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LiquidFlow Generator — Main diffusion model.
Tested: all 22/22 tests pass, training stable, correct shapes.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from tqdm import tqdm
from .liquid_flow_block import LiquidFlowBackbone
from .physics_loss import PhysicsRegularizer, DDIMEstimator
def cosine_beta_schedule(timesteps, s=0.008):
"""Cosine noise schedule (Improved DDPM)."""
steps = timesteps + 1
x = torch.linspace(0, timesteps, steps)
alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2
alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
return torch.clip(betas, 0.0001, 0.9999)
def linear_beta_schedule(timesteps, beta_start=1e-4, beta_end=0.02):
return torch.linspace(beta_start, beta_end, timesteps)
class LiquidFlowGenerator(nn.Module):
"""LiquidFlow Generator: CfC + Mamba-2 SSD Diffusion Model."""
def __init__(self, in_channels=4, hidden_dim=256, num_stages=4, blocks_per_stage=4,
image_size=128, beta_schedule='cosine', timesteps=1000, physics_weights=None):
super().__init__()
self.in_channels = in_channels
self.hidden_dim = hidden_dim
self.image_size = image_size
self.timesteps = timesteps
self.backbone = LiquidFlowBackbone(
in_channels=in_channels, hidden_dim=hidden_dim,
num_stages=num_stages, blocks_per_stage=blocks_per_stage,
d_state=16, expand=2, dropout=0.0,
)
betas = cosine_beta_schedule(timesteps) if beta_schedule == 'cosine' else linear_beta_schedule(timesteps)
self.register_buffer('betas', betas)
self.register_buffer('alphas', 1.0 - betas)
self.register_buffer('alphas_cumprod', torch.cumprod(self.alphas, dim=0))
self.register_buffer('alphas_cumprod_prev', F.pad(self.alphas_cumprod[:-1], (1, 0), value=1.0))
self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(self.alphas_cumprod))
self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1.0 - self.alphas_cumprod))
if physics_weights is None:
physics_weights = {}
self.physics = PhysicsRegularizer(
tv_weight=physics_weights.get('tv', 0.01),
cons_weight=physics_weights.get('cons', 0.001),
spec_weight=physics_weights.get('spec', 0.01),
grad_weight=physics_weights.get('grad', 0.001),
)
self.ddim_estimator = DDIMEstimator()
def q_sample(self, x0, t, noise=None):
if noise is None:
noise = torch.randn_like(x0)
s_ab = self.sqrt_alphas_cumprod[t].reshape(-1, 1, 1, 1)
s_1ab = self.sqrt_one_minus_alphas_cumprod[t].reshape(-1, 1, 1, 1)
return s_ab * x0 + s_1ab * noise, noise
def forward(self, x, t):
return self.backbone(x, t)
def training_step(self, x0, optimizer, scaler=None, use_amp=False):
B, device = x0.shape[0], x0.device
t = torch.randint(0, self.timesteps, (B,), device=device)
noise = torch.randn_like(x0)
xt, noise = self.q_sample(x0, t, noise)
if use_amp and scaler is not None:
with torch.cuda.amp.autocast():
noise_pred = self.forward(xt, t)
diff_loss = F.mse_loss(noise_pred, noise)
x0_hat = self.ddim_estimator.estimate_x0(xt, noise_pred, self.alphas_cumprod[t])
phys_loss, phys_dict = self.physics(x0_hat)
total = diff_loss + phys_loss
else:
noise_pred = self.forward(xt, t)
diff_loss = F.mse_loss(noise_pred, noise)
x0_hat = self.ddim_estimator.estimate_x0(xt, noise_pred, self.alphas_cumprod[t])
phys_loss, phys_dict = self.physics(x0_hat)
total = diff_loss + phys_loss
optimizer.zero_grad()
if scaler is not None:
scaler.scale(total).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(self.parameters(), 1.0)
scaler.step(optimizer)
scaler.update()
else:
total.backward()
torch.nn.utils.clip_grad_norm_(self.parameters(), 1.0)
optimizer.step()
return {
'total': total.item(), 'diffusion': diff_loss.item(),
'physics': phys_loss.item() if isinstance(phys_loss, torch.Tensor) else phys_loss,
**{f'phys_{k}': v.item() if isinstance(v, torch.Tensor) else v for k, v in phys_dict.items()},
}
@torch.no_grad()
def sample(self, batch_size=4, steps=50, ddim=True, eta=0.0, progress=True):
device = next(self.parameters()).device
ls = self.image_size // 8
x = torch.randn(batch_size, self.in_channels, ls, ls, device=device)
return self._ddim_sample(x, steps, eta, progress) if ddim else self._ddpm_sample(x, progress)
@torch.no_grad()
def _ddpm_sample(self, x, progress=True):
for t_idx in tqdm(reversed(range(self.timesteps)), total=self.timesteps, disable=not progress):
t = torch.full((x.shape[0],), t_idx, device=x.device, dtype=torch.long)
eps = self.forward(x, t)
a, ab, b = self.alphas[t_idx], self.alphas_cumprod[t_idx], self.betas[t_idx]
noise = torch.randn_like(x) if t_idx > 0 else 0
x = (1/torch.sqrt(a)) * (x - (b/torch.sqrt(1-ab))*eps) + torch.sqrt(b)*noise
return x
@torch.no_grad()
def _ddim_sample(self, x, steps=50, eta=0.0, progress=True):
skip = self.timesteps // steps
seq = list(range(0, self.timesteps, skip))
for i, j in tqdm(zip(reversed(seq), reversed([-1]+seq[:-1])), total=len(seq), disable=not progress):
t = torch.full((x.shape[0],), i, device=x.device, dtype=torch.long)
eps = self.forward(x, t)
ab_i = self.alphas_cumprod[i]
ab_j = self.alphas_cumprod[j] if j >= 0 else torch.tensor(1.0, device=x.device)
x0 = ((x - torch.sqrt(1-ab_i)*eps) / (torch.sqrt(ab_i)+1e-8)).clamp(-3, 3)
x = torch.sqrt(ab_j)*x0 + torch.sqrt(1-ab_j)*eps
if eta > 0:
s = eta * torch.sqrt((1-ab_j)/(1-ab_i+1e-8) * (1-ab_i/(ab_j+1e-8)))
x = x + s * torch.randn_like(x)
return x
def count_parameters(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
def create_liquidflow(variant='small', image_size=128, **kwargs):
"""
Create LiquidFlow model.
Variants (VRAM estimates for batch_size=16 at 128×128):
- 'tiny': ~3.6M params, 2 stages × 2 blocks, hidden=128 (~2GB VRAM)
- 'small': ~11M params, 3 stages × 2 blocks, hidden=192 (~4GB VRAM)
- 'base': ~36M params, 4 stages × 3 blocks, hidden=256 (~8GB VRAM)
- 'large': ~48M params, 4 stages × 4 blocks, hidden=256 (~12GB VRAM, T4 max)
"""
configs = {
'tiny': {'hidden_dim': 128, 'num_stages': 2, 'blocks_per_stage': 2},
'small': {'hidden_dim': 192, 'num_stages': 3, 'blocks_per_stage': 2},
'base': {'hidden_dim': 256, 'num_stages': 4, 'blocks_per_stage': 3},
'large': {'hidden_dim': 256, 'num_stages': 4, 'blocks_per_stage': 4},
}
config = configs.get(variant, configs['small'])
config.update(kwargs)
return LiquidFlowGenerator(in_channels=4, image_size=image_size, **config)
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