Move the training example

Makefile

@@ -3,7 +3,7 @@
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
 
-check_dirs := tests src utils
+check_dirs := examples tests src utils
 
 modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))

src/diffusers/trainers/training_ddpm.py → examples/training_ddpm.py

@@ -8,14 +8,23 @@ import PIL.Image
 from accelerate import Accelerator
 from datasets import load_dataset
 from diffusers import DDPM, DDPMScheduler, UNetModel
-from torchvision.transforms import InterpolationMode, CenterCrop, Compose, Lambda, RandomRotation, RandomHorizontalFlip, Resize, ToTensor
+from torchvision.transforms import (
+    Compose,
+    InterpolationMode,
+    Lambda,
+    RandomCrop,
+    RandomHorizontalFlip,
+    RandomVerticalFlip,
+    Resize,
+    ToTensor,
+)
 from tqdm.auto import tqdm
 from transformers import get_linear_schedule_with_warmup
 
 
 def set_seed(seed):
-    #torch.backends.cudnn.deterministic = True
-    #torch.backends.cudnn.benchmark = False
+    # torch.backends.cudnn.deterministic = True
+    # torch.backends.cudnn.benchmark = False
     torch.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)
     np.random.seed(seed)
@@ -33,7 +42,7 @@ model = UNetModel(
     dropout=0.0,
     num_res_blocks=2,
     resamp_with_conv=True,
-    resolution=32
+    resolution=32,
 )
 noise_scheduler = DDPMScheduler(timesteps=1000)
 optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)
@@ -44,15 +53,15 @@ gradient_accumulation_steps = 2
 
 augmentations = Compose(
     [
-        RandomHorizontalFlip(),
-        RandomRotation(15, interpolation=InterpolationMode.BILINEAR, fill=1),
         Resize(32, interpolation=InterpolationMode.BILINEAR),
-        CenterCrop(32),
+        RandomHorizontalFlip(),
+        RandomVerticalFlip(),
+        RandomCrop(32),
         ToTensor(),
         Lambda(lambda x: x * 2 - 1),
     ]
 )
-dataset = load_dataset("huggan/pokemon", split="train")
+dataset = load_dataset("huggan/flowers-102-categories", split="train")
 
 
 def transforms(examples):
@@ -127,5 +136,5 @@ for epoch in range(num_epochs):
         image_pil = PIL.Image.fromarray(image_processed[0])
 
         # save image
-        pipeline.save_pretrained("./poke-ddpm")
-        image_pil.save(f"./poke-ddpm/test_{epoch}.png")
+        pipeline.save_pretrained("./flowers-ddpm")
+        image_pil.save(f"./flowers-ddpm/test_{epoch}.png")

tests/test_modeling_utils.py

@@ -19,7 +19,7 @@ import unittest
 
 import torch
 
-from diffusers import DDIM, DDPM, DDIMScheduler, DDPMScheduler, LatentDiffusion, UNetModel, PNDM, PNDMScheduler
+from diffusers import DDIM, DDPM, PNDM, DDIMScheduler, DDPMScheduler, LatentDiffusion, PNDMScheduler, UNetModel
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.testing_utils import floats_tensor, slow, torch_device