implement #3;

examples/train_ddpm.py

@@ -0,0 +1,83 @@
+import argparse
+import logging
+import os
+from pathlib import Path
+
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+
+from diffusers import DDPMPipeline, DDPMScheduler
+from diffusers.models.ddpm_model import DDPMModel
+
+logger = get_logger(__name__, log_level="INFO")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pretrained_model_name_or_path", type=str, required=True,
+                        help="Path to pretrained model or model identifier from huggingface.co/models.")
+    parser.add_argument("--output_dir", type=str, default="ddpm_training",
+                        help="The output directory where the model predictions and checkpoints will be written.")
+    parser.add_argument("--train_batch_size", type=int, default=16, help="Batch size for the training dataloader.")
+    parser.add_argument("--learning_rate", type=float, default=0.0001, help="Initial learning rate.")
+    parser.add_argument("--max_train_steps", type=int, default=1000, help="Total number of training steps.")
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    return parser.parse_args()
+
+
+def main(args):
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    accelerator = Accelerator()
+
+    if accelerator.is_main_process:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+    # Initialize model and scheduler
+    unet = ...  # Initialize UNet model
+    scheduler = DDPMScheduler()
+    model = DDPMModel(unet=unet, scheduler=scheduler)
+
+    # Prepare optimizer
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate)
+
+    # Prepare data
+    transform = transforms.Compose([
+        transforms.Resize((64, 64)),
+        transforms.ToTensor(),
+    ])
+    dataset = ...  # Load your dataset here
+    dataloader = DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True)
+
+    # Training loop
+    for step in range(args.max_train_steps):
+        for batch in dataloader:
+            # Forward diffusion
+            x_0 = batch["image"].to(accelerator.device)
+            timesteps = torch.randint(0, scheduler.num_train_timesteps, (x_0.size(0),), device=accelerator.device)
+            loss = model.forward_diffusion(x_0, timesteps)
+
+            # Backward pass
+            accelerator.backward(loss)
+            optimizer.step()
+            optimizer.zero_grad()
+
+            if step % 100 == 0:
+                logger.info(f"Step {step}: loss = {loss.item()}")
+
+            if step >= args.max_train_steps:
+                break
+
+    # Save the model
+    if accelerator.is_main_process:
+        model.save_pretrained(args.output_dir)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

src/diffusers/models/__init__.py

@@ -35,6 +35,7 @@
     _import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["autoencoders.vq_model"] = ["VQModel"]
     _import_structure["controlnet"] = ["ControlNetModel"]
+    _import_structure["ddpm_model"] = ["DDPMModel"]
     _import_structure["controlnet_flux"] = ["FluxControlNetModel", "FluxMultiControlNetModel"]
     _import_structure["controlnet_hunyuan"] = ["HunyuanDiT2DControlNetModel", "HunyuanDiT2DMultiControlNetModel"]
     _import_structure["controlnet_sd3"] = ["SD3ControlNetModel", "SD3MultiControlNetModel"]
@@ -134,4 +135,4 @@
 else:
     import sys
 
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

src/diffusers/models/ddpm_model.py

@@ -0,0 +1,19 @@
+import torch
+from diffusers import DDPMPipeline, DDPMScheduler
+
+class DDPMModel:
+    def __init__(self, unet, scheduler):
+        self.unet = unet
+        self.scheduler = scheduler
+
+    def forward_diffusion(self, x_0, timesteps):
+        # Implement forward diffusion process
+        pass
+
+    def denoise(self, x_t, timesteps):
+        # Implement denoising process
+        pass
+
+    def generate(self, batch_size=1, num_inference_steps=1000, output_type="pil"):
+        # Implement image generation process
+        pass