Add diffusion prepare

benchmarks/diffusion/main.py

@@ -45,9 +45,6 @@ class Arguments:
     lr_warmup_steps: int = 500
     epochs: int = 10
 
-def step():
-    pass
-
 
 def models(accelerator, args: Arguments):
     encoder = CLIPTextModel.from_pretrained(
@@ -135,6 +132,7 @@ def collate_fn(examples):
         collate_fn=collate_fn,
         batch_size=args.batch_size,
         num_workers=args.num_workers,
+        persistent_workers=True,
     )
 
 def train(args: Arguments):
@@ -214,16 +212,19 @@ def batch_size(x):
             lr_scheduler.step()
             optimizer.zero_grad()
 
-
-
 
 def main():
-    from argklass import ArgumentParser
-    parser = ArgumentParser()
-    parser.add_arguments(Arguments)
-    config, _ = parser.parse_known_args()
+    from benchmate.metrics import StopProgram
+
+    try:
+        from argklass import ArgumentParser
+        parser = ArgumentParser()
+        parser.add_arguments(Arguments)
+        config, _ = parser.parse_known_args()
 
-    train(config)
+        train(config)
+    except StopProgram:
+        pass
 
 
 if __name__ == "__main__":

benchmarks/diffusion/prepare.py

@@ -2,22 +2,44 @@
 from dataclasses import dataclass
 import os
 
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler
 from datasets import load_dataset
 
 
 @dataclass
 class TrainingConfig:
-    dataset_name: str = "huggan/smithsonian_butterflies_subset"
+    model: str = "runwayml/stable-diffusion-v1-5"
+    dataset: str = "lambdalabs/naruto-blip-captions"
 
 
 def main():
     from argklass import ArgumentParser
 
     parser = ArgumentParser()
     parser.add_arguments(TrainingConfig)
-    config, _ = parser.parse_known_args()
+    args, _ = parser.parse_known_args()
+
+    _ = load_dataset(args.dataset)
+
+    _ = CLIPTextModel.from_pretrained(
+        args.model, subfolder="text_encoder", revision=args.revision, variant=args.variant
+    )
+
+    _ = AutoencoderKL.from_pretrained(
+        args.model, subfolder="vae", revision=args.revision, variant=args.variant
+    )
+
+    _ = UNet2DConditionModel.from_pretrained(
+        args.model, subfolder="unet", revision=args.revision, variant=args.variant
+    )
+
+    _ = CLIPTokenizer.from_pretrained(
+        args.model, subfolder="tokenizer", revision=args.revision
+    )
 
-    _ = load_dataset(config.dataset_name, split="train")
+    _ = DDPMScheduler.from_pretrained(args.model, subfolder="scheduler")
 
 
 if __name__ == "__main__":