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import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision import transforms
from diffusers.models import AutoencoderKL
from DenoisingDiffusionProcess import *
class AutoEncoder(nn.Module):
def __init__(self,
model_type= "stabilityai/sd-vae-ft-ema"#@param ["stabilityai/sd-vae-ft-mse", "stabilityai/sd-vae-ft-ema"]
):
"""
A wrapper for an AutoEncoder model
By default, a pretrained AutoencoderKL is used from stabilitai
A custom AutoEncoder could be trained and used with the same interface.
Yet, this model works quite well for many tasks out of the box!
"""
super().__init__()
self.model=AutoencoderKL.from_pretrained(model_type)
def forward(self,input):
return self.model(input).sample
def encode(self,input,mode=False):
dist=self.model.encode(input).latent_dist
if mode:
return dist.mode()
else:
return dist.sample()
def decode(self,input):
return self.model.decode(input).sample
class LatentDiffusion(pl.LightningModule):
def __init__(self,
train_dataset,
valid_dataset=None,
num_timesteps=1000,
latent_scale_factor=0.1,
batch_size=1,
lr=1e-4):
"""
This is a simplified version of Latent Diffusion
"""
super().__init__()
self.train_dataset = train_dataset
self.valid_dataset = valid_dataset
self.lr = lr
self.register_buffer('latent_scale_factor', torch.tensor(latent_scale_factor))
self.batch_size=batch_size
self.ae=AutoEncoder()
with torch.no_grad():
self.latent_dim=self.ae.encode(torch.ones(1,3,256,256)).shape[1]
self.model=DenoisingDiffusionProcess(generated_channels=self.latent_dim,
num_timesteps=num_timesteps)
@torch.no_grad()
def forward(self,*args,**kwargs):
#return self.output_T(self.model(*args,**kwargs))
return self.output_T(self.ae.decode(self.model(*args,**kwargs)/self.latent_scale_factor))
def input_T(self, input):
# By default, let the model accept samples in [0,1] range, and transform them automatically
return (input.clip(0,1).mul_(2)).sub_(1)
def output_T(self, input):
# Inverse transform of model output from [-1,1] to [0,1] range
return (input.add_(1)).div_(2)
def training_step(self, batch, batch_idx):
latents=self.ae.encode(self.input_T(batch)).detach()*self.latent_scale_factor
loss = self.model.p_loss(latents)
self.log('train_loss',loss)
return loss
def validation_step(self, batch, batch_idx):
latents=self.ae.encode(self.input_T(batch)).detach()*self.latent_scale_factor
loss = self.model.p_loss(latents)
self.log('val_loss',loss)
return loss
def train_dataloader(self):
return DataLoader(self.train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=4)
def val_dataloader(self):
if self.valid_dataset is not None:
return DataLoader(self.valid_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=4)
else:
return None
def configure_optimizers(self):
return torch.optim.AdamW(list(filter(lambda p: p.requires_grad, self.model.parameters())), lr=self.lr)
class LatentDiffusionConditional(LatentDiffusion):
def __init__(self,
train_dataset,
valid_dataset=None,
num_timesteps=1000,
latent_scale_factor=0.1,
batch_size=1,
lr=1e-4):
pl.LightningModule.__init__(self)
self.train_dataset = train_dataset
self.valid_dataset = valid_dataset
self.lr = lr
self.register_buffer('latent_scale_factor', torch.tensor(latent_scale_factor))
self.batch_size=batch_size
self.ae=AutoEncoder()
with torch.no_grad():
self.latent_dim=self.ae.encode(torch.ones(1,3,128,128)).shape[1]
self.gender_emb = nn.Embedding(2, self.latent_dim) # 2 classes: male/female
self.model=DenoisingDiffusionConditionalProcess(generated_channels=self.latent_dim,
condition_channels=self.latent_dim*3,
num_timesteps=num_timesteps)
@torch.no_grad()
def forward(self,condition,*args,**kwargs):
cond_img, gender = condition
cond1, cond2 = torch.chunk(cond_img, 2, dim=1)
latent1 = self.ae.encode(self.input_T(cond1)).detach() * self.latent_scale_factor
latent2 = self.ae.encode(self.input_T(cond2)).detach() * self.latent_scale_factor
gender_vec = self.gender_emb(gender.long()) # [B, latent_dim]
gender_map = gender_vec.view(-1, self.latent_dim, 1, 1)
gender_map = gender_map.expand(-1, -1, latent1.shape[2], latent1.shape[3])
latents_condition = torch.cat([latent1, latent2, gender_map], dim=1)
output_code=self.model(latents_condition,*args,**kwargs)/self.latent_scale_factor
return self.output_T(self.ae.decode(output_code))
def training_step(self, batch, batch_idx):
(cond_img, gender), output = batch
with torch.no_grad():
latents=self.ae.encode(self.input_T(output)).detach()*self.latent_scale_factor
cond1, cond2 = torch.chunk(cond_img, 2, dim=1)
latent1 = self.ae.encode(self.input_T(cond1)).detach() * self.latent_scale_factor
latent2 = self.ae.encode(self.input_T(cond2)).detach() * self.latent_scale_factor
gender_vec = self.gender_emb(gender.long()) # [B, latent_dim]
gender_map = gender_vec.view(-1, self.latent_dim, 1, 1)
gender_map = gender_map.expand(-1, -1, latent1.shape[2], latent1.shape[3])
latents_condition = torch.cat([latent1, latent2, gender_map], dim=1)
loss = self.model.p_loss(latents, latents_condition)
self.log('train_loss',loss)
return loss
def validation_step(self, batch, batch_idx):
(cond_img, gender), output = batch
with torch.no_grad():
latents = self.ae.encode(self.input_T(output)).detach() * self.latent_scale_factor
cond1, cond2 = torch.chunk(cond_img, 2, dim=1)
latent1 = self.ae.encode(self.input_T(cond1)).detach() * self.latent_scale_factor
latent2 = self.ae.encode(self.input_T(cond2)).detach() * self.latent_scale_factor
gender_vec = self.gender_emb(gender.long()) # [B, latent_dim]
gender_map = gender_vec.view(-1, self.latent_dim, 1, 1)
gender_map = gender_map.expand(-1, -1, latent1.shape[2], latent1.shape[3])
latents_condition = torch.cat([latent1, latent2, gender_map], dim=1)
loss = self.model.p_loss(latents, latents_condition)
self.log('val_loss',loss)
return loss
@torch.no_grad()
def generate_from_parents(self, father_img, mother_img, gender, image_size=(128, 128)):
"""
Generate a child image given father image, mother image, and desired gender.
Args:
father_img: Tensor [3, H, W] or PIL image → must be in [0,1] range
mother_img: same
gender: int scalar (0 or 1)
image_size: (H, W)
Returns:
Generated image tensor in [0,1] range, shape [3, H, W]
"""
self.eval()
# Handle PIL images if needed
if not isinstance(father_img, torch.Tensor):
tfm = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor()
])
father_img = tfm(father_img)
if not isinstance(mother_img, torch.Tensor):
tfm = transforms.Compose([
transforms.Resize(image_size),
transforms.ToTensor()
])
mother_img = tfm(mother_img)
# Stack as batch
cond = torch.cat([father_img.unsqueeze(0), mother_img.unsqueeze(0)], dim=0) # [2, 3, H, W]
cond = cond.unsqueeze(0).view(1, 6, *father_img.shape[1:]) # [1, 6, H, W]
gender_tensor = torch.tensor([gender]).to(cond.device)
# Run forward
output = self.forward((cond.to(self.device), gender_tensor.to(self.device)))
return output[0] # remove batch dimension |