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+results/reflow_ep99_eval_8step/54_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/54_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/55_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/55_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/56_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/56_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/57_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/57_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/5_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/5_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/6_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/6_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/7_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/7_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/8_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/8_rain_input.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/9_rain_flow.png filter=lfs diff=lfs merge=lfs -text
+results/reflow_ep99_eval_8step/9_rain_input.png filter=lfs diff=lfs merge=lfs -text

diff2flow.py

@@ -0,0 +1,400 @@
+import argparse
+import os
+import yaml
+import torch
+import torch.nn as nn
+import numpy as np
+import torchvision
+import utils
+from models.unet import DiffusionUNet
+import torchdiffeq
+import math
+from torchvision.transforms.functional import crop
+
+
+def dict2namespace(config):
+    namespace = argparse.Namespace()
+    for key, value in config.items():
+        if isinstance(value, dict):
+            new_value = dict2namespace(value)
+        else:
+            new_value = value
+        setattr(namespace, key, new_value)
+    return namespace
+
+
+def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
+    def sigmoid(x):
+        return 1 / (np.exp(-x) + 1)
+
+    if beta_schedule == "quad":
+        betas = (
+            np.linspace(
+                beta_start**0.5,
+                beta_end**0.5,
+                num_diffusion_timesteps,
+                dtype=np.float64,
+            )
+            ** 2
+        )
+    elif beta_schedule == "linear":
+        betas = np.linspace(
+            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
+        )
+    elif beta_schedule == "const":
+        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
+        betas = 1.0 / np.linspace(
+            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
+        )
+    elif beta_schedule == "sigmoid":
+        betas = np.linspace(-6, 6, num_diffusion_timesteps)
+        betas = sigmoid(betas) * (beta_end - beta_start) + beta_start
+    else:
+        raise NotImplementedError(beta_schedule)
+    return betas
+
+
+class VPDiffusionFlow:
+    def __init__(self, args, config):
+        self.args = args
+        self.flow_mode = getattr(args, 'flow_mode', 'vp')
+        self.config = config
+        self.device = config.device
+
+        # Load model
+        self.model = DiffusionUNet(config)
+        self.model.to(self.device)
+        # self.model = nn.DataParallel(self.model)
+
+        # Schedules
+        self.num_timesteps = config.diffusion.num_diffusion_timesteps
+        betas = get_beta_schedule(
+            beta_schedule=config.diffusion.beta_schedule,
+            beta_start=config.diffusion.beta_start,
+            beta_end=config.diffusion.beta_end,
+            num_diffusion_timesteps=self.num_timesteps,
+        )
+        self.betas = torch.from_numpy(betas).float().to(self.device)
+
+        # Precompute alphas for continuous time interpolation if needed
+        # But for VP-ODE, we need beta(t) continuously.
+        # Linear schedule: beta(t) = beta_start + t * (beta_end - beta_start)
+        # where t is [0, 1].
+        # In discrete case: betas are discrete steps.
+        # The config says "linear", betas = linspace(start, end, N).
+        # This approximates beta(t) = start + k * t.
+        self.beta_start = config.diffusion.beta_start
+        self.beta_end = config.diffusion.beta_end
+
+        # Calculate alpha_bar (cumulative product) equivalent for continuous time
+        # Discrete: alpha = 1 - beta. alpha_bar = cumprod(alpha).
+        # Continuous: alpha_bar(t) = exp(- integral_0^t beta(s) ds).
+        # if beta(s) = a + b*s, integral is a*t + 0.5*b*t^2.
+        # log_alpha_bar(t) = - (a*t + 0.5*b*t^2)?
+        # Let's verify against discrete steps.
+        # Discrete index i corresponds to t = i / N (approx).
+        # Actually discrete usually means t_discrete = 1..N.
+        # We will treat t in [0, 1].
+
+        # Precompute alpha_bar array for lookups if we trust discrete more
+        alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+    def load_ckpt(self, load_path):
+        checkpoint = torch.load(load_path, map_location=self.device)
+        # Handle state dict
+        if "state_dict" in checkpoint:
+            state_dict = checkpoint["state_dict"]
+        else:
+            state_dict = checkpoint
+
+        # Strip module. prefix if present
+        new_state_dict = {}
+        for k, v in state_dict.items():
+            if k.startswith("module."):
+                new_state_dict[k[7:]] = v
+            else:
+                new_state_dict[k] = v
+        state_dict = new_state_dict
+
+        self.model.load_state_dict(state_dict, strict=True)
+        print(f"=> loaded checkpoint '{load_path}'")
+        self.model.eval()
+
+    def get_beta_t(self, t):
+        # t is float scalar or tensor in [0, 1]
+        # Linear interpolation of beta
+        scalar_t = t.item() if isinstance(t, torch.Tensor) else t
+        # Clamp t to [0, 1]
+        scalar_t = max(0.0, min(1.0, scalar_t))
+        return self.beta_start + scalar_t * (self.beta_end - self.beta_start)
+
+    def get_alpha_bar_t(self, t):
+        # Calculate alpha_bar analytically for linear beta schedule
+        scalar_t = t.item() if isinstance(t, torch.Tensor) else t
+        scalar_t = max(0.0, min(1.0, scalar_t))
+        
+        N = self.num_timesteps
+        
+        # Integral of N * (b0 + (b1-b0)*s) ds from 0 to t
+        # = N * [ b0*t + 0.5*(b1-b0)*t^2 ]
+        b0 = self.beta_start
+        b1 = self.beta_end
+        integral = N * (b0 * scalar_t + 0.5 * (b1 - b0) * scalar_t**2)
+        return math.exp(-integral)
+
+    def overlapping_grid_indices(self, x_cond, output_size, r=None):
+        _, c, h, w = x_cond.shape
+        r = 16 if r is None else r
+        h_list = [i for i in range(0, h - output_size + 1, r)]
+        w_list = [i for i in range(0, w - output_size + 1, r)]
+        return h_list, w_list
+
+    def get_velocity(self, x, t, x_cond, patch_size=None, r_stride=16):
+        # If no patching needed (x fits in patch_size or patch_size None), do normal
+        if patch_size is None or (
+            x.shape[2] == patch_size and x.shape[3] == patch_size
+        ):
+            return self._get_velocity_single(x, t, x_cond)
+
+        # Full image inference with patching
+        N = self.num_timesteps
+        t_idx = min(int(t * N), N - 1)
+        t_input_scalar = t_idx
+
+        # Grid setup
+        h_list, w_list = self.overlapping_grid_indices(x, patch_size, r=r_stride)
+        corners = [(i, j) for i in h_list for j in w_list]
+
+        # Mask for overlap averaging
+        # Cache this if t doesn't change grid? Grid depends on image size, which is constant during integration.
+        # But for now recompute is safer/easier.
+        x_grid_mask = torch.zeros_like(x, device=self.device)
+        for hi, wi in corners:
+            x_grid_mask[:, :, hi : hi + patch_size, wi : wi + patch_size] += 1
+
+        # Accumulate output (epsilon or velocity)
+        output_accum = torch.zeros_like(x, device=self.device)
+
+        # Process in batches
+        batch_size = 64  # From restoration.py logic or config
+
+        # Prepare params if VP
+        if self.flow_mode == 'vp':
+            beta_discrete = self.get_beta_t(t)
+            beta_cont = beta_discrete * N
+            ab = self.alphas_cumprod[t_idx]
+        
+        # Loop over patches
+        # NOTE: drift depends on x (noisy) and x_cond (clean/cond).
+        for i in range(0, len(corners), batch_size):
+            batch_corners = corners[i : i + batch_size]
+
+            # Crop batch
+            x_batch = torch.cat(
+                [crop(x, hi, wi, patch_size, patch_size) for (hi, wi) in batch_corners],
+                dim=0,
+            )
+            cond_batch = torch.cat(
+                [
+                    crop(x_cond, hi, wi, patch_size, patch_size)
+                    for (hi, wi) in batch_corners
+                ],
+                dim=0,
+            )
+            t_batch = torch.tensor(
+                [t_input_scalar] * x_batch.size(0), device=self.device
+            )
+
+            with torch.no_grad():
+                model_output = self.model(torch.cat([cond_batch, x_batch], dim=1), t_batch)
+
+            # Scatter back
+            for idx, (hi, wi) in enumerate(batch_corners):
+                output_accum[0, :, hi : hi + patch_size, wi : wi + patch_size] += (
+                    model_output[idx]
+                )
+
+        # Average
+        model_output_full = torch.div(output_accum, x_grid_mask)
+
+        # Compute v
+        if self.flow_mode == 'reflow':
+            # In Reflow, model output is velocity
+            v = model_output_full
+        else:
+            # VP-ODE
+            epsilon = model_output_full
+            coeff1 = -0.5 * beta_cont
+            coeff2 = 0.5 * beta_cont / torch.sqrt(1 - ab)
+            v = coeff1 * x + coeff2 * epsilon
+            
+        return v
+
+    def _get_velocity_single(self, x, t, x_cond):
+        # Single image / patch velocity
+        # x: [B, C, H, W]
+        # t: continuous time in [0, 1].
+        # x_cond: condition (clean image part or conditional input)
+
+        N = self.num_timesteps
+        t_idx = min(int(t * N), N - 1)
+        t_input = torch.tensor([t_idx] * x.size(0), device=self.device)
+
+        with torch.no_grad():
+            model_output = self.model(torch.cat([x_cond, x], dim=1), t_input)
+
+        if self.flow_mode == 'reflow':
+            return model_output
+        else:
+            epsilon = model_output
+            beta_discrete = self.get_beta_t(t)
+            beta_cont = beta_discrete * N
+            ab = self.alphas_cumprod[t_idx]
+
+            coeff1 = -0.5 * beta_cont
+            coeff2 = 0.5 * beta_cont / torch.sqrt(1 - ab)
+
+            v = coeff1 * x + coeff2 * epsilon
+            return v
+
+
+def ode_solve(
+    flow_model,
+    x_init,
+    x_cond,
+    steps=100,
+    method="dopri5",
+    patch_size=64,
+    atol=1e-4,
+    rtol=1e-4,
+):
+    # Define the drift function wrapper for torchdiffeq
+    step = 0
+
+    print(f"ODE Solve: Method={method}, Steps={steps}, atol={atol}, rtol={rtol}")
+
+    def drift_func(t, x):
+        nonlocal step
+        step += 1
+        print(f"Step {step}, t={t.item():.6f}")
+        # Assuming batch size 1 for full image inference usually
+        return flow_model.get_velocity(x, t, x_cond, patch_size=patch_size)
+
+    t_eval = torch.linspace(1.0, 0.0, steps + 1, device=x_init.device)
+    out = torchdiffeq.odeint(
+        drift_func, x_init, t_eval, method=method, atol=atol, rtol=rtol
+    )
+    return out[-1]
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--resume", type=str, required=True)
+    parser.add_argument(
+        "--data_dir", type=str, default=None, help="Override data_dir in config"
+    )
+    parser.add_argument(
+        "--dataset", type=str, default=None, help="Override dataset name"
+    )
+    parser.add_argument("--steps", type=int, default=100)
+    parser.add_argument("--output", type=str, default="results/diff2flow")
+    parser.add_argument("--seed", type=int, default=61)
+    parser.add_argument(
+        "--patch_size", type=int, default=64, help="Patch size for model"
+    )
+    parser.add_argument(
+        "--method", type=str, default="dopri5", help="ODE solver method"
+    )
+    parser.add_argument(
+        "--atol", type=float, default=1e-4, help="Absolute tolerance for ODE solver"
+    )
+    parser.add_argument(
+        "--rtol", type=float, default=1e-4, help="Relative tolerance for ODE solver"
+    )
+    parser.add_argument(
+        "--flow_mode", type=str, default="vp", choices=["vp", "reflow"], help="Flow mode: vp (default) or reflow"
+    )
+    args = parser.parse_args()
+
+    # ... (Config loading)
+    with open(os.path.join("configs", args.config), "r") as f:
+        config_dict = yaml.safe_load(f)
+    config = dict2namespace(config_dict)
+
+    if args.data_dir:
+        config.data.data_dir = args.data_dir
+    if args.dataset:
+        config.data.dataset = args.dataset
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    config.device = device
+
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+
+    print("Initializing VPDiffusionFlow...")
+    flow = VPDiffusionFlow(args, config)
+    flow.load_ckpt(args.resume)
+
+    os.makedirs(args.output, exist_ok=True)
+
+    import datasets
+
+    print(f"Loading dataset {config.data.dataset}...")
+    DATASET = datasets.__dict__[config.data.dataset](config)
+    # Use parse_patches=False to get full images
+    _, val_loader = DATASET.get_loaders(
+        parse_patches=False,
+        validation=config.data.dataset if args.dataset else "raindrop",
+    )
+
+    for i, (x_batch, img_id) in enumerate(val_loader):
+        print(f"Processing image {img_id}...")
+
+        x_batch = x_batch.to(device)
+        # x_batch shape [1, 6, H, W] usually for full image due to concat in dataset?
+
+        x_cond = x_batch[:, :3, :, :]  # Input
+        # x_target = x_batch[:, 3:, :, :] # GT
+
+        x_cond = utils.sampling.data_transform(x_cond)
+
+        B, C, H, W = x_cond.shape
+        x_init = torch.randn(B, 3, H, W, device=device)
+
+        print(f"Starting flow matching inference for image {img_id}, shape {H}x{W}...")
+        x_pred = ode_solve(
+            flow,
+            x_init,
+            x_cond,
+            steps=args.steps,
+            patch_size=args.patch_size,
+            method=args.method,
+            atol=args.atol,
+            rtol=args.rtol,
+        )
+
+        x_pred = utils.sampling.inverse_data_transform(x_pred)
+        x_cond_img = utils.sampling.inverse_data_transform(x_cond)
+
+        # Save
+        if isinstance(img_id, tuple) or isinstance(img_id, list):
+            idx = img_id[0]
+        else:
+            idx = img_id
+
+        utils.logging.save_image(
+            x_cond_img[0], os.path.join(args.output, f"{idx}_input.png")
+        )
+        utils.logging.save_image(
+            x_pred[0], os.path.join(args.output, f"{idx}_flow.png")
+        )
+
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

generate_reflow_pairs.py

@@ -0,0 +1,161 @@
+import argparse
+import os
+import yaml
+import torch
+import torch.nn as nn
+import numpy as np
+import torchdiffeq
+import utils
+from diff2flow import VPDiffusionFlow, dict2namespace
+import datasets
+from tqdm import tqdm
+
+def ode_inverse_solve(flow_model, x_data, x_cond, steps=100, method="dopri5", patch_size=64, atol=1e-5, rtol=1e-5):
+    """
+    Solves the ODE from t=0 (data) to t=1 (noise).
+    Returns x_1 (noise latent).
+    """
+    # Define the drift function wrapper for torchdiffeq
+    # For inversion, we integrate from 0 to 1.
+    # The drift v(x, t) is defined for t in [0, 1].
+    
+    def drift_func(t, x):
+        # flow_model.get_velocity expects t in [0, 1]
+        # When using torchdiffeq, t will be traversing 0->1.
+        return flow_model.get_velocity(x, t, x_cond, patch_size=patch_size)
+
+    # Time points from 0 to 1
+    t_eval = torch.linspace(0.0, 1.0, steps + 1, device=x_data.device)
+    
+    # Solve
+    out = torchdiffeq.odeint(
+        drift_func, x_data, t_eval, method=method, atol=atol, rtol=rtol
+    )
+    # Return final state at t=1
+    return out[-1]
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--resume", type=str, required=True)
+    parser.add_argument("--data_dir", type=str, default=None)
+    parser.add_argument("--dataset", type=str, default=None)
+    parser.add_argument("--steps", type=int, default=100)
+    parser.add_argument("--output_dir", type=str, default="reflow_data")
+    parser.add_argument("--seed", type=int, default=61)
+    parser.add_argument("--patch_size", type=int, default=64)
+    parser.add_argument("--method", type=str, default="dopri5")
+    parser.add_argument("--atol", type=float, default=1e-5)
+    parser.add_argument("--rtol", type=float, default=1e-5)
+    parser.add_argument("--max_images", type=int, default=None, help="Max images to generate (for testing)")
+    args = parser.parse_args()
+
+    # Load Config
+    with open(os.path.join("configs", args.config), "r") as f:
+        config_dict = yaml.safe_load(f)
+    config = dict2namespace(config_dict)
+    
+    if args.data_dir:
+        config.data.data_dir = args.data_dir
+    if args.dataset:
+        config.data.dataset = args.dataset
+        
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    config.device = device
+    
+    # Reproducibility
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    
+    # Load Model
+    print("Initializing VPDiffusionFlow...")
+    flow = VPDiffusionFlow(args, config)
+    flow.load_ckpt(args.resume)
+    
+    os.makedirs(args.output_dir, exist_ok=True)
+    
+    # Load Dataset
+    print(f"Loading dataset {config.data.dataset}...")
+    DATASET = datasets.__dict__[config.data.dataset](config)
+    
+    # We use the TRAINING set to generate pairs for training the reflow model
+    train_loader, _ = DATASET.get_loaders(parse_patches=False, validation=config.data.dataset if args.dataset else 'raindrop')
+    
+    # We want to iterate over training data. Note: get_loaders usually returns (train_loader, val_loader).
+    # RainDrop.get_loaders returns (train_loader, val_loader).
+    # train_loader usually parses patches = True for original training.
+    # But for generating full image pairs or consistent pairs, we might want full images or patching?
+    # The user asked for "very fast inference". If we train on patches, we can infer on patches (and then stitch).
+    # If we train on full images, that's better but memory intensive.
+    # The original training was likely on patches (RainDropDataset uses patch_size).
+    # For Reflow, we should probably train on PATCHES to match the original training distribution and efficiency.
+    # So let's use parse_patches=True for the loader to match training setup.
+    
+    # However, to use `ode_inverse_solve`, we need to follow the flow.
+    # If we use patches, we can solve ODE for each patch independently.
+    # This is consistent.
+    
+    # Re-get loaders with parse_patches=True to get training patches
+    train_loader, _ = DATASET.get_loaders(parse_patches=True)
+    
+    print(f"Starting generation of reflow pairs...")
+    
+    count = 0
+    
+    # Iterate through training patches
+    for i, (x_batch, img_id) in enumerate(tqdm(train_loader, desc="Generating Reflow Pairs")):
+        # x_batch: [B, N, 6, H, W] if parse_patches=True
+        # Flatten B and N to process all patches
+        if x_batch.ndim == 5:
+            x_batch = x_batch.flatten(start_dim=0, end_dim=1)
+        
+        input_img = x_batch[:, :3, :, :].to(device) # Input (Rainy)
+        gt_img = x_batch[:, 3:, :, :].to(device)    # GT (Clean)
+        
+        # Transform data to [-1, 1]
+        x_cond = utils.sampling.data_transform(input_img)
+        x_data = utils.sampling.data_transform(gt_img)
+        
+        # Run ODE Inversion: x_data (t=0) -> x_noise (t=1)
+        # Note: patch_size argument in ode_inverse_solve usually used for stitching.
+        # Here x_data IS a patch (e.g. 64x64 or config size).
+        # So we can pass patch_size=None or just let it handle it.
+        # Our VPDiffusionFlow.get_velocity handles patching if x > patch_size.
+        # Here x is likely small.
+        
+        with torch.no_grad():
+            x_noise = ode_inverse_solve(
+                flow, 
+                x_data, 
+                x_cond, 
+                steps=args.steps, 
+                method=args.method, 
+                patch_size=args.patch_size,
+                atol=args.atol, 
+                rtol=args.rtol
+            )
+        
+        # Save pair (x_noise, x_cond, x_data)
+        # x_noise is the 'target' input for the reflow model (at t=1)
+        # x_data is the 'target' output (at t=0)
+        # x_cond is the condition
+        
+        # We save this batch
+        batch_data = {
+            "x_noise": x_noise.cpu(),
+            "x_data": x_data.cpu(),
+            "x_cond": x_cond.cpu()
+        }
+        
+        save_path = os.path.join(args.output_dir, f"batch_{i}.pth")
+        torch.save(batch_data, save_path)
+        
+        print(f"Saved batch {i} to {save_path}")
+        
+        count += input_img.shape[0]
+        if args.max_images and count >= args.max_images:
+            print(f"Reached max images {args.max_images}")
+            break
+
+if __name__ == "__main__":
+    main()