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import torch
import os
from torch import nn
from safetensors.torch import load_file
import torch.nn.functional as F
from diffusers import AutoencoderTiny
from transformers import SiglipImageProcessor, SiglipVisionModel
import lpips
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler
class ResBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
self.norm1 = nn.GroupNorm(8, out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
self.norm2 = nn.GroupNorm(8, out_channels)
self.skip = nn.Conv2d(in_channels, out_channels,
1) if in_channels != out_channels else nn.Identity()
def forward(self, x):
identity = self.skip(x)
x = self.conv1(x)
x = self.norm1(x)
x = F.silu(x)
x = self.conv2(x)
x = self.norm2(x)
x = F.silu(x + identity)
return x
class DiffusionFeatureExtractor2(nn.Module):
def __init__(self, in_channels=32):
super().__init__()
self.version = 2
# Path 1: Upsample to 512x512 (1, 64, 512, 512)
self.up_path = nn.ModuleList([
nn.Conv2d(in_channels, 64, 3, padding=1),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ResBlock(64, 64),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ResBlock(64, 64),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ResBlock(64, 64),
nn.Conv2d(64, 64, 3, padding=1),
])
# Path 2: Upsample to 256x256 (1, 128, 256, 256)
self.path2 = nn.ModuleList([
nn.Conv2d(in_channels, 128, 3, padding=1),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ResBlock(128, 128),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ResBlock(128, 128),
nn.Conv2d(128, 128, 3, padding=1),
])
# Path 3: Upsample to 128x128 (1, 256, 128, 128)
self.path3 = nn.ModuleList([
nn.Conv2d(in_channels, 256, 3, padding=1),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
ResBlock(256, 256),
nn.Conv2d(256, 256, 3, padding=1)
])
# Path 4: Original size (1, 512, 64, 64)
self.path4 = nn.ModuleList([
nn.Conv2d(in_channels, 512, 3, padding=1),
ResBlock(512, 512),
ResBlock(512, 512),
nn.Conv2d(512, 512, 3, padding=1)
])
# Path 5: Downsample to 32x32 (1, 512, 32, 32)
self.path5 = nn.ModuleList([
nn.Conv2d(in_channels, 512, 3, padding=1),
ResBlock(512, 512),
nn.AvgPool2d(2),
ResBlock(512, 512),
nn.Conv2d(512, 512, 3, padding=1)
])
def forward(self, x):
outputs = []
# Path 1: 512x512
x1 = x
for layer in self.up_path:
x1 = layer(x1)
outputs.append(x1) # [1, 64, 512, 512]
# Path 2: 256x256
x2 = x
for layer in self.path2:
x2 = layer(x2)
outputs.append(x2) # [1, 128, 256, 256]
# Path 3: 128x128
x3 = x
for layer in self.path3:
x3 = layer(x3)
outputs.append(x3) # [1, 256, 128, 128]
# Path 4: 64x64
x4 = x
for layer in self.path4:
x4 = layer(x4)
outputs.append(x4) # [1, 512, 64, 64]
# Path 5: 32x32
x5 = x
for layer in self.path5:
x5 = layer(x5)
outputs.append(x5) # [1, 512, 32, 32]
return outputs
class DFEBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
self.act = nn.GELU()
def forward(self, x):
x_in = x
x = self.conv1(x)
x = self.conv2(x)
x = self.act(x)
x = x + x_in
return x
class DiffusionFeatureExtractor(nn.Module):
def __init__(self, in_channels=32):
super().__init__()
self.version = 1
num_blocks = 6
self.conv_in = nn.Conv2d(in_channels, 512, 1)
self.blocks = nn.ModuleList([DFEBlock(512) for _ in range(num_blocks)])
self.conv_out = nn.Conv2d(512, 512, 1)
def forward(self, x):
x = self.conv_in(x)
for block in self.blocks:
x = block(x)
x = self.conv_out(x)
return x
class DiffusionFeatureExtractor3(nn.Module):
def __init__(self, device=torch.device("cuda"), dtype=torch.bfloat16, vae=None):
super().__init__()
self.version = 3
if vae is None:
vae = AutoencoderTiny.from_pretrained(
"madebyollin/taef1", torch_dtype=torch.bfloat16)
self.vae = vae
# image_encoder_path = "google/siglip-so400m-patch14-384"
image_encoder_path = "google/siglip2-so400m-patch16-512"
try:
self.image_processor = SiglipImageProcessor.from_pretrained(
image_encoder_path)
except EnvironmentError:
self.image_processor = SiglipImageProcessor()
self.vision_encoder = SiglipVisionModel.from_pretrained(
image_encoder_path,
ignore_mismatched_sizes=True
).to(device, dtype=dtype)
self.lpips_model = lpips_model = lpips.LPIPS(net='vgg')
self.lpips_model = lpips_model.to(device, dtype=torch.float32)
self.losses = {}
self.log_every = 100
self.step = 0
def get_siglip_features(self, tensors_0_1):
dtype = torch.bfloat16
device = self.vae.device
# resize to 384x384
if 'height' in self.image_processor.size:
size = self.image_processor.size['height']
else:
size = self.image_processor.crop_size['height']
images = F.interpolate(tensors_0_1, size=(size, size),
mode='bicubic', align_corners=False)
mean = torch.tensor(self.image_processor.image_mean).to(
device, dtype=dtype
).detach()
std = torch.tensor(self.image_processor.image_std).to(
device, dtype=dtype
).detach()
# tensors_0_1 = torch.clip((255. * tensors_0_1), 0, 255).round() / 255.0
clip_image = (
images - mean.view([1, 3, 1, 1])) / std.view([1, 3, 1, 1])
id_embeds = self.vision_encoder(
clip_image,
output_hidden_states=True,
)
last_hidden_state = id_embeds['last_hidden_state']
return last_hidden_state
def get_lpips_features(self, tensors_0_1):
device = self.vae.device
tensors_n1p1 = (tensors_0_1 * 2) - 1
def get_lpips_features(img): # -1 to 1
in0_input = self.lpips_model.scaling_layer(img)
outs0 = self.lpips_model.net.forward(in0_input)
feats0 = {}
feats_list = []
for kk in range(self.lpips_model.L):
feats0[kk] = lpips.normalize_tensor(outs0[kk])
feats_list.append(feats0[kk])
# 512 in
# vgg
# 0 torch.Size([1, 64, 512, 512])
# 1 torch.Size([1, 128, 256, 256])
# 2 torch.Size([1, 256, 128, 128])
# 3 torch.Size([1, 512, 64, 64])
# 4 torch.Size([1, 512, 32, 32])
return feats_list
# do lpips
lpips_feat_list = [x for x in get_lpips_features(
tensors_n1p1.to(device, dtype=torch.float32))]
return lpips_feat_list
def forward(
self,
noise,
noise_pred,
noisy_latents,
timesteps,
batch: DataLoaderBatchDTO,
scheduler: CustomFlowMatchEulerDiscreteScheduler,
# lpips_weight=1.0,
lpips_weight=10.0,
clip_weight=0.1,
pixel_weight=0.1
):
dtype = torch.bfloat16
device = self.vae.device
# first we step the scheduler from current timestep to the very end for a full denoise
# bs = noise_pred.shape[0]
# noise_pred_chunks = torch.chunk(noise_pred, bs)
# timestep_chunks = torch.chunk(timesteps, bs)
# noisy_latent_chunks = torch.chunk(noisy_latents, bs)
# stepped_chunks = []
# for idx in range(bs):
# model_output = noise_pred_chunks[idx]
# timestep = timestep_chunks[idx]
# scheduler._step_index = None
# scheduler._init_step_index(timestep)
# sample = noisy_latent_chunks[idx].to(torch.float32)
# sigma = scheduler.sigmas[scheduler.step_index]
# sigma_next = scheduler.sigmas[-1] # use last sigma for final step
# prev_sample = sample + (sigma_next - sigma) * model_output
# stepped_chunks.append(prev_sample)
# stepped_latents = torch.cat(stepped_chunks, dim=0)
stepped_latents = noise - noise_pred
latents = stepped_latents.to(self.vae.device, dtype=self.vae.dtype)
latents = (
latents / self.vae.config['scaling_factor']) + self.vae.config['shift_factor']
tensors_n1p1 = self.vae.decode(latents).sample # -1 to 1
pred_images = (tensors_n1p1 + 1) / 2 # 0 to 1
lpips_feat_list_pred = self.get_lpips_features(pred_images.float())
total_loss = 0
with torch.no_grad():
target_img = batch.tensor.to(device, dtype=dtype)
# go from -1 to 1 to 0 to 1
target_img = (target_img + 1) / 2
lpips_feat_list_target = self.get_lpips_features(target_img.float())
if clip_weight > 0:
target_clip_output = self.get_siglip_features(target_img).detach()
if clip_weight > 0:
pred_clip_output = self.get_siglip_features(pred_images)
clip_loss = torch.nn.functional.mse_loss(
pred_clip_output.float(), target_clip_output.float()
) * clip_weight
if 'clip_loss' not in self.losses:
self.losses['clip_loss'] = clip_loss.item()
else:
self.losses['clip_loss'] += clip_loss.item()
total_loss += clip_loss
skip_lpips_layers = []
lpips_loss = 0
for idx, lpips_feat in enumerate(lpips_feat_list_pred):
if idx in skip_lpips_layers:
continue
lpips_loss += torch.nn.functional.mse_loss(
lpips_feat.float(), lpips_feat_list_target[idx].float()
) * lpips_weight
if f'lpips_loss_{idx}' not in self.losses:
self.losses[f'lpips_loss_{idx}'] = lpips_loss.item()
else:
self.losses[f'lpips_loss_{idx}'] += lpips_loss.item()
total_loss += lpips_loss
# mse_loss = torch.nn.functional.mse_loss(
# stepped_latents.float(), batch.latents.float()
# ) * pixel_weight
# if 'pixel_loss' not in self.losses:
# self.losses['pixel_loss'] = mse_loss.item()
# else:
# self.losses['pixel_loss'] += mse_loss.item()
if self.step % self.log_every == 0 and self.step > 0:
print(f"DFE losses:")
for key in self.losses:
self.losses[key] /= self.log_every
# print in 2.000e-01 format
print(f" - {key}: {self.losses[key]:.3e}")
self.losses[key] = 0.0
# total_loss += mse_loss
self.step += 1
return total_loss
def load_dfe(model_path, vae=None) -> DiffusionFeatureExtractor:
if model_path == "v3":
dfe = DiffusionFeatureExtractor3(vae=vae)
dfe.eval()
return dfe
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found: {model_path}")
# if it ende with safetensors
if model_path.endswith('.safetensors'):
state_dict = load_file(model_path)
else:
state_dict = torch.load(model_path, weights_only=True)
if 'model_state_dict' in state_dict:
state_dict = state_dict['model_state_dict']
if 'conv_in.weight' in state_dict:
dfe = DiffusionFeatureExtractor()
else:
dfe = DiffusionFeatureExtractor2()
dfe.load_state_dict(state_dict)
dfe.eval()
return dfe
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