diff --git a/src/diffusers/loaders/__init__.py b/src/diffusers/loaders/__init__.py
index b59150376599..6ea382d721de 100644
--- a/src/diffusers/loaders/__init__.py
+++ b/src/diffusers/loaders/__init__.py
@@ -70,6 +70,7 @@ def text_encoder_attn_modules(text_encoder):
"FluxLoraLoaderMixin",
"CogVideoXLoraLoaderMixin",
"Mochi1LoraLoaderMixin",
+ "HunyuanVideoLoraLoaderMixin",
"SanaLoraLoaderMixin",
]
_import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
@@ -90,6 +91,7 @@ def text_encoder_attn_modules(text_encoder):
AmusedLoraLoaderMixin,
CogVideoXLoraLoaderMixin,
FluxLoraLoaderMixin,
+ HunyuanVideoLoraLoaderMixin,
LoraLoaderMixin,
LTXVideoLoraLoaderMixin,
Mochi1LoraLoaderMixin,
diff --git a/src/diffusers/loaders/lora_pipeline.py b/src/diffusers/loaders/lora_pipeline.py
index b8c44e480093..46d744233014 100644
--- a/src/diffusers/loaders/lora_pipeline.py
+++ b/src/diffusers/loaders/lora_pipeline.py
@@ -3870,6 +3870,314 @@ def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], *
super().unfuse_lora(components=components)
+class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
+ r"""
+ Load LoRA layers into [`HunyuanVideoTransformer3DModel`]. Specific to [`HunyuanVideoPipeline`].
+ """
+
+ _lora_loadable_modules = ["transformer"]
+ transformer_name = TRANSFORMER_NAME
+
+ @classmethod
+ @validate_hf_hub_args
+ # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.lora_state_dict
+ def lora_state_dict(
+ cls,
+ pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+ **kwargs,
+ ):
+ r"""
+ Return state dict for lora weights and the network alphas.
+
+
+
+ We support loading A1111 formatted LoRA checkpoints in a limited capacity.
+
+ This function is experimental and might change in the future.
+
+
+
+ Parameters:
+ pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+ Can be either:
+
+ - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+ the Hub.
+ - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+ with [`ModelMixin.save_pretrained`].
+ - A [torch state
+ dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+ cache_dir (`Union[str, os.PathLike]`, *optional*):
+ Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
+ is not used.
+ force_download (`bool`, *optional*, defaults to `False`):
+ Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+ cached versions if they exist.
+
+ proxies (`Dict[str, str]`, *optional*):
+ A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
+ 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+ local_files_only (`bool`, *optional*, defaults to `False`):
+ Whether to only load local model weights and configuration files or not. If set to `True`, the model
+ won't be downloaded from the Hub.
+ token (`str` or *bool*, *optional*):
+ The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
+ `diffusers-cli login` (stored in `~/.huggingface`) is used.
+ revision (`str`, *optional*, defaults to `"main"`):
+ The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
+ allowed by Git.
+ subfolder (`str`, *optional*, defaults to `""`):
+ The subfolder location of a model file within a larger model repository on the Hub or locally.
+
+ """
+ # Load the main state dict first which has the LoRA layers for either of
+ # transformer and text encoder or both.
+ cache_dir = kwargs.pop("cache_dir", None)
+ force_download = kwargs.pop("force_download", False)
+ proxies = kwargs.pop("proxies", None)
+ local_files_only = kwargs.pop("local_files_only", None)
+ token = kwargs.pop("token", None)
+ revision = kwargs.pop("revision", None)
+ subfolder = kwargs.pop("subfolder", None)
+ weight_name = kwargs.pop("weight_name", None)
+ use_safetensors = kwargs.pop("use_safetensors", None)
+
+ allow_pickle = False
+ if use_safetensors is None:
+ use_safetensors = True
+ allow_pickle = True
+
+ user_agent = {
+ "file_type": "attn_procs_weights",
+ "framework": "pytorch",
+ }
+
+ state_dict = _fetch_state_dict(
+ pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
+ weight_name=weight_name,
+ use_safetensors=use_safetensors,
+ local_files_only=local_files_only,
+ cache_dir=cache_dir,
+ force_download=force_download,
+ proxies=proxies,
+ token=token,
+ revision=revision,
+ subfolder=subfolder,
+ user_agent=user_agent,
+ allow_pickle=allow_pickle,
+ )
+
+ is_dora_scale_present = any("dora_scale" in k for k in state_dict)
+ if is_dora_scale_present:
+ warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you think this is a mistake please open an issue https://github.com/huggingface/diffusers/issues/new."
+ logger.warning(warn_msg)
+ state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
+
+ return state_dict
+
+ # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
+ def load_lora_weights(
+ self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
+ ):
+ """
+ Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
+ `self.text_encoder`. All kwargs are forwarded to `self.lora_state_dict`. See
+ [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
+ See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer`] for more details on how the state
+ dict is loaded into `self.transformer`.
+
+ Parameters:
+ pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+ See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
+ adapter_name (`str`, *optional*):
+ Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+ `default_{i}` where i is the total number of adapters being loaded.
+ low_cpu_mem_usage (`bool`, *optional*):
+ Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+ weights.
+ kwargs (`dict`, *optional*):
+ See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
+ """
+ if not USE_PEFT_BACKEND:
+ raise ValueError("PEFT backend is required for this method.")
+
+ low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
+ if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+ raise ValueError(
+ "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+ )
+
+ # if a dict is passed, copy it instead of modifying it inplace
+ if isinstance(pretrained_model_name_or_path_or_dict, dict):
+ pretrained_model_name_or_path_or_dict = pretrained_model_name_or_path_or_dict.copy()
+
+ # First, ensure that the checkpoint is a compatible one and can be successfully loaded.
+ state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
+
+ is_correct_format = all("lora" in key for key in state_dict.keys())
+ if not is_correct_format:
+ raise ValueError("Invalid LoRA checkpoint.")
+
+ self.load_lora_into_transformer(
+ state_dict,
+ transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
+ adapter_name=adapter_name,
+ _pipeline=self,
+ low_cpu_mem_usage=low_cpu_mem_usage,
+ )
+
+ @classmethod
+ # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->HunyuanVideoTransformer3DModel
+ def load_lora_into_transformer(
+ cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False
+ ):
+ """
+ This will load the LoRA layers specified in `state_dict` into `transformer`.
+
+ Parameters:
+ state_dict (`dict`):
+ A standard state dict containing the lora layer parameters. The keys can either be indexed directly
+ into the unet or prefixed with an additional `unet` which can be used to distinguish between text
+ encoder lora layers.
+ transformer (`HunyuanVideoTransformer3DModel`):
+ The Transformer model to load the LoRA layers into.
+ adapter_name (`str`, *optional*):
+ Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+ `default_{i}` where i is the total number of adapters being loaded.
+ low_cpu_mem_usage (`bool`, *optional*):
+ Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+ weights.
+ """
+ if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
+ raise ValueError(
+ "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+ )
+
+ # Load the layers corresponding to transformer.
+ logger.info(f"Loading {cls.transformer_name}.")
+ transformer.load_lora_adapter(
+ state_dict,
+ network_alphas=None,
+ adapter_name=adapter_name,
+ _pipeline=_pipeline,
+ low_cpu_mem_usage=low_cpu_mem_usage,
+ )
+
+ @classmethod
+ # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.save_lora_weights
+ def save_lora_weights(
+ cls,
+ save_directory: Union[str, os.PathLike],
+ transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ is_main_process: bool = True,
+ weight_name: str = None,
+ save_function: Callable = None,
+ safe_serialization: bool = True,
+ ):
+ r"""
+ Save the LoRA parameters corresponding to the UNet and text encoder.
+
+ Arguments:
+ save_directory (`str` or `os.PathLike`):
+ Directory to save LoRA parameters to. Will be created if it doesn't exist.
+ transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
+ State dict of the LoRA layers corresponding to the `transformer`.
+ is_main_process (`bool`, *optional*, defaults to `True`):
+ Whether the process calling this is the main process or not. Useful during distributed training and you
+ need to call this function on all processes. In this case, set `is_main_process=True` only on the main
+ process to avoid race conditions.
+ save_function (`Callable`):
+ The function to use to save the state dictionary. Useful during distributed training when you need to
+ replace `torch.save` with another method. Can be configured with the environment variable
+ `DIFFUSERS_SAVE_MODE`.
+ safe_serialization (`bool`, *optional*, defaults to `True`):
+ Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
+ """
+ state_dict = {}
+
+ if not transformer_lora_layers:
+ raise ValueError("You must pass `transformer_lora_layers`.")
+
+ if transformer_lora_layers:
+ state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
+
+ # Save the model
+ cls.write_lora_layers(
+ state_dict=state_dict,
+ save_directory=save_directory,
+ is_main_process=is_main_process,
+ weight_name=weight_name,
+ save_function=save_function,
+ safe_serialization=safe_serialization,
+ )
+
+ # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.fuse_lora with unet->transformer
+ def fuse_lora(
+ self,
+ components: List[str] = ["transformer", "text_encoder"],
+ lora_scale: float = 1.0,
+ safe_fusing: bool = False,
+ adapter_names: Optional[List[str]] = None,
+ **kwargs,
+ ):
+ r"""
+ Fuses the LoRA parameters into the original parameters of the corresponding blocks.
+
+
+
+ This is an experimental API.
+
+
+
+ Args:
+ components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
+ lora_scale (`float`, defaults to 1.0):
+ Controls how much to influence the outputs with the LoRA parameters.
+ safe_fusing (`bool`, defaults to `False`):
+ Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
+ adapter_names (`List[str]`, *optional*):
+ Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
+
+ Example:
+
+ ```py
+ from diffusers import DiffusionPipeline
+ import torch
+
+ pipeline = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+ ).to("cuda")
+ pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+ pipeline.fuse_lora(lora_scale=0.7)
+ ```
+ """
+ super().fuse_lora(
+ components=components, lora_scale=lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names
+ )
+
+ # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.unfuse_lora with unet->transformer
+ def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], **kwargs):
+ r"""
+ Reverses the effect of
+ [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
+
+
+
+ This is an experimental API.
+
+
+
+ Args:
+ components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
+ unfuse_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
+ unfuse_text_encoder (`bool`, defaults to `True`):
+ Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
+ LoRA parameters then it won't have any effect.
+ """
+ super().unfuse_lora(components=components)
+
+
class LoraLoaderMixin(StableDiffusionLoraLoaderMixin):
def __init__(self, *args, **kwargs):
deprecation_message = "LoraLoaderMixin is deprecated and this will be removed in a future version. Please use `StableDiffusionLoraLoaderMixin`, instead."
diff --git a/src/diffusers/loaders/peft.py b/src/diffusers/loaders/peft.py
index a791a250af08..9c00012ebc65 100644
--- a/src/diffusers/loaders/peft.py
+++ b/src/diffusers/loaders/peft.py
@@ -53,6 +53,7 @@
"FluxTransformer2DModel": lambda model_cls, weights: weights,
"CogVideoXTransformer3DModel": lambda model_cls, weights: weights,
"MochiTransformer3DModel": lambda model_cls, weights: weights,
+ "HunyuanVideoTransformer3DModel": lambda model_cls, weights: weights,
"LTXVideoTransformer3DModel": lambda model_cls, weights: weights,
"SanaTransformer2DModel": lambda model_cls, weights: weights,
}
diff --git a/src/diffusers/models/transformers/transformer_hunyuan_video.py b/src/diffusers/models/transformers/transformer_hunyuan_video.py
index 737be99c5a10..089389b5f9ad 100644
--- a/src/diffusers/models/transformers/transformer_hunyuan_video.py
+++ b/src/diffusers/models/transformers/transformer_hunyuan_video.py
@@ -19,7 +19,8 @@
import torch.nn.functional as F
from ...configuration_utils import ConfigMixin, register_to_config
-from ...utils import is_torch_version
+from ...loaders import PeftAdapterMixin
+from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
from ..attention import FeedForward
from ..attention_processor import Attention, AttentionProcessor
from ..embeddings import (
@@ -32,6 +33,9 @@
from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
class HunyuanVideoAttnProcessor2_0:
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
@@ -496,7 +500,7 @@ def forward(
return hidden_states, encoder_hidden_states
-class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin):
+class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
r"""
A Transformer model for video-like data used in [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo).
@@ -670,8 +674,24 @@ def forward(
encoder_attention_mask: torch.Tensor,
pooled_projections: torch.Tensor,
guidance: torch.Tensor = None,
+ attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+ if attention_kwargs is not None:
+ attention_kwargs = attention_kwargs.copy()
+ lora_scale = attention_kwargs.pop("scale", 1.0)
+ else:
+ lora_scale = 1.0
+
+ if USE_PEFT_BACKEND:
+ # weight the lora layers by setting `lora_scale` for each PEFT layer
+ scale_lora_layers(self, lora_scale)
+ else:
+ if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+ logger.warning(
+ "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+ )
+
batch_size, num_channels, num_frames, height, width = hidden_states.shape
p, p_t = self.config.patch_size, self.config.patch_size_t
post_patch_num_frames = num_frames // p_t
@@ -757,6 +777,10 @@ def custom_forward(*inputs):
hidden_states = hidden_states.permute(0, 4, 1, 5, 2, 6, 3, 7)
hidden_states = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+ if USE_PEFT_BACKEND:
+ # remove `lora_scale` from each PEFT layer
+ unscale_lora_layers(self, lora_scale)
+
if not return_dict:
return (hidden_states,)
diff --git a/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py b/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
index bd3d3c1e8485..4423ccf97932 100644
--- a/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
+++ b/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
@@ -20,6 +20,7 @@
from transformers import CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizerFast
from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...loaders import HunyuanVideoLoraLoaderMixin
from ...models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import logging, replace_example_docstring
@@ -132,7 +133,7 @@ def retrieve_timesteps(
return timesteps, num_inference_steps
-class HunyuanVideoPipeline(DiffusionPipeline):
+class HunyuanVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
r"""
Pipeline for text-to-video generation using HunyuanVideo.
@@ -447,6 +448,10 @@ def guidance_scale(self):
def num_timesteps(self):
return self._num_timesteps
+ @property
+ def attention_kwargs(self):
+ return self._attention_kwargs
+
@property
def interrupt(self):
return self._interrupt
@@ -471,6 +476,7 @@ def __call__(
prompt_attention_mask: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
+ attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
@@ -525,6 +531,10 @@ def __call__(
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`HunyuanVideoPipelineOutput`] instead of a plain tuple.
+ attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
@@ -562,6 +572,7 @@ def __call__(
)
self._guidance_scale = guidance_scale
+ self._attention_kwargs = attention_kwargs
self._interrupt = False
device = self._execution_device
@@ -640,6 +651,7 @@ def __call__(
encoder_attention_mask=prompt_attention_mask,
pooled_projections=pooled_prompt_embeds,
guidance=guidance,
+ attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
diff --git a/tests/lora/test_lora_layers_hunyuanvideo.py b/tests/lora/test_lora_layers_hunyuanvideo.py
new file mode 100644
index 000000000000..59464c052684
--- /dev/null
+++ b/tests/lora/test_lora_layers_hunyuanvideo.py
@@ -0,0 +1,228 @@
+# Copyright 2024 HuggingFace Inc.
+#
+# 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.
+
+import sys
+import unittest
+
+import numpy as np
+import pytest
+import torch
+from transformers import CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizerFast
+
+from diffusers import (
+ AutoencoderKLHunyuanVideo,
+ FlowMatchEulerDiscreteScheduler,
+ HunyuanVideoPipeline,
+ HunyuanVideoTransformer3DModel,
+)
+from diffusers.utils.testing_utils import (
+ floats_tensor,
+ is_torch_version,
+ require_peft_backend,
+ skip_mps,
+ torch_device,
+)
+
+
+sys.path.append(".")
+
+from utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa: E402
+
+
+@require_peft_backend
+@skip_mps
+class HunyuanVideoLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
+ pipeline_class = HunyuanVideoPipeline
+ scheduler_cls = FlowMatchEulerDiscreteScheduler
+ scheduler_classes = [FlowMatchEulerDiscreteScheduler]
+ scheduler_kwargs = {}
+
+ transformer_kwargs = {
+ "in_channels": 4,
+ "out_channels": 4,
+ "num_attention_heads": 2,
+ "attention_head_dim": 10,
+ "num_layers": 1,
+ "num_single_layers": 1,
+ "num_refiner_layers": 1,
+ "patch_size": 1,
+ "patch_size_t": 1,
+ "guidance_embeds": True,
+ "text_embed_dim": 16,
+ "pooled_projection_dim": 8,
+ "rope_axes_dim": (2, 4, 4),
+ }
+ transformer_cls = HunyuanVideoTransformer3DModel
+ vae_kwargs = {
+ "in_channels": 3,
+ "out_channels": 3,
+ "latent_channels": 4,
+ "down_block_types": (
+ "HunyuanVideoDownBlock3D",
+ "HunyuanVideoDownBlock3D",
+ "HunyuanVideoDownBlock3D",
+ "HunyuanVideoDownBlock3D",
+ ),
+ "up_block_types": (
+ "HunyuanVideoUpBlock3D",
+ "HunyuanVideoUpBlock3D",
+ "HunyuanVideoUpBlock3D",
+ "HunyuanVideoUpBlock3D",
+ ),
+ "block_out_channels": (8, 8, 8, 8),
+ "layers_per_block": 1,
+ "act_fn": "silu",
+ "norm_num_groups": 4,
+ "scaling_factor": 0.476986,
+ "spatial_compression_ratio": 8,
+ "temporal_compression_ratio": 4,
+ "mid_block_add_attention": True,
+ }
+ vae_cls = AutoencoderKLHunyuanVideo
+ has_two_text_encoders = True
+ tokenizer_cls, tokenizer_id, tokenizer_subfolder = (
+ LlamaTokenizerFast,
+ "hf-internal-testing/tiny-random-hunyuanvideo",
+ "tokenizer",
+ )
+ tokenizer_2_cls, tokenizer_2_id, tokenizer_2_subfolder = (
+ CLIPTokenizer,
+ "hf-internal-testing/tiny-random-hunyuanvideo",
+ "tokenizer_2",
+ )
+ text_encoder_cls, text_encoder_id, text_encoder_subfolder = (
+ LlamaModel,
+ "hf-internal-testing/tiny-random-hunyuanvideo",
+ "text_encoder",
+ )
+ text_encoder_2_cls, text_encoder_2_id, text_encoder_2_subfolder = (
+ CLIPTextModel,
+ "hf-internal-testing/tiny-random-hunyuanvideo",
+ "text_encoder_2",
+ )
+
+ @property
+ def output_shape(self):
+ return (1, 9, 32, 32, 3)
+
+ def get_dummy_inputs(self, with_generator=True):
+ batch_size = 1
+ sequence_length = 16
+ num_channels = 4
+ num_frames = 9
+ num_latent_frames = 3 # (num_frames - 1) // temporal_compression_ratio + 1
+ sizes = (4, 4)
+
+ generator = torch.manual_seed(0)
+ noise = floats_tensor((batch_size, num_latent_frames, num_channels) + sizes)
+ input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
+
+ pipeline_inputs = {
+ "prompt": "",
+ "num_frames": num_frames,
+ "num_inference_steps": 1,
+ "guidance_scale": 6.0,
+ "height": 32,
+ "width": 32,
+ "max_sequence_length": sequence_length,
+ "prompt_template": {"template": "{}", "crop_start": 0},
+ "output_type": "np",
+ }
+ if with_generator:
+ pipeline_inputs.update({"generator": generator})
+
+ return noise, input_ids, pipeline_inputs
+
+ @pytest.mark.xfail(
+ condition=torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
+ reason="Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.",
+ strict=True,
+ )
+ def test_lora_fuse_nan(self):
+ for scheduler_cls in self.scheduler_classes:
+ components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+ pipe = self.pipeline_class(**components)
+ pipe = pipe.to(torch_device)
+ pipe.set_progress_bar_config(disable=None)
+ _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+ pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+
+ self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
+
+ # corrupt one LoRA weight with `inf` values
+ with torch.no_grad():
+ pipe.transformer.transformer_blocks[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
+
+ # with `safe_fusing=True` we should see an Error
+ with self.assertRaises(ValueError):
+ pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
+
+ # without we should not see an error, but every image will be black
+ pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
+
+ out = pipe(
+ prompt=inputs["prompt"],
+ height=inputs["height"],
+ width=inputs["width"],
+ num_frames=inputs["num_frames"],
+ num_inference_steps=inputs["num_inference_steps"],
+ max_sequence_length=inputs["max_sequence_length"],
+ output_type="np",
+ )[0]
+
+ self.assertTrue(np.isnan(out).all())
+
+ def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
+ super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
+
+ def test_simple_inference_with_text_denoiser_lora_unfused(self):
+ super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
+
+ # TODO(aryan): Fix the following test
+ @unittest.skip("This test fails with an error I haven't been able to debug yet.")
+ def test_simple_inference_save_pretrained(self):
+ pass
+
+ @unittest.skip("Not supported in HunyuanVideo.")
+ def test_simple_inference_with_text_denoiser_block_scale(self):
+ pass
+
+ @unittest.skip("Not supported in HunyuanVideo.")
+ def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
+ pass
+
+ @unittest.skip("Not supported in HunyuanVideo.")
+ def test_modify_padding_mode(self):
+ pass
+
+ @unittest.skip("Text encoder LoRA is not supported in HunyuanVideo.")
+ def test_simple_inference_with_partial_text_lora(self):
+ pass
+
+ @unittest.skip("Text encoder LoRA is not supported in HunyuanVideo.")
+ def test_simple_inference_with_text_lora(self):
+ pass
+
+ @unittest.skip("Text encoder LoRA is not supported in HunyuanVideo.")
+ def test_simple_inference_with_text_lora_and_scale(self):
+ pass
+
+ @unittest.skip("Text encoder LoRA is not supported in HunyuanVideo.")
+ def test_simple_inference_with_text_lora_fused(self):
+ pass
+
+ @unittest.skip("Text encoder LoRA is not supported in HunyuanVideo.")
+ def test_simple_inference_with_text_lora_save_load(self):
+ pass
diff --git a/tests/lora/utils.py b/tests/lora/utils.py
index ac7a944cd026..73ed17049c1b 100644
--- a/tests/lora/utils.py
+++ b/tests/lora/utils.py
@@ -89,12 +89,12 @@ class PeftLoraLoaderMixinTests:
has_two_text_encoders = False
has_three_text_encoders = False
- text_encoder_cls, text_encoder_id = None, None
- text_encoder_2_cls, text_encoder_2_id = None, None
- text_encoder_3_cls, text_encoder_3_id = None, None
- tokenizer_cls, tokenizer_id = None, None
- tokenizer_2_cls, tokenizer_2_id = None, None
- tokenizer_3_cls, tokenizer_3_id = None, None
+ text_encoder_cls, text_encoder_id, text_encoder_subfolder = None, None, None
+ text_encoder_2_cls, text_encoder_2_id, text_encoder_2_subfolder = None, None, None
+ text_encoder_3_cls, text_encoder_3_id, text_encoder_3_subfolder = None, None, None
+ tokenizer_cls, tokenizer_id, tokenizer_subfolder = None, None, None
+ tokenizer_2_cls, tokenizer_2_id, tokenizer_2_subfolder = None, None, None
+ tokenizer_3_cls, tokenizer_3_id, tokenizer_3_subfolder = None, None, None
unet_kwargs = None
transformer_cls = None
@@ -124,16 +124,26 @@ def get_dummy_components(self, scheduler_cls=None, use_dora=False):
torch.manual_seed(0)
vae = self.vae_cls(**self.vae_kwargs)
- text_encoder = self.text_encoder_cls.from_pretrained(self.text_encoder_id)
- tokenizer = self.tokenizer_cls.from_pretrained(self.tokenizer_id)
+ text_encoder = self.text_encoder_cls.from_pretrained(
+ self.text_encoder_id, subfolder=self.text_encoder_subfolder
+ )
+ tokenizer = self.tokenizer_cls.from_pretrained(self.tokenizer_id, subfolder=self.tokenizer_subfolder)
if self.text_encoder_2_cls is not None:
- text_encoder_2 = self.text_encoder_2_cls.from_pretrained(self.text_encoder_2_id)
- tokenizer_2 = self.tokenizer_2_cls.from_pretrained(self.tokenizer_2_id)
+ text_encoder_2 = self.text_encoder_2_cls.from_pretrained(
+ self.text_encoder_2_id, subfolder=self.text_encoder_2_subfolder
+ )
+ tokenizer_2 = self.tokenizer_2_cls.from_pretrained(
+ self.tokenizer_2_id, subfolder=self.tokenizer_2_subfolder
+ )
if self.text_encoder_3_cls is not None:
- text_encoder_3 = self.text_encoder_3_cls.from_pretrained(self.text_encoder_3_id)
- tokenizer_3 = self.tokenizer_3_cls.from_pretrained(self.tokenizer_3_id)
+ text_encoder_3 = self.text_encoder_3_cls.from_pretrained(
+ self.text_encoder_3_id, subfolder=self.text_encoder_3_subfolder
+ )
+ tokenizer_3 = self.tokenizer_3_cls.from_pretrained(
+ self.tokenizer_3_id, subfolder=self.tokenizer_3_subfolder
+ )
text_lora_config = LoraConfig(
r=rank,