Merge branch 'main' into refactor-pkg-imports

Author: ishan-modi

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

@@ -50,7 +50,8 @@ The following models are available for the image-to-video pipeline:
 | Model name | Description |
 |:---|:---|
 | [`Skywork/SkyReels-V1-Hunyuan-I2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution. Performs best at `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
-| [`hunyuanvideo-community/HunyuanVideo-I2V`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20) |
+| [`hunyuanvideo-community/HunyuanVideo-I2V-33ch`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo 33-channel I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20). |
+| [`hunyuanvideo-community/HunyuanVideo-I2V`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo 16-channel I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20) |
 
 ## Quantization
 

docs/source/en/optimization/memory.md

@@ -198,6 +198,18 @@ export_to_video(video, "output.mp4", fps=8)
 
 Group offloading (for CUDA devices with support for asynchronous data transfer streams) overlaps data transfer and computation to reduce the overall execution time compared to sequential offloading. This is enabled using layer prefetching with CUDA streams. The next layer to be executed is loaded onto the accelerator device while the current layer is being executed - this increases the memory requirements slightly. Group offloading also supports leaf-level offloading (equivalent to sequential CPU offloading) but can be made much faster when using streams.
 
+<Tip>
+
+- Group offloading may not work with all models out-of-the-box. If the forward implementations of the model contain weight-dependent device-casting of inputs, it may clash with the offloading mechanism's handling of device-casting.
+- The `offload_type` parameter can be set to either `block_level` or `leaf_level`. `block_level` offloads groups of `torch::nn::ModuleList` or `torch::nn:Sequential` modules based on a configurable attribute `num_blocks_per_group`. For example, if you set `num_blocks_per_group=2` on a standard transformer model containing 40 layers, it will onload/offload 2 layers at a time for a total of 20 onload/offloads. This drastically reduces the VRAM requirements. `leaf_level` offloads individual layers at the lowest level, which is equivalent to sequential offloading. However, unlike sequential offloading, group offloading can be made much faster when using streams, with minimal compromise to end-to-end generation time.
+- The `use_stream` parameter can be used with CUDA devices to enable prefetching layers for onload. It defaults to `False`. Layer prefetching allows overlapping computation and data transfer of model weights, which drastically reduces the overall execution time compared to other offloading methods. However, it can increase the CPU RAM usage significantly. Ensure that available CPU RAM that is at least twice the size of the model when setting `use_stream=True`. You can find more information about CUDA streams [here](https://pytorch.org/docs/stable/generated/torch.cuda.Stream.html)
+- If specifying `use_stream=True` on VAEs with tiling enabled, make sure to do a dummy forward pass (possibly with dummy inputs) before the actual inference to avoid device-mismatch errors. This may not work on all implementations. Please open an issue if you encounter any problems.
+- The parameter `low_cpu_mem_usage` can be set to `True` to reduce CPU memory usage when using streams for group offloading. This is useful when the CPU memory is the bottleneck, but it may counteract the benefits of using streams and increase the overall execution time. The CPU memory savings come from creating pinned-tensors on-the-fly instead of pre-pinning them. This parameter is better suited for using `leaf_level` offloading.
+
+For more information about available parameters and an explanation of how group offloading works, refer to [`~hooks.group_offloading.apply_group_offloading`].
+
+</Tip>
+
 ## FP8 layerwise weight-casting
 
 PyTorch supports `torch.float8_e4m3fn` and `torch.float8_e5m2` as weight storage dtypes, but they can't be used for computation in many different tensor operations due to unimplemented kernel support. However, you can use these dtypes to store model weights in fp8 precision and upcast them on-the-fly when the layers are used in the forward pass. This is known as layerwise weight-casting.
@@ -235,6 +247,14 @@ In the above example, layerwise casting is enabled on the transformer component
 
 However, you gain more control and flexibility by directly utilizing the [`~hooks.layerwise_casting.apply_layerwise_casting`] function instead of [`~ModelMixin.enable_layerwise_casting`].
 
+<Tip>
+
+- Layerwise casting may not work with all models out-of-the-box. Sometimes, the forward implementations of the model might contain internal typecasting of weight values. Such implementations are not supported due to the currently simplistic implementation of layerwise casting, which assumes that the forward pass is independent of the weight precision and that the input dtypes are always in `compute_dtype`. An example of an incompatible implementation can be found [here](https://github.com/huggingface/transformers/blob/7f5077e53682ca855afc826162b204ebf809f1f9/src/transformers/models/t5/modeling_t5.py#L294-L299).
+- Layerwise casting may fail on custom modeling implementations that make use of [PEFT](https://github.com/huggingface/peft) layers. Some minimal checks to handle this case is implemented but is not extensively tested or guaranteed to work in all cases.
+- It can be also be applied partially to specific layers of a model. Partially applying layerwise casting can either be done manually by calling the `apply_layerwise_casting` function on specific internal modules, or by specifying the `skip_modules_pattern` and `skip_modules_classes` parameters for a root module. These parameters are particularly useful for layers such as normalization and modulation.
+
+</Tip>
+
 ## Channels-last memory format
 
 The channels-last memory format is an alternative way of ordering NCHW tensors in memory to preserve dimension ordering. Channels-last tensors are ordered in such a way that the channels become the densest dimension (storing images pixel-per-pixel). Since not all operators currently support the channels-last format, it may result in worst performance but you should still try and see if it works for your model.

docs/source/ko/training/controlnet.md

@@ -66,12 +66,6 @@ from accelerate.utils import write_basic_config
 write_basic_config()
 ```
 
-## 원을 채우는 데이터셋
-
-원본 데이터셋은 ControlNet [repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip)에 올라와있지만, 우리는 [여기](https://huggingface.co/datasets/fusing/fill50k)에 새롭게 다시 올려서 🤗 Datasets 과 호환가능합니다. 그래서 학습 스크립트 상에서 데이터 불러오기를 다룰 수 있습니다.
-
-우리의 학습 예시는 원래 ControlNet의 학습에 쓰였던 [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)을 사용합니다. 그렇지만 ControlNet은 대응되는 어느 Stable Diffusion 모델([`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4)) 혹은 [`stabilityai/stable-diffusion-2-1`](https://huggingface.co/stabilityai/stable-diffusion-2-1)의 증가를 위해 학습될 수 있습니다.
-
 자체 데이터셋을 사용하기 위해서는 [학습을 위한 데이터셋 생성하기](create_dataset) 가이드를 확인하세요.
 
 ## 학습

examples/research_projects/anytext/README.md

@@ -1,20 +1,27 @@
-# AnyTextPipeline Pipeline
+# AnyTextPipeline
 
 Project page: https://aigcdesigngroup.github.io/homepage_anytext
 
 "AnyText comprises a diffusion pipeline with two primary elements: an auxiliary latent module and a text embedding module. The former uses inputs like text glyph, position, and masked image to generate latent features for text generation or editing. The latter employs an OCR model for encoding stroke data as embeddings, which blend with image caption embeddings from the tokenizer to generate texts that seamlessly integrate with the background. We employed text-control diffusion loss and text perceptual loss for training to further enhance writing accuracy."
 
-Each text line that needs to be generated should be enclosed in double quotes. For any usage questions, please refer to the [paper](https://arxiv.org/abs/2311.03054).
+> **Note:** Each text line that needs to be generated should be enclosed in double quotes.
 
+For any usage questions, please refer to the [paper](https://arxiv.org/abs/2311.03054).
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/tolgacangoz/b87ec9d2f265b448dd947c9d4a0da389/anytext.ipynb)
 
 ```py
+# This example requires the `anytext_controlnet.py` file:
+# !git clone --depth 1 https://github.com/huggingface/diffusers.git
+# %cd diffusers/examples/research_projects/anytext
+# Let's choose a font file shared by an HF staff:
+# !wget https://huggingface.co/spaces/ysharma/TranslateQuotesInImageForwards/resolve/main/arial-unicode-ms.ttf
+
 import torch
 from diffusers import DiffusionPipeline
 from anytext_controlnet import AnyTextControlNetModel
 from diffusers.utils import load_image
 
-# I chose a font file shared by an HF staff:
-# !wget https://huggingface.co/spaces/ysharma/TranslateQuotesInImageForwards/resolve/main/arial-unicode-ms.ttf
 
 anytext_controlnet = AnyTextControlNetModel.from_pretrained("tolgacangoz/anytext-controlnet", torch_dtype=torch.float16,
                                                             variant="fp16",)
@@ -26,6 +33,7 @@ pipe = DiffusionPipeline.from_pretrained("tolgacangoz/anytext", font_path="arial
 # generate image
 prompt = 'photo of caramel macchiato coffee on the table, top-down perspective, with "Any" "Text" written on it using cream'
 draw_pos = load_image("https://raw.githubusercontent.com/tyxsspa/AnyText/refs/heads/main/example_images/gen9.png")
+# There are two modes: "generate" and "edit". "edit" mode requires `ori_image` parameter for the image to be edited.
 image = pipe(prompt, num_inference_steps=20, mode="generate", draw_pos=draw_pos,
              ).images[0]
 image

examples/research_projects/anytext/anytext.py

@@ -146,14 +146,17 @@ def _is_whitespace(self, char):
 EXAMPLE_DOC_STRING = """
     Examples:
         ```py
+        >>> # This example requires the `anytext_controlnet.py` file:
+        >>> # !git clone --depth 1 https://github.com/huggingface/diffusers.git
+        >>> # %cd diffusers/examples/research_projects/anytext
+        >>> # Let's choose a font file shared by an HF staff:
+        >>> # !wget https://huggingface.co/spaces/ysharma/TranslateQuotesInImageForwards/resolve/main/arial-unicode-ms.ttf
+
         >>> import torch
         >>> from diffusers import DiffusionPipeline
         >>> from anytext_controlnet import AnyTextControlNetModel
         >>> from diffusers.utils import load_image
 
-        >>> # I chose a font file shared by an HF staff:
-        >>> !wget https://huggingface.co/spaces/ysharma/TranslateQuotesInImageForwards/resolve/main/arial-unicode-ms.ttf
-
         >>> anytext_controlnet = AnyTextControlNetModel.from_pretrained("tolgacangoz/anytext-controlnet", torch_dtype=torch.float16,
         ...                                                             variant="fp16",)
         >>> pipe = DiffusionPipeline.from_pretrained("tolgacangoz/anytext", font_path="arial-unicode-ms.ttf",
@@ -165,6 +168,7 @@ def _is_whitespace(self, char):
         >>> # generate image
         >>> prompt = 'photo of caramel macchiato coffee on the table, top-down perspective, with "Any" "Text" written on it using cream'
         >>> draw_pos = load_image("https://raw.githubusercontent.com/tyxsspa/AnyText/refs/heads/main/example_images/gen9.png")
+        >>> # There are two modes: "generate" and "edit". "edit" mode requires `ori_image` parameter for the image to be edited.
         >>> image = pipe(prompt, num_inference_steps=20, mode="generate", draw_pos=draw_pos,
         ...              ).images[0]
         >>> image
@@ -257,11 +261,11 @@ def forward(
             idx = tokenized_text[i] == self.placeholder_token.to(device)
             if sum(idx) > 0:
                 if i >= len(self.text_embs_all):
-                    print("truncation for log images...")
+                    logger.warning("truncation for log images...")
                     break
                 text_emb = torch.cat(self.text_embs_all[i], dim=0)
                 if sum(idx) != len(text_emb):
-                    print("truncation for long caption...")
+                    logger.warning("truncation for long caption...")
                 text_emb = text_emb.to(embedded_text.device)
                 embedded_text[i][idx] = text_emb[: sum(idx)]
         return embedded_text
@@ -1058,6 +1062,8 @@ def forward(
                     raise ValueError(f"Can't read ori_image image from {ori_image}!")
             elif isinstance(ori_image, torch.Tensor):
                 ori_image = ori_image.cpu().numpy()
+            elif isinstance(ori_image, PIL.Image.Image):
+                ori_image = np.array(ori_image.convert("RGB"))
             else:
                 if not isinstance(ori_image, np.ndarray):
                     raise ValueError(f"Unknown format of ori_image: {type(ori_image)}")

scripts/convert_hunyuan_video_to_diffusers.py

@@ -160,8 +160,9 @@ def remap_single_transformer_blocks_(key, state_dict):
         "pooled_projection_dim": 768,
         "rope_theta": 256.0,
         "rope_axes_dim": (16, 56, 56),
+        "image_condition_type": None,
     },
-    "HYVideo-T/2-I2V": {
+    "HYVideo-T/2-I2V-33ch": {
         "in_channels": 16 * 2 + 1,
         "out_channels": 16,
         "num_attention_heads": 24,
@@ -178,6 +179,26 @@ def remap_single_transformer_blocks_(key, state_dict):
         "pooled_projection_dim": 768,
         "rope_theta": 256.0,
         "rope_axes_dim": (16, 56, 56),
+        "image_condition_type": "latent_concat",
+    },
+    "HYVideo-T/2-I2V-16ch": {
+        "in_channels": 16,
+        "out_channels": 16,
+        "num_attention_heads": 24,
+        "attention_head_dim": 128,
+        "num_layers": 20,
+        "num_single_layers": 40,
+        "num_refiner_layers": 2,
+        "mlp_ratio": 4.0,
+        "patch_size": 2,
+        "patch_size_t": 1,
+        "qk_norm": "rms_norm",
+        "guidance_embeds": True,
+        "text_embed_dim": 4096,
+        "pooled_projection_dim": 768,
+        "rope_theta": 256.0,
+        "rope_axes_dim": (16, 56, 56),
+        "image_condition_type": "token_replace",
     },
 }
 

scripts/convert_sana_to_diffusers.py

@@ -27,7 +27,10 @@
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
 ckpt_ids = [
+    "Efficient-Large-Model/Sana_Sprint_0.6B_1024px/checkpoints/Sana_Sprint_0.6B_1024px.pth"
+    "Efficient-Large-Model/Sana_Sprint_1.6B_1024px/checkpoints/Sana_Sprint_1.6B_1024px.pth"
     "Efficient-Large-Model/SANA1.5_4.8B_1024px/checkpoints/SANA1.5_4.8B_1024px.pth",
+    "Efficient-Large-Model/SANA1.5_1.6B_1024px/checkpoints/SANA1.5_1.6B_1024px.pth",
     "Efficient-Large-Model/Sana_1600M_4Kpx_BF16/checkpoints/Sana_1600M_4Kpx_BF16.pth",
     "Efficient-Large-Model/Sana_1600M_2Kpx_BF16/checkpoints/Sana_1600M_2Kpx_BF16.pth",
     "Efficient-Large-Model/Sana_1600M_1024px_MultiLing/checkpoints/Sana_1600M_1024px_MultiLing.pth",
@@ -314,7 +317,6 @@ def main(args):
 
             # SCM Scheduler for Sana Sprint
             scheduler_config = {
-                "num_train_timesteps": 1000,
                 "prediction_type": "trigflow",
                 "sigma_data": 0.5,
             }
@@ -378,7 +380,8 @@ def main(args):
         choices=[
             "SanaMS_1600M_P1_D20",
             "SanaMS_600M_P1_D28",
-            "SanaMS_4800M_P1_D60",
+            "SanaMS1.5_1600M_P1_D20",
+            "SanaMS1.5_4800M_P1_D60",
             "SanaSprint_1600M_P1_D20",
             "SanaSprint_600M_P1_D28",
         ],
@@ -421,7 +424,7 @@ def main(args):
             "cross_attention_dim": 2240,
             "num_layers": 20,
         },
-        "SanaMS1.5__4800M_P1_D60": {
+        "SanaMS1.5_4800M_P1_D60": {
             "num_attention_heads": 70,
             "attention_head_dim": 32,
             "num_cross_attention_heads": 20,

src/diffusers/hooks/group_offloading.py

@@ -331,7 +331,7 @@ def apply_group_offloading(
     num_blocks_per_group: Optional[int] = None,
     non_blocking: bool = False,
     use_stream: bool = False,
-    low_cpu_mem_usage=False,
+    low_cpu_mem_usage: bool = False,
 ) -> None:
     r"""
     Applies group offloading to the internal layers of a torch.nn.Module. To understand what group offloading is, and
@@ -378,6 +378,10 @@ def apply_group_offloading(
         use_stream (`bool`, defaults to `False`):
             If True, offloading and onloading is done asynchronously using a CUDA stream. This can be useful for
             overlapping computation and data transfer.
+        low_cpu_mem_usage (`bool`, defaults to `False`):
+            If True, the CPU memory usage is minimized by pinning tensors on-the-fly instead of pre-pinning them. This
+            option only matters when using streamed CPU offloading (i.e. `use_stream=True`). This can be useful when
+            the CPU memory is a bottleneck but may counteract the benefits of using streams.
 
     Example:
         ```python

src/diffusers/loaders/peft.py

@@ -307,6 +307,9 @@ def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="trans
             try:
                 inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
                 incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
+                # Set peft config loaded flag to True if module has been successfully injected and incompatible keys retrieved
+                if not self._hf_peft_config_loaded:
+                    self._hf_peft_config_loaded = True
             except Exception as e:
                 # In case `inject_adapter_in_model()` was unsuccessful even before injecting the `peft_config`.
                 if hasattr(self, "peft_config"):

src/diffusers/loaders/single_file_model.py

@@ -282,6 +282,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         if quantization_config is not None:
             hf_quantizer = DiffusersAutoQuantizer.from_config(quantization_config)
             hf_quantizer.validate_environment()
+            torch_dtype = hf_quantizer.update_torch_dtype(torch_dtype)
 
         else:
             hf_quantizer = None

src/diffusers/models/transformers/sana_transformer.py

@@ -326,6 +326,10 @@ class SanaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrig
             Whether to use elementwise affinity in the normalization layer.
         norm_eps (`float`, defaults to `1e-6`):
             The epsilon value for the normalization layer.
+        qk_norm (`str`, *optional*, defaults to `None`):
+            The normalization to use for the query and key.
+        timestep_scale (`float`, defaults to `1.0`):
+            The scale to use for the timesteps.
     """
 
     _supports_gradient_checkpointing = True
@@ -355,6 +359,7 @@ def __init__(
         guidance_embeds: bool = False,
         guidance_embeds_scale: float = 0.1,
         qk_norm: Optional[str] = None,
+        timestep_scale: float = 1.0,
     ) -> None:
         super().__init__()