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f0395ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | # Copyright (c) 2025 Hansheng Chen
import torch
import inspect
from copy import deepcopy
from mmgen.models.builder import MODELS, build_module
from .base_diffusion import BaseDiffusion
from lakonlab.utils import rgetattr
@MODELS.register_module()
class LatentDiffusionTextImage(BaseDiffusion):
def __init__(self,
*args,
vae=None,
text_encoder=None,
**kwargs):
super().__init__(*args, **kwargs)
self.vae = build_module(vae) if vae is not None else None
self.text_encoder = build_module(text_encoder) if text_encoder is not None else None
def _prepare_train_minibatch_diffusion_args(self, data):
if 'prompt_embed_kwargs' in data:
prompt_embed_kwargs = data['prompt_embed_kwargs']
elif 'prompt_kwargs' in data:
assert self.text_encoder is not None, 'Text encoder must be provided for encoding text to embeddings.'
prompt_embed_kwargs = self.text_encoder(**data['prompt_kwargs'])
else:
raise ValueError('Either `prompt_embed_kwargs` or `prompt_kwargs` should be provided in the input data.')
if 'latents' in data:
latents = data['latents']
elif 'images' in data:
assert self.vae is not None, 'VAE must be provided for encoding images to latents.'
with torch.no_grad():
if hasattr(self.vae, 'dtype'):
vae_dtype = self.vae.dtype
else:
vae_dtype = next(self.vae.parameters()).dtype
latents = self.vae.encode((data['images'] * 2 - 1).to(vae_dtype)).float()
else:
raise ValueError('Either `latents` or `images` should be provided in the input data.')
v = next(iter(prompt_embed_kwargs.values()))
bs = v.size(0)
device = v.device
diffusion_args = (self.patchify(latents), )
diffusion_kwargs = prompt_embed_kwargs.copy()
distilled_guidance_scale = self.train_cfg.get('distilled_guidance_scale', None)
if distilled_guidance_scale is not None:
distilled_guidance_scale = torch.full(
(bs,), distilled_guidance_scale, dtype=torch.float32, device=device)
diffusion_kwargs.update(guidance=distilled_guidance_scale)
return diffusion_args, diffusion_kwargs, prompt_embed_kwargs, bs, device
def _prepare_train_minibatch_teacher_args(self, data, prompt_embed_kwargs, bs, device):
teacher_guidance_scale = self.train_cfg.get('teacher_guidance_scale', None)
teacher_use_guidance = (teacher_guidance_scale is not None
and teacher_guidance_scale != 0.0 and teacher_guidance_scale != 1.0)
if teacher_use_guidance:
if 'negative_prompt_embed_kwargs' in data:
negative_prompt_embed_kwargs = data['negative_prompt_embed_kwargs']
elif 'negative_prompt_kwargs' in data:
negative_prompt_embed_kwargs = self.text_encoder(**data['negative_prompt_kwargs'])
else:
raise ValueError(
'Either `negative_prompt_embed_kwargs` or `negative_prompt_kwargs` should be provided in the '
'input data for classifier-free guidance.')
teacher_kwargs = {
k: torch.cat([negative_prompt_embed_kwargs[k], v], dim=0)
for k, v in prompt_embed_kwargs.items()}
teacher_kwargs.update(guidance_scale=teacher_guidance_scale)
else:
teacher_kwargs = prompt_embed_kwargs.copy()
teacher_distilled_guidance_scale = self.train_cfg.get('teacher_distilled_guidance_scale', None)
if teacher_distilled_guidance_scale is not None:
teacher_distilled_guidance_scale = torch.full(
(bs * 2,) if teacher_use_guidance else (bs,),
teacher_distilled_guidance_scale, dtype=torch.float32, device=device)
teacher_kwargs.update(guidance=teacher_distilled_guidance_scale)
return teacher_kwargs
def _prepare_train_minibatch_args(self, data, running_status=None):
diffusion_args, diffusion_kwargs, prompt_embed_kwargs, bs, device = \
self._prepare_train_minibatch_diffusion_args(data)
parameters = inspect.signature(rgetattr(self.diffusion, 'forward_train')).parameters
if 'running_status' in parameters:
diffusion_kwargs['running_status'] = running_status
if 'teacher' in parameters and 'teacher_kwargs' in parameters and self.teacher is not None:
teacher_kwargs = self._prepare_train_minibatch_teacher_args(
data, prompt_embed_kwargs, bs, device)
diffusion_kwargs.update(
teacher=self.teacher,
teacher_kwargs=teacher_kwargs)
return bs, diffusion_args, diffusion_kwargs
def val_step(self, data, test_cfg_override=dict(), **kwargs):
if 'prompt_embed_kwargs' in data:
prompt_embed_kwargs = data['prompt_embed_kwargs']
elif 'prompt_kwargs' in data:
assert self.text_encoder is not None, 'Text encoder must be provided for encoding text to embeddings.'
prompt_embed_kwargs = self.text_encoder(**data['prompt_kwargs'])
else:
raise ValueError('Either `prompt_embed_kwargs` or `prompt_kwargs` should be provided in the input data.')
v = next(iter(prompt_embed_kwargs.values()))
bs = v.size(0)
device = v.device
cfg = deepcopy(self.test_cfg)
cfg.update(test_cfg_override)
guidance_scale = cfg.get('guidance_scale', 1.0)
diffusion = self.diffusion_ema if self.diffusion_use_ema else self.diffusion
with torch.no_grad():
use_guidance = guidance_scale != 0.0 and guidance_scale != 1.0
if use_guidance:
if 'negative_prompt_embed_kwargs' in data:
negative_prompt_embed_kwargs = data['negative_prompt_embed_kwargs']
elif 'negative_prompt_kwargs' in data:
negative_prompt_embed_kwargs = self.text_encoder(**data['negative_prompt_kwargs'])
else:
raise ValueError(
'Either `negative_prompt_embed_kwargs` or `negative_prompt_kwargs` should be provided in the '
'input data for classifier-free guidance.')
kwargs = {
k: torch.cat([negative_prompt_embed_kwargs[k], v], dim=0)
for k, v in prompt_embed_kwargs.items()}
else:
kwargs = prompt_embed_kwargs.copy()
distilled_guidance_scale = cfg.get('distilled_guidance_scale', None)
if distilled_guidance_scale is not None:
distilled_guidance_scale = torch.full(
(bs * 2,) if use_guidance else (bs,),
distilled_guidance_scale, dtype=torch.float32, device=device)
kwargs.update(guidance=distilled_guidance_scale)
if 'noise' in data:
noise = data['noise']
else:
latent_size = cfg['latent_size']
noise = torch.randn((bs, *latent_size), device=device)
noise = self.patchify(noise)
latents_out = diffusion(
noise=noise,
guidance_scale=guidance_scale,
test_cfg_override=test_cfg_override,
**kwargs)
latents_out = self.unpatchify(latents_out)
if hasattr(self.vae, 'dtype'):
vae_dtype = self.vae.dtype
else:
vae_dtype = next(self.vae.parameters()).dtype
latents_out = latents_out.to(vae_dtype)
out_images = (self.vae.decode(latents_out).float() / 2 + 0.5).clamp(min=0, max=1)
return dict(num_samples=bs, pred_imgs=out_images)
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