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# -*- 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
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)
import gradio as gr
gr.Interface(fn=compose_language_descriptions, inputs='text', outputs='image').launch();