoder, lora_scale) - - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - if prompt_embeds is None: - # textual inversion: process multi-vector tokens if necessary - if isinstance(self, TextualInversionLoaderMixin): - prompt = self.maybe_convert_prompt(prompt, self.tokenizer) - - text_inputs = self.tokenizer( - prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer( - prompt, padding="longest", return_tensors="pt" - ).input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[ - -1 - ] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode( - untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] - ) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {self.tokenizer.model_max_length} tokens: {removed_text}" - ) - - if ( - hasattr(self.text_encoder.config, "use_attention_mask") - and self.text_encoder.config.use_attention_mask - ): - attention_mask = text_inputs.attention_mask.to(device) - else: - attention_mask = None - - if clip_skip is None: - prompt_embeds = self.text_encoder( - text_input_ids.to(device), attention_mask=attention_mask - ) - prompt_embeds = prompt_embeds[0] - else: - prompt_embeds = self.text_encoder( - text_input_ids.to(device), - attention_mask=attention_mask, - output_hidden_states=True, - ) - # Access the `hidden_states` first, that contains a tuple of - # all the hidden states from the encoder layers. Then index into - # the tuple to access the hidden states from the desired layer. - prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] - # We also need to apply the final LayerNorm here to not mess with the - # representations. The `last_hidden_states` that we typically use for - # obtaining the final prompt representations passes through the LayerNorm - # layer. - prompt_embeds = self.text_encoder.text_model.final_layer_norm( - prompt_embeds - ) - - if self.text_encoder is not None: - prompt_embeds_dtype = self.text_encoder.dtype - elif self.unet is not None: - prompt_embeds_dtype = self.unet.dtype - else: - prompt_embeds_dtype = prompt_embeds.dtype - - prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) - - bs_embed, seq_len, _ = prompt_embeds.shape - # duplicate text embeddings for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view( - bs_embed * num_images_per_prompt, seq_len, -1 - ) - - # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance and negative_prompt_embeds is None: - uncond_tokens: List[str] - if negative_prompt is None: - uncond_tokens = [""] * batch_size - elif prompt is not None and type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}." - ) - elif isinstance(negative_prompt, str): - uncond_tokens = [negative_prompt] - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`." - ) - else: - uncond_tokens = negative_prompt - - # textual inversion: process multi-vector tokens if necessary - if isinstance(self, TextualInversionLoaderMixin): - uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) - - max_length = prompt_embeds.shape[1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - if ( - hasattr(self.text_encoder.config, "use_attention_mask") - and self.text_encoder.config.use_attention_mask - ): - attention_mask = uncond_input.attention_mask.to(device) - else: - attention_mask = None - - negative_prompt_embeds = self.text_encoder( - uncond_input.input_ids.to(device), - attention_mask=attention_mask, - ) - negative_prompt_embeds = negative_prompt_embeds[0] - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - negative_prompt_embeds = negative_prompt_embeds.to( - dtype=prompt_embeds_dtype, device=device - ) - - negative_prompt_embeds = negative_prompt_embeds.repeat( - 1, num_images_per_prompt, 1 - ) - negative_prompt_embeds = negative_prompt_embeds.view( - batch_size * num_images_per_prompt, seq_len, -1 - ) - - if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder, lora_scale) - - return prompt_embeds, negative_prompt_embeds - - def encode_image( - self, image, device, num_images_per_prompt, output_hidden_states=None - ): - dtype = next(self.image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = self.feature_extractor(image, return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - if output_hidden_states: - image_enc_hidden_states = self.image_encoder( - image, output_hidden_states=True - ).hidden_states[-2] - image_enc_hidden_states = image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - uncond_image_enc_hidden_states = self.image_encoder( - torch.zeros_like(image), output_hidden_states=True - ).hidden_states[-2] - uncond_image_enc_hidden_states = ( - uncond_image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - ) - return image_enc_hidden_states, uncond_image_enc_hidden_states - else: - image_embeds = self.image_encoder(image).image_embeds - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_embeds = torch.zeros_like(image_embeds) - - return image_embeds, uncond_image_embeds - - def prepare_ip_adapter_image_embeds( - self, - ip_adapter_image, - ip_adapter_image_embeds, - device, - num_images_per_prompt, - do_classifier_free_guidance, - ): - if ip_adapter_image_embeds is None: - if not isinstance(ip_adapter_image, list): - ip_adapter_image = [ip_adapter_image] - - if len(ip_adapter_image) != len( - self.unet.encoder_hid_proj.image_projection_layers - ): - raise ValueError( - f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - ) - - image_embeds = [] - for single_ip_adapter_image, image_proj_layer in zip( - ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers - ): - output_hidden_state = not isinstance(image_proj_layer, ImageProjection) - single_image_embeds, single_negative_image_embeds = self.encode_image( - single_ip_adapter_image, device, 1, output_hidden_state - ) - single_image_embeds = torch.stack( - [single_image_embeds] * num_images_per_prompt, dim=0 - ) - single_negative_image_embeds = torch.stack( - [single_negative_image_embeds] * num_images_per_prompt, dim=0 - ) - - if do_classifier_free_guidance: - single_image_embeds = torch.cat( - [single_negative_image_embeds, single_image_embeds] - ) - single_image_embeds = single_image_embeds.to(device) - - image_embeds.append(single_image_embeds) - else: - repeat_dims = [1] - image_embeds = [] - for single_image_embeds in ip_adapter_image_embeds: - if do_classifier_free_guidance: - single_negative_image_embeds, single_image_embeds = ( - single_image_embeds.chunk(2) - ) - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, - *(repeat_dims * len(single_image_embeds.shape[1:])), - ) - single_negative_image_embeds = single_negative_image_embeds.repeat( - num_images_per_prompt, - *(repeat_dims * len(single_negative_image_embeds.shape[1:])), - ) - single_image_embeds = torch.cat( - [single_negative_image_embeds, single_image_embeds] - ) - else: - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, - *(repeat_dims * len(single_image_embeds.shape[1:])), - ) - image_embeds.append(single_image_embeds) - - return image_embeds - - def run_safety_checker(self, image, device, dtype): - if self.safety_checker is None: - has_nsfw_concept = None - else: - if torch.is_tensor(image): - feature_extractor_input = self.image_processor.postprocess( - image, output_type="pil" - ) - else: - feature_extractor_input = self.image_processor.numpy_to_pil(image) - safety_checker_input = self.feature_extractor( - feature_extractor_input, return_tensors="pt" - ).to(device) - image, has_nsfw_concept = self.safety_checker( - images=image, clip_input=safety_checker_input.pixel_values.to(dtype) - ) - return image, has_nsfw_concept - - def decode_latents(self, latents): - deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" - deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) - - latents = 1 / self.vae.config.scaling_factor * latents - image = self.vae.decode(latents, return_dict=False)[0] - image = (image / 2 + 0.5).clamp(0, 1) - # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 - image = image.cpu().permute(0, 2, 3, 1).float().numpy() - return image - - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set( - inspect.signature(self.scheduler.step).parameters.keys() - ) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set( - inspect.signature(self.scheduler.step).parameters.keys() - ) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs( - self, - prompt, - height, - width, - callback_steps, - negative_prompt=None, - prompt_embeds=None, - negative_prompt_embeds=None, - ip_adapter_image=None, - ip_adapter_image_embeds=None, - callback_on_step_end_tensor_inputs=None, - ): - if height % 8 != 0 or width % 8 != 0: - raise ValueError( - f"`height` and `width` have to be divisible by 8 but are {height} and {width}." - ) - - if callback_steps is not None and ( - not isinstance(callback_steps, int) or callback_steps <= 0 - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs - for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) - - if prompt is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two." - ) - elif prompt is None and prompt_embeds is None: - raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and ( - not isinstance(prompt, str) and not isinstance(prompt, list) - ): - raise ValueError( - f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" - ) - - if negative_prompt is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - - if prompt_embeds is not None and negative_prompt_embeds is not None: - if prompt_embeds.shape != negative_prompt_embeds.shape: - raise ValueError( - "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" - f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" - f" {negative_prompt_embeds.shape}." - ) - - if ip_adapter_image is not None and ip_adapter_image_embeds is not None: - raise ValueError( - "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." - ) - - if ip_adapter_image_embeds is not None: - if not isinstance(ip_adapter_image_embeds, list): - raise ValueError( - f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" - ) - elif ip_adapter_image_embeds[0].ndim not in [3, 4]: - raise ValueError( - f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" - ) - - def prepare_latents( - self, - batch_size, - num_channels_latents, - height, - width, - dtype, - device, - generator, - latents=None, - ): - shape = ( - batch_size, - num_channels_latents, - int(height) // self.vae_scale_factor, - int(width) // self.vae_scale_factor, - ) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - - if latents is None: - latents = randn_tensor( - shape, generator=generator, device=device, dtype=dtype - ) - else: - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding - def get_guidance_scale_embedding( - self, - w: torch.Tensor, - embedding_dim: int = 512, - dtype: torch.dtype = torch.float32, - ) -> torch.Tensor: - """ - See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 - - Args: - w (`torch.Tensor`): - Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. - embedding_dim (`int`, *optional*, defaults to 512): - Dimension of the embeddings to generate. - dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): - Data type of the generated embeddings. - - Returns: - `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`. - """ - assert len(w.shape) == 1 - w = w * 1000.0 - - half_dim = embedding_dim // 2 - emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) - emb = w.to(dtype)[:, None] * emb[None, :] - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) - if embedding_dim % 2 == 1: # zero pad - emb = torch.nn.functional.pad(emb, (0, 1)) - assert emb.shape == (w.shape[0], embedding_dim) - return emb - - @property - def guidance_scale(self): - return self._guidance_scale - - @property - def guidance_rescale(self): - return self._guidance_rescale - - @property - def clip_skip(self): - return self._clip_skip - - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - @property - def do_classifier_free_guidance(self): - return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None - - @property - def cross_attention_kwargs(self): - return self._cross_attention_kwargs - - @property - def num_timesteps(self): - return self._num_timesteps - - @property - def interrupt(self): - return self._interrupt - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - input_image: PipelineImageInput, - text_mask_image: PipelineImageInput, - prompt: Union[str, List[str]] = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - timesteps: List[int] = None, - sigmas: List[float] = None, - guidance_scale: float = 7.5, - negative_prompt: Optional[Union[str, List[str]]] = None, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.Tensor] = None, - prompt_embeds: Optional[torch.Tensor] = None, - negative_prompt_embeds: Optional[torch.Tensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guidance_rescale: float = 0.0, - clip_skip: Optional[int] = None, - callback_on_step_end: Optional[ - Union[ - Callable[[int, int, Dict], None], - PipelineCallback, - MultiPipelineCallbacks, - ] - ] = None, - callback_on_step_end_tensor_inputs: List[str] = ["latents"], - **kwargs, - ): - r""" - The call function to the pipeline for generation. - - Args: - prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. - height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): - The height in pixels of the generated image. - width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): - The width in pixels of the generated image. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - timesteps (`List[int]`, *optional*): - Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument - in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is - passed will be used. Must be in descending order. - sigmas (`List[float]`, *optional*): - Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in - their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed - will be used. - guidance_scale (`float`, *optional*, defaults to 7.5): - A higher guidance scale value encourages the model to generate images closely linked to the text - `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide what to not include in image generation. If not defined, you need to - pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies - to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make - generation deterministic. - latents (`torch.Tensor`, *optional*): - Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor is generated by sampling using the supplied random `generator`. - prompt_embeds (`torch.Tensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not - provided, text embeddings are generated from the `prompt` input argument. - negative_prompt_embeds (`torch.Tensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If - not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of - IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should - contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not - provided, embeddings are computed from the `ip_adapter_image` input argument. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generated image. Choose between `PIL.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in - [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - guidance_rescale (`float`, *optional*, defaults to 0.0): - Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are - Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when - using zero terminal SNR. - clip_skip (`int`, *optional*): - Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that - the output of the pre-final layer will be used for computing the prompt embeddings. - callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): - A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of - each denoising step during the inference. with the following arguments: `callback_on_step_end(self: - DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a - list of all tensors as specified by `callback_on_step_end_tensor_inputs`. - callback_on_step_end_tensor_inputs (`List`, *optional*): - The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list - will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the - `._callback_tensor_inputs` attribute of your pipeline class. - - Examples: - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, - otherwise a `tuple` is returned where the first element is a list with the generated images and the - second element is a list of `bool`s indicating whether the corresponding generated image contains - "not-safe-for-work" (nsfw) content. - """ - - callback = kwargs.pop("callback", None) - callback_steps = kwargs.pop("callback_steps", None) - - if callback is not None: - deprecate( - "callback", - "1.0.0", - "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", - ) - if callback_steps is not None: - deprecate( - "callback_steps", - "1.0.0", - "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", - ) - - if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): - callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs - - # 0. Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - # to deal with lora scaling and other possible forward hooks - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, - height, - width, - callback_steps, - negative_prompt, - prompt_embeds, - negative_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - callback_on_step_end_tensor_inputs, - ) - - self._guidance_scale = guidance_scale - self._guidance_rescale = guidance_rescale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._interrupt = False - - # 2. Define call parameters - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - sample_num = batch_size * num_images_per_prompt - device = self._execution_device - dtype = self.unet.dtype - - ##### For Text Diff - # 1.1 Prepare text mask image - if text_mask_image is not None: - threshold = 50 - _, text_mask = cv2.threshold( - text_mask_image, threshold, 255, cv2.THRESH_BINARY - ) - text_mask = cv2.resize( - text_mask, (width, height), interpolation=cv2.INTER_NEAREST - ) - # make textmask to RGB 3 channel - from PIL import Image - from torchvision.transforms import ToTensor - - text_mask = Image.fromarray(text_mask).convert("RGB") - - text_mask_tensor = ( - ToTensor()(text_mask) - .unsqueeze(0) - .sub_(0.5) - .div_(0.5) - .to(device=device, dtype=dtype) - ) - - segmentation_mask: torch.Tensor = self.segmenter(text_mask_tensor) - segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) - segmentation_mask = filter_segmentation_mask(segmentation_mask) - segmentation_mask = torch.nn.functional.interpolate( - segmentation_mask.unsqueeze(0).unsqueeze(0).to(dtype=dtype), - size=(256, 256), # TODO: Why 256? - mode="nearest", - ) - - img = text_mask_image - img = cv2.resize(img, (width, height), interpolation=cv2.INTER_NEAREST) - gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - # making the image mask for inpainting ? - _, binary = cv2.threshold( - gray, 250, 255, cv2.THRESH_BINARY - ) # pixel value is set to 0 or 255 according to the threshold - image_mask = 1 - (binary.astype(np.float32) / 255) - image_mask = torch.from_numpy(image_mask).unsqueeze(0).unsqueeze(0).to(device=device, dtype=dtype) - - image = input_image.convert("RGB").resize((width, height)) - image_tensor = ( - ToTensor()(image) - .unsqueeze(0) - .sub_(0.5) - .div_(0.5) - .to(device=device, dtype=dtype) - ) - - # 1.2 prepare mask for inpainting - - masked_image = image_tensor * (1 - image_mask) - masked_feature = ( - self.vae.encode(masked_image) - .latent_dist.sample() - .repeat(sample_num, 1, 1, 1) - ) - - #### Original ##### - # masked_feature = masked_feature * vae.config.scaling_factor - # masked_image = torch.zeros(sample_num, 3, 512, 512).to( - # "cuda" - # ) # (b, 3, 512, 512) - # masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64) - # masked_feature = masked_feature * vae.config.scaling_factor - - ##################### - - ##### Background ##### - - masked_image = torch.zeros(sample_num, 3, width, height).to( - device - ) # (b, 3, 512, 512) - masked_feature = self.vae.encode( - masked_image - ).latent_dist.sample() # (b, 4, 64, 64) - masked_feature = masked_feature * self.vae.config.scaling_factor - - # TODO: Hard coded for 256x256 - image_mask = torch.nn.functional.interpolate( - image_mask, size=(256, 256), mode="nearest" - ).repeat(sample_num, 1, 1, 1) - - segmentation_mask = segmentation_mask * image_mask # (b, 1, 512, 512) - - # feature_mask = torch.nn.functional.interpolate( - # image_mask, size=(64, 64), mode="nearest" - # ) # 원본 - - feature_mask = torch.ones(sample_num, 1, 64, 64).to(device) # (b, 1, 64, 64) - - # 3. Encode input prompt - lora_scale = ( - self.cross_attention_kwargs.get("scale", None) - if self.cross_attention_kwargs is not None - else None - ) - - prompt_embeds, negative_prompt_embeds = self.encode_prompt( - prompt, - device, - num_images_per_prompt, - self.do_classifier_free_guidance, - negative_prompt, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - lora_scale=lora_scale, - clip_skip=self.clip_skip, - ) - - # For classifier free guidance, we need to do two forward passes. - # Here we concatenate the unconditional and text embeddings into a single batch - # to avoid doing two forward passes - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) - - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 4. Prepare timesteps - timesteps, num_inference_steps = retrieve_timesteps( - self.scheduler, num_inference_steps, device, timesteps, sigmas - ) - - # 5. Prepare latent variables - # num_channels_latents = self.unet.config.in_channels ## 원본 - num_channels_latents = 4 - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - 4, #FIXME: Hardcoded... - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - ) - - # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 6.1 Add image embeds for IP-Adapter - added_cond_kwargs = ( - {"image_embeds": image_embeds} - if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) - else None - ) - - # 6.2 Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat( - batch_size * num_images_per_prompt - ) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim - ).to(device=device, dtype=latents.dtype) - - # 7. Denoising loop - num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order - self._num_timesteps = len(timesteps) - - print("latent shape", latents.shape) - print("prompt_embeds shape or enc hidden state", prompt_embeds.shape) - print("feature_mask shape", feature_mask.shape) - print("masked_feature shape", masked_feature.shape) - print("segmentation_mask shape", segmentation_mask.shape) - feature_mask = torch.cat([feature_mask] * 2, dim=0) - masked_feature = torch.cat([masked_feature] * 2, dim=0) - segmentation_mask = torch.cat([segmentation_mask] * 2, dim=0) - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - if self.interrupt: - continue - - # expand the latents if we are doing classifier free guidance - latent_model_input = ( - torch.cat([latents] * 2) - if self.do_classifier_free_guidance - else latents - ) - - latent_model_input = self.scheduler.scale_model_input( - latent_model_input, t - ) - - # predict the noise residual - noise_pred = self.unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - timestep_cond=timestep_cond, - cross_attention_kwargs=self.cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - #### ADDED#### - segmentation_mask=segmentation_mask, - feature_mask=feature_mask, - masked_feature=masked_feature, - ############## - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * ( - noise_pred_text - noise_pred_uncond - ) - - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg( - noise_pred, - noise_pred_text, - guidance_rescale=self.guidance_rescale, - ) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step( - noise_pred, t, latents, **extra_step_kwargs, return_dict=False - )[0] - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop( - "negative_prompt_embeds", negative_prompt_embeds - ) - - # call the callback, if provided - if i == len(timesteps) - 1 or ( - (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 - ): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if not output_type == "latent": - image = self.vae.decode( - latents / self.vae.config.scaling_factor, - return_dict=False, - generator=generator, - )[0] - image, has_nsfw_concept = self.run_safety_checker( - image, device, prompt_embeds.dtype - ) - else: - image = latents - has_nsfw_concept = None - - if has_nsfw_concept is None: - do_denormalize = [True] * image.shape[0] - else: - do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] - - image = self.image_processor.postprocess( - image, output_type=output_type, do_denormalize=do_denormalize - ) - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return (image, has_nsfw_concept) - - return StableDiffusionPipelineOutput( - images=image, nsfw_content_detected=has_nsfw_concept - ) diff --git a/text_diffuser/t_diffusers/__init__.py b/text_diffuser/t_diffusers/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/text_diffuser/t_diffusers/callbacks.py b/text_diffuser/t_diffusers/callbacks.py deleted file mode 100644 index 84cc50a..0000000 --- a/text_diffuser/t_diffusers/callbacks.py +++ /dev/null @@ -1,193 +0,0 @@ -# From diffusers main branch - -from typing import Any, Dict, List - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.utils import CONFIG_NAME - - -class PipelineCallback(ConfigMixin): - """ - Base class for all the official callbacks used in a pipeline. This class provides a structure for implementing - custom callbacks and ensures that all callbacks have a consistent interface. - - Please implement the following: - `tensor_inputs`: This should return a list of tensor inputs specific to your callback. You will only be able to - include - variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. - `callback_fn`: This method defines the core functionality of your callback. - """ - - config_name = CONFIG_NAME - - @register_to_config - def __init__(self, cutoff_step_ratio=1.0, cutoff_step_index=None): - super().__init__() - - if (cutoff_step_ratio is None and cutoff_step_index is None) or ( - cutoff_step_ratio is not None and cutoff_step_index is not None - ): - raise ValueError( - "Either cutoff_step_ratio or cutoff_step_index should be provided, not both or none." - ) - - if cutoff_step_ratio is not None and ( - not isinstance(cutoff_step_ratio, float) - or not (0.0 <= cutoff_step_ratio <= 1.0) - ): - raise ValueError("cutoff_step_ratio must be a float between 0.0 and 1.0.") - - @property - def tensor_inputs(self) -> List[str]: - raise NotImplementedError( - f"You need to set the attribute `tensor_inputs` for {self.__class__}" - ) - - def callback_fn( - self, pipeline, step_index, timesteps, callback_kwargs - ) -> Dict[str, Any]: - raise NotImplementedError( - f"You need to implement the method `callback_fn` for {self.__class__}" - ) - - def __call__( - self, pipeline, step_index, timestep, callback_kwargs - ) -> Dict[str, Any]: - return self.callback_fn(pipeline, step_index, timestep, callback_kwargs) - - -class MultiPipelineCallbacks: - """ - This class is designed to handle multiple pipeline callbacks. It accepts a list of PipelineCallback objects and - provides a unified interface for calling all of them. - """ - - def __init__(self, callbacks: List[PipelineCallback]): - self.callbacks = callbacks - - @property - def tensor_inputs(self) -> List[str]: - return [ - input for callback in self.callbacks for input in callback.tensor_inputs - ] - - def __call__( - self, pipeline, step_index, timestep, callback_kwargs - ) -> Dict[str, Any]: - """ - Calls all the callbacks in order with the given arguments and returns the final callback_kwargs. - """ - for callback in self.callbacks: - callback_kwargs = callback(pipeline, step_index, timestep, callback_kwargs) - - return callback_kwargs - - -class SDCFGCutoffCallback(PipelineCallback): - """ - Callback function for Stable Diffusion Pipelines. After certain number of steps (set by `cutoff_step_ratio` or - `cutoff_step_index`), this callback will disable the CFG. - - Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step. - """ - - tensor_inputs = ["prompt_embeds"] - - def callback_fn( - self, pipeline, step_index, timestep, callback_kwargs - ) -> Dict[str, Any]: - cutoff_step_ratio = self.config.cutoff_step_ratio - cutoff_step_index = self.config.cutoff_step_index - - # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio - cutoff_step = ( - cutoff_step_index - if cutoff_step_index is not None - else int(pipeline.num_timesteps * cutoff_step_ratio) - ) - - if step_index == cutoff_step: - prompt_embeds = callback_kwargs[self.tensor_inputs[0]] - prompt_embeds = prompt_embeds[ - -1: - ] # "-1" denotes the embeddings for conditional text tokens. - - pipeline._guidance_scale = 0.0 - - callback_kwargs[self.tensor_inputs[0]] = prompt_embeds - return callback_kwargs - - -class SDXLCFGCutoffCallback(PipelineCallback): - """ - Callback function for Stable Diffusion XL Pipelines. After certain number of steps (set by `cutoff_step_ratio` or - `cutoff_step_index`), this callback will disable the CFG. - - Note: This callback mutates the pipeline by changing the `_guidance_scale` attribute to 0.0 after the cutoff step. - """ - - tensor_inputs = ["prompt_embeds", "add_text_embeds", "add_time_ids"] - - def callback_fn( - self, pipeline, step_index, timestep, callback_kwargs - ) -> Dict[str, Any]: - cutoff_step_ratio = self.config.cutoff_step_ratio - cutoff_step_index = self.config.cutoff_step_index - - # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio - cutoff_step = ( - cutoff_step_index - if cutoff_step_index is not None - else int(pipeline.num_timesteps * cutoff_step_ratio) - ) - - if step_index == cutoff_step: - prompt_embeds = callback_kwargs[self.tensor_inputs[0]] - prompt_embeds = prompt_embeds[ - -1: - ] # "-1" denotes the embeddings for conditional text tokens. - - add_text_embeds = callback_kwargs[self.tensor_inputs[1]] - add_text_embeds = add_text_embeds[ - -1: - ] # "-1" denotes the embeddings for conditional pooled text tokens - - add_time_ids = callback_kwargs[self.tensor_inputs[2]] - add_time_ids = add_time_ids[ - -1: - ] # "-1" denotes the embeddings for conditional added time vector - - pipeline._guidance_scale = 0.0 - - callback_kwargs[self.tensor_inputs[0]] = prompt_embeds - callback_kwargs[self.tensor_inputs[1]] = add_text_embeds - callback_kwargs[self.tensor_inputs[2]] = add_time_ids - return callback_kwargs - - -class IPAdapterScaleCutoffCallback(PipelineCallback): - """ - Callback function for any pipeline that inherits `IPAdapterMixin`. After certain number of steps (set by - `cutoff_step_ratio` or `cutoff_step_index`), this callback will set the IP Adapter scale to `0.0`. - - Note: This callback mutates the IP Adapter attention processors by setting the scale to 0.0 after the cutoff step. - """ - - tensor_inputs = [] - - def callback_fn( - self, pipeline, step_index, timestep, callback_kwargs - ) -> Dict[str, Any]: - cutoff_step_ratio = self.config.cutoff_step_ratio - cutoff_step_index = self.config.cutoff_step_index - - # Use cutoff_step_index if it's not None, otherwise use cutoff_step_ratio - cutoff_step = ( - cutoff_step_index - if cutoff_step_index is not None - else int(pipeline.num_timesteps * cutoff_step_ratio) - ) - - if step_index == cutoff_step: - pipeline.set_ip_adapter_scale(0.0) - return callback_kwargs diff --git a/text_diffuser/t_diffusers/modeling_utils.py b/text_diffuser/t_diffusers/modeling_utils.py deleted file mode 100644 index 73ea5fb..0000000 --- a/text_diffuser/t_diffusers/modeling_utils.py +++ /dev/null @@ -1,1021 +0,0 @@ -# coding=utf-8 -# Copyright 2024 The HuggingFace Inc. team. -# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import inspect -import itertools -import os -import re -from collections import OrderedDict -from functools import partial -from typing import Any, Callable, List, Optional, Tuple, Union - -import safetensors -import torch -from huggingface_hub import create_repo -from huggingface_hub.utils import validate_hf_hub_args -from torch import Tensor, nn - -from .. import __version__ -from ..utils import ( - CONFIG_NAME, - FLAX_WEIGHTS_NAME, - SAFETENSORS_FILE_EXTENSION, - SAFETENSORS_WEIGHTS_NAME, - WEIGHTS_NAME, - _add_variant, - _get_model_file, - deprecate, - is_accelerate_available, - is_torch_version, - logging, -) -from ..utils.hub_utils import PushToHubMixin, load_or_create_model_card, populate_model_card - - -logger = logging.get_logger(__name__) - - -if is_torch_version(">=", "1.9.0"): - _LOW_CPU_MEM_USAGE_DEFAULT = True -else: - _LOW_CPU_MEM_USAGE_DEFAULT = False - - -if is_accelerate_available(): - import accelerate - from accelerate.utils import set_module_tensor_to_device - from accelerate.utils.versions import is_torch_version - - -def get_parameter_device(parameter: torch.nn.Module) -> torch.device: - try: - parameters_and_buffers = itertools.chain(parameter.parameters(), parameter.buffers()) - return next(parameters_and_buffers).device - except StopIteration: - # For torch.nn.DataParallel compatibility in PyTorch 1.5 - - def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]: - tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] - return tuples - - gen = parameter._named_members(get_members_fn=find_tensor_attributes) - first_tuple = next(gen) - return first_tuple[1].device - - -def get_parameter_dtype(parameter: torch.nn.Module) -> torch.dtype: - try: - params = tuple(parameter.parameters()) - if len(params) > 0: - return params[0].dtype - - buffers = tuple(parameter.buffers()) - if len(buffers) > 0: - return buffers[0].dtype - - except StopIteration: - # For torch.nn.DataParallel compatibility in PyTorch 1.5 - - def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]: - tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] - return tuples - - gen = parameter._named_members(get_members_fn=find_tensor_attributes) - first_tuple = next(gen) - return first_tuple[1].dtype - - -def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None): - """ - Reads a checkpoint file, returning properly formatted errors if they arise. - """ - try: - file_extension = os.path.basename(checkpoint_file).split(".")[-1] - if file_extension == SAFETENSORS_FILE_EXTENSION: - return safetensors.torch.load_file(checkpoint_file, device="cpu") - else: - return torch.load(checkpoint_file, map_location="cpu") - except Exception as e: - try: - with open(checkpoint_file) as f: - if f.read().startswith("version"): - raise OSError( - "You seem to have cloned a repository without having git-lfs installed. Please install " - "git-lfs and run `git lfs install` followed by `git lfs pull` in the folder " - "you cloned." - ) - else: - raise ValueError( - f"Unable to locate the file {checkpoint_file} which is necessary to load this pretrained " - "model. Make sure you have saved the model properly." - ) from e - except (UnicodeDecodeError, ValueError): - raise OSError( - f"Unable to load weights from checkpoint file for '{checkpoint_file}' " f"at '{checkpoint_file}'. " - ) - - -def load_model_dict_into_meta( - model, - state_dict: OrderedDict, - device: Optional[Union[str, torch.device]] = None, - dtype: Optional[Union[str, torch.dtype]] = None, - model_name_or_path: Optional[str] = None, -) -> List[str]: - device = device or torch.device("cpu") - dtype = dtype or torch.float32 - - accepts_dtype = "dtype" in set(inspect.signature(set_module_tensor_to_device).parameters.keys()) - - unexpected_keys = [] - empty_state_dict = model.state_dict() - for param_name, param in state_dict.items(): - if param_name not in empty_state_dict: - unexpected_keys.append(param_name) - continue - - if empty_state_dict[param_name].shape != param.shape: - model_name_or_path_str = f"{model_name_or_path} " if model_name_or_path is not None else "" - raise ValueError( - f"Cannot load {model_name_or_path_str}because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example." - ) - - if accepts_dtype: - set_module_tensor_to_device(model, param_name, device, value=param, dtype=dtype) - else: - set_module_tensor_to_device(model, param_name, device, value=param) - return unexpected_keys - - -def _load_state_dict_into_model(model_to_load, state_dict: OrderedDict) -> List[str]: - # Convert old format to new format if needed from a PyTorch state_dict - # copy state_dict so _load_from_state_dict can modify it - state_dict = state_dict.copy() - error_msgs = [] - - # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants - # so we need to apply the function recursively. - def load(module: torch.nn.Module, prefix: str = ""): - args = (state_dict, prefix, {}, True, [], [], error_msgs) - module._load_from_state_dict(*args) - - for name, child in module._modules.items(): - if child is not None: - load(child, prefix + name + ".") - - load(model_to_load) - - return error_msgs - - -class ModelMixin(torch.nn.Module, PushToHubMixin): - r""" - Base class for all models. - - [`ModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and - saving models. - - - **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`]. - """ - - config_name = CONFIG_NAME - _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"] - _supports_gradient_checkpointing = False - _keys_to_ignore_on_load_unexpected = None - - def __init__(self): - super().__init__() - - def __getattr__(self, name: str) -> Any: - """The only reason we overwrite `getattr` here is to gracefully deprecate accessing - config attributes directly. See https://github.com/huggingface/diffusers/pull/3129 We need to overwrite - __getattr__ here in addition so that we don't trigger `torch.nn.Module`'s __getattr__': - https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module - """ - - is_in_config = "_internal_dict" in self.__dict__ and hasattr(self.__dict__["_internal_dict"], name) - is_attribute = name in self.__dict__ - - if is_in_config and not is_attribute: - deprecation_message = f"Accessing config attribute `{name}` directly via '{type(self).__name__}' object attribute is deprecated. Please access '{name}' over '{type(self).__name__}'s config object instead, e.g. 'unet.config.{name}'." - deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False, stacklevel=3) - return self._internal_dict[name] - - # call PyTorch's https://pytorch.org/docs/stable/_modules/torch/nn/modules/module.html#Module - return super().__getattr__(name) - - @property - def is_gradient_checkpointing(self) -> bool: - """ - Whether gradient checkpointing is activated for this model or not. - """ - return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules()) - - def enable_gradient_checkpointing(self) -> None: - """ - Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or - *checkpoint activations* in other frameworks). - """ - if not self._supports_gradient_checkpointing: - raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.") - self.apply(partial(self._set_gradient_checkpointing, value=True)) - - def disable_gradient_checkpointing(self) -> None: - """ - Deactivates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or - *checkpoint activations* in other frameworks). - """ - if self._supports_gradient_checkpointing: - self.apply(partial(self._set_gradient_checkpointing, value=False)) - - def set_use_memory_efficient_attention_xformers( - self, valid: bool, attention_op: Optional[Callable] = None - ) -> None: - # Recursively walk through all the children. - # Any children which exposes the set_use_memory_efficient_attention_xformers method - # gets the message - def fn_recursive_set_mem_eff(module: torch.nn.Module): - if hasattr(module, "set_use_memory_efficient_attention_xformers"): - module.set_use_memory_efficient_attention_xformers(valid, attention_op) - - for child in module.children(): - fn_recursive_set_mem_eff(child) - - for module in self.children(): - if isinstance(module, torch.nn.Module): - fn_recursive_set_mem_eff(module) - - def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None) -> None: - r""" - Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). - - When this option is enabled, you should observe lower GPU memory usage and a potential speed up during - inference. Speed up during training is not guaranteed. - - - - ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes - precedent. - - - - Parameters: - attention_op (`Callable`, *optional*): - Override the default `None` operator for use as `op` argument to the - [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention) - function of xFormers. - - Examples: - - ```py - >>> import torch - >>> from diffusers import UNet2DConditionModel - >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp - - >>> model = UNet2DConditionModel.from_pretrained( - ... "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16 - ... ) - >>> model = model.to("cuda") - >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) - ``` - """ - self.set_use_memory_efficient_attention_xformers(True, attention_op) - - def disable_xformers_memory_efficient_attention(self) -> None: - r""" - Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). - """ - self.set_use_memory_efficient_attention_xformers(False) - - def save_pretrained( - self, - save_directory: Union[str, os.PathLike], - is_main_process: bool = True, - save_function: Optional[Callable] = None, - safe_serialization: bool = True, - variant: Optional[str] = None, - push_to_hub: bool = False, - **kwargs, - ): - """ - Save a model and its configuration file to a directory so that it can be reloaded using the - [`~models.ModelMixin.from_pretrained`] class method. - - Arguments: - save_directory (`str` or `os.PathLike`): - Directory to save a model and its configuration file to. Will be created if it doesn't exist. - is_main_process (`bool`, *optional*, defaults to `True`): - Whether the process calling this is the main process or not. Useful during distributed training and you - need to call this function on all processes. In this case, set `is_main_process=True` only on the main - process to avoid race conditions. - save_function (`Callable`): - The function to use to save the state dictionary. Useful during distributed training when you need to - replace `torch.save` with another method. Can be configured with the environment variable - `DIFFUSERS_SAVE_MODE`. - safe_serialization (`bool`, *optional*, defaults to `True`): - Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`. - variant (`str`, *optional*): - If specified, weights are saved in the format `pytorch_model..bin`. - push_to_hub (`bool`, *optional*, defaults to `False`): - Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the - repository you want to push to with `repo_id` (will default to the name of `save_directory` in your - namespace). - kwargs (`Dict[str, Any]`, *optional*): - Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. - """ - if os.path.isfile(save_directory): - logger.error(f"Provided path ({save_directory}) should be a directory, not a file") - return - - os.makedirs(save_directory, exist_ok=True) - - if push_to_hub: - commit_message = kwargs.pop("commit_message", None) - private = kwargs.pop("private", False) - create_pr = kwargs.pop("create_pr", False) - token = kwargs.pop("token", None) - repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1]) - repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id - - # Only save the model itself if we are using distributed training - model_to_save = self - - # Attach architecture to the config - # Save the config - if is_main_process: - model_to_save.save_config(save_directory) - - # Save the model - state_dict = model_to_save.state_dict() - - weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME - weights_name = _add_variant(weights_name, variant) - - # Save the model - if safe_serialization: - safetensors.torch.save_file( - state_dict, os.path.join(save_directory, weights_name), metadata={"format": "pt"} - ) - else: - torch.save(state_dict, os.path.join(save_directory, weights_name)) - - logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}") - - if push_to_hub: - # Create a new empty model card and eventually tag it - model_card = load_or_create_model_card(repo_id, token=token) - model_card = populate_model_card(model_card) - model_card.save(os.path.join(save_directory, "README.md")) - - self._upload_folder( - save_directory, - repo_id, - token=token, - commit_message=commit_message, - create_pr=create_pr, - ) - - @classmethod - @validate_hf_hub_args - def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): - r""" - Instantiate a pretrained PyTorch model from a pretrained model configuration. - - The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To - train the model, set it back in training mode with `model.train()`. - - Parameters: - pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): - Can be either: - - - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on - the Hub. - - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved - with [`~ModelMixin.save_pretrained`]. - - cache_dir (`Union[str, os.PathLike]`, *optional*): - Path to a directory where a downloaded pretrained model configuration is cached if the standard cache - is not used. - torch_dtype (`str` or `torch.dtype`, *optional*): - Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the - dtype is automatically derived from the model's weights. - force_download (`bool`, *optional*, defaults to `False`): - Whether or not to force the (re-)download of the model weights and configuration files, overriding the - cached versions if they exist. - resume_download (`bool`, *optional*, defaults to `False`): - Whether or not to resume downloading the model weights and configuration files. If set to `False`, any - incompletely downloaded files are deleted. - proxies (`Dict[str, str]`, *optional*): - A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', - 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. - output_loading_info (`bool`, *optional*, defaults to `False`): - Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. - local_files_only(`bool`, *optional*, defaults to `False`): - Whether to only load local model weights and configuration files or not. If set to `True`, the model - won't be downloaded from the Hub. - token (`str` or *bool*, *optional*): - The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from - `diffusers-cli login` (stored in `~/.huggingface`) is used. - revision (`str`, *optional*, defaults to `"main"`): - The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier - allowed by Git. - from_flax (`bool`, *optional*, defaults to `False`): - Load the model weights from a Flax checkpoint save file. - subfolder (`str`, *optional*, defaults to `""`): - The subfolder location of a model file within a larger model repository on the Hub or locally. - mirror (`str`, *optional*): - Mirror source to resolve accessibility issues if you're downloading a model in China. We do not - guarantee the timeliness or safety of the source, and you should refer to the mirror site for more - information. - device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): - A map that specifies where each submodule should go. It doesn't need to be defined for each - parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the - same device. - - Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For - more information about each option see [designing a device - map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). - max_memory (`Dict`, *optional*): - A dictionary device identifier for the maximum memory. Will default to the maximum memory available for - each GPU and the available CPU RAM if unset. - offload_folder (`str` or `os.PathLike`, *optional*): - The path to offload weights if `device_map` contains the value `"disk"`. - offload_state_dict (`bool`, *optional*): - If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if - the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` - when there is some disk offload. - low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): - Speed up model loading only loading the pretrained weights and not initializing the weights. This also - tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. - Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this - argument to `True` will raise an error. - variant (`str`, *optional*): - Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when - loading `from_flax`. - use_safetensors (`bool`, *optional*, defaults to `None`): - If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the - `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors` - weights. If set to `False`, `safetensors` weights are not loaded. - - - - To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with - `huggingface-cli login`. You can also activate the special - ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a - firewalled environment. - - - - Example: - - ```py - from diffusers import UNet2DConditionModel - - unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") - ``` - - If you get the error message below, you need to finetune the weights for your downstream task: - - ```bash - Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated - You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. - ``` - """ - cache_dir = kwargs.pop("cache_dir", None) - ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) - force_download = kwargs.pop("force_download", False) - from_flax = kwargs.pop("from_flax", False) - resume_download = kwargs.pop("resume_download", False) - proxies = kwargs.pop("proxies", None) - output_loading_info = kwargs.pop("output_loading_info", False) - local_files_only = kwargs.pop("local_files_only", None) - token = kwargs.pop("token", None) - revision = kwargs.pop("revision", None) - torch_dtype = kwargs.pop("torch_dtype", None) - subfolder = kwargs.pop("subfolder", None) - device_map = kwargs.pop("device_map", None) - max_memory = kwargs.pop("max_memory", None) - offload_folder = kwargs.pop("offload_folder", None) - offload_state_dict = kwargs.pop("offload_state_dict", False) - low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) - variant = kwargs.pop("variant", None) - use_safetensors = kwargs.pop("use_safetensors", None) - - allow_pickle = False - if use_safetensors is None: - use_safetensors = True - allow_pickle = True - - if low_cpu_mem_usage and not is_accelerate_available(): - low_cpu_mem_usage = False - logger.warning( - "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" - " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" - " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" - " install accelerate\n```\n." - ) - - if device_map is not None and not is_accelerate_available(): - raise NotImplementedError( - "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" - " `device_map=None`. You can install accelerate with `pip install accelerate`." - ) - - # Check if we can handle device_map and dispatching the weights - if device_map is not None and not is_torch_version(">=", "1.9.0"): - raise NotImplementedError( - "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" - " `device_map=None`." - ) - - if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): - raise NotImplementedError( - "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" - " `low_cpu_mem_usage=False`." - ) - - if low_cpu_mem_usage is False and device_map is not None: - raise ValueError( - f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and" - " dispatching. Please make sure to set `low_cpu_mem_usage=True`." - ) - - # Load config if we don't provide a configuration - config_path = pretrained_model_name_or_path - - user_agent = { - "diffusers": __version__, - "file_type": "model", - "framework": "pytorch", - } - - # load config - config, unused_kwargs, commit_hash = cls.load_config( - config_path, - cache_dir=cache_dir, - return_unused_kwargs=True, - return_commit_hash=True, - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - token=token, - revision=revision, - subfolder=subfolder, - device_map=device_map, - max_memory=max_memory, - offload_folder=offload_folder, - offload_state_dict=offload_state_dict, - user_agent=user_agent, - **kwargs, - ) - - # load model - model_file = None - if from_flax: - model_file = _get_model_file( - pretrained_model_name_or_path, - weights_name=FLAX_WEIGHTS_NAME, - cache_dir=cache_dir, - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - token=token, - revision=revision, - subfolder=subfolder, - user_agent=user_agent, - commit_hash=commit_hash, - ) - model = cls.from_config(config, **unused_kwargs) - - # Convert the weights - from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model - - model = load_flax_checkpoint_in_pytorch_model(model, model_file) - else: - if use_safetensors: - try: - model_file = _get_model_file( - pretrained_model_name_or_path, - weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), - cache_dir=cache_dir, - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - token=token, - revision=revision, - subfolder=subfolder, - user_agent=user_agent, - commit_hash=commit_hash, - ) - except IOError as e: - if not allow_pickle: - raise e - pass - if model_file is None: - model_file = _get_model_file( - pretrained_model_name_or_path, - weights_name=_add_variant(WEIGHTS_NAME, variant), - cache_dir=cache_dir, - force_download=force_download, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - token=token, - revision=revision, - subfolder=subfolder, - user_agent=user_agent, - commit_hash=commit_hash, - ) - - if low_cpu_mem_usage: - # Instantiate model with empty weights - with accelerate.init_empty_weights(): - model = cls.from_config(config, **unused_kwargs) - - # if device_map is None, load the state dict and move the params from meta device to the cpu - if device_map is None: - param_device = "cpu" - state_dict = load_state_dict(model_file, variant=variant) - model._convert_deprecated_attention_blocks(state_dict) - # move the params from meta device to cpu - missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) - if len(missing_keys) > 0: - raise ValueError( - f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are" - f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass" - " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize" - " those weights or else make sure your checkpoint file is correct." - ) - - unexpected_keys = load_model_dict_into_meta( - model, - state_dict, - device=param_device, - dtype=torch_dtype, - model_name_or_path=pretrained_model_name_or_path, - ) - - if cls._keys_to_ignore_on_load_unexpected is not None: - for pat in cls._keys_to_ignore_on_load_unexpected: - unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] - - if len(unexpected_keys) > 0: - logger.warning( - f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}" - ) - - else: # else let accelerate handle loading and dispatching. - # Load weights and dispatch according to the device_map - # by default the device_map is None and the weights are loaded on the CPU - try: - accelerate.load_checkpoint_and_dispatch( - model, - model_file, - device_map, - max_memory=max_memory, - offload_folder=offload_folder, - offload_state_dict=offload_state_dict, - dtype=torch_dtype, - ) - except AttributeError as e: - # When using accelerate loading, we do not have the ability to load the state - # dict and rename the weight names manually. Additionally, accelerate skips - # torch loading conventions and directly writes into `module.{_buffers, _parameters}` - # (which look like they should be private variables?), so we can't use the standard hooks - # to rename parameters on load. We need to mimic the original weight names so the correct - # attributes are available. After we have loaded the weights, we convert the deprecated - # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert - # the weights so we don't have to do this again. - - if "'Attention' object has no attribute" in str(e): - logger.warning( - f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}" - " was saved with deprecated attention block weight names. We will load it with the deprecated attention block" - " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion," - " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint," - " please also re-upload it or open a PR on the original repository." - ) - model._temp_convert_self_to_deprecated_attention_blocks() - accelerate.load_checkpoint_and_dispatch( - model, - model_file, - device_map, - max_memory=max_memory, - offload_folder=offload_folder, - offload_state_dict=offload_state_dict, - dtype=torch_dtype, - ) - model._undo_temp_convert_self_to_deprecated_attention_blocks() - else: - raise e - - loading_info = { - "missing_keys": [], - "unexpected_keys": [], - "mismatched_keys": [], - "error_msgs": [], - } - else: - model = cls.from_config(config, **unused_kwargs) - - state_dict = load_state_dict(model_file, variant=variant) - model._convert_deprecated_attention_blocks(state_dict) - - model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( - model, - state_dict, - model_file, - pretrained_model_name_or_path, - ignore_mismatched_sizes=ignore_mismatched_sizes, - ) - - loading_info = { - "missing_keys": missing_keys, - "unexpected_keys": unexpected_keys, - "mismatched_keys": mismatched_keys, - "error_msgs": error_msgs, - } - - if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): - raise ValueError( - f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." - ) - elif torch_dtype is not None: - model = model.to(torch_dtype) - - model.register_to_config(_name_or_path=pretrained_model_name_or_path) - - # Set model in evaluation mode to deactivate DropOut modules by default - model.eval() - if output_loading_info: - return model, loading_info - - return model - - @classmethod - def _load_pretrained_model( - cls, - model, - state_dict: OrderedDict, - resolved_archive_file, - pretrained_model_name_or_path: Union[str, os.PathLike], - ignore_mismatched_sizes: bool = False, - ): - # Retrieve missing & unexpected_keys - model_state_dict = model.state_dict() - loaded_keys = list(state_dict.keys()) - - expected_keys = list(model_state_dict.keys()) - - original_loaded_keys = loaded_keys - - missing_keys = list(set(expected_keys) - set(loaded_keys)) - unexpected_keys = list(set(loaded_keys) - set(expected_keys)) - - # Make sure we are able to load base models as well as derived models (with heads) - model_to_load = model - - def _find_mismatched_keys( - state_dict, - model_state_dict, - loaded_keys, - ignore_mismatched_sizes, - ): - mismatched_keys = [] - if ignore_mismatched_sizes: - for checkpoint_key in loaded_keys: - model_key = checkpoint_key - - if ( - model_key in model_state_dict - and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape - ): - mismatched_keys.append( - (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) - ) - del state_dict[checkpoint_key] - return mismatched_keys - - if state_dict is not None: - # Whole checkpoint - mismatched_keys = _find_mismatched_keys( - state_dict, - model_state_dict, - original_loaded_keys, - ignore_mismatched_sizes, - ) - error_msgs = _load_state_dict_into_model(model_to_load, state_dict) - - if len(error_msgs) > 0: - error_msg = "\n\t".join(error_msgs) - if "size mismatch" in error_msg: - error_msg += ( - "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method." - ) - raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}") - - if len(unexpected_keys) > 0: - logger.warning( - f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when" - f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are" - f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task" - " or with another architecture (e.g. initializing a BertForSequenceClassification model from a" - " BertForPreTraining model).\n- This IS NOT expected if you are initializing" - f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly" - " identical (initializing a BertForSequenceClassification model from a" - " BertForSequenceClassification model)." - ) - else: - logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n") - if len(missing_keys) > 0: - logger.warning( - f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" - f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably" - " TRAIN this model on a down-stream task to be able to use it for predictions and inference." - ) - elif len(mismatched_keys) == 0: - logger.info( - f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at" - f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the" - f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions" - " without further training." - ) - if len(mismatched_keys) > 0: - mismatched_warning = "\n".join( - [ - f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" - for key, shape1, shape2 in mismatched_keys - ] - ) - logger.warning( - f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" - f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not" - f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be" - " able to use it for predictions and inference." - ) - - return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs - - @property - def device(self) -> torch.device: - """ - `torch.device`: The device on which the module is (assuming that all the module parameters are on the same - device). - """ - return get_parameter_device(self) - - @property - def dtype(self) -> torch.dtype: - """ - `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype). - """ - return get_parameter_dtype(self) - - def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int: - """ - Get number of (trainable or non-embedding) parameters in the module. - - Args: - only_trainable (`bool`, *optional*, defaults to `False`): - Whether or not to return only the number of trainable parameters. - exclude_embeddings (`bool`, *optional*, defaults to `False`): - Whether or not to return only the number of non-embedding parameters. - - Returns: - `int`: The number of parameters. - - Example: - - ```py - from diffusers import UNet2DConditionModel - - model_id = "runwayml/stable-diffusion-v1-5" - unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet") - unet.num_parameters(only_trainable=True) - 859520964 - ``` - """ - - if exclude_embeddings: - embedding_param_names = [ - f"{name}.weight" - for name, module_type in self.named_modules() - if isinstance(module_type, torch.nn.Embedding) - ] - non_embedding_parameters = [ - parameter for name, parameter in self.named_parameters() if name not in embedding_param_names - ] - return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable) - else: - return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable) - - def _convert_deprecated_attention_blocks(self, state_dict: OrderedDict) -> None: - deprecated_attention_block_paths = [] - - def recursive_find_attn_block(name, module): - if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block: - deprecated_attention_block_paths.append(name) - - for sub_name, sub_module in module.named_children(): - sub_name = sub_name if name == "" else f"{name}.{sub_name}" - recursive_find_attn_block(sub_name, sub_module) - - recursive_find_attn_block("", self) - - # NOTE: we have to check if the deprecated parameters are in the state dict - # because it is possible we are loading from a state dict that was already - # converted - - for path in deprecated_attention_block_paths: - # group_norm path stays the same - - # query -> to_q - if f"{path}.query.weight" in state_dict: - state_dict[f"{path}.to_q.weight"] = state_dict.pop(f"{path}.query.weight") - if f"{path}.query.bias" in state_dict: - state_dict[f"{path}.to_q.bias"] = state_dict.pop(f"{path}.query.bias") - - # key -> to_k - if f"{path}.key.weight" in state_dict: - state_dict[f"{path}.to_k.weight"] = state_dict.pop(f"{path}.key.weight") - if f"{path}.key.bias" in state_dict: - state_dict[f"{path}.to_k.bias"] = state_dict.pop(f"{path}.key.bias") - - # value -> to_v - if f"{path}.value.weight" in state_dict: - state_dict[f"{path}.to_v.weight"] = state_dict.pop(f"{path}.value.weight") - if f"{path}.value.bias" in state_dict: - state_dict[f"{path}.to_v.bias"] = state_dict.pop(f"{path}.value.bias") - - # proj_attn -> to_out.0 - if f"{path}.proj_attn.weight" in state_dict: - state_dict[f"{path}.to_out.0.weight"] = state_dict.pop(f"{path}.proj_attn.weight") - if f"{path}.proj_attn.bias" in state_dict: - state_dict[f"{path}.to_out.0.bias"] = state_dict.pop(f"{path}.proj_attn.bias") - - def _temp_convert_self_to_deprecated_attention_blocks(self) -> None: - deprecated_attention_block_modules = [] - - def recursive_find_attn_block(module): - if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block: - deprecated_attention_block_modules.append(module) - - for sub_module in module.children(): - recursive_find_attn_block(sub_module) - - recursive_find_attn_block(self) - - for module in deprecated_attention_block_modules: - module.query = module.to_q - module.key = module.to_k - module.value = module.to_v - module.proj_attn = module.to_out[0] - - # We don't _have_ to delete the old attributes, but it's helpful to ensure - # that _all_ the weights are loaded into the new attributes and we're not - # making an incorrect assumption that this model should be converted when - # it really shouldn't be. - del module.to_q - del module.to_k - del module.to_v - del module.to_out - - def _undo_temp_convert_self_to_deprecated_attention_blocks(self) -> None: - deprecated_attention_block_modules = [] - - def recursive_find_attn_block(module) -> None: - if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block: - deprecated_attention_block_modules.append(module) - - for sub_module in module.children(): - recursive_find_attn_block(sub_module) - - recursive_find_attn_block(self) - - for module in deprecated_attention_block_modules: - module.to_q = module.query - module.to_k = module.key - module.to_v = module.value - module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)]) - - del module.query - del module.key - del module.value - del module.proj_attn diff --git a/text_diffuser/t_diffusers/scheduling_ddpm.py b/text_diffuser/t_diffusers/scheduling_ddpm.py deleted file mode 100644 index 6798041..0000000 --- a/text_diffuser/t_diffusers/scheduling_ddpm.py +++ /dev/null @@ -1,627 +0,0 @@ -# Copyright 2024 UC Berkeley Team and The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim - -import math -from dataclasses import dataclass -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.schedulers.scheduling_utils import ( - KarrasDiffusionSchedulers, - SchedulerMixin, -) -from diffusers.utils import BaseOutput -from diffusers.utils.torch_utils import randn_tensor - - -@dataclass -class DDPMSchedulerOutput(BaseOutput): - """ - Output class for the scheduler's `step` function output. - - Args: - prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): - Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the - denoising loop. - pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): - The predicted denoised sample `(x_{0})` based on the model output from the current timestep. - `pred_original_sample` can be used to preview progress or for guidance. - """ - - prev_sample: torch.FloatTensor - pred_original_sample: Optional[torch.FloatTensor] = None - - -def betas_for_alpha_bar( - num_diffusion_timesteps, - max_beta=0.999, - alpha_transform_type="cosine", -): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of - (1-beta) over time from t = [0,1]. - - Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up - to that part of the diffusion process. - - - Args: - num_diffusion_timesteps (`int`): the number of betas to produce. - max_beta (`float`): the maximum beta to use; use values lower than 1 to - prevent singularities. - alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. - Choose from `cosine` or `exp` - - Returns: - betas (`np.ndarray`): the betas used by the scheduler to step the model outputs - """ - if alpha_transform_type == "cosine": - - def alpha_bar_fn(t): - return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 - - elif alpha_transform_type == "exp": - - def alpha_bar_fn(t): - return math.exp(t * -12.0) - - else: - raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}") - - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) - return torch.tensor(betas, dtype=torch.float32) - - -# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr -def rescale_zero_terminal_snr(betas): - """ - Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) - - - Args: - betas (`torch.FloatTensor`): - the betas that the scheduler is being initialized with. - - Returns: - `torch.FloatTensor`: rescaled betas with zero terminal SNR - """ - # Convert betas to alphas_bar_sqrt - alphas = 1.0 - betas - alphas_cumprod = torch.cumprod(alphas, dim=0) - alphas_bar_sqrt = alphas_cumprod.sqrt() - - # Store old values. - alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() - alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() - - # Shift so the last timestep is zero. - alphas_bar_sqrt -= alphas_bar_sqrt_T - - # Scale so the first timestep is back to the old value. - alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) - - # Convert alphas_bar_sqrt to betas - alphas_bar = alphas_bar_sqrt**2 # Revert sqrt - alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod - alphas = torch.cat([alphas_bar[0:1], alphas]) - betas = 1 - alphas - - return betas - - -class DDPMScheduler(SchedulerMixin, ConfigMixin): - """ - `DDPMScheduler` explores the connections between denoising score matching and Langevin dynamics sampling. - - This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic - methods the library implements for all schedulers such as loading and saving. - - Args: - num_train_timesteps (`int`, defaults to 1000): - The number of diffusion steps to train the model. - beta_start (`float`, defaults to 0.0001): - The starting `beta` value of inference. - beta_end (`float`, defaults to 0.02): - The final `beta` value. - beta_schedule (`str`, defaults to `"linear"`): - The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from - `linear`, `scaled_linear`, or `squaredcos_cap_v2`. - trained_betas (`np.ndarray`, *optional*): - An array of betas to pass directly to the constructor without using `beta_start` and `beta_end`. - variance_type (`str`, defaults to `"fixed_small"`): - Clip the variance when adding noise to the denoised sample. Choose from `fixed_small`, `fixed_small_log`, - `fixed_large`, `fixed_large_log`, `learned` or `learned_range`. - clip_sample (`bool`, defaults to `True`): - Clip the predicted sample for numerical stability. - clip_sample_range (`float`, defaults to 1.0): - The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. - prediction_type (`str`, defaults to `epsilon`, *optional*): - Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), - `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen - Video](https://imagen.research.google/video/paper.pdf) paper). - thresholding (`bool`, defaults to `False`): - Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such - as Stable Diffusion. - dynamic_thresholding_ratio (`float`, defaults to 0.995): - The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. - sample_max_value (`float`, defaults to 1.0): - The threshold value for dynamic thresholding. Valid only when `thresholding=True`. - timestep_spacing (`str`, defaults to `"leading"`): - The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. - steps_offset (`int`, defaults to 0): - An offset added to the inference steps, as required by some model families. - rescale_betas_zero_snr (`bool`, defaults to `False`): - Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and - dark samples instead of limiting it to samples with medium brightness. Loosely related to - [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). - """ - - _compatibles = [e.name for e in KarrasDiffusionSchedulers] - order = 1 - - @register_to_config - def __init__( - self, - num_train_timesteps: int = 1000, - beta_start: float = 0.0001, - beta_end: float = 0.02, - beta_schedule: str = "linear", - trained_betas: Optional[Union[np.ndarray, List[float]]] = None, - variance_type: str = "fixed_small", - clip_sample: bool = True, - prediction_type: str = "epsilon", - thresholding: bool = False, - dynamic_thresholding_ratio: float = 0.995, - clip_sample_range: float = 1.0, - sample_max_value: float = 1.0, - timestep_spacing: str = "leading", - steps_offset: int = 0, - rescale_betas_zero_snr: int = False, - ): - if trained_betas is not None: - self.betas = torch.tensor(trained_betas, dtype=torch.float32) - elif beta_schedule == "linear": - self.betas = torch.linspace( - beta_start, beta_end, num_train_timesteps, dtype=torch.float32 - ) - elif beta_schedule == "scaled_linear": - # this schedule is very specific to the latent diffusion model. - self.betas = ( - torch.linspace( - beta_start**0.5, - beta_end**0.5, - num_train_timesteps, - dtype=torch.float32, - ) - ** 2 - ) - elif beta_schedule == "squaredcos_cap_v2": - # Glide cosine schedule - self.betas = betas_for_alpha_bar(num_train_timesteps) - elif beta_schedule == "sigmoid": - # GeoDiff sigmoid schedule - betas = torch.linspace(-6, 6, num_train_timesteps) - self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start - else: - raise NotImplementedError( - f"{beta_schedule} does is not implemented for {self.__class__}" - ) - - # Rescale for zero SNR - if rescale_betas_zero_snr: - self.betas = rescale_zero_terminal_snr(self.betas) - - self.alphas = 1.0 - self.betas - self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) - self.one = torch.tensor(1.0) - - # standard deviation of the initial noise distribution - self.init_noise_sigma = 1.0 - - # setable values - self.custom_timesteps = False - self.num_inference_steps = None - self.timesteps = torch.from_numpy( - np.arange(0, num_train_timesteps)[::-1].copy() - ) - - self.variance_type = variance_type - - def scale_model_input( - self, sample: torch.FloatTensor, timestep: Optional[int] = None - ) -> torch.FloatTensor: - """ - Ensures interchangeability with schedulers that need to scale the denoising model input depending on the - current timestep. - - Args: - sample (`torch.FloatTensor`): - The input sample. - timestep (`int`, *optional*): - The current timestep in the diffusion chain. - - Returns: - `torch.FloatTensor`: - A scaled input sample. - """ - return sample - - def set_timesteps( - self, - num_inference_steps: Optional[int] = None, - device: Union[str, torch.device] = None, - timesteps: Optional[List[int]] = None, - ): - """ - Sets the discrete timesteps used for the diffusion chain (to be run before inference). - - Args: - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. If used, - `timesteps` must be `None`. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - timesteps (`List[int]`, *optional*): - Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default - timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed, - `num_inference_steps` must be `None`. - - """ - if num_inference_steps is not None and timesteps is not None: - raise ValueError( - "Can only pass one of `num_inference_steps` or `custom_timesteps`." - ) - - if timesteps is not None: - for i in range(1, len(timesteps)): - if timesteps[i] >= timesteps[i - 1]: - raise ValueError("`custom_timesteps` must be in descending order.") - - if timesteps[0] >= self.config.num_train_timesteps: - raise ValueError( - f"`timesteps` must start before `self.config.train_timesteps`:" - f" {self.config.num_train_timesteps}." - ) - - timesteps = np.array(timesteps, dtype=np.int64) - self.custom_timesteps = True - else: - if num_inference_steps > self.config.num_train_timesteps: - raise ValueError( - f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" - f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" - f" maximal {self.config.num_train_timesteps} timesteps." - ) - - self.num_inference_steps = num_inference_steps - self.custom_timesteps = False - - # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891 - if self.config.timestep_spacing == "linspace": - timesteps = ( - np.linspace( - 0, self.config.num_train_timesteps - 1, num_inference_steps - ) - .round()[::-1] - .copy() - .astype(np.int64) - ) - elif self.config.timestep_spacing == "leading": - step_ratio = self.config.num_train_timesteps // self.num_inference_steps - # creates integer timesteps by multiplying by ratio - # casting to int to avoid issues when num_inference_step is power of 3 - timesteps = ( - (np.arange(0, num_inference_steps) * step_ratio) - .round()[::-1] - .copy() - .astype(np.int64) - ) - timesteps += self.config.steps_offset - elif self.config.timestep_spacing == "trailing": - step_ratio = self.config.num_train_timesteps / self.num_inference_steps - # creates integer timesteps by multiplying by ratio - # casting to int to avoid issues when num_inference_step is power of 3 - timesteps = np.round( - np.arange(self.config.num_train_timesteps, 0, -step_ratio) - ).astype(np.int64) - timesteps -= 1 - else: - raise ValueError( - f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." - ) - - self.timesteps = torch.from_numpy(timesteps).to(device) - - def _get_variance(self, t, predicted_variance=None, variance_type=None): - prev_t = self.previous_timestep(t) - - alpha_prod_t = self.alphas_cumprod[t] - alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one - current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev - - # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) - # and sample from it to get previous sample - # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample - variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t - - # we always take the log of variance, so clamp it to ensure it's not 0 - variance = torch.clamp(variance, min=1e-20) - - if variance_type is None: - variance_type = self.config.variance_type - - # hacks - were probably added for training stability - if variance_type == "fixed_small": - variance = variance - # for rl-diffuser https://arxiv.org/abs/2205.09991 - elif variance_type == "fixed_small_log": - variance = torch.log(variance) - variance = torch.exp(0.5 * variance) - elif variance_type == "fixed_large": - variance = current_beta_t - elif variance_type == "fixed_large_log": - # Glide max_log - variance = torch.log(current_beta_t) - elif variance_type == "learned": - return predicted_variance - elif variance_type == "learned_range": - min_log = torch.log(variance) - max_log = torch.log(current_beta_t) - frac = (predicted_variance + 1) / 2 - variance = frac * max_log + (1 - frac) * min_log - - return variance - - def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: - """ - "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the - prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by - s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing - pixels from saturation at each step. We find that dynamic thresholding results in significantly better - photorealism as well as better image-text alignment, especially when using very large guidance weights." - - https://arxiv.org/abs/2205.11487 - """ - dtype = sample.dtype - batch_size, channels, *remaining_dims = sample.shape - - if dtype not in (torch.float32, torch.float64): - sample = ( - sample.float() - ) # upcast for quantile calculation, and clamp not implemented for cpu half - - # Flatten sample for doing quantile calculation along each image - sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) - - abs_sample = sample.abs() # "a certain percentile absolute pixel value" - - s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) - s = torch.clamp( - s, min=1, max=self.config.sample_max_value - ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] - s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 - sample = ( - torch.clamp(sample, -s, s) / s - ) # "we threshold xt0 to the range [-s, s] and then divide by s" - - sample = sample.reshape(batch_size, channels, *remaining_dims) - sample = sample.to(dtype) - - return sample - - def step( - self, - model_output: torch.FloatTensor, - timestep: int, - sample: torch.FloatTensor, - generator=None, - return_dict: bool = True, - ) -> Union[DDPMSchedulerOutput, Tuple]: - """ - Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion - process from the learned model outputs (most often the predicted noise). - - Args: - model_output (`torch.FloatTensor`): - The direct output from learned diffusion model. - timestep (`float`): - The current discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - A current instance of a sample created by the diffusion process. - generator (`torch.Generator`, *optional*): - A random number generator. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`. - - Returns: - [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`: - If return_dict is `True`, [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] is returned, otherwise a - tuple is returned where the first element is the sample tensor. - - """ - t = timestep - - prev_t = self.previous_timestep(t) - - if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in [ - "learned", - "learned_range", - ]: - model_output, predicted_variance = torch.split( - model_output, sample.shape[1], dim=1 - ) - else: - predicted_variance = None - - # 1. compute alphas, betas - alpha_prod_t = self.alphas_cumprod[t] - alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one - beta_prod_t = 1 - alpha_prod_t - beta_prod_t_prev = 1 - alpha_prod_t_prev - current_alpha_t = alpha_prod_t / alpha_prod_t_prev - current_beta_t = 1 - current_alpha_t - - # 2. compute predicted original sample from predicted noise also called - # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf - if self.config.prediction_type == "epsilon": - pred_original_sample = ( - sample - beta_prod_t ** (0.5) * model_output - ) / alpha_prod_t ** (0.5) - elif self.config.prediction_type == "sample": - pred_original_sample = model_output - elif self.config.prediction_type == "v_prediction": - pred_original_sample = (alpha_prod_t**0.5) * sample - ( - beta_prod_t**0.5 - ) * model_output - else: - raise ValueError( - f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or" - " `v_prediction` for the DDPMScheduler." - ) - - # 3. Clip or threshold "predicted x_0" - if self.config.thresholding: - pred_original_sample = self._threshold_sample(pred_original_sample) - elif self.config.clip_sample: - pred_original_sample = pred_original_sample.clamp( - -self.config.clip_sample_range, self.config.clip_sample_range - ) - - # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t - # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf - pred_original_sample_coeff = ( - alpha_prod_t_prev ** (0.5) * current_beta_t - ) / beta_prod_t - current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t - - # 5. Compute predicted previous sample µ_t - # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf - pred_prev_sample = ( - pred_original_sample_coeff * pred_original_sample - + current_sample_coeff * sample - ) - - # 6. Add noise - variance = 0 - if t > 0: - device = model_output.device - variance_noise = randn_tensor( - model_output.shape, - generator=generator, - device=device, - dtype=model_output.dtype, - ) - if self.variance_type == "fixed_small_log": - variance = ( - self._get_variance(t, predicted_variance=predicted_variance) - * variance_noise - ) - elif self.variance_type == "learned_range": - variance = self._get_variance(t, predicted_variance=predicted_variance) - variance = torch.exp(0.5 * variance) * variance_noise - else: - variance = ( - self._get_variance(t, predicted_variance=predicted_variance) ** 0.5 - ) * variance_noise - - pred_prev_sample = pred_prev_sample + variance - - if not return_dict: - return (pred_prev_sample,) - - return DDPMSchedulerOutput( - prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample - ) - - def add_noise( - self, - original_samples: torch.FloatTensor, - noise: torch.FloatTensor, - timesteps: torch.IntTensor, - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as original_samples - # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement - # for the subsequent add_noise calls - self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device) - alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype) - timesteps = timesteps.to(original_samples.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(original_samples.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - noisy_samples = ( - sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise - ) - return noisy_samples - - def get_velocity( - self, - sample: torch.FloatTensor, - noise: torch.FloatTensor, - timesteps: torch.IntTensor, - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as sample - self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device) - alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype) - timesteps = timesteps.to(sample.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(sample.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample - return velocity - - def __len__(self): - return self.config.num_train_timesteps - - def previous_timestep(self, timestep): - if self.custom_timesteps: - index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0] - if index == self.timesteps.shape[0] - 1: - prev_t = torch.tensor(-1) - else: - prev_t = self.timesteps[index + 1] - else: - num_inference_steps = ( - self.num_inference_steps - if self.num_inference_steps - else self.config.num_train_timesteps - ) - prev_t = timestep - self.config.num_train_timesteps // num_inference_steps - - return prev_t diff --git a/text_diffuser/t_diffusers/unet_2d_condition.py b/text_diffuser/t_diffusers/unet_2d_condition.py deleted file mode 100644 index f94aba7..0000000 --- a/text_diffuser/t_diffusers/unet_2d_condition.py +++ /dev/null @@ -1,1527 +0,0 @@ -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.utils.checkpoint - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin -from diffusers.models.activations import get_activation -from diffusers.models.attention_processor import ( - ADDED_KV_ATTENTION_PROCESSORS, - CROSS_ATTENTION_PROCESSORS, - Attention, - AttentionProcessor, - AttnAddedKVProcessor, - AttnProcessor, -) -from diffusers.models.embeddings import ( - GaussianFourierProjection, - GLIGENTextBoundingboxProjection, - ImageHintTimeEmbedding, - ImageProjection, - ImageTimeEmbedding, - TextImageProjection, - TextImageTimeEmbedding, - TextTimeEmbedding, - TimestepEmbedding, - Timesteps, -) -from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.unets.unet_2d_blocks import ( - get_down_block, - get_mid_block, - get_up_block, -) -from diffusers.utils import ( - USE_PEFT_BACKEND, - BaseOutput, - deprecate, - logging, - scale_lora_layers, - unscale_lora_layers, -) - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class UNet2DConditionOutput(BaseOutput): - """ - The output of [`UNet2DConditionModel`]. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. - """ - - sample: torch.FloatTensor = None - - -class UNet2DConditionModel( - ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin -): - r""" - A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample - shaped output. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented - for all models (such as downloading or saving). - - Parameters: - sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): - Height and width of input/output sample. - in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. - out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. - center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. - flip_sin_to_cos (`bool`, *optional*, defaults to `True`): - Whether to flip the sin to cos in the time embedding. - freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. - down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): - The tuple of downsample blocks to use. - mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): - Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or - `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. - up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): - The tuple of upsample blocks to use. - only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): - Whether to include self-attention in the basic transformer blocks, see - [`~models.attention.BasicTransformerBlock`]. - block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): - The tuple of output channels for each block. - layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. - downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. - mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. - dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. - If `None`, normalization and activation layers is skipped in post-processing. - norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. - cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): - The dimension of the cross attention features. - transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): - The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling - blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for - [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], - [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. - encoder_hid_dim (`int`, *optional*, defaults to None): - If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` - dimension to `cross_attention_dim`. - encoder_hid_dim_type (`str`, *optional*, defaults to `None`): - If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text - embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. - attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. - num_attention_heads (`int`, *optional*): - The number of attention heads. If not defined, defaults to `attention_head_dim` - resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config - for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. - class_embed_type (`str`, *optional*, defaults to `None`): - The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, - `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. - addition_embed_type (`str`, *optional*, defaults to `None`): - Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or - "text". "text" will use the `TextTimeEmbedding` layer. - addition_time_embed_dim: (`int`, *optional*, defaults to `None`): - Dimension for the timestep embeddings. - num_class_embeds (`int`, *optional*, defaults to `None`): - Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing - class conditioning with `class_embed_type` equal to `None`. - time_embedding_type (`str`, *optional*, defaults to `positional`): - The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. - time_embedding_dim (`int`, *optional*, defaults to `None`): - An optional override for the dimension of the projected time embedding. - time_embedding_act_fn (`str`, *optional*, defaults to `None`): - Optional activation function to use only once on the time embeddings before they are passed to the rest of - the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. - timestep_post_act (`str`, *optional*, defaults to `None`): - The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. - time_cond_proj_dim (`int`, *optional*, defaults to `None`): - The dimension of `cond_proj` layer in the timestep embedding. - conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. - conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. - projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when - `class_embed_type="projection"`. Required when `class_embed_type="projection"`. - class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time - embeddings with the class embeddings. - mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): - Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If - `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the - `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` - otherwise. - """ - - _supports_gradient_checkpointing = True - _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D", "CrossAttnUpBlock2D"] - - @register_to_config - def __init__( - self, - sample_size: Optional[int] = None, - in_channels: int = 17, # TODO: Change This... - out_channels: int = 4, - center_input_sample: bool = False, - flip_sin_to_cos: bool = True, - freq_shift: int = 0, - down_block_types: Tuple[str] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ), - mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", - up_block_types: Tuple[str] = ( - "UpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - ), - only_cross_attention: Union[bool, Tuple[bool]] = False, - block_out_channels: Tuple[int] = (320, 640, 1280, 1280), - layers_per_block: Union[int, Tuple[int]] = 2, - downsample_padding: int = 1, - mid_block_scale_factor: float = 1, - dropout: float = 0.0, - act_fn: str = "silu", - norm_num_groups: Optional[int] = 32, - norm_eps: float = 1e-5, - cross_attention_dim: Union[int, Tuple[int]] = 1280, - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, - reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, - encoder_hid_dim: Optional[int] = None, - encoder_hid_dim_type: Optional[str] = None, - attention_head_dim: Union[int, Tuple[int]] = 8, - num_attention_heads: Optional[Union[int, Tuple[int]]] = None, - dual_cross_attention: bool = False, - use_linear_projection: bool = False, - class_embed_type: Optional[str] = None, - addition_embed_type: Optional[str] = None, - addition_time_embed_dim: Optional[int] = None, - num_class_embeds: Optional[int] = None, - upcast_attention: bool = False, - resnet_time_scale_shift: str = "default", - resnet_skip_time_act: bool = False, - resnet_out_scale_factor: float = 1.0, - time_embedding_type: str = "positional", - time_embedding_dim: Optional[int] = None, - time_embedding_act_fn: Optional[str] = None, - timestep_post_act: Optional[str] = None, - time_cond_proj_dim: Optional[int] = None, - conv_in_kernel: int = 3, - conv_out_kernel: int = 3, - projection_class_embeddings_input_dim: Optional[int] = None, - attention_type: str = "default", - class_embeddings_concat: bool = False, - mid_block_only_cross_attention: Optional[bool] = None, - cross_attention_norm: Optional[str] = None, - addition_embed_type_num_heads: int = 64, - ): - super().__init__() - - # char embedding layer - # 128개의 임베딩 만들고 각자 벡터 차원이 8인 - self.word_embedding = nn.Embedding( - 128, 8 - ) # 최대 128개의 char(word)를 8개의 vector로 embedding(char인가 word인가?) - # convolution layer - # input.shape = (batchsize, 8, 256 256) - self.segmap_conv = nn.Sequential( - nn.Conv2d(8, 32, 3, 1, 1), - nn.ReLU(), - nn.BatchNorm2d(32), - nn.MaxPool2d(2, 2), - nn.Conv2d(32, 64, 3, 1, 1), - nn.ReLU(), - nn.BatchNorm2d(64), - nn.MaxPool2d(2, 2), - nn.Conv2d(64, 8, 3, 1, 1), - ) - # output.shape = (batchsize, 8, 64, 64) - - self.sample_size = sample_size # ? - - if num_attention_heads is not None: - raise ValueError( - "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." - ) - - # If `num_attention_heads` is not defined (which is the case for most models) - # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. - # The reason for this behavior is to correct for incorrectly named variables that were introduced - # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 - # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking - # which is why we correct for the naming here. - num_attention_heads = num_attention_heads or attention_head_dim - - # Check inputs - self._check_config( - down_block_types=down_block_types, - up_block_types=up_block_types, - only_cross_attention=only_cross_attention, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - cross_attention_dim=cross_attention_dim, - transformer_layers_per_block=transformer_layers_per_block, - reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, - attention_head_dim=attention_head_dim, - num_attention_heads=num_attention_heads, - ) - - # input - conv_in_padding = (conv_in_kernel - 1) // 2 # ex) 3-1/2 = 1 - self.conv_in = nn.Conv2d( - in_channels, # the input channel is modified to 17 (4+8+1+4)s - block_out_channels[0], # the output channel is 320 - kernel_size=conv_in_kernel, # the kernel size is 3 - padding=conv_in_padding, # the padding is 1 - ) # the output size is (320, 64, 64) - - # time - time_embed_dim, timestep_input_dim = self._set_time_proj( - time_embedding_type, - block_out_channels=block_out_channels, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - time_embedding_dim=time_embedding_dim, - ) - - self.time_embedding = TimestepEmbedding( - timestep_input_dim, - time_embed_dim, - act_fn=act_fn, - post_act_fn=timestep_post_act, - cond_proj_dim=time_cond_proj_dim, - ) - - self._set_encoder_hid_proj( - encoder_hid_dim_type, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - ) - - # class embedding - self._set_class_embedding( - class_embed_type, - act_fn=act_fn, - num_class_embeds=num_class_embeds, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - timestep_input_dim=timestep_input_dim, - ) - - self._set_add_embedding( - addition_embed_type, - addition_embed_type_num_heads=addition_embed_type_num_heads, - addition_time_embed_dim=addition_time_embed_dim, - cross_attention_dim=cross_attention_dim, - encoder_hid_dim=encoder_hid_dim, - flip_sin_to_cos=flip_sin_to_cos, - freq_shift=freq_shift, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - time_embed_dim=time_embed_dim, - ) - - if time_embedding_act_fn is None: - self.time_embed_act = None - else: - self.time_embed_act = get_activation(time_embedding_act_fn) - - self.down_blocks = nn.ModuleList([]) - self.up_blocks = nn.ModuleList([]) - - if isinstance(only_cross_attention, bool): - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = only_cross_attention - - only_cross_attention = [only_cross_attention] * len(down_block_types) - - if mid_block_only_cross_attention is None: - mid_block_only_cross_attention = False - - if isinstance(num_attention_heads, int): - num_attention_heads = (num_attention_heads,) * len(down_block_types) - - if isinstance(attention_head_dim, int): - attention_head_dim = (attention_head_dim,) * len(down_block_types) - - if isinstance(cross_attention_dim, int): - cross_attention_dim = (cross_attention_dim,) * len(down_block_types) - - if isinstance(layers_per_block, int): - layers_per_block = [layers_per_block] * len(down_block_types) - - if isinstance(transformer_layers_per_block, int): - transformer_layers_per_block = [transformer_layers_per_block] * len( - down_block_types - ) - - if class_embeddings_concat: - # The time embeddings are concatenated with the class embeddings. The dimension of the - # time embeddings passed to the down, middle, and up blocks is twice the dimension of the - # regular time embeddings - blocks_time_embed_dim = time_embed_dim * 2 - else: - blocks_time_embed_dim = time_embed_dim - - # down - output_channel = block_out_channels[0] # 320 - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block[i], - transformer_layers_per_block=transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - temb_channels=blocks_time_embed_dim, - add_downsample=not is_final_block, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - cross_attention_dim=cross_attention_dim[i], - num_attention_heads=num_attention_heads[i], - downsample_padding=downsample_padding, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=( - attention_head_dim[i] - if attention_head_dim[i] is not None - else output_channel - ), - dropout=dropout, - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = get_mid_block( - mid_block_type, - temb_channels=blocks_time_embed_dim, - in_channels=block_out_channels[-1], - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - output_scale_factor=mid_block_scale_factor, - transformer_layers_per_block=transformer_layers_per_block[-1], - num_attention_heads=num_attention_heads[-1], - cross_attention_dim=cross_attention_dim[-1], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - mid_block_only_cross_attention=mid_block_only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - cross_attention_norm=cross_attention_norm, - attention_head_dim=attention_head_dim[-1], - dropout=dropout, - ) - - # count how many layers upsample the images - self.num_upsamplers = 0 - - # for up_blocks - reversed_block_out_channels = list(reversed(block_out_channels)) - reversed_num_attention_heads = list(reversed(num_attention_heads)) - reversed_layers_per_block = list(reversed(layers_per_block)) - reversed_cross_attention_dim = list(reversed(cross_attention_dim)) - reversed_transformer_layers_per_block = ( - list(reversed(transformer_layers_per_block)) - if reverse_transformer_layers_per_block is None - else reverse_transformer_layers_per_block - ) - only_cross_attention = list(reversed(only_cross_attention)) - - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - is_final_block = ( - i == len(block_out_channels) - 1 - ) # if True it is final block - - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - input_channel = reversed_block_out_channels[ - min(i + 1, len(block_out_channels) - 1) - ] - - # add upsample block for all BUT final layer - if not is_final_block: - add_upsample = True - self.num_upsamplers += 1 - else: - add_upsample = False - - up_block = get_up_block( - up_block_type, - num_layers=reversed_layers_per_block[i] + 1, - transformer_layers_per_block=reversed_transformer_layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - prev_output_channel=prev_output_channel, - temb_channels=blocks_time_embed_dim, - add_upsample=add_upsample, - resnet_eps=norm_eps, - resnet_act_fn=act_fn, - resolution_idx=i, - resnet_groups=norm_num_groups, - cross_attention_dim=reversed_cross_attention_dim[i], - num_attention_heads=reversed_num_attention_heads[i], - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention[i], - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - attention_type=attention_type, - resnet_skip_time_act=resnet_skip_time_act, - resnet_out_scale_factor=resnet_out_scale_factor, - cross_attention_norm=cross_attention_norm, - attention_head_dim=( - attention_head_dim[i] - if attention_head_dim[i] is not None - else output_channel - ), - dropout=dropout, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - if norm_num_groups is not None: - self.conv_norm_out = nn.GroupNorm( - num_channels=block_out_channels[0], - num_groups=norm_num_groups, - eps=norm_eps, - ) - - self.conv_act = get_activation(act_fn) - - else: - self.conv_norm_out = None - self.conv_act = None - - conv_out_padding = (conv_out_kernel - 1) // 2 - self.conv_out = nn.Conv2d( - block_out_channels[0], - out_channels, - kernel_size=conv_out_kernel, - padding=conv_out_padding, - ) - - self._set_pos_net_if_use_gligen( - attention_type=attention_type, cross_attention_dim=cross_attention_dim - ) - - def _check_config( - self, - down_block_types: Tuple[str], - up_block_types: Tuple[str], - only_cross_attention: Union[bool, Tuple[bool]], - block_out_channels: Tuple[int], - layers_per_block: Union[int, Tuple[int]], - cross_attention_dim: Union[int, Tuple[int]], - transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]], - reverse_transformer_layers_per_block: bool, - attention_head_dim: int, - num_attention_heads: Optional[Union[int, Tuple[int]]], - ): - if len(down_block_types) != len(up_block_types): - raise ValueError( - f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." - ) - - if len(block_out_channels) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(only_cross_attention, bool) and len( - only_cross_attention - ) != len(down_block_types): - raise ValueError( - f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len( - down_block_types - ): - raise ValueError( - f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len( - down_block_types - ): - raise ValueError( - f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." - ) - - if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len( - down_block_types - ): - raise ValueError( - f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." - ) - - if not isinstance(layers_per_block, int) and len(layers_per_block) != len( - down_block_types - ): - raise ValueError( - f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." - ) - if ( - isinstance(transformer_layers_per_block, list) - and reverse_transformer_layers_per_block is None - ): - for layer_number_per_block in transformer_layers_per_block: - if isinstance(layer_number_per_block, list): - raise ValueError( - "Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet." - ) - - # positional encoding - def _set_time_proj( - self, - time_embedding_type: str, - block_out_channels: int, - flip_sin_to_cos: bool, - freq_shift: float, - time_embedding_dim: int, - ) -> Tuple[int, int]: - if time_embedding_type == "fourier": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 - if time_embed_dim % 2 != 0: - raise ValueError( - f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}." - ) - self.time_proj = GaussianFourierProjection( - time_embed_dim // 2, - set_W_to_weight=False, - log=False, - flip_sin_to_cos=flip_sin_to_cos, - ) - timestep_input_dim = time_embed_dim - elif time_embedding_type == "positional": - time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 - - self.time_proj = Timesteps( - block_out_channels[0], flip_sin_to_cos, freq_shift - ) - timestep_input_dim = block_out_channels[0] - else: - raise ValueError( - f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." - ) - - return time_embed_dim, timestep_input_dim - - def _set_encoder_hid_proj( - self, - encoder_hid_dim_type: Optional[str], - cross_attention_dim: Union[int, Tuple[int]], - encoder_hid_dim: Optional[int], - ): - if encoder_hid_dim_type is None and encoder_hid_dim is not None: - encoder_hid_dim_type = "text_proj" - self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) - logger.info( - "encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined." - ) - - if encoder_hid_dim is None and encoder_hid_dim_type is not None: - raise ValueError( - f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." - ) - - if encoder_hid_dim_type == "text_proj": - self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) - elif encoder_hid_dim_type == "text_image_proj": - # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image_proj"` (Kandinsky 2.1)` - self.encoder_hid_proj = TextImageProjection( - text_embed_dim=encoder_hid_dim, - image_embed_dim=cross_attention_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type == "image_proj": - # Kandinsky 2.2 - self.encoder_hid_proj = ImageProjection( - image_embed_dim=encoder_hid_dim, - cross_attention_dim=cross_attention_dim, - ) - elif encoder_hid_dim_type is not None: - raise ValueError( - f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." - ) - else: - self.encoder_hid_proj = None - - def _set_class_embedding( - self, - class_embed_type: Optional[str], - act_fn: str, - num_class_embeds: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - timestep_input_dim: int, - ): - if class_embed_type is None and num_class_embeds is not None: - self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) - elif class_embed_type == "timestep": - self.class_embedding = TimestepEmbedding( - timestep_input_dim, time_embed_dim, act_fn=act_fn - ) - elif class_embed_type == "identity": - self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) - elif class_embed_type == "projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" - ) - # The projection `class_embed_type` is the same as the timestep `class_embed_type` except - # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings - # 2. it projects from an arbitrary input dimension. - # - # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. - # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. - # As a result, `TimestepEmbedding` can be passed arbitrary vectors. - self.class_embedding = TimestepEmbedding( - projection_class_embeddings_input_dim, time_embed_dim - ) - elif class_embed_type == "simple_projection": - if projection_class_embeddings_input_dim is None: - raise ValueError( - "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" - ) - self.class_embedding = nn.Linear( - projection_class_embeddings_input_dim, time_embed_dim - ) - else: - self.class_embedding = None - - def _set_add_embedding( - self, - addition_embed_type: str, - addition_embed_type_num_heads: int, - addition_time_embed_dim: Optional[int], - flip_sin_to_cos: bool, - freq_shift: float, - cross_attention_dim: Optional[int], - encoder_hid_dim: Optional[int], - projection_class_embeddings_input_dim: Optional[int], - time_embed_dim: int, - ): - if addition_embed_type == "text": - if encoder_hid_dim is not None: - text_time_embedding_from_dim = encoder_hid_dim - else: - text_time_embedding_from_dim = cross_attention_dim - - self.add_embedding = TextTimeEmbedding( - text_time_embedding_from_dim, - time_embed_dim, - num_heads=addition_embed_type_num_heads, - ) - elif addition_embed_type == "text_image": - # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much - # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use - # case when `addition_embed_type == "text_image"` (Kandinsky 2.1)` - self.add_embedding = TextImageTimeEmbedding( - text_embed_dim=cross_attention_dim, - image_embed_dim=cross_attention_dim, - time_embed_dim=time_embed_dim, - ) - elif addition_embed_type == "text_time": - self.add_time_proj = Timesteps( - addition_time_embed_dim, flip_sin_to_cos, freq_shift - ) - self.add_embedding = TimestepEmbedding( - projection_class_embeddings_input_dim, time_embed_dim - ) - elif addition_embed_type == "image": - # Kandinsky 2.2 - self.add_embedding = ImageTimeEmbedding( - image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim - ) - elif addition_embed_type == "image_hint": - # Kandinsky 2.2 ControlNet - self.add_embedding = ImageHintTimeEmbedding( - image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim - ) - elif addition_embed_type is not None: - raise ValueError( - f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'." - ) - - def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int): - if attention_type in ["gated", "gated-text-image"]: - positive_len = 768 - if isinstance(cross_attention_dim, int): - positive_len = cross_attention_dim - elif isinstance(cross_attention_dim, tuple) or isinstance( - cross_attention_dim, list - ): - positive_len = cross_attention_dim[0] - - feature_type = "text-only" if attention_type == "gated" else "text-image" - self.position_net = GLIGENTextBoundingboxProjection( - positive_len=positive_len, - out_dim=cross_attention_dim, - feature_type=feature_type, - ) - - @property - def attn_processors(self) -> Dict[str, AttentionProcessor]: - r""" - Returns: - `dict` of attention processors: A dictionary containing all attention processors used in the model with - indexed by its weight name. - """ - # set recursively - processors = {} - - def fn_recursive_add_processors( - name: str, - module: torch.nn.Module, - processors: Dict[str, AttentionProcessor], - ): - if hasattr(module, "get_processor"): - processors[f"{name}.processor"] = module.get_processor( - return_deprecated_lora=True - ) - - for sub_name, child in module.named_children(): - fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) - - return processors - - for name, module in self.named_children(): - fn_recursive_add_processors(name, module, processors) - - return processors - - def set_attn_processor( - self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]] - ): - r""" - Sets the attention processor to use to compute attention. - - Parameters: - processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): - The instantiated processor class or a dictionary of processor classes that will be set as the processor - for **all** `Attention` layers. - - If `processor` is a dict, the key needs to define the path to the corresponding cross attention - processor. This is strongly recommended when setting trainable attention processors. - - """ - count = len(self.attn_processors.keys()) - - if isinstance(processor, dict) and len(processor) != count: - raise ValueError( - f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" - f" number of attention layers: {count}. Please make sure to pass {count} processor classes." - ) - - def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): - if hasattr(module, "set_processor"): - if not isinstance(processor, dict): - module.set_processor(processor) - else: - module.set_processor(processor.pop(f"{name}.processor")) - - for sub_name, child in module.named_children(): - fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) - - for name, module in self.named_children(): - fn_recursive_attn_processor(name, module, processor) - - def set_default_attn_processor(self): - """ - Disables custom attention processors and sets the default attention implementation. - """ - if all( - proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS - for proc in self.attn_processors.values() - ): - processor = AttnAddedKVProcessor() - elif all( - proc.__class__ in CROSS_ATTENTION_PROCESSORS - for proc in self.attn_processors.values() - ): - processor = AttnProcessor() - else: - raise ValueError( - f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" - ) - - self.set_attn_processor(processor) - - def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"): - r""" - Enable sliced attention computation. - - When this option is enabled, the attention module splits the input tensor in slices to compute attention in - several steps. This is useful for saving some memory in exchange for a small decrease in speed. - - Args: - slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): - When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If - `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is - provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` - must be a multiple of `slice_size`. - """ - sliceable_head_dims = [] - - def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): - if hasattr(module, "set_attention_slice"): - sliceable_head_dims.append(module.sliceable_head_dim) - - for child in module.children(): - fn_recursive_retrieve_sliceable_dims(child) - - # retrieve number of attention layers - for module in self.children(): - fn_recursive_retrieve_sliceable_dims(module) - - num_sliceable_layers = len(sliceable_head_dims) - - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = [dim // 2 for dim in sliceable_head_dims] - elif slice_size == "max": - # make smallest slice possible - slice_size = num_sliceable_layers * [1] - - slice_size = ( - num_sliceable_layers * [slice_size] - if not isinstance(slice_size, list) - else slice_size - ) - - if len(slice_size) != len(sliceable_head_dims): - raise ValueError( - f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" - f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." - ) - - for i in range(len(slice_size)): - size = slice_size[i] - dim = sliceable_head_dims[i] - if size is not None and size > dim: - raise ValueError(f"size {size} has to be smaller or equal to {dim}.") - - # Recursively walk through all the children. - # Any children which exposes the set_attention_slice method - # gets the message - def fn_recursive_set_attention_slice( - module: torch.nn.Module, slice_size: List[int] - ): - if hasattr(module, "set_attention_slice"): - module.set_attention_slice(slice_size.pop()) - - for child in module.children(): - fn_recursive_set_attention_slice(child, slice_size) - - reversed_slice_size = list(reversed(slice_size)) - for module in self.children(): - fn_recursive_set_attention_slice(module, reversed_slice_size) - - def _set_gradient_checkpointing(self, module, value=False): - if hasattr(module, "gradient_checkpointing"): - module.gradient_checkpointing = value - - def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): - r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. - - The suffixes after the scaling factors represent the stage blocks where they are being applied. - - Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that - are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. - - Args: - s1 (`float`): - Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - s2 (`float`): - Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to - mitigate the "oversmoothing effect" in the enhanced denoising process. - b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. - """ - for i, upsample_block in enumerate(self.up_blocks): - setattr(upsample_block, "s1", s1) - setattr(upsample_block, "s2", s2) - setattr(upsample_block, "b1", b1) - setattr(upsample_block, "b2", b2) - - def disable_freeu(self): - """Disables the FreeU mechanism.""" - freeu_keys = {"s1", "s2", "b1", "b2"} - for i, upsample_block in enumerate(self.up_blocks): - for k in freeu_keys: - if ( - hasattr(upsample_block, k) - or getattr(upsample_block, k, None) is not None - ): - setattr(upsample_block, k, None) - - def fuse_qkv_projections(self): - """ - Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) - are fused. For cross-attention modules, key and value projection matrices are fused. - - - - This API is 🧪 experimental. - - - """ - self.original_attn_processors = None - - for _, attn_processor in self.attn_processors.items(): - if "Added" in str(attn_processor.__class__.__name__): - raise ValueError( - "`fuse_qkv_projections()` is not supported for models having added KV projections." - ) - - self.original_attn_processors = self.attn_processors - - for module in self.modules(): - if isinstance(module, Attention): - module.fuse_projections(fuse=True) - - def unfuse_qkv_projections(self): - """Disables the fused QKV projection if enabled. - - - - This API is 🧪 experimental. - - - - """ - if self.original_attn_processors is not None: - self.set_attn_processor(self.original_attn_processors) - - def unload_lora(self): - """Unloads LoRA weights.""" - deprecate( - "unload_lora", - "0.28.0", - "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", - ) - for module in self.modules(): - if hasattr(module, "set_lora_layer"): - module.set_lora_layer(None) - - def get_time_embed( - self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int] - ) -> Optional[torch.Tensor]: - timesteps = timestep - if not torch.is_tensor(timesteps): - # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can - # This would be a good case for the `match` statement (Python 3.10+) - is_mps = sample.device.type == "mps" - if isinstance(timestep, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(sample.device) - - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(sample.shape[0]) - - t_emb = self.time_proj(timesteps) - # `Timesteps` does not contain any weights and will always return f32 tensors - # but time_embedding might actually be running in fp16. so we need to cast here. - # there might be better ways to encapsulate this. - t_emb = t_emb.to(dtype=sample.dtype) - return t_emb - - def get_class_embed( - self, sample: torch.Tensor, class_labels: Optional[torch.Tensor] - ) -> Optional[torch.Tensor]: - class_emb = None - if self.class_embedding is not None: - if class_labels is None: - raise ValueError( - "class_labels should be provided when num_class_embeds > 0" - ) - - if self.config.class_embed_type == "timestep": - class_labels = self.time_proj(class_labels) - - # `Timesteps` does not contain any weights and will always return f32 tensors - # there might be better ways to encapsulate this. - class_labels = class_labels.to(dtype=sample.dtype) - - class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) - return class_emb - - def get_aug_embed( - self, - emb: torch.Tensor, - encoder_hidden_states: torch.Tensor, - added_cond_kwargs: Dict[str, Any], - ) -> Optional[torch.Tensor]: - aug_emb = None - if self.config.addition_embed_type == "text": - aug_emb = self.add_embedding(encoder_hidden_states) - elif self.config.addition_embed_type == "text_image": - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - - image_embs = added_cond_kwargs.get("image_embeds") - text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) - aug_emb = self.add_embedding(text_embs, image_embs) - elif self.config.addition_embed_type == "text_time": - # SDXL - style - if "text_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" - ) - text_embeds = added_cond_kwargs.get("text_embeds") - if "time_ids" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" - ) - time_ids = added_cond_kwargs.get("time_ids") - time_embeds = self.add_time_proj(time_ids.flatten()) - time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) - add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) - add_embeds = add_embeds.to(emb.dtype) - aug_emb = self.add_embedding(add_embeds) - elif self.config.addition_embed_type == "image": - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - aug_emb = self.add_embedding(image_embs) - elif self.config.addition_embed_type == "image_hint": - # Kandinsky 2.2 - style - if ( - "image_embeds" not in added_cond_kwargs - or "hint" not in added_cond_kwargs - ): - raise ValueError( - f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" - ) - image_embs = added_cond_kwargs.get("image_embeds") - hint = added_cond_kwargs.get("hint") - aug_emb = self.add_embedding(image_embs, hint) - return aug_emb - - def process_encoder_hidden_states( - self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] - ) -> torch.Tensor: - if ( - self.encoder_hid_proj is not None - and self.config.encoder_hid_dim_type == "text_proj" - ): - encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) - elif ( - self.encoder_hid_proj is not None - and self.config.encoder_hid_dim_type == "text_image_proj" - ): - # Kandinsky 2.1 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj( - encoder_hidden_states, image_embeds - ) - elif ( - self.encoder_hid_proj is not None - and self.config.encoder_hid_dim_type == "image_proj" - ): - # Kandinsky 2.2 - style - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - encoder_hidden_states = self.encoder_hid_proj(image_embeds) - elif ( - self.encoder_hid_proj is not None - and self.config.encoder_hid_dim_type == "ip_image_proj" - ): - if "image_embeds" not in added_cond_kwargs: - raise ValueError( - f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" - ) - image_embeds = added_cond_kwargs.get("image_embeds") - image_embeds = self.encoder_hid_proj(image_embeds) - encoder_hidden_states = (encoder_hidden_states, image_embeds) - print("IP-Adapter here! ", len(encoder_hidden_states), encoder_hidden_states[0].shape) - return encoder_hidden_states - - def forward( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - timestep_cond: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - mid_block_additional_residual: Optional[torch.Tensor] = None, - down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - return_dict: bool = True, - segmentation_mask: torch.Tensor = None, # added - masked_feature: torch.Tensor = None, # added - feature_mask: torch.Tensor = None, # added - ) -> Union[UNet2DConditionOutput, Tuple]: - r""" - Args: - sample (`torch.FloatTensor`): - The noisy input tensor with the following shape `(batch, channel, height, width)`. - timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. - encoder_hidden_states (`torch.FloatTensor`): - The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. - class_labels (`torch.Tensor`, *optional*, defaults to `None`): - Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. - timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): - Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed - through the `self.time_embedding` layer to obtain the timestep embeddings. - attention_mask (`torch.Tensor`, *optional*, defaults to `None`): - An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask - is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large - negative values to the attention scores corresponding to "discard" tokens. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - added_cond_kwargs: (`dict`, *optional*): - A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that - are passed along to the UNet blocks. - down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): - A tuple of tensors that if specified are added to the residuals of down unet blocks. - mid_block_additional_residual: (`torch.Tensor`, *optional*): - A tensor that if specified is added to the residual of the middle unet block. - down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): - additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) - encoder_attention_mask (`torch.Tensor`): - A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If - `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, - which adds large negative values to the attention scores corresponding to "discard" tokens. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain - tuple. - - segmentation_mask (`torch.Tensor`): - A tensor of shape `(batch, number of segmentations ?, height, width)` representing the character-level segmentation mask. - masked_feature (`torch.Tensor`): - A masked feature tensor - feature_mask (`torch.Tensor`): - A feature mask tensor - - Returns: - [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: - If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, - otherwise a `tuple` is returned where the first element is the sample tensor. - """ - # By default samples have to be AT least a multiple of the overall upsampling factor. - # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). - # However, the upsampling interpolation output size can be forced to fit any upsampling size - # on the fly if necessary. - default_overall_up_factor = 2**self.num_upsamplers - - # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` - forward_upsample_size = False - upsample_size = None - for dim in sample.shape[-2:]: - if dim % default_overall_up_factor != 0: - # Forward upsample size to force interpolation output size. - forward_upsample_size = True - break - - # ensure attention_mask is a bias, and give it a singleton query_tokens dimension - # expects mask of shape: - # [batch, key_tokens] - # adds singleton query_tokens dimension: - # [batch, 1, key_tokens] - # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: - # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) - # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) - if attention_mask is not None: - # assume that mask is expressed as: - # (1 = keep, 0 = discard) - # convert mask into a bias that can be added to attention scores: - # (keep = +0, discard = -10000.0) - attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 - attention_mask = attention_mask.unsqueeze(1) - - # 0. concat all the feature together (Seg mask, masked feature.) - sample = torch.cat([sample, feature_mask, masked_feature], dim=1) - print(sample.shape) - - # convert encoder_attention_mask to a bias the same way we do for attention_mask - if encoder_attention_mask is not None: - encoder_attention_mask = ( - 1 - encoder_attention_mask.to(sample.dtype) - ) * -10000.0 - encoder_attention_mask = encoder_attention_mask.unsqueeze(1) - - # 0. center input if necessary - if self.config.center_input_sample: - sample = 2 * sample - 1.0 - - # 1. time - t_emb = self.get_time_embed(sample=sample, timestep=timestep) - emb = self.time_embedding(t_emb, timestep_cond) - aug_emb = None - - class_emb = self.get_class_embed(sample=sample, class_labels=class_labels) - if class_emb is not None: - if self.config.class_embeddings_concat: - emb = torch.cat([emb, class_emb], dim=-1) - else: - emb = emb + class_emb - if added_cond_kwargs is not None: - aug_emb = self.get_aug_embed( - emb=emb, - encoder_hidden_states=encoder_hidden_states, - added_cond_kwargs=added_cond_kwargs, - ) - if self.config.addition_embed_type == "image_hint": - aug_emb, hint = aug_emb - sample = torch.cat([sample, hint], dim=1) - - emb = emb + aug_emb if aug_emb is not None else emb - - if self.time_embed_act is not None: - emb = self.time_embed_act(emb) - - # Process encoder hidden state!!! for ip adapter and others - if added_cond_kwargs is not None: - encoder_hidden_states = self.process_encoder_hidden_states( - encoder_hidden_states=encoder_hidden_states, - added_cond_kwargs=added_cond_kwargs, - ) - - # 2. pre-process - segmentation_mask_embedding = self.word_embedding( - segmentation_mask.squeeze(1).long() - ).permute( - 0, 3, 1, 2 - ) # get 8-d embedding from character-level segmentation mask, with 128 indices - segmentation_mask_embedding = self.segmap_conv( - segmentation_mask_embedding - ) # resize the mask using cnn to match the feature space - sample = torch.cat([sample, segmentation_mask_embedding], 1) - - sample = self.conv_in(sample) - - # 2.5 GLIGEN position net - if ( - cross_attention_kwargs is not None - and cross_attention_kwargs.get("gligen", None) is not None - ): - cross_attention_kwargs = cross_attention_kwargs.copy() - gligen_args = cross_attention_kwargs.pop("gligen") - cross_attention_kwargs["gligen"] = { - "objs": self.position_net(**gligen_args) - } - - # 3. down - # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated - # to the internal blocks and will raise deprecation warnings. this will be confusing for our users. - if cross_attention_kwargs is not None: - cross_attention_kwargs = cross_attention_kwargs.copy() - lora_scale = cross_attention_kwargs.pop("scale", 1.0) - else: - lora_scale = 1.0 - - if USE_PEFT_BACKEND: - # weight the lora layers by setting `lora_scale` for each PEFT layer - scale_lora_layers(self, lora_scale) - - is_controlnet = ( - mid_block_additional_residual is not None - and down_block_additional_residuals is not None - ) - # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets - is_adapter = down_intrablock_additional_residuals is not None - # maintain backward compatibility for legacy usage, where - # T2I-Adapter and ControlNet both use down_block_additional_residuals arg - # but can only use one or the other - if ( - not is_adapter - and mid_block_additional_residual is None - and down_block_additional_residuals is not None - ): - deprecate( - "T2I should not use down_block_additional_residuals", - "1.3.0", - "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ - and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ - for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", - standard_warn=False, - ) - down_intrablock_additional_residuals = down_block_additional_residuals - is_adapter = True - - down_block_res_samples = (sample,) - for downsample_block in self.down_blocks: - if ( - hasattr(downsample_block, "has_cross_attention") - and downsample_block.has_cross_attention - ): - # For t2i-adapter CrossAttnDownBlock2D - additional_residuals = {} - if is_adapter and len(down_intrablock_additional_residuals) > 0: - additional_residuals["additional_residuals"] = ( - down_intrablock_additional_residuals.pop(0) - ) - - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - **additional_residuals, - ) - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - if is_adapter and len(down_intrablock_additional_residuals) > 0: - sample += down_intrablock_additional_residuals.pop(0) - - down_block_res_samples += res_samples - - if is_controlnet: - new_down_block_res_samples = () - - for down_block_res_sample, down_block_additional_residual in zip( - down_block_res_samples, down_block_additional_residuals - ): - down_block_res_sample = ( - down_block_res_sample + down_block_additional_residual - ) - new_down_block_res_samples = new_down_block_res_samples + ( - down_block_res_sample, - ) - - down_block_res_samples = new_down_block_res_samples - - # 4. mid - if self.mid_block is not None: - if ( - hasattr(self.mid_block, "has_cross_attention") - and self.mid_block.has_cross_attention - ): - sample = self.mid_block( - sample, - emb, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - cross_attention_kwargs=cross_attention_kwargs, - encoder_attention_mask=encoder_attention_mask, - ) - else: - sample = self.mid_block(sample, emb) - - # To support T2I-Adapter-XL - if ( - is_adapter - and len(down_intrablock_additional_residuals) > 0 - and sample.shape == down_intrablock_additional_residuals[0].shape - ): - sample += down_intrablock_additional_residuals.pop(0) - - if is_controlnet: - sample = sample + mid_block_additional_residual - - # 5. up - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[ - : -len(upsample_block.resnets) - ] - - # if we have not reached the final block and need to forward the - # upsample size, we do it here - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if ( - hasattr(upsample_block, "has_cross_attention") - and upsample_block.has_cross_attention - ): - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - upsample_size=upsample_size, - attention_mask=attention_mask, - encoder_attention_mask=encoder_attention_mask, - ) - else: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - upsample_size=upsample_size, - ) - - # 6. post-process - if self.conv_norm_out: - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if USE_PEFT_BACKEND: - # remove `lora_scale` from each PEFT layer - unscale_lora_layers(self, lora_scale) - - if not return_dict: - return (sample,) - - return UNet2DConditionOutput(sample=sample) diff --git a/text_diffuser/train.py b/text_diffuser/train.py deleted file mode 100644 index 8f5f120..0000000 --- a/text_diffuser/train.py +++ /dev/null @@ -1,1237 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file provides the inference script. -# ------------------------------------------ - -import argparse -import logging -import math -import os -import random -from collections import OrderedDict -from pathlib import Path -from typing import Optional - -import accelerate -import cv2 -import datasets -import numpy as np -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -import transformers -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from huggingface_hub import HfFolder, Repository, create_repo, whoami -from packaging import version -from PIL import Image, ImageDraw -from termcolor import colored -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer - -import diffusers -from diffusers import ( - AutoencoderKL, - DDPMScheduler, - StableDiffusionPipeline, - UNet2DConditionModel, -) -from diffusers.optimization import get_scheduler -from diffusers.training_utils import EMAModel -from diffusers.utils import check_min_version, deprecate -from diffusers.utils.import_utils import is_xformers_available - - -# Will error if the minimal version of diffusers is not installed. Remove at your own risks. -check_min_version("0.15.0.dev0") - -logger = get_logger(__name__, log_level="INFO") - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default="runwayml/stable-diffusion-v1-5", - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--max_train_samples", - type=int, - default=None, - help=( - "For debugging purposes or quicker training, truncate the number of training examples to this " - "value if set." - ), - ) - parser.add_argument( - "--output_dir", - type=str, - default="sd-model-finetuned", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="The directory where the downloaded models and datasets will be stored.", - ) - parser.add_argument( - "--seed", type=int, default=None, help="A seed for reproducible training." - ) - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--character_aware_loss_lambda", - type=float, - default=0.01, - help="Lambda for the character-aware loss", - ) - parser.add_argument( - "--character_aware_loss_ckpt", - type=str, - default="ckpt/character_aware_loss_unet.pth", - help="The checkpoint for unet providing the charactere-aware loss.", - ) - parser.add_argument( - "--train_batch_size", - type=int, - default=16, - help="Batch size (per device) for the training dataloader.", - ) - parser.add_argument("--num_train_epochs", type=int, default=2) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-5, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--no_pos_con", - action="store_true", - default=False, - help="If it is activated, the position and the content of character are not avaible during training.", - ) - parser.add_argument( - "--no_con", - action="store_true", - default=False, - help="If it is activated, the content of character is not avaible during training.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", - type=int, - default=0, - help="Number of steps for the warmup in the lr scheduler.", - ) - parser.add_argument( - "--use_8bit_adam", - action="store_true", - help="Whether or not to use 8-bit Adam from bitsandbytes.", - ) - parser.add_argument( - "--drop_caption", - action="store_true", - help="Whether or not to drop captions during training.", - ) - parser.add_argument( - "--dataset_name", - type=str, - default="MARIO-10M", - help=( - "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," - " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," - " or to a folder containing files that 🤗 Datasets can understand." - ), - ) - parser.add_argument( - "--use_ema", action="store_true", help="Whether to use EMA model." - ) - parser.add_argument( - "--segmentation_mask_aug", - action="store_true", - help="Whether to augment the segmentation masks (inspired by https://arxiv.org/abs/2211.13227).", - ) - parser.add_argument( - "--non_ema_revision", - type=str, - default=None, - required=False, - help=( - "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" - " remote repository specified with --pretrained_model_name_or_path." - ), - ) - parser.add_argument( - "--image_column", - type=str, - default="image", - help="The column of the dataset containing an image.", - ) - parser.add_argument( - "--caption_column", - type=str, - default="text", - help="The column of the dataset containing a caption or a list of captions.", - ) - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=0, - help=( - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." - ), - ) - parser.add_argument( - "--mask_all_ratio", - type=float, - default=0.5, - help="The training ratio of two branches.", - ) - parser.add_argument( - "--adam_beta1", - type=float, - default=0.9, - help="The beta1 parameter for the Adam optimizer.", - ) - parser.add_argument( - "--adam_beta2", - type=float, - default=0.999, - help="The beta2 parameter for the Adam optimizer.", - ) - parser.add_argument( - "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use." - ) - parser.add_argument( - "--adam_epsilon", - type=float, - default=1e-08, - help="Epsilon value for the Adam optimizer", - ) - parser.add_argument( - "--max_grad_norm", default=1.0, type=float, help="Max gradient norm." - ) - parser.add_argument( - "--push_to_hub", - action="store_true", - help="Whether or not to push the model to the Hub.", - ) - parser.add_argument( - "--hub_token", - type=str, - default=None, - help="The token to use to push to the Model Hub.", - ) - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default=None, - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), - ) - parser.add_argument( - "--report_to", - type=str, - default="tensorboard", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' - ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' - ), - ) - parser.add_argument( - "--local_rank", - type=int, - default=-1, - help="For distributed training: local_rank", - ) - parser.add_argument( - "--checkpointing_steps", - type=int, - default=500, - help=( - "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" - " training using `--resume_from_checkpoint`." - ), - ) - parser.add_argument( - "--checkpoints_total_limit", - type=int, - default=5, - help=( - "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." - " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" - " for more docs" - ), - ) - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default=None, - help=( - "Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), - ) - parser.add_argument( - "--enable_xformers_memory_efficient_attention", - action="store_true", - help="Whether or not to use xformers.", - ) - parser.add_argument( - "--noise_offset", type=float, default=0, help="The scale of noise offset." - ) - parser.add_argument( - "--dataset_path", - type=str, - default="/home/cjy/cjy/TextDiffusion/data/laion-ocr-unzip", - help="The path of dataset.", - ) - parser.add_argument( - "--train_dataset_index_file", - type=str, - default="/home/jingyechen/jingyechen/amlt_test/diffusers_combine/examples/text_to_image/train_dataset_index.txt", - help="The txt file that provides the index of training samples. The format of each line should be XXXXX_XXXXXXXXX.", - ) - parser.add_argument( - "--vis_num", - type=int, - default=16, - help="The number of images to be visualized during training.", - ) - parser.add_argument( - "--vis_interval", type=int, default=500, help="The interval for visualization." - ) - - args = parser.parse_args() - - print("***************") - print(args) - print("***************") - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - # default to using the same revision for the non-ema model if not specified - if args.non_ema_revision is None: - args.non_ema_revision = args.revision - - return args - - -def get_full_repo_name( - model_id: str, organization: Optional[str] = None, token: Optional[str] = None -): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - - -def main(): - args = parse_args() - - if args.non_ema_revision is not None: - deprecate( - "non_ema_revision!=None", - "0.15.0", - message=( - "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to" - " use `--variant=non_ema` instead." - ), - ) - logging_dir = os.path.join(args.output_dir, args.logging_dir) - - accelerator_project_config = ProjectConfiguration( - total_limit=args.checkpoints_total_limit - ) - - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - logging_dir=logging_dir, - project_config=accelerator_project_config, - ) - - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state, main_process_only=False) - if accelerator.is_local_main_process: - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_warning() - diffusers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - diffusers.utils.logging.set_verbosity_error() - - # If passed along, set the training seed now. - args.seed = random.randint(0, 1000000) if args.seed is None else args.seed - - print(f'{colored("[√]", "green")} Arguments are loaded.') - print(args) - - set_seed(args.seed) - print(f'{colored("[√]", "green")} Seed is set to {args.seed}.') - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name( - Path(args.output_dir).name, token=args.hub_token - ) - else: - repo_name = args.hub_model_id - create_repo(repo_name, exist_ok=True, token=args.hub_token) - repo = Repository( - args.output_dir, clone_from=repo_name, token=args.hub_token - ) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - print(args.output_dir) - - # Load scheduler, tokenizer and models. - noise_scheduler = DDPMScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder="scheduler" - ) - tokenizer = CLIPTokenizer.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer", - revision=args.revision, - ) - text_encoder = CLIPTextModel.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="text_encoder", - revision=args.revision, - ) - vae = AutoencoderKL.from_pretrained( - args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision - ) - unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="unet", - revision=args.non_ema_revision, - ) - - # Freeze vae and text_encoder - vae.requires_grad_(False) - text_encoder.requires_grad_(False) - - if args.enable_xformers_memory_efficient_attention: - if is_xformers_available(): - import xformers - - xformers_version = version.parse(xformers.__version__) - if xformers_version == version.parse("0.0.16"): - logger.warn( - "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." - ) - unet.enable_xformers_memory_efficient_attention() - else: - raise ValueError( - "xformers is not available. Make sure it is installed correctly" - ) - - # `accelerate` 0.16.0 will have better support for customized saving - if version.parse(accelerate.__version__) >= version.parse("0.16.0"): - # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format - def save_model_hook(models, weights, output_dir): - if args.use_ema: - ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema")) # noqa - - for i, model in enumerate(models): - model.save_pretrained(os.path.join(output_dir, "unet")) - - # make sure to pop weight so that corresponding model is not saved again - weights.pop() - - def load_model_hook(models, input_dir): - if args.use_ema: - load_model = EMAModel.from_pretrained( - os.path.join(input_dir, "unet_ema"), UNet2DConditionModel - ) - ema_unet.load_state_dict(load_model.state_dict()) # noqa - ema_unet.to(accelerator.device) # noqa - del load_model - - for i in range(len(models)): - # pop models so that they are not loaded again - model = models.pop() - - # load diffusers style into model - load_model = UNet2DConditionModel.from_pretrained( - input_dir, subfolder="unet" - ) - #register to config? - model.register_to_config(**load_model.config) - - model.load_state_dict(load_model.state_dict()) - del load_model - - accelerator.register_save_state_pre_hook(save_model_hook) - accelerator.register_load_state_pre_hook(load_model_hook) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate - * args.gradient_accumulation_steps - * args.train_batch_size - * accelerator.num_processes - ) - - # Initialize the optimizer - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError( - "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" - ) - - optimizer_cls = bnb.optim.AdamW8bit - else: - optimizer_cls = torch.optim.AdamW - - optimizer = optimizer_cls( - unet.parameters(), - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - from datasets import Dataset - - lines = open(args.train_dataset_index_file).readlines() - random.shuffle(lines) - train_dataset = Dataset.from_dict({"image": lines, "text": lines}) - dataset = { - "train": train_dataset, - } - - # Preprocessing the datasets. - # We need to tokenize inputs and targets. - column_names = dataset["train"].column_names - - dataset_name_mapping = { - "MARIO-10M": ("image", "text"), - } - - # 6. Get the column names for input/target. - dataset_columns = dataset_name_mapping.get(args.dataset_name, None) #mario-10m - if args.image_column is None: - image_column = ( - dataset_columns[0] if dataset_columns is not None else column_names[0] #image - ) - else: - image_column = args.image_column #user input - if image_column not in column_names: - raise ValueError( - f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" - ) - if args.caption_column is None: - caption_column = ( - dataset_columns[1] if dataset_columns is not None else column_names[1] #text - ) - else: - caption_column = args.caption_column #user input - if caption_column not in column_names: - raise ValueError( - f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" - ) - - # Preprocessing the datasets. - # We need to tokenize input captions and transform the images. - def tokenize_captions(examples, is_train=True): - captions = [] - for caption in examples[caption_column]: #ex) examples['text'] - caption = caption.strip() #ex) cat_box - first, second = caption.split("_") #ex) cat, box - try: - caption = open( - f"{args.dataset_path}/{first}/{second}/caption.txt" - ).readlines()[0] #default dataset_path: /home/cjy/cjy/TextDiffusion/data/laion-ocr-unzip - except: - caption = "null" - print("erorr of caption") - - if args.drop_caption and is_train and random.random() < 0.1: - caption = "" # drop caption with 10% probability - - if isinstance(caption, str): - captions.append(caption) - elif isinstance(caption, (list, np.ndarray)): - # take a random caption if there are multiple - captions.append(random.choice(caption) if is_train else caption[0]) - #train시에는 random choice로 caption을 선택 - else: - raise ValueError( - f"Caption column `{caption_column}` should contain either strings or lists of strings." - ) - inputs = tokenizer( - captions, - max_length=tokenizer.model_max_length, - padding="max_length", - truncation=True, - return_tensors="pt", - ) - #return the tokenized captions by pretrained tokenizer - return inputs.input_ids - - # Preprocessing the datasets. - # Please not that Crop is not suitable for this task as texts may be incomplete during cropping - train_transforms = transforms.Compose( - [ - transforms.ToTensor(), - ] - ) - - def generate_random_rectangles(image): - # randomly generate 0~3 masks - rectangles = [] - box_num = random.randint(0, 3) - for i in range(box_num): - x = random.randint(0, image.size[0]) - y = random.randint(0, image.size[1]) - w = random.randint(16, 256) - h = random.randint(16, 96) - angle = random.randint(-45, 45) - p1 = (x, y) - p2 = (x + w, y) - p3 = (x + w, y + h) - p4 = (x, y + h) - center = ((x + x + w) / 2, (y + y + h) / 2) - p1 = rotate_point(p1, center, angle) - p2 = rotate_point(p2, center, angle) - p3 = rotate_point(p3, center, angle) - p4 = rotate_point(p4, center, angle) - rectangles.append((p1, p2, p3, p4)) - return rectangles - - def rotate_point(point, center, angle): - # rotation - angle = math.radians(angle) - x = point[0] - center[0] - y = point[1] - center[1] - x1 = x * math.cos(angle) - y * math.sin(angle) - y1 = x * math.sin(angle) + y * math.cos(angle) - x1 += center[0] - y1 += center[1] - return int(x1), int(y1) - - def box2point(box): - # convert string of points to list of points - box = box.split(",") - box = [int(i) // (512 // 512) for i in box] - points = [ - (box[0], box[1]), - (box[2], box[3]), - (box[4], box[5]), - (box[6], box[7]), - ] - return points - - def get_mask(ocrs): - # the two branches are trained at a certain ratio - if random.random() <= args.mask_all_ratio: #ex) args.mask_all_ratio = 0.1 - image_mask = Image.new("L", (512, 512), 1) #all white 512x512 image mask - return image_mask - - image_mask = Image.new("L", (512, 512), 0) - draw_image_mask = ImageDraw.ImageDraw(image_mask) #draw on the image mask - for ocr in ocrs: - ocr = ocr.strip() - _, box, _ = ocr.split() #box from stage1 - if random.random() < 0.5: # each box is masked with 50% probability - points = box2point(box) - draw_image_mask.polygon(points, fill=1) - - blank = Image.new("RGB", (512, 512), (0, 0, 0)) - rectangles = generate_random_rectangles( - blank - ) # get additional masks (can mask non-text areas): why is it needed? - for rectangle in rectangles: - draw_image_mask.polygon(rectangle, fill=1) - - return image_mask #random mask word mask랑 섞음 - - def preprocess_train(examples): - # preprocess the training data - - images = [] - segmentation_masks = [] - image_masks = [] - for image in examples[image_column]: - image = image.strip() - first, second = image.split("_") - image_path = f"{args.dataset_path}/{first}/{second}/image.jpg" - ocrs = open(f"{args.dataset_path}/{first}/{second}/ocr.txt").readlines() - - image = Image.open(image_path).convert("RGB") - - image_mask = get_mask(ocrs) - image_mask_np = np.array(image_mask) - image_mask_tensor = torch.from_numpy(image_mask_np) - images.append(image) - - if args.no_pos_con: - segmentation_mask = ( - np.load(f"{args.dataset_path}/{first}/{second}/charseg.npy") * 0 - ) - elif args.no_con: - segmentation_mask = ( - np.load(f"{args.dataset_path}/{first}/{second}/charseg.npy") > 0 - ).astype(np.float32) - else: - segmentation_mask = np.load( - f"{args.dataset_path}/{first}/{second}/charseg.npy" - ) - - if args.segmentation_mask_aug: # 10% dilate / 10% erode / 10% drop - random_value = random.random() - if random_value < 0.6: - pass - elif random_value < 0.7: - kernel = np.ones((3, 3), dtype=np.uint8) - segmentation_mask = cv2.dilate( - segmentation_mask.astype(np.uint8), kernel, iterations=1 - ) - elif random_value < 0.8: - kernel = np.ones((3, 3), dtype=np.uint8) - segmentation_mask = cv2.erode( - segmentation_mask.astype(np.uint8), kernel, iterations=1 - ) - elif random_value < 0.85: - kernel = np.ones((3, 3), dtype=np.uint8) - segmentation_mask = cv2.dilate( - segmentation_mask.astype(np.uint8), kernel, iterations=2 - ) - elif random_value < 0.9: - kernel = np.ones((3, 3), dtype=np.uint8) - segmentation_mask = cv2.erode( - segmentation_mask.astype(np.uint8), kernel, iterations=2 - ) - else: - drop_mask = np.random.rand(*segmentation_mask.shape) < 0.1 - segmentation_mask[drop_mask] = ( - 0 # set character to non-character with 10% probability - ) - - segmentation_masks.append(segmentation_mask) - image_masks.append(image_mask_tensor) - - examples["images"] = [ - train_transforms(image).sub_(0.5).div_(0.5) for image in images - ] - examples["prompts"] = tokenize_captions(examples) - examples["segmentation_masks"] = segmentation_masks - examples["image_masks"] = image_masks - - return examples - - with accelerator.main_process_first(): - if args.max_train_samples is not None: - dataset["train"] = ( - dataset["train"] - .shuffle(seed=args.seed) - .select(range(args.max_train_samples)) - ) - # Set the training transforms - train_dataset = dataset["train"].with_transform(preprocess_train) - - def collate_fn(examples): - images = torch.stack([example["images"] for example in examples]) - images = images.to(memory_format=torch.contiguous_format).float() - prompts = torch.stack([example["prompts"] for example in examples]) - image_masks = torch.cat( - [example["image_masks"].unsqueeze(0) for example in examples], 0 - ) - segmentation_masks = torch.cat( - [ - torch.from_numpy(example["segmentation_masks"]) - .unsqueeze(0) - .unsqueeze(0) - for example in examples - ], - dim=0, - ) - #size: (batch_size, 1, 512, 512) - return { - "images": images, - "prompts": prompts, - } - - # DataLoaders creation: - train_dataloader = torch.utils.data.DataLoader( - train_dataset, - shuffle=True, - collate_fn=collate_fn, - batch_size=args.train_batch_size, - num_workers=args.dataloader_num_workers, - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil( - len(train_dataloader) / args.gradient_accumulation_steps - ) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - # Prepare everything with our `accelerator`. - unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, optimizer, train_dataloader, lr_scheduler - ) - - if args.use_ema: - ema_unet.to(accelerator.device) # noqa - - # For mixed precision training we cast the text_encoder and vae weights to half-precision - # as these models are only used for inference, keeping weights in full precision is not required. - weight_dtype = torch.float32 - if accelerator.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif accelerator.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - # Move text_encode and vae to gpu and cast to weight_dtype - text_encoder.to(accelerator.device, dtype=weight_dtype) - vae.to(accelerator.device, dtype=weight_dtype) - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil( - len(train_dataloader) / args.gradient_accumulation_steps - ) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - accelerator.init_trackers("text2image-fine-tune", config=vars(args)) - - # Train! - total_batch_size = ( - args.train_batch_size - * accelerator.num_processes - * args.gradient_accumulation_steps - ) - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info( - f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}" - ) - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - global_step = 0 - first_epoch = 0 - - # Potentially load in the weights and states from a previous save - if args.resume_from_checkpoint: - if args.resume_from_checkpoint != "latest": - path = os.path.basename(args.resume_from_checkpoint) - else: - # Get the most recent checkpoint - dirs = os.listdir(args.output_dir) - dirs = [d for d in dirs if d.startswith("checkpoint")] - dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) - path = dirs[-1] if len(dirs) > 0 else None - - if path is None: - accelerator.print( - f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." - ) - args.resume_from_checkpoint = None - else: - accelerator.print(f"Resuming from checkpoint {path}") - accelerator.load_state(os.path.join(args.output_dir, path)) - global_step = int(path.split("-")[1]) - - resume_global_step = global_step * args.gradient_accumulation_steps - first_epoch = global_step // num_update_steps_per_epoch - resume_step = resume_global_step % ( - num_update_steps_per_epoch * args.gradient_accumulation_steps - ) - - # Only show the progress bar once on each machine. - progress_bar = tqdm( - range(global_step, args.max_train_steps), - disable=not accelerator.is_local_main_process, - ) - progress_bar.set_description("Steps") - - ce_criterion = torch.nn.CrossEntropyLoss() - - # import segmenter for calculating loss - from model.text_segmenter.unet import UNet - - segmenter = UNet(4, 96, True).cuda() - state_dict = torch.load(args.character_aware_loss_ckpt, map_location="cpu") - # create new OrderedDict that does not contain `module.` - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - name = k[7:] - new_state_dict[name] = v - segmenter.load_state_dict(new_state_dict) - segmenter.eval() - - for epoch in range(first_epoch, args.num_train_epochs): - unet.train() - train_loss = 0.0 - for step, batch in enumerate(train_dataloader): - - with accelerator.accumulate(unet): - # Convert images to latent space - features = vae.encode( - batch["images"].to(weight_dtype) - ).latent_dist.sample() - features = features * vae.config.scaling_factor - - image_masks = batch["image_masks"] - - masked_images = batch["images"] * (1 - image_masks).unsqueeze(1) - #size of image_masks: (batch_size, 1, 512, 512) - #size of masked_images: (batch_size, 3, 512, 512) - masked_features = vae.encode( - masked_images.to(weight_dtype) - ).latent_dist.sample() - masked_features = masked_features * vae.config.scaling_factor - - segmentation_masks = batch["segmentation_masks"] - image_masks_256 = F.interpolate( - image_masks.unsqueeze(1), size=(256, 256), mode="nearest" - ) - #256x256로 resize - #size of image_masks_256: (batch_size, 1, 256, 256) - segmentation_masks = image_masks_256 * segmentation_masks - #element-wise multiplication - feature_masks = F.interpolate( - image_masks.unsqueeze(1), size=(64, 64), mode="nearest" - ) - #size of feature_masks: (batch_size, 1, 64, 64) - #64x64로 resize - - # Sample noise that we'll add to the latents - noise = torch.randn_like(features) - if args.noise_offset: - # https://www.crosslabs.org//blog/diffusion-with-offset-noise #entire image에 대해서 같은 noise를 추가 - noise += args.noise_offset * torch.randn( - (features.shape[0], features.shape[1], 1, 1), - device=features.device, - ) - - bsz = features.shape[0] #batch size - timesteps = torch.randint( - 0, - noise_scheduler.num_train_timesteps, - (bsz,), - device=features.device, - ) #ramdomly selected timestep - timesteps = timesteps.long() - - noisy_latents = noise_scheduler.add_noise(features, noise, timesteps) - - encoder_hidden_states = text_encoder(batch["prompts"])[0] - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": # √ - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(features, noise, timesteps) - else: - raise ValueError( - f"Unknown prediction type {noise_scheduler.config.prediction_type}" - ) - - if accelerator.is_main_process: - if (step + 1) % args.vis_interval == 0: - scheduler = DDPMScheduler.from_pretrained( - args.pretrained_model_name_or_path, subfolder="scheduler" - ) - scheduler.set_timesteps(50) - noise = torch.randn((args.vis_num, 4, 64, 64)).to("cuda") - input = noise - - for t in tqdm(scheduler.timesteps): - with torch.no_grad(): - noisy_residual = unet( - input, - t, - encoder_hidden_states[: args.vis_num], - masked_feature=masked_features[:16], - feature_mask=feature_masks[:16], - segmentation_mask=segmentation_masks[:16], - ).sample - prev_noisy_sample = scheduler.step( - noisy_residual, t, input - ).prev_sample - input = prev_noisy_sample - - # decode - input = 1 / vae.config.scaling_factor * input - images = vae.decode(input.half(), return_dict=False)[0] - - ## save predicted images - width, height = 512, 512 - new_image = Image.new("RGB", (4 * width, 4 * height)) - for index, image in enumerate(images.float()): - image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) - image = image.cpu().permute(0, 2, 3, 1).numpy()[0] - image = Image.fromarray( - (image * 255).round().astype("uint8") - ).convert("RGB") - row = index // 4 - col = index % 4 - new_image.paste(image, (col * width, row * height)) - new_image.save( - f"{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_pred_img.png" - ) - - ## save segmentation masks - width, height = 512, 512 - new_image = Image.new("L", (4 * width, 4 * height)) - for index, image in enumerate( - segmentation_masks[: args.vis_num] - ): - segmap_pil = Image.fromarray( - ((image != 0) * 255) - .squeeze() - .cpu() - .numpy() - .astype("uint8") - ) - row = index // 4 - col = index % 4 - new_image.paste(segmap_pil, (col * width, row * height)) - new_image.save( - f"{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_segmentation_mask.png" - ) - - ## save original images - width, height = 512, 512 - new_image = Image.new("RGB", (4 * width, 4 * height)) - for index, image in enumerate(batch["images"][: args.vis_num]): - image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) - image = image.cpu().permute(0, 2, 3, 1).numpy()[0] - image = Image.fromarray( - (image * 255).round().astype("uint8") - ).convert("RGB") - # pred_images.append(image) - row = index // 4 - col = index % 4 - new_image.paste(image, (col * width, row * height)) - new_image.save( - f"{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_orig_img.png" - ) - - ## save masked original images - width, height = 512, 512 - new_image = Image.new("RGB", (4 * width, 4 * height)) - for index, image in enumerate(masked_images[: args.vis_num]): - image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) - image = image.cpu().permute(0, 2, 3, 1).numpy()[0] - image = Image.fromarray( - (image * 255).round().astype("uint8") - ).convert("RGB") - # pred_images.append(image) - row = index // 4 - col = index % 4 - new_image.paste(image, (col * width, row * height)) - new_image.save( - f"{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_masked_orig_img.png" - ) - print("inference successfully") - - model_pred = unet( - sample=noisy_latents, - timestep=timesteps, - encoder_hidden_states=encoder_hidden_states, - masked_feature=masked_features, - feature_mask=feature_masks, - segmentation_mask=segmentation_masks, - ).sample - #size of model_pred: (batch_size, 4, 64, 64) - - pred_x0 = noise_scheduler.get_x0_from_noise( - model_pred, timesteps, noisy_latents - ) - resized_charmap = F.interpolate( - batch["segmentation_masks"].float(), size=(64, 64), mode="nearest" - ).long() - - ce_loss = ce_criterion( - segmenter(pred_x0.float()), resized_charmap.squeeze(1) - ) - mse_loss = F.mse_loss( - model_pred.float(), target.float(), reduction="mean" - ) - loss = mse_loss + ce_loss * args.character_aware_loss_lambda - - avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() - train_loss += avg_loss.item() / args.gradient_accumulation_steps - - # Backpropagate - accelerator.backward(loss) - if accelerator.sync_gradients: - accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm) - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - if args.use_ema: - ema_unet.step(unet.parameters()) # noqa - progress_bar.update(1) - global_step += 1 - accelerator.log({"train_loss": train_loss}, step=global_step) - train_loss = 0.0 - - if global_step % args.checkpointing_steps == 0: - if accelerator.is_main_process: - save_path = os.path.join( - args.output_dir, f"checkpoint-{global_step}" - ) - accelerator.save_state(save_path) - logger.info(f"Saved state to {save_path}") - - logs = { - "step_loss": loss.detach().item(), - "lr": lr_scheduler.get_last_lr()[0], - "mse_loss": mse_loss.detach().item(), - "ce_loss": ce_loss.detach().item(), - } - progress_bar.set_postfix(**logs) - - if global_step >= args.max_train_steps: - break - - # Create the pipeline using the trained modules and save it. - accelerator.wait_for_everyone() - if accelerator.is_main_process: - unet = accelerator.unwrap_model(unet) - if args.use_ema: - ema_unet.copy_to(unet.parameters()) # noqa - - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - text_encoder=text_encoder, - vae=vae, - unet=unet, - revision=args.revision, - ) - pipeline.save_pretrained(args.output_dir) - - if args.push_to_hub: - repo.push_to_hub( - commit_message="End of training", blocking=False, auto_lfs_prune=True - ) - - accelerator.end_training() - - -if __name__ == "__main__": - main() diff --git a/text_diffuser/util.py b/text_diffuser/util.py deleted file mode 100644 index 9ba63d3..0000000 --- a/text_diffuser/util.py +++ /dev/null @@ -1,387 +0,0 @@ -# ------------------------------------------ -# TextDiffuser: Diffusion Models as Text Painters -# Paper Link: https://arxiv.org/abs/2305.10855 -# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser -# Copyright (c) Microsoft Corporation. -# This file defines a set of commonly used utility functions. -# ------------------------------------------ - -import math -import re -import string -import textwrap -from typing import List - -import cv2 -import numpy as np -from PIL import Image, ImageDraw, ImageFont, ImageOps - - -# define alphabet and alphabet_dic -alphabet = ( - string.digits - + string.ascii_lowercase - + string.ascii_uppercase - + string.punctuation - + " " -) # len(aphabet) = 95 -alphabet_dic = {} -for index, c in enumerate(alphabet): - alphabet_dic[c] = index + 1 # the index 0 stands for non-character - - -def transform_mask_pil(mask_root): - """ - This function extracts the mask area and text area from the images. - - Args: - mask_root (str): The path of mask image. - * The white area is the unmasked area - * The gray area is the masked area - * The white area is the text area - """ - img = np.array(mask_root) - img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_NEAREST) - gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - ret, binary = cv2.threshold( - gray, 250, 255, cv2.THRESH_BINARY - ) # pixel value is set to 0 or 255 according to the threshold - return 1 - (binary.astype(np.float32) / 255) - - -def transform_mask(mask_root: str): - """ - This function extracts the mask area and text area from the images. - - Args: - mask_root (str): The path of mask image. - * The white area is the unmasked area - * The gray area is the masked area - * The white area is the text area - """ - img = cv2.imread(mask_root) - img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_NEAREST) - gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - ret, binary = cv2.threshold( - gray, 250, 255, cv2.THRESH_BINARY - ) # pixel value is set to 0 or 255 according to the threshold - return 1 - (binary.astype(np.float32) / 255) - - -def segmentation_mask_visualization(font_path: str, segmentation_mask: np.array): - """ - This function visualizes the segmentaiton masks with characters. - - Args: - font_path (str): The path of font. We recommand to use Arial.ttf - segmentation_mask (np.array): The character-level segmentation mask. - """ - segmentation_mask = cv2.resize( - segmentation_mask, (64, 64), interpolation=cv2.INTER_NEAREST - ) - font = ImageFont.truetype(font_path, 8) - blank = Image.new("RGB", (512, 512), (0, 0, 0)) - d = ImageDraw.Draw(blank) - for i in range(64): - for j in range(64): - if int(segmentation_mask[i][j]) == 0 or int( - segmentation_mask[i][j] - ) - 1 >= len(alphabet): - continue - else: - d.text( - (j * 8, i * 8), - alphabet[int(segmentation_mask[i][j]) - 1], - font=font, - fill=(0, 255, 0), - ) - return blank - - -def make_caption_pil(font_path: str, captions: List[str]): - """ - This function converts captions into pil images. - - Args: - font_path (str): The path of font. We recommand to use Arial.ttf - captions (List[str]): List of captions. - """ - caption_pil_list = [] - font = ImageFont.truetype(font_path, 18) - - for caption in captions: - border_size = 2 - img = Image.new("RGB", (512 - 4, 48 - 4), (255, 255, 255)) - img = ImageOps.expand( - img, - border=(border_size, border_size, border_size, border_size), - fill=(127, 127, 127), - ) - draw = ImageDraw.Draw(img) - border_size = 2 - text = caption - lines = textwrap.wrap(text, width=40) - x, y = 4, 4 - line_height = getsize(font,"A")[1] + 4 - - start = 0 - for line in lines: - draw.text((x, y + start), line, font=font, fill=(200, 127, 0)) - y += line_height - - caption_pil_list.append(img) - return caption_pil_list - - -def filter_segmentation_mask(segmentation_mask: np.array): - """ - This function removes some noisy predictions of segmentation masks. - - Args: - segmentation_mask (np.array): The character-level segmentation mask. - """ - segmentation_mask[segmentation_mask == alphabet_dic["-"]] = 0 - segmentation_mask[segmentation_mask == alphabet_dic[" "]] = 0 - return segmentation_mask - - -def combine_image( - args, - sub_output_dir: str, - pred_image_list: List, - image_pil: Image, - character_mask_pil: Image, - character_mask_highlight_pil: Image, - caption_pil_list: List, -): - """ - This function combines all the outputs and useful inputs together. - - Args: - args (argparse.ArgumentParser): The arguments. - pred_image_list (List): List of predicted images. - image_pil (Image): The original image. - character_mask_pil (Image): The character-level segmentation mask. - character_mask_highlight_pil (Image): The character-level segmentation mask highlighting character regions with green color. - caption_pil_list (List): List of captions. - """ - - # # create a "latest" folder to store the results - # if os.path.exists(f'{args.output_dir}/latest'): - # shutil.rmtree(f'{args.output_dir}/latest') - # os.mkdir(f'{args.output_dir}/latest') - - # save each predicted image - # os.makedirs(f'{args.output_dir}/{sub_output_dir}', exist_ok=True) - for index, img in enumerate(pred_image_list): - img.save(f"{args.output_dir}/{sub_output_dir}/{index}.jpg") - # img.save(f'{args.output_dir}/latest/{index}.jpg') - - length = len(pred_image_list) - lines = math.ceil(length / 3) - - blank = Image.new("RGB", (512 * 3, 512 * (lines + 1) + 48 * lines), (0, 0, 0)) - blank.paste(image_pil, (0, 0)) - blank.paste(character_mask_pil, (512, 0)) - blank.paste(character_mask_highlight_pil, (512 * 2, 0)) - - for i in range(length): - row, col = i // 3, i % 3 - blank.paste(pred_image_list[i], (512 * col, 512 * (row + 1) + 48 * row)) - blank.paste(caption_pil_list[i], (512 * col, 512 * (row + 1) + 48 * row + 512)) - - blank.save(f"{args.output_dir}/{sub_output_dir}/combine.jpg") - # blank.save(f'{args.output_dir}/latest/combine.jpg') - - return blank.convert("RGB") - - -def combine_image_gradio( - args, - sub_output_dir: str, - pred_image_list: List, - image_pil: Image, - character_mask_pil: Image, - character_mask_highlight_pil: Image, - caption_pil_list: List, -): - """ - This function combines all the outputs and useful inputs together. - - Args: - args (argparse.ArgumentParser): The arguments. - pred_image_list (List): List of predicted images. - image_pil (Image): The original image. - character_mask_pil (Image): The character-level segmentation mask. - character_mask_highlight_pil (Image): The character-level segmentation mask highlighting character regions with green color. - caption_pil_list (List): List of captions. - """ - - size = len(pred_image_list) - - if size == 1: - return pred_image_list[0] - elif size == 2: - blank = Image.new("RGB", (512 * 2, 512), (0, 0, 0)) - blank.paste(pred_image_list[0], (0, 0)) - blank.paste(pred_image_list[1], (512, 0)) - elif size == 3: - blank = Image.new("RGB", (512 * 3, 512), (0, 0, 0)) - blank.paste(pred_image_list[0], (0, 0)) - blank.paste(pred_image_list[1], (512, 0)) - blank.paste(pred_image_list[2], (1024, 0)) - elif size == 4: - blank = Image.new("RGB", (512 * 2, 512 * 2), (0, 0, 0)) - blank.paste(pred_image_list[0], (0, 0)) - blank.paste(pred_image_list[1], (512, 0)) - blank.paste(pred_image_list[2], (0, 512)) - blank.paste(pred_image_list[3], (512, 512)) - - return blank - -def getsize(font, text): - left, top, right, bottom = font.getbbox(text) - return right - left, bottom - top - -def get_width(font_path, text): - """ - This function calculates the width of the text. - - Args: - font_path (str): user prompt. - text (str): user prompt. - """ - font = ImageFont.truetype(font_path, 24) - width = getsize(font, text)[0] - return width - - -def get_key_words(text: str): - """ - This function detect keywords (enclosed by quotes) from user prompts. The keywords are used to guide the layout generation. - - Args: - text (str): user prompt. - """ - - words = [] - text = text - matches = re.findall(r"'(.*?)'", text) # find the keywords enclosed by '' - if matches: - for match in matches: - words.extend(match.split()) - - if len(words) >= 8: - return [] - - return words - - -def adjust_overlap_box(box_output, current_index): - """ - This function adjust the overlapping boxes. - - Args: - box_output (List): List of predicted boxes. - current_index (int): the index of current box. - """ - - if current_index == 0: - return box_output - else: - # judge whether it contains overlap with the last output - last_box = box_output[0, current_index - 1, :] - xmin_last, ymin_last, xmax_last, ymax_last = last_box - - current_box = box_output[0, current_index, :] - xmin, ymin, xmax, ymax = current_box - - if xmin_last <= xmin <= xmax_last and ymin_last <= ymin <= ymax_last: - print("adjust overlapping") - distance_x = xmax_last - xmin - distance_y = ymax_last - ymin - if distance_x <= distance_y: - # avoid overlap - new_x_min = xmax_last + 0.025 - new_x_max = xmax - xmin + xmax_last + 0.025 - box_output[0, current_index, 0] = new_x_min - box_output[0, current_index, 2] = new_x_max - else: - new_y_min = ymax_last + 0.025 - new_y_max = ymax - ymin + ymax_last + 0.025 - box_output[0, current_index, 1] = new_y_min - box_output[0, current_index, 3] = new_y_max - - elif xmin_last <= xmin <= xmax_last and ymin_last <= ymax <= ymax_last: - print("adjust overlapping") - new_x_min = xmax_last + 0.05 - new_x_max = xmax - xmin + xmax_last + 0.05 - box_output[0, current_index, 0] = new_x_min - box_output[0, current_index, 2] = new_x_max - - return box_output - - -def shrink_box(box, scale_factor=0.9): - """ - This function shrinks the box. - - Args: - box (List): List of predicted boxes. - scale_factor (float): The scale factor of shrinking. - """ - - x1, y1, x2, y2 = box - x1_new = x1 + (x2 - x1) * (1 - scale_factor) / 2 - y1_new = y1 + (y2 - y1) * (1 - scale_factor) / 2 - x2_new = x2 - (x2 - x1) * (1 - scale_factor) / 2 - y2_new = y2 - (y2 - y1) * (1 - scale_factor) / 2 - return (x1_new, y1_new, x2_new, y2_new) - - -def adjust_font_size(args, width, height, draw, text): - """ - This function adjusts the font size. - - Args: - args (argparse.ArgumentParser): The arguments. - width (int): The width of the text. - height (int): The height of the text. - draw (ImageDraw): The ImageDraw object. - text (str): The text. - """ - - size_start = height - while True: - font = ImageFont.truetype(args.font_path, size_start) - text_width = draw.textlength(text, font=font) - if text_width >= width: - size_start = size_start - 1 - else: - return size_start - - -def inpainting_merge_image(original_image, mask_image, inpainting_image): - """ - This function merges the original image, mask image and inpainting image. - - Args: - original_image (PIL.Image): The original image. - mask_image (PIL.Image): The mask images. - inpainting_image (PIL.Image): The inpainting images. - """ - - original_image = original_image.resize((512, 512)) - mask_image = mask_image.resize((512, 512)) - inpainting_image = inpainting_image.resize((512, 512)) - mask_image.convert("L") - threshold = 250 - table = [] - for i in range(256): - if i < threshold: - table.append(1) - else: - table.append(0) - mask_image = mask_image.point(table, "1") - merged_image = Image.composite(inpainting_image, original_image, mask_image) - return merged_image From 482260464e2bcff71f1adbd03f8770fc4fe3a426 Mon Sep 17 00:00:00 2001 From: ksyint <82583462+ksyint@users.noreply.github.com> Date: Tue, 21 May 2024 22:42:52 +0900 Subject: [PATCH 2/2] Add files via upload --- text_diffuser/__init__.py | 0 text_diffuser/a.py | 14 + text_diffuser/any_text.py | 519 ++ text_diffuser/assets/font/Arial.ttf | Bin 0 -> 1036584 bytes text_diffuser/assets/mask_1.png | Bin 0 -> 1559 bytes text_diffuser/assets/mask_2.png | Bin 0 -> 2214 bytes text_diffuser/assets/original_input.jpeg 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.../fid/src/pytorch_fid/inception.py | 344 + text_diffuser/fid/tests/test_fid_score.py | 102 + text_diffuser/inference.py | 1070 +++ text_diffuser/model/layout_generator.py | 290 + text_diffuser/model/layout_transformer.py | 153 + text_diffuser/model/text_segmenter/unet.py | 54 + .../model/text_segmenter/unet_parts.py | 81 + text_diffuser/pipeline_demo.ipynb | 359 + text_diffuser/pipeline_text_diffuser_sd15.py | 1368 +++ text_diffuser/t_diffusers/__init__.py | 0 text_diffuser/t_diffusers/callbacks.py | 193 + text_diffuser/t_diffusers/modeling_utils.py | 1021 +++ text_diffuser/t_diffusers/scheduling_ddpm.py | 627 ++ .../t_diffusers/unet_2d_condition.py | 1527 ++++ text_diffuser/to.py | 9 + text_diffuser/train.py | 1237 +++ text_diffuser/util.py | 387 + 64 files changed, 25221 insertions(+) create mode 100644 text_diffuser/__init__.py create mode 100644 text_diffuser/a.py create mode 100644 text_diffuser/any_text.py create mode 100644 text_diffuser/assets/font/Arial.ttf create mode 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create mode 100644 text_diffuser/inference.py create mode 100644 text_diffuser/model/layout_generator.py create mode 100644 text_diffuser/model/layout_transformer.py create mode 100644 text_diffuser/model/text_segmenter/unet.py create mode 100644 text_diffuser/model/text_segmenter/unet_parts.py create mode 100644 text_diffuser/pipeline_demo.ipynb create mode 100644 text_diffuser/pipeline_text_diffuser_sd15.py create mode 100644 text_diffuser/t_diffusers/__init__.py create mode 100644 text_diffuser/t_diffusers/callbacks.py create mode 100644 text_diffuser/t_diffusers/modeling_utils.py create mode 100644 text_diffuser/t_diffusers/scheduling_ddpm.py create mode 100644 text_diffuser/t_diffusers/unet_2d_condition.py create mode 100644 text_diffuser/to.py create mode 100644 text_diffuser/train.py create mode 100644 text_diffuser/util.py diff --git a/text_diffuser/__init__.py b/text_diffuser/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/text_diffuser/a.py b/text_diffuser/a.py new file mode 100644 index 0000000..f1134f9 --- /dev/null +++ b/text_diffuser/a.py @@ -0,0 +1,14 @@ + + +target_list=[] + +target_images=[1,2,3,4] + +while len(target_list)<5: + + for img in target_images: + if len(target_list)==5: + break + + target_list.append(img) +print(target_list) \ No newline at end of file diff --git a/text_diffuser/any_text.py b/text_diffuser/any_text.py new file mode 100644 index 0000000..96af0b5 --- /dev/null +++ b/text_diffuser/any_text.py @@ -0,0 +1,519 @@ +# type: ignore + +""" +AnyText: Multilingual Visual Text Generation And Editing +Paper: https://arxiv.org/abs/2311.03054 +Code: https://github.com/tyxsspa/AnyText +Copyright (c) Alibaba, Inc. and its affiliates. +""" + +# ruff: noqa +import os +import random +import re +import time + +import cv2 +import einops +import numpy as np +import torch +from bert_tokenizer import BasicTokenizer +from cldm.ddim_hacked import DDIMSampler +from cldm.model import create_model, load_state_dict +from modelscope.hub.snapshot_download import snapshot_download +from modelscope.models.base import TorchModel +from modelscope.models.builder import MODELS +from modelscope.pipelines import pipeline +from modelscope.pipelines.base import Model, Pipeline +from modelscope.pipelines.builder import PIPELINES +from modelscope.preprocessors.base import Preprocessor +from modelscope.preprocessors.builder import PREPROCESSORS +from modelscope.utils.config import Config +from modelscope.utils.constant import Tasks +from PIL import ImageFont +from pytorch_lightning import seed_everything +from t3_dataset import draw_glyph, draw_glyph2 +from util import check_channels, resize_image, save_images + + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +checker = BasicTokenizer() +BBOX_MAX_NUM = 8 +PLACE_HOLDER = "*" +max_chars = 20 + + +@MODELS.register_module("my-anytext-task", module_name="anytext-model") +class AnyTextModel(TorchModel): + + def __init__(self, model_dir, *args, **kwargs): + super().__init__(model_dir, *args, **kwargs) + self.use_fp16 = kwargs.get("use_fp16", True) + self.use_translator = kwargs.get("use_translator", True) + self.init_model(**kwargs) + + """ + return: + result: list of images in numpy.ndarray format + rst_code: 0: normal -1: error 1:warning + str_warning: string of error or warning + debug_info: string for debug, only valid if show_debug=True + """ + + def forward(self, input_tensor, **forward_params): + tic = time.time() + str_warning = "" + # get inputs + seed = input_tensor.get("seed", -1) + if seed == -1: + seed = random.randint(0, 99999999) + seed_everything(seed) + prompt = input_tensor.get("prompt") + draw_pos = input_tensor.get("draw_pos") + ori_image = input_tensor.get("ori_image") + + mode = forward_params.get("mode") + sort_priority = forward_params.get("sort_priority", "↕") + show_debug = forward_params.get("show_debug", False) + revise_pos = forward_params.get("revise_pos", False) + img_count = forward_params.get("image_count", 4) + ddim_steps = forward_params.get("ddim_steps", 20) + w = forward_params.get("image_width", 512) + h = forward_params.get("image_height", 512) + strength = forward_params.get("strength", 1.0) + cfg_scale = forward_params.get("cfg_scale", 9.0) + eta = forward_params.get("eta", 0.0) + a_prompt = forward_params.get( + "a_prompt", + "best quality, extremely detailed,4k, HD, supper legible text, clear text edges, clear strokes, neat writing, no watermarks", + ) + n_prompt = forward_params.get( + "n_prompt", + "low-res, bad anatomy, extra digit, fewer digits, cropped, worst quality, low quality, watermark, unreadable text, messy words, distorted text, disorganized writing, advertising picture", + ) + + prompt, texts = self.modify_prompt(prompt) + if prompt is None and texts is None: + return ( + None, + -1, + "You have input Chinese prompt but the translator is not loaded!", + "", + ) + n_lines = len(texts) + if mode in ["text-generation", "gen"]: + edit_image = np.ones((h, w, 3)) * 127.5 # empty mask image + elif mode in ["text-editing", "edit"]: + if draw_pos is None or ori_image is None: + return ( + None, + -1, + "Reference image and position image are needed for text editing!", + "", + ) + if isinstance(ori_image, str): + ori_image = cv2.imread(ori_image)[..., ::-1] + assert ( + ori_image is not None + ), f"Can't read ori_image image from{ori_image}!" + elif isinstance(ori_image, torch.Tensor): + ori_image = ori_image.cpu().numpy() + else: + assert isinstance( + ori_image, np.ndarray + ), f"Unknown format of ori_image: {type(ori_image)}" + edit_image = ori_image.clip(1, 255) # for mask reason + edit_image = check_channels(edit_image) + edit_image = resize_image( + edit_image, max_length=768 + ) # make w h multiple of 64, resize if w or h > max_length + h, w = edit_image.shape[:2] # change h, w by input ref_img + # preprocess pos_imgs(if numpy, make sure it's white pos in black bg) + if draw_pos is None: + pos_imgs = np.zeros((w, h, 1)) + if isinstance(draw_pos, str): + draw_pos = cv2.imread(draw_pos)[..., ::-1] + assert draw_pos is not None, f"Can't read draw_pos image from{draw_pos}!" + pos_imgs = 255 - draw_pos + elif isinstance(draw_pos, torch.Tensor): + pos_imgs = draw_pos.cpu().numpy() + else: + assert isinstance( + draw_pos, np.ndarray + ), f"Unknown format of draw_pos: {type(draw_pos)}" + pos_imgs = pos_imgs[..., 0:1] + pos_imgs = cv2.convertScaleAbs(pos_imgs) + _, pos_imgs = cv2.threshold(pos_imgs, 254, 255, cv2.THRESH_BINARY) + # seprate pos_imgs + pos_imgs = self.separate_pos_imgs(pos_imgs, sort_priority) + if len(pos_imgs) == 0: + pos_imgs = [np.zeros((h, w, 1))] + if len(pos_imgs) < n_lines: + if n_lines == 1 and texts[0] == " ": + pass # text-to-image without text + else: + return ( + None, + -1, + f"Found {len(pos_imgs)} positions that < needed {n_lines} from prompt, check and try again!", + "", + ) + elif len(pos_imgs) > n_lines: + str_warning = f"Warning: found {len(pos_imgs)} positions that > needed {n_lines} from prompt." + # get pre_pos, poly_list, hint that needed for anytext + pre_pos = [] + poly_list = [] + for input_pos in pos_imgs: + if input_pos.mean() != 0: + input_pos = ( + input_pos[..., np.newaxis] + if len(input_pos.shape) == 2 + else input_pos + ) + poly, pos_img = self.find_polygon(input_pos) + pre_pos += [pos_img / 255.0] + poly_list += [poly] + else: + pre_pos += [np.zeros((h, w, 1))] + poly_list += [None] + np_hint = np.sum(pre_pos, axis=0).clip(0, 1) + # prepare info dict + info = {} + info["glyphs"] = [] + info["gly_line"] = [] + info["positions"] = [] + info["n_lines"] = [len(texts)] * img_count + gly_pos_imgs = [] + for i in range(len(texts)): + text = texts[i] + if len(text) > max_chars: + str_warning = ( + f'"{text}" length > max_chars: {max_chars}, will be cut off...' + ) + text = text[:max_chars] + gly_scale = 2 + if pre_pos[i].mean() != 0: + gly_line = draw_glyph(self.font, text) + glyphs = draw_glyph2( + self.font, + text, + poly_list[i], + scale=gly_scale, + width=w, + height=h, + add_space=False, + ) + gly_pos_img = cv2.drawContours( + glyphs * 255, [poly_list[i] * gly_scale], 0, (255, 255, 255), 1 + ) + if revise_pos: + resize_gly = cv2.resize( + glyphs, (pre_pos[i].shape[1], pre_pos[i].shape[0]) + ) + new_pos = cv2.morphologyEx( + (resize_gly * 255).astype(np.uint8), + cv2.MORPH_CLOSE, + kernel=np.ones( + (resize_gly.shape[0] // 10, resize_gly.shape[1] // 10), + dtype=np.uint8, + ), + iterations=1, + ) + new_pos = ( + new_pos[..., np.newaxis] if len(new_pos.shape) == 2 else new_pos + ) + contours, _ = cv2.findContours( + new_pos, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + if len(contours) != 1: + str_warning = f"Fail to revise position {i} to bounding rect, remain position unchanged..." + else: + rect = cv2.minAreaRect(contours[0]) + poly = np.int0(cv2.boxPoints(rect)) + pre_pos[i] = ( + cv2.drawContours(new_pos, [poly], -1, 255, -1) / 255.0 + ) + gly_pos_img = cv2.drawContours( + glyphs * 255, [poly * gly_scale], 0, (255, 255, 255), 1 + ) + gly_pos_imgs += [gly_pos_img] # for show + else: + glyphs = np.zeros((h * gly_scale, w * gly_scale, 1)) + gly_line = np.zeros((80, 512, 1)) + gly_pos_imgs += [ + np.zeros((h * gly_scale, w * gly_scale, 1)) + ] # for show + pos = pre_pos[i] + info["glyphs"] += [self.arr2tensor(glyphs, img_count)] + info["gly_line"] += [self.arr2tensor(gly_line, img_count)] + info["positions"] += [self.arr2tensor(pos, img_count)] + # get masked_x + masked_img = ((edit_image.astype(np.float32) / 127.5) - 1.0) * (1 - np_hint) + masked_img = np.transpose(masked_img, (2, 0, 1)) + masked_img = torch.from_numpy(masked_img.copy()).float().cuda() + if self.use_fp16: + masked_img = masked_img.half() + encoder_posterior = self.model.encode_first_stage(masked_img[None, ...]) + masked_x = self.model.get_first_stage_encoding(encoder_posterior).detach() + if self.use_fp16: + masked_x = masked_x.half() + info["masked_x"] = torch.cat([masked_x for _ in range(img_count)], dim=0) + + hint = self.arr2tensor(np_hint, img_count) + cond = self.model.get_learned_conditioning( + dict( + c_concat=[hint], + c_crossattn=[[prompt + " , " + a_prompt] * img_count], + text_info=info, + ) + ) + un_cond = self.model.get_learned_conditioning( + dict(c_concat=[hint], c_crossattn=[[n_prompt] * img_count], text_info=info) + ) + shape = (4, h // 8, w // 8) + self.model.control_scales = [strength] * 13 + samples, intermediates = self.ddim_sampler.sample( + ddim_steps, + img_count, + shape, + cond, + verbose=False, + eta=eta, + unconditional_guidance_scale=cfg_scale, + unconditional_conditioning=un_cond, + ) + if self.use_fp16: + samples = samples.half() + x_samples = self.model.decode_first_stage(samples) + x_samples = ( + (einops.rearrange(x_samples, "b c h w -> b h w c") * 127.5 + 127.5) + .cpu() + .numpy() + .clip(0, 255) + .astype(np.uint8) + ) + results = [x_samples[i] for i in range(img_count)] + if ( + mode == "edit" and False + ): # replace backgound in text editing but not ideal yet + results = [r * np_hint + edit_image * (1 - np_hint) for r in results] + results = [r.clip(0, 255).astype(np.uint8) for r in results] + if len(gly_pos_imgs) > 0 and show_debug: + glyph_bs = np.stack(gly_pos_imgs, axis=2) + glyph_img = np.sum(glyph_bs, axis=2) * 255 + glyph_img = glyph_img.clip(0, 255).astype(np.uint8) + results += [np.repeat(glyph_img, 3, axis=2)] + input_prompt = prompt + for t in texts: + input_prompt = input_prompt.replace("*", f'"{t}"', 1) + print(f"Prompt: {input_prompt}") + # debug_info + if not show_debug: + debug_info = "" + else: + debug_info = f'Prompt: {input_prompt}
\ + Size: {w}x{h}
\ + Image Count: {img_count}
\ + Seed: {seed}
\ + Use FP16: {self.use_fp16}
\ + Cost Time: {(time.time()-tic):.2f}s' + rst_code = 1 if str_warning else 0 + return results, rst_code, str_warning, debug_info + + def init_model(self, **kwargs): + font_path = kwargs.get("font_path", "font/Arial_Unicode.ttf") + self.font = ImageFont.truetype(font_path, size=60) + cfg_path = kwargs.get("cfg_path", "models_yaml/anytext_sd15.yaml") + ckpt_path = kwargs.get( + "model_path", os.path.join(self.model_dir, "anytext_v1.1.ckpt") + ) + clip_path = os.path.join(self.model_dir, "clip-vit-large-patch14") + self.model = create_model( + cfg_path, cond_stage_path=clip_path, use_fp16=self.use_fp16 + ) + if self.use_fp16: + self.model = self.model.half() + self.model.load_state_dict( + load_state_dict(ckpt_path, location="cuda"), strict=False + ) + self.model.eval() + self.ddim_sampler = DDIMSampler(self.model) + if self.use_translator: + self.trans_pipe = pipeline( + task=Tasks.translation, + model=os.path.join(self.model_dir, "nlp_csanmt_translation_zh2en"), + ) + else: + self.trans_pipe = None + + def modify_prompt(self, prompt): + prompt = prompt.replace("“", '"') + prompt = prompt.replace("”", '"') + p = '"(.*?)"' + strs = re.findall(p, prompt) + if len(strs) == 0: + strs = [" "] + else: + for s in strs: + prompt = prompt.replace(f'"{s}"', f" {PLACE_HOLDER} ", 1) + if self.is_chinese(prompt): + if self.trans_pipe is None: + return None, None + old_prompt = prompt + prompt = self.trans_pipe(input=prompt + " .")["translation"][:-1] + print(f"Translate: {old_prompt} --> {prompt}") + return prompt, strs + + def is_chinese(self, text): + text = checker._clean_text(text) + for char in text: + cp = ord(char) + if checker._is_chinese_char(cp): + return True + return False + + def separate_pos_imgs(self, img, sort_priority, gap=102): + num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(img) + components = [] + for label in range(1, num_labels): + component = np.zeros_like(img) + component[labels == label] = 255 + components.append((component, centroids[label])) + if sort_priority == "↕": + fir, sec = 1, 0 # top-down first + elif sort_priority == "↔": + fir, sec = 0, 1 # left-right first + components.sort(key=lambda c: (c[1][fir] // gap, c[1][sec] // gap)) + sorted_components = [c[0] for c in components] + return sorted_components + + def find_polygon(self, image, min_rect=False): + contours, hierarchy = cv2.findContours( + image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + max_contour = max(contours, key=cv2.contourArea) # get contour with max area + if min_rect: + # get minimum enclosing rectangle + rect = cv2.minAreaRect(max_contour) + poly = np.int0(cv2.boxPoints(rect)) + else: + # get approximate polygon + epsilon = 0.01 * cv2.arcLength(max_contour, True) + poly = cv2.approxPolyDP(max_contour, epsilon, True) + n, _, xy = poly.shape + poly = poly.reshape(n, xy) + cv2.drawContours(image, [poly], -1, 255, -1) + return poly, image + + def arr2tensor(self, arr, bs): + arr = np.transpose(arr, (2, 0, 1)) + _arr = torch.from_numpy(arr.copy()).float().cuda() + if self.use_fp16: + _arr = _arr.half() + _arr = torch.stack([_arr for _ in range(bs)], dim=0) + return _arr + + +@PREPROCESSORS.register_module("my-anytext-task", module_name="anytext-preprocessor") +class AnyTextPreprocessor(Preprocessor): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.trainsforms = self.init_preprocessor(**kwargs) + + def __call__(self, results): + return self.trainsforms(results) + + def init_preprocessor(self, **kwarg): + """Provide default implementation based on preprocess_cfg and user can reimplement it. + if nothing to do, then return lambda x: x + """ + return lambda x: x + + +@PIPELINES.register_module("my-anytext-task", module_name="anytext-pipeline") +class AnyTextPipeline(Pipeline): + + def __init__(self, model, preprocessor=None, **kwargs): + assert isinstance(model, str) or isinstance( + model, Model + ), "model must be a single str or Model" + if isinstance(model, str): + if not os.path.exists(model): + model = snapshot_download(model) + pipe_model = AnyTextModel(model_dir=model, **kwargs) + elif isinstance(model, Model): + pipe_model = model + else: + raise NotImplementedError + pipe_model.eval() + if preprocessor is None: + preprocessor = AnyTextPreprocessor() + super().__init__(model=pipe_model, preprocessor=preprocessor, **kwargs) + + def _sanitize_parameters(self, **pipeline_parameters): + return {}, pipeline_parameters, {} + + def _check_input(self, inputs): + pass + + def _check_output(self, outputs): + pass + + def forward(self, inputs, **forward_params): + return super().forward(inputs, **forward_params) + + def postprocess(self, inputs): + return inputs + + +usr_config_path = "models" +config = Config( + { + "framework": "pytorch", + "task": "my-anytext-task", + "model": {"type": "anytext-model"}, + "pipeline": {"type": "anytext-pipeline"}, + "allow_remote": True, + } +) +# config.dump('models/' + 'configuration.json') + +if __name__ == "__main__": + img_save_folder = "SaveImages" + inference = pipeline( + "my-anytext-task", model=usr_config_path, use_fp16=True, use_translator=False + ) + params = { + "show_debug": True, + "image_count": 2, + "ddim_steps": 20, + } + + # 1. text generation + mode = "text-generation" + input_data = { + "prompt": 'photo of caramel macchiato coffee on the table, top-down perspective, with "Any" "Text" written on it using cream', + "seed": 66273235, + "draw_pos": "example_images/gen9.png", + } + results, rtn_code, rtn_warning, debug_info = inference( + input_data, mode=mode, **params + ) + if rtn_code >= 0: + save_images(results, img_save_folder) + # 2. text editing + mode = "text-editing" + input_data = { + "prompt": 'A cake with colorful characters that reads "EVERYDAY"', + "seed": 8943410, + "draw_pos": "example_images/edit7.png", + "ori_image": "example_images/ref7.jpg", + } + results, rtn_code, rtn_warning, debug_info = inference( + input_data, mode=mode, **params + ) + if rtn_code >= 0: + save_images(results, img_save_folder) + print(f"Done, result images are saved in: {img_save_folder}") diff --git a/text_diffuser/assets/font/Arial.ttf b/text_diffuser/assets/font/Arial.ttf new file mode 100644 index 0000000000000000000000000000000000000000..8682d94623e575a4ecc9586a35fc909dff37fb2c GIT binary patch literal 1036584 zcmeFa37lO;mB(Lix%cgBcW0wX0%T9o(9uzF19Jc0->F-#U%yT#@E@H2XZ|1SrK)b#t>sjm zQ|FvIRk!0Z&bf&Md3XNO6?^V}?RR(F+tvDRc5dsNcVGI7WrfM*yIpO;an9uq*nPiO zu2|R_|5aCe!IxZiLjUe7R_^xcGgp7XHTM3tb7%Z;`HBOVt(kLt>>A&`mR2Y4ec*~c zuKVJl4>-5S_nm89^2!zS7xpiI^_h;!ygy>Um+yVxGjCh+a{7Ba=@%Zfbe}`^`{3)J zbndW|DgU9P)*N~Ak}qC;qsz8$aBk+mANA%_rhf9apP%Ql2kh)zeBv=DAG_v7SC5