Create sample.py

base/sample.py

@@ -0,0 +1,86 @@
+import os
+import torch
+import argparse
+import torchvision
+import sys
+
+sys.path.append(os.path.split(sys.path[0])[0])
+from pipelines.pipeline_videogen import VideoGenPipeline
+
+from download import find_model
+from diffusers.schedulers import DDIMScheduler, DDPMScheduler, PNDMScheduler, EulerDiscreteScheduler
+from diffusers.models import AutoencoderKL
+from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection
+from omegaconf import OmegaConf
+from models import get_models
+import imageio
+
+def main(args):
+	torch.manual_seed(args.seed)
+	torch.set_grad_enabled(False)
+	device = "cuda" if torch.cuda.is_available() else "cpu"
+
+	sd_path = args.pretrained_path + "/stable-diffusion-v1-4"
+	unet = get_models(args, sd_path).to(device, dtype=torch.float16)
+	state_dict = find_model(args.pretrained_path + "/lavie_base.pt")
+	unet.load_state_dict(state_dict)
+
+	vae = AutoencoderKL.from_pretrained(sd_path, subfolder="vae", torch_dtype=torch.float16).to(device)
+	tokenizer_one = CLIPTokenizer.from_pretrained(sd_path, subfolder="tokenizer")
+	text_encoder_one = CLIPTextModel.from_pretrained(sd_path, subfolder="text_encoder", torch_dtype=torch.float16).to(device) # huge
+
+	# set eval mode
+	unet.eval()
+	vae.eval()
+	text_encoder_one.eval()
+
+	if args.sample_method == 'ddim':
+		scheduler = DDIMScheduler.from_pretrained(sd_path, 
+											   subfolder="scheduler",
+											   beta_start=args.beta_start, 
+											   beta_end=args.beta_end, 
+											   beta_schedule=args.beta_schedule)
+	elif args.sample_method == 'eulerdiscrete':
+		scheduler = EulerDiscreteScheduler.from_pretrained(sd_path,
+											   subfolder="scheduler",
+											   beta_start=args.beta_start,
+											   beta_end=args.beta_end,
+											   beta_schedule=args.beta_schedule)
+	elif args.sample_method == 'ddpm':
+		scheduler = DDPMScheduler.from_pretrained(sd_path,
+											  subfolder="scheduler",
+											  beta_start=args.beta_start,
+											  beta_end=args.beta_end,
+											  beta_schedule=args.beta_schedule)
+	else:
+		raise NotImplementedError
+
+	videogen_pipeline = VideoGenPipeline(vae=vae, 
+								 text_encoder=text_encoder_one, 
+								 tokenizer=tokenizer_one, 
+								 scheduler=scheduler, 
+								 unet=unet).to(device)
+	videogen_pipeline.enable_xformers_memory_efficient_attention()
+
+	if not os.path.exists(args.output_folder):
+		os.makedirs(args.output_folder)
+
+	video_grids = []
+	for prompt in args.text_prompt:
+		print('Processing the ({}) prompt'.format(prompt))
+		videos = videogen_pipeline(prompt, 
+								video_length=args.video_length, 
+								height=args.image_size[0], 
+								width=args.image_size[1], 
+								num_inference_steps=args.num_sampling_steps,
+								guidance_scale=args.guidance_scale).video
+		imageio.mimwrite(args.output_folder + prompt.replace(' ', '_') + '.mp4', videos[0], fps=8, quality=9) # highest quality is 10, lowest is 0
+
+	print('save path {}'.format(args.output_folder))
+
+if __name__ == "__main__":
+	parser = argparse.ArgumentParser()
+	parser.add_argument("--config", type=str, default="")
+	args = parser.parse_args()
+
+	main(OmegaConf.load(args.config))