glide.py

#!/usr/bin/env python3
# coding: utf-8

import os

PEN_PROMPT = os.environ.get("PEN_PROMPT")
PEN_N_COMPLETIONS = os.environ.get("PEN_N_COMPLETIONS") or "1"

from PIL import Image
# from IPython.display import display
import torch as th

from glide_text2im.download import load_checkpoint
from glide_text2im.model_creation import (
    create_model_and_diffusion,
    model_and_diffusion_defaults,
    model_and_diffusion_defaults_upsampler
)

os.chdir("/root")

# This notebook supports both CPU and GPU.
# On CPU, generating one sample may take on the order of 20 minutes.
# On a GPU, it should be under a minute.

has_cuda = th.cuda.is_available()
device = th.device('cpu' if not has_cuda else 'cuda')

# Create base model.
options = model_and_diffusion_defaults()
options['use_fp16'] = has_cuda
options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling
model, diffusion = create_model_and_diffusion(**options)
model.eval()
if has_cuda:
    model.convert_to_fp16()
model.to(device)
model.load_state_dict(load_checkpoint('base', device))
print('total base parameters', sum(x.numel() for x in model.parameters()))

# Create upsampler model.
options_up = model_and_diffusion_defaults_upsampler()
options_up['use_fp16'] = has_cuda
options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling
model_up, diffusion_up = create_model_and_diffusion(**options_up)
model_up.eval()
if has_cuda:
    model_up.convert_to_fp16()
model_up.to(device)
model_up.load_state_dict(load_checkpoint('upsample', device))
print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))

def b(c, inputstring="", timeout=0):
    """Runs a shell command
    This function always has stdin and stdout.
    Don't do anything fancy here with ttys, handling stdin and stdout.
    If I wan't to use tty programs, then use a ttyize/ttyify script.
    echo hi | ttyify vim | cat"""

    p = subprocess.Popen(
        c,
        shell=True,
        executable="/bin/sh",
        stdin=subprocess.PIPE,
        stdout=subprocess.PIPE,
        stderr=subprocess.STDOUT,
        close_fds=True,
    )
    p.stdin.write(str(inputstring).encode("utf-8"))
    p.stdin.close()
    output = p.stdout.read().decode("utf-8")
    p.wait()
    return [str(output), p.returncode]

def show_images(batch: th.Tensor):
    """ Display a batch of images inline. """
    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()
    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])
    # At this stage I need to verify that it's what I want before proceeding
    # TODO I should make some kind of acceptance dialog
    Image.fromarray(reshaped.numpy()).show()

# Sampling parameters
# prompt = PEN_PROMPT or "A surreal painting of a cat and a strawberry"
# prompt = PEN_PROMPT or "A sitting cat with strawberries for eyes"
prompt = PEN_PROMPT or "A strawberry behind a cat"
batch_size = int(PEN_N_COMPLETIONS)
guidance_scale = 3.0

# Tune this parameter to control the sharpness of 256x256 images.
# A value of 1.0 is sharper, but sometimes results in grainy artifacts.
upsample_temp = 0.997

##############################
# Sample from the base model #
##############################

# Create the text tokens to feed to the model.
tokens = model.tokenizer.encode(prompt)
tokens, mask = model.tokenizer.padded_tokens_and_mask(
    tokens, options['text_ctx']
)

# Create the classifier-free guidance tokens (empty)
full_batch_size = batch_size * 2
uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask(
    [], options['text_ctx']
)

# Pack the tokens together into model kwargs.
model_kwargs = dict(
    tokens=th.tensor(
        [tokens] * batch_size + [uncond_tokens] * batch_size, device=device
    ),
    mask=th.tensor(
        [mask] * batch_size + [uncond_mask] * batch_size,
        dtype=th.bool,
        device=device,
    ),
)

# Create a classifier-free guidance sampling function
def model_fn(x_t, ts, **kwargs):
    half = x_t[: len(x_t) // 2]
    combined = th.cat([half, half], dim=0)
    model_out = model(combined, ts, **kwargs)
    eps, rest = model_out[:, :3], model_out[:, 3:]
    cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)
    half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
    eps = th.cat([half_eps, half_eps], dim=0)
    return th.cat([eps, rest], dim=1)

# Sample from the base model.
model.del_cache()
samples = diffusion.p_sample_loop(
    model_fn,
    (full_batch_size, 3, options["image_size"], options["image_size"]),
    device=device,
    clip_denoised=True,
    progress=True,
    model_kwargs=model_kwargs,
    cond_fn=None,
)[:batch_size]
model.del_cache()

# Show the output
show_images(samples)

##############################
# Upsample the 64x64 samples #
##############################

tokens = model_up.tokenizer.encode(prompt)
tokens, mask = model_up.tokenizer.padded_tokens_and_mask(
    tokens, options_up['text_ctx']
)

# Create the model conditioning dict.
model_kwargs = dict(
    # Low-res image to upsample.
    low_res=((samples+1)*127.5).round()/127.5 - 1,

    # Text tokens
    tokens=th.tensor(
        [tokens] * batch_size, device=device
    ),
    mask=th.tensor(
        [mask] * batch_size,
        dtype=th.bool,
        device=device,
    ),
)

# Sample from the base model.
model_up.del_cache()
up_shape = (batch_size, 3, options_up["image_size"], options_up["image_size"])
up_samples = diffusion_up.ddim_sample_loop(
    model_up,
    up_shape,
    noise=th.randn(up_shape, device=device) * upsample_temp,
    device=device,
    clip_denoised=True,
    progress=True,
    model_kwargs=model_kwargs,
    cond_fn=None,
)[:batch_size]
model_up.del_cache()

# Show the output
show_images(up_samples)