# -*- 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
"""
import streamlit as st
import gradio as gr
import torch as th
from composable_diffusion.download import download_model
from composable_diffusion.model_creation import create_model_and_diffusion as create_model_and_diffusion_for_clevr
from composable_diffusion.model_creation import model_and_diffusion_defaults as model_and_diffusion_defaults_for_clevr
from torch import autocast
from composable_stable_diffusion_pipeline import ComposableStableDiffusionPipeline
# 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 th.cuda.is_available() else 'cuda')
print(device)
# init stable diffusion model
pipe = ComposableStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
use_auth_token=st.secrets["USER_TOKEN"]
).to(device)
# create model for CLEVR Objects
clevr_options = model_and_diffusion_defaults_for_clevr()
flags = {
"image_size": 128,
"num_channels": 192,
"num_res_blocks": 2,
"learn_sigma": True,
"use_scale_shift_norm": False,
"raw_unet": True,
"noise_schedule": "squaredcos_cap_v2",
"rescale_learned_sigmas": False,
"rescale_timesteps": False,
"num_classes": '2',
"dataset": "clevr_pos",
"use_fp16": has_cuda,
"timestep_respacing": '100'
}
for key, val in flags.items():
clevr_options[key] = val
clevr_model, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options)
clevr_model.eval()
if has_cuda:
clevr_model.convert_to_fp16()
clevr_model.to(device)
clevr_model.load_state_dict(th.load(download_model('clevr_pos'), device))
print('total clevr_pos parameters', sum(x.numel() for x in clevr_model.parameters()))
def compose_clevr_objects(prompt, guidance_scale, steps):
coordinates = [[float(x.split(',')[0].strip()), float(x.split(',')[1].strip())]
for x in prompt.split('|')]
coordinates += [[-1, -1]] # add unconditional score label
batch_size = 1
clevr_options['timestep_respacing'] = str(int(steps))
_, clevr_diffusion = create_model_and_diffusion_for_clevr(**clevr_options)
def model_fn(x_t, ts, **kwargs):
half = x_t[:1]
combined = th.cat([half] * kwargs['y'].size(0), dim=0)
model_out = clevr_model(combined, ts, **kwargs)
eps, rest = model_out[:, :3], model_out[:, 3:]
masks = kwargs.get('masks')
cond_eps = eps[masks].mean(dim=0, keepdim=True)
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)
def sample(coordinates):
masks = [True] * (len(coordinates) - 1) + [False]
model_kwargs = dict(
y=th.tensor(coordinates, dtype=th.float, device=device),
masks=th.tensor(masks, dtype=th.bool, device=device)
)
samples = clevr_diffusion.p_sample_loop(
model_fn,
(len(coordinates), 3, clevr_options["image_size"], clevr_options["image_size"]),
device=device,
clip_denoised=True,
progress=True,
model_kwargs=model_kwargs,
cond_fn=None,
)[:batch_size]
return samples
samples = sample(coordinates)
out_img = samples[0].permute(1, 2, 0)
out_img = (out_img + 1) / 2
out_img = (out_img.detach().cpu() * 255.).to(th.uint8)
out_img = out_img.numpy()
return out_img
def stable_diffusion_compose(prompt, scale, steps):
with autocast('cpu' if not th.cuda.is_available() else 'cuda'):
image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps)["sample"][0]
return image
def compose(prompt, version, guidance_scale, steps):
try:
with th.no_grad():
if version == 'Stable_Diffusion_1v_4':
return stable_diffusion_compose(prompt, guidance_scale, steps)
else:
return compose_clevr_objects(prompt, guidance_scale, steps)
except Exception as e:
print(e)
return None
examples_1 = 'a camel | a forest'
examples_2 = 'A blue sky | A mountain in the horizon | Cherry Blossoms in front of the mountain'
examples_3 = '0.1, 0.5 | 0.3, 0.5 | 0.5, 0.5 | 0.7, 0.5 | 0.9, 0.5'
examples_4 = 'a blue house | a desert'
examples_5 = 'a white church | lightning in the background'
examples_6 = 'a camel | arctic'
examples_7 = 'A lake | A mountain | Cherry Blossoms next to the lake'
examples = [
[examples_7, 'Stable_Diffusion_1v_4', 15, 50],
[examples_5, 'Stable_Diffusion_1v_4', 15, 50],
[examples_4, 'Stable_Diffusion_1v_4', 15, 50],
[examples_6, 'Stable_Diffusion_1v_4', 15, 50],
[examples_3, 'CLEVR Objects', 10, 100]
]
title = 'Compositional Visual Generation with Composable Diffusion Models'
description = 'Demo for Composable Diffusion
- ~30s per Stable-Diffusion example
- ~10s per CLEVR Object example
(Note: time is varied depending on what gpu is used.)
See more information from our Project Page.
- One version is based on the released GLIDE and Stable Diffusion for composing natural language description.
- Another is based on our pre-trained CLEVR Object Model for composing objects.
(Note: We recommend using x in range [0.1, 0.9] and y in range [0.25, 0.7], since the training dataset labels are in given ranges.)
When composing multiple sentences, use `|` as the delimiter, see given examples below.
Note: When using Stable Diffusion, black images will be returned if the given prompt is detected as problematic. For composing GLIDE model, we recommend using the Colab demo in our Project Page.
'
iface = gr.Interface(compose,
inputs=[
"text",
gr.Radio(['Stable_Diffusion_1v_4', 'CLEVR Objects'], type="value", label='version'),
gr.Slider(2, 30),
gr.Slider(10, 200)
],
outputs='image', cache_examples=False,
title=title, description=description, examples=examples)
iface.launch()