app.py

# -*- coding: utf-8 -*-
"""Copy of compose_glide.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/19xx6Nu4FeiGj-TzTUFxBf-15IkeuFx_F
"""


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

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
)

# 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.
timestep_respacing =  100 #@param{type: 'number'}
options = model_and_diffusion_defaults()
options['use_fp16'] = has_cuda
options['timestep_respacing'] = str(timestep_respacing) # 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 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])
    display(Image.fromarray(reshaped.numpy()))

def compose_language_descriptions(prompt):
  #@markdown `prompt`: when composing  multiple sentences, using `|` as the delimiter.
  prompts = [x.strip() for x in prompt.split('|')]

  batch_size = 1
  guidance_scale = 10 #@param{type: 'number'}
  # 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 = 0.980 #@param{type: 'number'}



  masks = [True] * len(prompts) + [False]
  # coefficients = th.tensor([0.5, 0.5], device=device).reshape(-1, 1, 1, 1)
  masks = th.tensor(masks, dtype=th.bool, device=device)
  # sampling function
  def model_fn(x_t, ts, **kwargs):
    half = x_t[:1]
    combined = th.cat([half] * x_t.size(0), dim=0)
    model_out = model(combined, ts, **kwargs)
    eps, rest = model_out[:, :3], model_out[:, 3:]
    cond_eps = eps[masks].mean(dim=0, keepdim=True)
    # cond_eps = (coefficients * eps[masks]).sum(dim=0)[None]
    uncond_eps = eps[~masks].mean(dim=0, keepdim=True)
    half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
    eps = th.cat([half_eps] * x_t.size(0), dim=0)
    return th.cat([eps, rest], dim=1)


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

  # Create the text tokens to feed to the model.
  def sample_64(prompts):
    tokens_list = [model.tokenizer.encode(prompt) for prompt in prompts]
    outputs = [model.tokenizer.padded_tokens_and_mask(
        tokens, options['text_ctx']
    ) for tokens in tokens_list]

    cond_tokens, cond_masks = zip(*outputs)
    cond_tokens, cond_masks = list(cond_tokens), list(cond_masks)

    full_batch_size = batch_size * (len(prompts) + 1)
    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(
            cond_tokens + [uncond_tokens], device=device
        ),
        mask=th.tensor(
            cond_masks + [uncond_mask],
            dtype=th.bool,
            device=device,
        ),
    )
    
    # 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
    return samples


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

  def upsampling_256(prompts, samples):
    tokens = model_up.tokenizer.encode("".join(prompts))
    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
    return up_samples


  # sampling 64x64 images
  samples = sample_64(prompts)
  # show_images(samples)

  # upsample from 64x64 to 256x256
  upsamples = upsampling_256(prompts, samples)
  # show_images(upsamples)

  out_img = upsamples[0].permute(1,2,0)
  out_img = (out_img+1)/2
  out_img = np.array(out_img.data.to('cpu'))
  return out_img

# prompt = "a camel | a forest" #@param{type: 'string'}
# out_img = compose_language_descriptions(prompt)

examples = ['a camel | a forest', 'A cloudy blue sky  | A mountain in the horizon | Cherry Blossoms in front of the mountain']


import gradio as gr
gr.Interface(title='Compositional Visual Generation with Composable Diffusion Models', 
    description='Demo for Composable Diffusion (~20s per example). Project Page: https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/. This demo is based on the released GLIDE model (https://github.com/openai/glide-text2im) for composing natural language descriptions. For composing objects and object relations, see the project page for more information. When composing  multiple sentences, use `|` as the delimiter. For example "a camel | a forest" composes "a camel" and "a forest" together.',
    fn=compose_language_descriptions, inputs='text', outputs='image', examples=examples).launch();