Merge branch 'main' into Add-Lora-Alpha-For-HiDream-Lora

Author: ParagEkbote

docs/source/en/_toctree.yml

@@ -26,6 +26,7 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
 - [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
 - [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
+- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
 - [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
@@ -92,6 +93,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
 
+## SkyReelsV2LoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
+
 ## AmusedLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
@@ -100,6 +105,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
 
-## WanLoraLoaderMixin
+## LoraBaseMixin
 
-[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
+[[autodoc]] loaders.lora_base.LoraBaseMixin

docs/source/en/api/loaders/lora.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# SkyReelsV2Transformer3DModel
+
+A Diffusion Transformer model for 3D video-like data was introduced in [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2) by the Skywork AI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import SkyReelsV2Transformer3DModel
+
+transformer = SkyReelsV2Transformer3DModel.from_pretrained("Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## SkyReelsV2Transformer3DModel
+
+[[autodoc]] SkyReelsV2Transformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/skyreels_v2_transformer_3d.md

@@ -239,6 +239,12 @@ The `step()` function is [called](https://github.com/huggingface/diffusers/blob/
 
 In general, the `sigmas` should [stay on the CPU](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240) to avoid the communication sync and latency.
 
+<Tip>
+
+Refer to the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post for maximizing performance with `torch.compile` for diffusion models.
+
+</Tip>
+
 ### Benchmarks
 
 Refer to the [diffusers/benchmarks](https://huggingface.co/datasets/diffusers/benchmarks) dataset to see inference latency and memory usage data for compiled pipelines.
@@ -298,4 +304,6 @@ pipeline.fuse_qkv_projections()
 
 - Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup using recipes from [flux-fast](https://github.com/huggingface/flux-fast).
 
-    These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
+    These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
+- Read the [torch.compile and Diffusers: A Hands-On Guide to Peak Performance](https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/) blog post
+to maximize performance when using `torch.compile`.

docs/source/en/api/pipelines/skyreels_v2.md

@@ -971,6 +971,7 @@ class DreamBoothDataset(Dataset):
 
     def __init__(
         self,
+        args,
         instance_data_root,
         instance_prompt,
         class_prompt,
@@ -980,10 +981,8 @@ def __init__(
         class_num=None,
         size=1024,
         repeats=1,
-        center_crop=False,
     ):
         self.size = size
-        self.center_crop = center_crop
 
         self.instance_prompt = instance_prompt
         self.custom_instance_prompts = None
@@ -1058,7 +1057,7 @@ def __init__(
         if interpolation is None:
             raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
         train_resize = transforms.Resize(size, interpolation=interpolation)
-        train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
+        train_crop = transforms.CenterCrop(size) if args.center_crop else transforms.RandomCrop(size)
         train_flip = transforms.RandomHorizontalFlip(p=1.0)
         train_transforms = transforms.Compose(
             [
@@ -1075,11 +1074,11 @@ def __init__(
                 # flip
                 image = train_flip(image)
             if args.center_crop:
-                y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
-                x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
+                y1 = max(0, int(round((image.height - self.size) / 2.0)))
+                x1 = max(0, int(round((image.width - self.size) / 2.0)))
                 image = train_crop(image)
             else:
-                y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
+                y1, x1, h, w = train_crop.get_params(image, (self.size, self.size))
                 image = crop(image, y1, x1, h, w)
             image = train_transforms(image)
             self.pixel_values.append(image)
@@ -1102,7 +1101,7 @@ def __init__(
         self.image_transforms = transforms.Compose(
             [
                 transforms.Resize(size, interpolation=interpolation),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
+                transforms.CenterCrop(size) if args.center_crop else transforms.RandomCrop(size),
                 transforms.ToTensor(),
                 transforms.Normalize([0.5], [0.5]),
             ]
@@ -1827,6 +1826,7 @@ def load_model_hook(models, input_dir):
 
     # Dataset and DataLoaders creation:
     train_dataset = DreamBoothDataset(
+        args=args,
         instance_data_root=args.instance_data_dir,
         instance_prompt=args.instance_prompt,
         train_text_encoder_ti=args.train_text_encoder_ti,
@@ -1836,7 +1836,6 @@ def load_model_hook(models, input_dir):
         class_num=args.num_class_images,
         size=args.resolution,
         repeats=args.repeats,
-        center_crop=args.center_crop,
     )
 
     train_dataloader = torch.utils.data.DataLoader(

docs/source/en/optimization/fp16.md

@@ -1614,7 +1614,7 @@ def load_model_hook(models, input_dir):
     )
     if args.cond_image_column is not None:
         logger.info("I2I fine-tuning enabled.")
-    batch_sampler = BucketBatchSampler(train_dataset, batch_size=args.train_batch_size, drop_last=False)
+    batch_sampler = BucketBatchSampler(train_dataset, batch_size=args.train_batch_size, drop_last=True)
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset,
         batch_sampler=batch_sampler,