* fix issue that training flux controlnet was unstable and validation results were unstable
* del unused code pieces, fix grammar
---------
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Fix: Inherit `StableDiffusionXLLoraLoaderMixin`
`StableDiffusionXLControlNetAdapterInpaintPipeline`
used to incorrectly inherit
`StableDiffusionLoraLoaderMixin`
instead of `StableDiffusionXLLoraLoaderMixin`
* Update pe_selection_index_based_on_dim
* Make pe_selection_index_based_on_dim work with torh.compile
* Fix AuraFlowTransformer2DModel's dpcstring default values
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
loose expected_max_diff from 5e-1 to 8e-1 to make
KandinskyV22PipelineInpaintCombinedFastTests::test_float16_inference
pass on XPU
Signed-off-by: Matrix Yao <matrix.yao@intel.com>
Before this if txt_ids was 3d tensor, line with txt_ids[:1] concat txt_ids by batch dim. Now we first check that txt_ids is 2d tensor (or take first batch element) and then concat by token dim
* loose test_float16_inference's tolerance from 5e-2 to 6e-2, so XPU can
pass UT
Signed-off-by: Matrix Yao <matrix.yao@intel.com>
* fix test_pipeline_flux_redux fail on XPU
Signed-off-by: Matrix Yao <matrix.yao@intel.com>
---------
Signed-off-by: Matrix Yao <matrix.yao@intel.com>
* [WIP][LoRA] Implement hot-swapping of LoRA
This PR adds the possibility to hot-swap LoRA adapters. It is WIP.
Description
As of now, users can already load multiple LoRA adapters. They can
offload existing adapters or they can unload them (i.e. delete them).
However, they cannot "hotswap" adapters yet, i.e. substitute the weights
from one LoRA adapter with the weights of another, without the need to
create a separate LoRA adapter.
Generally, hot-swapping may not appear not super useful but when the
model is compiled, it is necessary to prevent recompilation. See #9279
for more context.
Caveats
To hot-swap a LoRA adapter for another, these two adapters should target
exactly the same layers and the "hyper-parameters" of the two adapters
should be identical. For instance, the LoRA alpha has to be the same:
Given that we keep the alpha from the first adapter, the LoRA scaling
would be incorrect for the second adapter otherwise.
Theoretically, we could override the scaling dict with the alpha values
derived from the second adapter's config, but changing the dict will
trigger a guard for recompilation, defeating the main purpose of the
feature.
I also found that compilation flags can have an impact on whether this
works or not. E.g. when passing "reduce-overhead", there will be errors
of the type:
> input name: arg861_1. data pointer changed from 139647332027392 to
139647331054592
I don't know enough about compilation to determine whether this is
problematic or not.
Current state
This is obviously WIP right now to collect feedback and discuss which
direction to take this. If this PR turns out to be useful, the
hot-swapping functions will be added to PEFT itself and can be imported
here (or there is a separate copy in diffusers to avoid the need for a
min PEFT version to use this feature).
Moreover, more tests need to be added to better cover this feature,
although we don't necessarily need tests for the hot-swapping
functionality itself, since those tests will be added to PEFT.
Furthermore, as of now, this is only implemented for the unet. Other
pipeline components have yet to implement this feature.
Finally, it should be properly documented.
I would like to collect feedback on the current state of the PR before
putting more time into finalizing it.
* Reviewer feedback
* Reviewer feedback, adjust test
* Fix, doc
* Make fix
* Fix for possible g++ error
* Add test for recompilation w/o hotswapping
* Make hotswap work
Requires https://github.com/huggingface/peft/pull/2366
More changes to make hotswapping work. Together with the mentioned PEFT
PR, the tests pass for me locally.
List of changes:
- docstring for hotswap
- remove code copied from PEFT, import from PEFT now
- adjustments to PeftAdapterMixin.load_lora_adapter (unfortunately, some
state dict renaming was necessary, LMK if there is a better solution)
- adjustments to UNet2DConditionLoadersMixin._process_lora: LMK if this
is even necessary or not, I'm unsure what the overall relationship is
between this and PeftAdapterMixin.load_lora_adapter
- also in UNet2DConditionLoadersMixin._process_lora, I saw that there is
no LoRA unloading when loading the adapter fails, so I added it
there (in line with what happens in PeftAdapterMixin.load_lora_adapter)
- rewritten tests to avoid shelling out, make the test more precise by
making sure that the outputs align, parametrize it
- also checked the pipeline code mentioned in this comment:
https://github.com/huggingface/diffusers/pull/9453#issuecomment-2418508871;
when running this inside the with
torch._dynamo.config.patch(error_on_recompile=True) context, there is
no error, so I think hotswapping is now working with pipelines.
* Address reviewer feedback:
- Revert deprecated method
- Fix PEFT doc link to main
- Don't use private function
- Clarify magic numbers
- Add pipeline test
Moreover:
- Extend docstrings
- Extend existing test for outputs != 0
- Extend existing test for wrong adapter name
* Change order of test decorators
parameterized.expand seems to ignore skip decorators if added in last
place (i.e. innermost decorator).
* Split model and pipeline tests
Also increase test coverage by also targeting conv2d layers (support of
which was added recently on the PEFT PR).
* Reviewer feedback: Move decorator to test classes
... instead of having them on each test method.
* Apply suggestions from code review
Co-authored-by: hlky <hlky@hlky.ac>
* Reviewer feedback: version check, TODO comment
* Add enable_lora_hotswap method
* Reviewer feedback: check _lora_loadable_modules
* Revert changes in unet.py
* Add possibility to ignore enabled at wrong time
* Fix docstrings
* Log possible PEFT error, test
* Raise helpful error if hotswap not supported
I.e. for the text encoder
* Formatting
* More linter
* More ruff
* Doc-builder complaint
* Update docstring:
- mention no text encoder support yet
- make it clear that LoRA is meant
- mention that same adapter name should be passed
* Fix error in docstring
* Update more methods with hotswap argument
- SDXL
- SD3
- Flux
No changes were made to load_lora_into_transformer.
* Add hotswap argument to load_lora_into_transformer
For SD3 and Flux. Use shorter docstring for brevity.
* Extend docstrings
* Add version guards to tests
* Formatting
* Fix LoRA loading call to add prefix=None
See:
https://github.com/huggingface/diffusers/pull/10187#issuecomment-2717571064
* Run make fix-copies
* Add hot swap documentation to the docs
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: hlky <hlky@hlky.ac>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Refactor `LTXConditionPipeline` to add text-only conditioning
* style
* up
* Refactor `LTXConditionPipeline` to streamline condition handling and improve clarity
* Improve condition checks
* Simplify latents handling based on conditioning type
* Refactor rope_interpolation_scale preparation for clarity and efficiency
* Update LTXConditionPipeline docstring to clarify supported input types
* Add LTX Video 0.9.5 model to documentation
* Clarify documentation to indicate support for text-only conditioning without passing `conditions`
* refactor: comment out unused parameters in LTXConditionPipeline
* fix: restore previously commented parameters in LTXConditionPipeline
* fix: remove unused parameters from LTXConditionPipeline
* refactor: remove unnecessary lines in LTXConditionPipeline
* model card gen code
* push modelcard creation
* remove optional from params
* add import
* add use_dora check
* correct lora var use in tags
* make style && make quality
---------
Co-authored-by: Aryan <aryan@huggingface.co>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* allow models to run with a user-provided dtype map instead of a single dtype
* make style
* Add warning, change `_` to `default`
* make style
* add test
* handle shared tensors
* remove warning
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
set self._hf_peft_config_loaded to True on successful lora load
Sets the `_hf_peft_config_loaded` flag if a LoRA is successfully loaded in `load_lora_adapter`. Fixes bug huggingface/diffusers/issues/11148
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* [Documentation] Update README and example code with additional usage instructions for AnyText
* [Documentation] Update README for AnyTextPipeline and improve logging in code
* Remove wget command for font file from example docstring in anytext.py
* Don't use `torch_dtype` when `quantization_config` is set
* up
* djkajka
* Apply suggestions from code review
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* fix bug when pixart-dmd inference with `num_inference_steps=1`
* use return_dict=False and return [1] element for 1-step pixart model, which works for both lcm and dmd
reverts accidental change that removes attn_mask in attn. Improves flux ptxla by using flash block sizes. Moves encoding outside the for loop.
Co-authored-by: Juan Acevedo <jfacevedo@google.com>
* Add initial template
* Second template
* feat: Add TextEmbeddingModule to AnyTextPipeline
* feat: Add AuxiliaryLatentModule template to AnyTextPipeline
* Add bert tokenizer from the anytext repo for now
* feat: Update AnyTextPipeline's modify_prompt method
This commit adds improvements to the modify_prompt method in the AnyTextPipeline class. The method now handles special characters and replaces selected string prompts with a placeholder. Additionally, it includes a check for Chinese text and translation using the trans_pipe.
* Fill in the `forward` pass of `AuxiliaryLatentModule`
* `make style && make quality`
* `chore: Update bert_tokenizer.py with a TODO comment suggesting the use of the transformers library`
* Update error handling to raise and logging
* Add `create_glyph_lines` function into `TextEmbeddingModule`
* make style
* Up
* Up
* Up
* Up
* Remove several comments
* refactor: Remove ControlNetConditioningEmbedding and update code accordingly
* Up
* Up
* up
* refactor: Update AnyTextPipeline to include new optional parameters
* up
* feat: Add OCR model and its components
* chore: Update `TextEmbeddingModule` to include OCR model components and dependencies
* chore: Update `AuxiliaryLatentModule` to include VAE model and its dependencies for masked image in the editing task
* `make style`
* refactor: Update `AnyTextPipeline`'s docstring
* Update `AuxiliaryLatentModule` to include info dictionary so that text processing is done once
* simplify
* `make style`
* Converting `TextEmbeddingModule` to ordinary `encode_prompt()` function
* Simplify for now
* `make style`
* Up
* feat: Add scripts to convert AnyText controlnet to diffusers
* `make style`
* Fix: Move glyph rendering to `TextEmbeddingModule` from `AuxiliaryLatentModule`
* make style
* Up
* Simplify
* Up
* feat: Add safetensors module for loading model file
* Fix device issues
* Up
* Up
* refactor: Simplify
* refactor: Simplify code for loading models and handling data types
* `make style`
* refactor: Update to() method in FrozenCLIPEmbedderT3 and TextEmbeddingModule
* refactor: Update dtype in embedding_manager.py to match proj.weight
* Up
* Add attribution and adaptation information to pipeline_anytext.py
* Update usage example
* Will refactor `controlnet_cond_embedding` initialization
* Add `AnyTextControlNetConditioningEmbedding` template
* Refactor organization
* style
* style
* Move custom blocks from `AuxiliaryLatentModule` to `AnyTextControlNetConditioningEmbedding`
* Follow one-file policy
* style
* [Docs] Update README and pipeline_anytext.py to use AnyTextControlNetModel
* [Docs] Update import statement for AnyTextControlNetModel in pipeline_anytext.py
* [Fix] Update import path for ControlNetModel, ControlNetOutput in anytext_controlnet.py
* Refactor AnyTextControlNet to use configurable conditioning embedding channels
* Complete control net conditioning embedding in AnyTextControlNetModel
* up
* [FIX] Ensure embeddings use correct device in AnyTextControlNetModel
* up
* up
* style
* [UPDATE] Revise README and example code for AnyTextPipeline integration with DiffusionPipeline
* [UPDATE] Update example code in anytext.py to use correct font file and improve clarity
* down
* [UPDATE] Refactor BasicTokenizer usage to a new Checker class for text processing
* update pillow
* [UPDATE] Remove commented-out code and unnecessary docstring in anytext.py and anytext_controlnet.py for improved clarity
* [REMOVE] Delete frozen_clip_embedder_t3.py as it is in the anytext.py file
* [UPDATE] Replace edict with dict for configuration in anytext.py and RecModel.py for consistency
* 🆙
* style
* [UPDATE] Revise README.md for clarity, remove unused imports in anytext.py, and add author credits in anytext_controlnet.py
* style
* Update examples/research_projects/anytext/README.md
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
* Remove commented-out image preparation code in AnyTextPipeline
* Remove unnecessary blank line in README.md
* updated train_dreambooth_lora to fix the LR schedulers for `num_train_epochs` in distributed training env
* fixed formatting
* remove trailing newlines
* fixed style error
* Fix SD2.X clip single file load projection_dim
Infer projection_dim from the checkpoint before loading
from pretrained, override any incorrect hub config.
Hub configuration for SD2.X specifies projection_dim=512
which is incorrect for SD2.X checkpoints loaded from civitai
and similar.
Exception was previously thrown upon attempting to
load_model_dict_into_meta for SD2.X single file checkpoints.
Such LDM models usually require projection_dim=1024
* convert_open_clip_checkpoint use hidden_size for text_proj_dim
* convert_open_clip_checkpoint, revert checkpoint[text_proj_key].shape[1] -> [0]
values are identical
---------
Co-authored-by: Teriks <Teriks@users.noreply.github.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* fix-copies went uncaught it seems.
* remove more unneeded encode_prompt() tests
* Revert "fix-copies went uncaught it seems."
This reverts commit eefb302791.
* empty
PIPELINE_USAGE_CUTOFF:1000000000# set high cutoff so that only always-test pipelines run
jobs:
check_code_quality:
runs-on:ubuntu-22.04
steps:
- uses:actions/checkout@v3
- name:Set up Python
uses:actions/setup-python@v4
with:
python-version:"3.8"
- name:Install dependencies
run:|
python -m pip install --upgrade pip
pip install .[quality]
- name:Check quality
run:make quality
- name:Check if failure
if:${{ failure() }}
run:|
echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make style && make quality'" >> $GITHUB_STEP_SUMMARY
check_repository_consistency:
needs:check_code_quality
runs-on:ubuntu-22.04
steps:
- uses:actions/checkout@v3
- name:Set up Python
uses:actions/setup-python@v4
with:
python-version:"3.8"
- name:Install dependencies
run:|
python -m pip install --upgrade pip
pip install .[quality]
- name:Check repo consistency
run:|
python utils/check_copies.py
python utils/check_dummies.py
python utils/check_support_list.py
make deps_table_check_updated
- name:Check if failure
if:${{ failure() }}
run:|
echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
[FasterCache](https://huggingface.co/papers/2410.19355) from Zhengyao Lv, Chenyang Si, Junhao Song, Zhenyu Yang, Yu Qiao, Ziwei Liu, Kwan-Yee K. Wong.
FasterCache is a method that speeds up inference in diffusion transformers by:
- Reusing attention states between successive inference steps, due to high similarity between them
- Skipping unconditional branch prediction used in classifier-free guidance by revealing redundancies between unconditional and conditional branch outputs for the same timestep, and therefore approximating the unconditional branch output using the conditional branch output
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
<!--Copyright 2025 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
@@ -20,10 +20,13 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
- [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
- [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox).
- [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
- [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
- [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
- [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
- [`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).
- [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
@@ -56,6 +59,9 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
<!--Copyright 2025 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.
-->
# AutoModel
The `AutoModel` is designed to make it easy to load a checkpoint without needing to know the specific model class. `AutoModel` automatically retrieves the correct model class from the checkpoint `config.json` file.
<!--Copyright 2025 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.
-->
# SanaControlNetModel
The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
The abstract from the paper is:
*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
This model was contributed by [ishan24](https://huggingface.co/ishan24). ❤️
The original codebase can be found at [NVlabs/Sana](https://github.com/NVlabs/Sana), and you can find official ControlNet checkpoints on [Efficient-Large-Model's](https://huggingface.co/Efficient-Large-Model) Hub profile.
AuraFlow can be compiled with `torch.compile()` to speed up inference latency even for different resolutions. First, install PyTorch nightly following the instructions from [here](https://pytorch.org/). The snippet below shows the changes needed to enable this:
Specifying `use_duck_shape` to be `False` instructs the compiler if it should use the same symbolic variable to represent input sizes that are the same. For more details, check out [this comment](https://github.com/huggingface/diffusers/pull/11327#discussion_r2047659790).
This enables from 100% (on low resolutions) to a 30% (on 1536x1536 resolution) speed improvements.
Thanks to [AstraliteHeart](https://github.com/huggingface/diffusers/pull/11297/) who helped us rewrite the [`AuraFlowTransformer2DModel`] class so that the above works for different resolutions ([PR](https://github.com/huggingface/diffusers/pull/11297/)).
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
<!--Copyright 2025 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
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