rich-text-to-image / models /region_diffusion.py
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udpate to sdxl
99e3c03
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
from utils.attention_utils import CrossAttentionLayers, SelfAttentionLayers
# 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.selfattn_maps = None
self.crossattn_maps = None
self.color_loss = torch.nn.functional.mse_loss
self.forward_hooks = []
self.forward_replacement_hooks = []
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_guidance=False, text_format_dict={}, inject_selfattn=0, bg_aug_end=1000):
if latents is None:
latents = torch.randn(
(1, self.unet.in_channels, height // 8, width // 8), device=self.device)
if inject_selfattn > 0:
latents_reference = latents.clone().detach()
self.scheduler.set_timesteps(num_inference_steps)
n_styles = text_embeddings.shape[0]-1
print(n_styles, len(self.masks))
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():
# tokens without any attributes
feat_inject_step = t > (1-inject_selfattn) * 1000
noise_pred_uncond_cur = self.unet(latents, t, encoder_hidden_states=text_embeddings[:1],
# text_format_dict={})['sample']
)['sample']
# tokens without any style or footnote
self.register_fontsize_hooks(text_format_dict)
noise_pred_text_cur = self.unet(latents, t, encoder_hidden_states=text_embeddings[-1:],
# text_format_dict=text_format_dict)['sample']
)['sample']
self.remove_fontsize_hooks()
if inject_selfattn > 0 or inject_background > 0:
noise_pred_uncond_refer = self.unet(latents_reference, t, encoder_hidden_states=text_embeddings[:1],
# text_format_dict={})['sample']
)['sample']
self.register_selfattn_hooks(feat_inject_step)
noise_pred_text_refer = self.unet(latents_reference, t, encoder_hidden_states=text_embeddings[-1:],
# text_format_dict={})['sample']
)['sample']
self.remove_selfattn_hooks()
noise_pred_uncond = noise_pred_uncond_cur * self.masks[-1]
noise_pred_text = noise_pred_text_cur * self.masks[-1]
# tokens with attributes
for style_i, mask in enumerate(self.masks[:-1]):
if t > bg_aug_end:
rand_rgb = torch.rand([1, 3, 1, 1]).cuda()
black_background = torch.ones(
[1, 3, height, width]).cuda()*rand_rgb
black_latent = self.encode_imgs(
black_background)
noise = torch.randn_like(black_latent)
black_latent_noisy = self.scheduler.add_noise(
black_latent, noise, t)
masked_latent = (
mask > 0.001) * latents + (mask < 0.001) * black_latent_noisy
noise_pred_uncond_cur = self.unet(masked_latent, t, encoder_hidden_states=text_embeddings[:1],
text_format_dict={})['sample']
else:
masked_latent = latents
self.register_replacement_hooks(feat_inject_step)
noise_pred_text_cur = self.unet(latents, t, encoder_hidden_states=text_embeddings[style_i+1:style_i+2],
# text_format_dict={})['sample']
)['sample']
self.remove_replacement_hooks()
noise_pred_uncond = noise_pred_uncond + noise_pred_uncond_cur*mask
noise_pred_text = noise_pred_text + noise_pred_text_cur*mask
# perform guidance
noise_pred = noise_pred_uncond + guidance_scale * \
(noise_pred_text - noise_pred_uncond)
if inject_selfattn > 0:
noise_pred_refer = noise_pred_uncond_refer + guidance_scale * \
(noise_pred_text_refer - noise_pred_uncond_refer)
# compute the previous noisy sample x_t -> x_t-1
latents_reference = self.scheduler.step(torch.cat([noise_pred, noise_pred_refer]), t,
torch.cat([latents, latents_reference]))[
'prev_sample']
latents, latents_reference = torch.chunk(
latents_reference, 2, dim=0)
else:
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents)[
'prev_sample']
# apply guidance
if use_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)
# save_path = 'results/font_color/20230425/church_process/orange/'
# os.makedirs(save_path, exist_ok=True)
# torchvision.utils.save_image(
# imgs, os.path.join(save_path, 'step%d.png' % t))
# loss = (((imgs - text_format_dict['target_RGB'])*text_format_dict['color_obj_atten'][:, 0])**2).mean()*100
loss_total = 0.
for attn_map, rgb_val in zip(text_format_dict['color_obj_atten'], text_format_dict['target_RGB']):
# loss = self.color_loss(
# imgs*attn_map[:, 0], rgb_val*attn_map[:, 0])*100
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'] * self.masks[0]).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)
self.remove_replacement_hooks()
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 encode_imgs(self, imgs):
# imgs: [B, 3, H, W]
imgs = 2 * imgs - 1
posterior = self.vae.encode(imgs).latent_dist
latents = posterior.sample() * 0.18215
return latents
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_guidance=False, inject_selfattn=0, bg_aug_end=1000):
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]
# else:
latents = self.produce_latents(text_embeds, height=height, width=width, latents=latents,
num_inference_steps=num_inference_steps, guidance_scale=guidance_scale,
use_guidance=use_guidance, text_format_dict=text_format_dict,
inject_selfattn=inject_selfattn, bg_aug_end=bg_aug_end) # [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.selfattn_maps:
self.selfattn_maps[key] = []
for key in self.crossattn_maps:
self.crossattn_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 register_selfattn_hooks(self, feat_inject_step=False):
r"""Function for registering hooks during evaluation.
We mainly store activation maps averaged over queries.
"""
self.selfattn_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][1].shape[-1] == 77
# cross attention injection
# activations[name] = out[1][1].detach()
else:
assert out[1][1].shape[-1] != 77
activations[name] = out[1][1].detach()
def save_resnet_activations(activations, name, module, inp, out):
r"""
PyTorch Forward hook to save outputs at each forward pass.
"""
# out[0] - final output of residual layer
# out[1] - residual hidden feature
# import ipdb
# ipdb.set_trace()
assert out[1].shape[-1] == 16
activations[name] = out[1].detach()
attention_dict = collections.defaultdict(list)
for name, module in self.unet.named_modules():
leaf_name = name.split('.')[-1]
if 'attn' in leaf_name and feat_inject_step:
# Register hook to obtain outputs at every attention layer.
self.selfattn_forward_hooks.append(module.register_forward_hook(
partial(save_activations, attention_dict, name)
))
if name == 'up_blocks.1.resnets.1' and feat_inject_step:
self.selfattn_forward_hooks.append(module.register_forward_hook(
partial(save_resnet_activations, attention_dict, name)
))
# attention_dict is a dictionary containing attention maps for every attention layer
self.self_attention_maps_cur = attention_dict
def register_replacement_hooks(self, feat_inject_step=False):
r"""Function for registering hooks to replace self attention.
"""
self.forward_replacement_hooks = []
def replace_activations(name, module, args):
r"""
PyTorch Forward hook to save outputs at each forward pass.
"""
if 'attn1' in name:
modified_args = (args[0], self.self_attention_maps_cur[name])
return modified_args
# cross attention injection
# elif 'attn2' in name:
# modified_map = {
# 'reference': self.self_attention_maps_cur[name],
# 'inject_pos': self.inject_pos,
# }
# modified_args = (args[0], modified_map)
# return modified_args
def replace_resnet_activations(name, module, args):
r"""
PyTorch Forward hook to save outputs at each forward pass.
"""
modified_args = (args[0], args[1],
self.self_attention_maps_cur[name])
return modified_args
for name, module in self.unet.named_modules():
leaf_name = name.split('.')[-1]
if 'attn' in leaf_name and feat_inject_step:
# Register hook to obtain outputs at every attention layer.
self.forward_replacement_hooks.append(module.register_forward_pre_hook(
partial(replace_activations, name)
))
if name == 'up_blocks.1.resnets.1' and feat_inject_step:
# Register hook to obtain outputs at every attention layer.
self.forward_replacement_hooks.append(module.register_forward_pre_hook(
partial(replace_resnet_activations, name)
))
def register_tokenmap_hooks(self):
r"""Function for registering hooks during evaluation.
We mainly store activation maps averaged over queries.
"""
self.forward_hooks = []
def save_activations(selfattn_maps, crossattn_maps, n_maps, 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 matrices
if name in n_maps:
n_maps[name] += 1
else:
n_maps[name] = 1
if 'attn2' in name:
assert out[1][0].shape[-1] == 77
if name in CrossAttentionLayers and n_maps[name] > 10:
if name in crossattn_maps:
crossattn_maps[name] += out[1][0].detach().cpu()[1:2]
else:
crossattn_maps[name] = out[1][0].detach().cpu()[1:2]
else:
assert out[1][0].shape[-1] != 77
if name in SelfAttentionLayers and n_maps[name] > 10:
if name in crossattn_maps:
selfattn_maps[name] += out[1][0].detach().cpu()[1:2]
else:
selfattn_maps[name] = out[1][0].detach().cpu()[1:2]
selfattn_maps = collections.defaultdict(list)
crossattn_maps = collections.defaultdict(list)
n_maps = 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, selfattn_maps,
crossattn_maps, n_maps, name)
))
# attention_dict is a dictionary containing attention maps for every attention layer
self.selfattn_maps = selfattn_maps
self.crossattn_maps = crossattn_maps
self.n_maps = n_maps
def remove_tokenmap_hooks(self):
for hook in self.forward_hooks:
hook.remove()
self.selfattn_maps = None
self.crossattn_maps = None
self.n_maps = None
def remove_evaluation_hooks(self):
for hook in self.forward_hooks:
hook.remove()
self.attention_maps = None
def remove_replacement_hooks(self):
for hook in self.forward_replacement_hooks:
hook.remove()
def remove_selfattn_hooks(self):
for hook in self.selfattn_forward_hooks:
hook.remove()
def register_fontsize_hooks(self, text_format_dict={}):
r"""Function for registering hooks to replace self attention.
"""
self.forward_fontsize_hooks = []
def adjust_attn_weights(name, module, args):
r"""
PyTorch Forward hook to save outputs at each forward pass.
"""
if 'attn2' in name:
modified_args = (args[0], None, attn_weights)
return modified_args
if text_format_dict['word_pos'] is not None and text_format_dict['font_size'] is not None:
attn_weights = {'word_pos': text_format_dict['word_pos'], 'font_size': text_format_dict['font_size']}
else:
attn_weights = None
for name, module in self.unet.named_modules():
leaf_name = name.split('.')[-1]
if 'attn' in leaf_name and attn_weights is not None:
# Register hook to obtain outputs at every attention layer.
self.forward_fontsize_hooks.append(module.register_forward_pre_hook(
partial(adjust_attn_weights, name)
))
def remove_fontsize_hooks(self):
for hook in self.forward_fontsize_hooks:
hook.remove()