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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextModel, CLIPTokenizer
class TimeEmbedding(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
half_dim = dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
self.register_buffer('emb', emb)
def forward(self, time):
emb = time[:, None] * self.emb[None, :]
emb = torch.cat((torch.sin(emb), torch.cos(emb)), dim=-1)
return emb
class AttentionBlock(nn.Module):
def __init__(self, channels, num_heads=4):
super().__init__()
self.num_heads = num_heads
self.scale = (channels // num_heads) ** -0.5
self.norm = nn.GroupNorm(32, channels)
self.qkv = nn.Conv2d(channels, channels * 3, 1)
self.proj = nn.Conv2d(channels, channels, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.qkv(self.norm(x))
q, k, v = qkv.chunk(3, dim=1)
q = q.view(b, self.num_heads, -1, h * w).permute(0, 1, 3, 2)
k = k.view(b, self.num_heads, -1, h * w)
v = v.view(b, self.num_heads, -1, h * w)
attn = torch.softmax((q @ k) * self.scale, dim=-1)
x = (attn @ v).permute(0, 1, 3, 2).reshape(b, -1, h, w)
return self.proj(x) + x
class ResBlock(nn.Module):
def __init__(self, in_ch, out_ch, time_emb_dim, text_emb_dim, dropout=0.1):
super().__init__()
self.mlp = nn.Sequential(
nn.SiLU(),
nn.Linear(time_emb_dim + text_emb_dim, out_ch * 2)
self.block1 = nn.Sequential(
nn.GroupNorm(32, in_ch),
nn.SiLU(),
nn.Conv2d(in_ch, out_ch, 3, padding=1))
self.block2 = nn.Sequential(
nn.GroupNorm(32, out_ch),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv2d(out_ch, out_ch, 3, padding=1))
self.res_conv = nn.Conv2d(in_ch, out_ch, 1) if in_ch != out_ch else nn.Identity()
def forward(self, x, time_emb, text_emb):
emb = self.mlp(torch.cat([time_emb, text_emb], dim=-1))
scale, shift = torch.chunk(emb, 2, dim=1)
h = self.block1(x)
h = h * (1 + scale[:, :, None, None]) + shift[:, :, None, None]
h = self.block2(h)
return h + self.res_conv(x)
class UNet(nn.Module):
def __init__(self, in_channels=3, out_channels=3, dim=64, dim_mults=(1, 2, 4, 8)):
super().__init__()
dims = [dim * m for m in dim_mults]
in_out = list(zip(dims[:-1], dims[1:]))
# Time and text embeddings
self.time_mlp = nn.Sequential(
TimeEmbedding(dim),
nn.Linear(dim, dim * 4),
nn.SiLU(),
nn.Linear(dim * 4, dim))
# Text conditioning
self.text_proj = nn.Linear(768, dim * 4)
# Initial convolution
self.init_conv = nn.Conv2d(in_channels, dim, 3, padding=1)
# Downsample blocks
self.downs = nn.ModuleList()
for ind, (in_dim, out_dim) in enumerate(in_out):
is_last = ind >= (len(in_out) - 1)
self.downs.append(nn.ModuleList([
ResBlock(in_dim, in_dim, dim, dim * 4),
ResBlock(in_dim, in_dim, dim, dim * 4),
AttentionBlock(in_dim),
nn.Conv2d(in_dim, out_dim, 3, stride=2, padding=1) if not is_last else nn.Conv2d(in_dim, out_dim, 3, padding=1)
]))
# Middle blocks
self.mid_block1 = ResBlock(dims[-1], dims[-1], dim, dim * 4)
self.mid_attn = AttentionBlock(dims[-1])
self.mid_block2 = ResBlock(dims[-1], dims[-1], dim, dim * 4)
# Upsample blocks
self.ups = nn.ModuleList()
for ind, (in_dim, out_dim) in enumerate(reversed(in_out)):
is_last = ind >= (len(in_out) - 1)
self.ups.append(nn.ModuleList([
ResBlock(out_dim + in_dim, out_dim, dim, dim * 4),
ResBlock(out_dim + in_dim, out_dim, dim, dim * 4),
AttentionBlock(out_dim),
nn.ConvTranspose2d(out_dim, out_dim, 4, 2, 1) if not is_last else nn.Identity()
]))
# Final blocks
self.final_block1 = ResBlock(dim * 2, dim, dim, dim * 4)
self.final_block2 = ResBlock(dim, dim, dim, dim * 4)
self.final_conv = nn.Conv2d(dim, out_channels, 3, padding=1)
def forward(self, x, time, text_emb):
t = self.time_mlp(time)
text_emb = self.text_proj(text_emb)
x = self.init_conv(x)
h = [x]
# Downsample
for block1, block2, attn, downsample in self.downs:
x = block1(x, t, text_emb)
x = block2(x, t, text_emb)
x = attn(x)
h.append(x)
x = downsample(x)
# Bottleneck
x = self.mid_block1(x, t, text_emb)
x = self.mid_attn(x)
x = self.mid_block2(x, t, text_emb)
# Upsample
for block1, block2, attn, upsample in self.ups:
x = torch.cat([x, h.pop()], dim=1)
x = block1(x, t, text_emb)
x = block2(x, t, text_emb)
x = attn(x)
x = upsample(x)
# Final
x = torch.cat([x, h.pop()], dim=1)
x = self.final_block1(x, t, text_emb)
x = self.final_block2(x, t, text_emb)
return self.final_conv(x)
class DiffusionModel(nn.Module):
def __init__(self, model, betas, device):
super().__init__()
self.model = model
self.betas = betas
self.alphas = 1. - betas
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1. - self.alphas_cumprod)
self.device = device
# CLIP model for text conditioning
self.clip = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
for param in self.clip.parameters():
param.requires_grad = False
def get_text_emb(self, prompts):
inputs = self.tokenizer(prompts, padding=True, return_tensors="pt").to(self.device)
return self.clip(**inputs).last_hidden_state.mean(dim=1)
def q_sample(self, x_start, t, noise=None):
if noise is None:
noise = torch.randn_like(x_start)
sqrt_alpha_cumprod = self.sqrt_alphas_cumprod[t].view(-1, 1, 1, 1)
sqrt_one_minus_alpha_cumprod = self.sqrt_one_minus_alphas_cumprod[t].view(-1, 1, 1, 1)
return sqrt_alpha_cumprod * x_start + sqrt_one_minus_alpha_cumprod * noise
def p_losses(self, x_start, text, t, noise=None):
if noise is None:
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start, t, noise)
text_emb = self.get_text_emb(text)
predicted_noise = self.model(x_noisy, t, text_emb)
return F.mse_loss(noise, predicted_noise)
@torch.no_grad()
def sample(self, prompts, image_size=256, batch_size=4, channels=3, cfg_scale=7.5):
shape = (batch_size, channels, image_size, image_size)
x = torch.randn(shape, device=self.device)
text_emb = self.get_text_emb(prompts)
uncond_emb = self.get_text_emb([""] * batch_size)
for i in reversed(range(0, len(self.betas))):
t = torch.full((batch_size,), i, device=self.device, dtype=torch.long)
# Classifier-free guidance
noise_pred = self.model(x, t, text_emb)
noise_pred_uncond = self.model(x, t, uncond_emb)
noise_pred = noise_pred_uncond + cfg_scale * (noise_pred - noise_pred_uncond)
alpha = self.alphas[t].view(-1, 1, 1, 1)
alpha_cumprod = self.alphas_cumprod[t].view(-1, 1, 1, 1)
beta = self.betas[t].view(-1, 1, 1, 1)
if i > 0:
noise = torch.randn_like(x)
else:
noise = torch.zeros_like(x)
x = (1 / torch.sqrt(alpha)) * (x - ((1 - alpha) / torch.sqrt(1 - alpha_cumprod)) * noise_pred) + torch.sqrt(beta) * noise
x = (x.clamp(-1, 1) + 1) / 2
x = (x * 255).type(torch.uint8)
return x |