rich-text-to-image / models /region_diffusion.py
Songwei Ge
demo
9d776c8
raw
history blame
12 kB
import os
import torch
import collections
import torch.nn as nn
from functools import partial
from transformers import CLIPTextModel, CLIPTokenizer, logging
from diffusers import AutoencoderKL, PNDMScheduler, EulerDiscreteScheduler, DPMSolverMultistepScheduler
from models.unet_2d_condition import UNet2DConditionModel
# suppress partial model loading warning
logging.set_verbosity_error()
class RegionDiffusion(nn.Module):
def __init__(self, device):
super().__init__()
self.device = device
self.num_train_timesteps = 1000
self.clip_gradient = False
print(f'[INFO] loading stable diffusion...')
model_id = 'runwayml/stable-diffusion-v1-5'
self.vae = AutoencoderKL.from_pretrained(
model_id, subfolder="vae").to(self.device)
self.tokenizer = CLIPTokenizer.from_pretrained(
model_id, subfolder='tokenizer')
self.text_encoder = CLIPTextModel.from_pretrained(
model_id, subfolder='text_encoder').to(self.device)
self.unet = UNet2DConditionModel.from_pretrained(
model_id, subfolder="unet").to(self.device)
self.scheduler = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
num_train_timesteps=self.num_train_timesteps, skip_prk_steps=True, steps_offset=1)
self.alphas_cumprod = self.scheduler.alphas_cumprod.to(self.device)
self.masks = []
self.attention_maps = None
self.color_loss = torch.nn.functional.mse_loss
print(f'[INFO] loaded stable diffusion!')
def get_text_embeds(self, prompt, negative_prompt):
# prompt, negative_prompt: [str]
# Tokenize text and get embeddings
text_input = self.tokenizer(
prompt, padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')
with torch.no_grad():
text_embeddings = self.text_encoder(
text_input.input_ids.to(self.device))[0]
# Do the same for unconditional embeddings
uncond_input = self.tokenizer(negative_prompt, padding='max_length',
max_length=self.tokenizer.model_max_length, return_tensors='pt')
with torch.no_grad():
uncond_embeddings = self.text_encoder(
uncond_input.input_ids.to(self.device))[0]
# Cat for final embeddings
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
return text_embeddings
def get_text_embeds_list(self, prompts):
# prompts: [list]
text_embeddings = []
for prompt in prompts:
# Tokenize text and get embeddings
text_input = self.tokenizer(
[prompt], padding='max_length', max_length=self.tokenizer.model_max_length, truncation=True, return_tensors='pt')
with torch.no_grad():
text_embeddings.append(self.text_encoder(
text_input.input_ids.to(self.device))[0])
return text_embeddings
def produce_latents(self, text_embeddings, height=512, width=512, num_inference_steps=50, guidance_scale=7.5,
latents=None, use_grad_guidance=False, text_format_dict={}):
if latents is None:
latents = torch.randn(
(1, self.unet.in_channels, height // 8, width // 8), device=self.device)
self.scheduler.set_timesteps(num_inference_steps)
n_styles = text_embeddings.shape[0]-1
assert n_styles == len(self.masks)
with torch.autocast('cuda'):
for i, t in enumerate(self.scheduler.timesteps):
# predict the noise residual
with torch.no_grad():
noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=text_embeddings[:1],
text_format_dict={})['sample']
noise_pred_text = None
for style_i, mask in enumerate(self.masks):
if style_i < len(self.masks) - 1:
masked_latent = latents
noise_pred_text_cur = self.unet(masked_latent, t, encoder_hidden_states=text_embeddings[style_i+1:style_i+2],
text_format_dict={})['sample']
else:
noise_pred_text_cur = self.unet(latents, t, encoder_hidden_states=text_embeddings[style_i+1:style_i+2],
text_format_dict=text_format_dict)['sample']
if noise_pred_text is None:
noise_pred_text = noise_pred_text_cur * mask
else:
noise_pred_text = noise_pred_text + noise_pred_text_cur*mask
# perform classifier-free guidance
noise_pred = noise_pred_uncond + guidance_scale * \
(noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents)[
'prev_sample']
# apply gradient guidance
if use_grad_guidance and t < text_format_dict['guidance_start_step']:
with torch.enable_grad():
if not latents.requires_grad:
latents.requires_grad = True
latents_0 = self.predict_x0(latents, noise_pred, t)
latents_inp = 1 / 0.18215 * latents_0
imgs = self.vae.decode(latents_inp).sample
imgs = (imgs / 2 + 0.5).clamp(0, 1)
loss_total = 0.
for attn_map, rgb_val in zip(text_format_dict['color_obj_atten'], text_format_dict['target_RGB']):
avg_rgb = (
imgs*attn_map[:, 0]).sum(2).sum(2)/attn_map[:, 0].sum()
loss = self.color_loss(
avg_rgb, rgb_val[:, :, 0, 0])*100
# print(loss)
loss_total += loss
loss_total.backward()
latents = (
latents - latents.grad * text_format_dict['color_guidance_weight']).detach().clone()
return latents
def predict_x0(self, x_t, eps_t, t):
alpha_t = self.scheduler.alphas_cumprod[t]
return (x_t - eps_t * torch.sqrt(1-alpha_t)) / torch.sqrt(alpha_t)
def produce_attn_maps(self, prompts, negative_prompts='', height=512, width=512, num_inference_steps=50,
guidance_scale=7.5, latents=None):
if isinstance(prompts, str):
prompts = [prompts]
if isinstance(negative_prompts, str):
negative_prompts = [negative_prompts]
# Prompts -> text embeds
text_embeddings = self.get_text_embeds(
prompts, negative_prompts) # [2, 77, 768]
if latents is None:
latents = torch.randn(
(text_embeddings.shape[0] // 2, self.unet.in_channels, height // 8, width // 8), device=self.device)
self.scheduler.set_timesteps(num_inference_steps)
with torch.autocast('cuda'):
for i, t in enumerate(self.scheduler.timesteps):
# expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
latent_model_input = torch.cat([latents] * 2)
# predict the noise residual
with torch.no_grad():
noise_pred = self.unet(
latent_model_input, t, encoder_hidden_states=text_embeddings)['sample']
# perform guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * \
(noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents)[
'prev_sample']
# Img latents -> imgs
imgs = self.decode_latents(latents) # [1, 3, 512, 512]
# Img to Numpy
imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy()
imgs = (imgs * 255).round().astype('uint8')
return imgs
def decode_latents(self, latents):
latents = 1 / 0.18215 * latents
with torch.no_grad():
imgs = self.vae.decode(latents).sample
imgs = (imgs / 2 + 0.5).clamp(0, 1)
return imgs
def prompt_to_img(self, prompts, negative_prompts='', height=512, width=512, num_inference_steps=50,
guidance_scale=7.5, latents=None, text_format_dict={}, use_grad_guidance=False):
if isinstance(prompts, str):
prompts = [prompts]
if isinstance(negative_prompts, str):
negative_prompts = [negative_prompts]
# Prompts -> text embeds
text_embeds = self.get_text_embeds(
prompts, negative_prompts) # [2, 77, 768]
if len(text_format_dict) > 0:
if 'font_styles' in text_format_dict and text_format_dict['font_styles'] is not None:
text_format_dict['font_styles_embs'] = self.get_text_embeds_list(
text_format_dict['font_styles']) # [2, 77, 768]
else:
text_format_dict['font_styles_embs'] = None
# else:
latents = self.produce_latents(text_embeds, height=height, width=width, latents=latents,
num_inference_steps=num_inference_steps, guidance_scale=guidance_scale,
use_grad_guidance=use_grad_guidance, text_format_dict=text_format_dict) # [1, 4, 64, 64]
# Img latents -> imgs
imgs = self.decode_latents(latents) # [1, 3, 512, 512]
# Img to Numpy
imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy()
imgs = (imgs * 255).round().astype('uint8')
return imgs
def reset_attention_maps(self):
r"""Function to reset attention maps.
We reset attention maps because we append them while getting hooks
to visualize attention maps for every step.
"""
for key in self.attention_maps:
self.attention_maps[key] = []
def register_evaluation_hooks(self):
r"""Function for registering hooks during evaluation.
We mainly store activation maps averaged over queries.
"""
self.forward_hooks = []
def save_activations(activations, name, module, inp, out):
r"""
PyTorch Forward hook to save outputs at each forward pass.
"""
# out[0] - final output of attention layer
# out[1] - attention probability matrix
if 'attn2' in name:
assert out[1].shape[-1] == 77
activations[name].append(out[1].detach().cpu())
else:
assert out[1].shape[-1] != 77
attention_dict = collections.defaultdict(list)
for name, module in self.unet.named_modules():
leaf_name = name.split('.')[-1]
if 'attn' in leaf_name:
# Register hook to obtain outputs at every attention layer.
self.forward_hooks.append(module.register_forward_hook(
partial(save_activations, attention_dict, name)
))
# attention_dict is a dictionary containing attention maps for every attention layer
self.attention_maps = attention_dict
def remove_evaluation_hooks(self):
for hook in self.forward_hooks:
hook.remove()
self.attention_maps = None