dad5cb55e6
* Fix QwenImage txt_seq_lens handling * formatting * formatting * remove txt_seq_lens and use bool mask * use compute_text_seq_len_from_mask * add seq_lens to dispatch_attention_fn * use joint_seq_lens * remove unused index_block * WIP: Remove seq_lens parameter and use mask-based approach - Remove seq_lens parameter from dispatch_attention_fn - Update varlen backends to extract seqlens from masks - Update QwenImage to pass 2D joint_attention_mask - Fix native backend to handle 2D boolean masks - Fix sage_varlen seqlens_q to match seqlens_k for self-attention Note: sage_varlen still producing black images, needs further investigation * fix formatting * undo sage changes * xformers support * hub fix * fix torch compile issues * fix tests * use _prepare_attn_mask_native * proper deprecation notice * add deprecate to txt_seq_lens * Update src/diffusers/models/transformers/transformer_qwenimage.py Co-authored-by: YiYi Xu <yixu310@gmail.com> * Update src/diffusers/models/transformers/transformer_qwenimage.py Co-authored-by: YiYi Xu <yixu310@gmail.com> * Only create the mask if there's actual padding * fix order of docstrings * Adds performance benchmarks and optimization details for QwenImage Enhances documentation with comprehensive performance insights for QwenImage pipeline: * rope_text_seq_len = text_seq_len * rename to max_txt_seq_len * removed deprecated args * undo unrelated change * Updates QwenImage performance documentation Removes detailed attention backend benchmarks and simplifies torch.compile performance description Focuses on key performance improvement with torch.compile, highlighting the specific speedup from 4.70s to 1.93s on an A100 GPU Streamlines the documentation to provide more concise and actionable performance insights * Updates deprecation warnings for txt_seq_lens parameter Extends deprecation timeline for txt_seq_lens from version 0.37.0 to 0.39.0 across multiple Qwen image-related models Adds a new unit test to verify the deprecation warning behavior for the txt_seq_lens parameter * fix compile * formatting * fix compile tests * rename helper * remove duplicate * smaller values * removed * use torch.cond for torch compile * Construct joint attention mask once * test different backends * construct joint attention mask once to avoid reconstructing in every block * Update src/diffusers/models/attention_dispatch.py Co-authored-by: YiYi Xu <yixu310@gmail.com> * formatting * raising an error from the EditPlus pipeline when batch_size > 1 --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: cdutr <dutra_carlos@hotmail.com>
🧨 Diffusers Examples
Diffusers examples are a collection of scripts to demonstrate how to effectively use the diffusers library
for a variety of use cases involving training or fine-tuning.
Note: If you are looking for official examples on how to use diffusers for inference, please have a look at src/diffusers/pipelines.
Our examples aspire to be self-contained, easy-to-tweak, beginner-friendly and for one-purpose-only. More specifically, this means:
- Self-contained: An example script shall only depend on "pip-install-able" Python packages that can be found in a
requirements.txtfile. Example scripts shall not depend on any local files. This means that one can simply download an example script, e.g. train_unconditional.py, install the required dependencies, e.g. requirements.txt and execute the example script. - Easy-to-tweak: While we strive to present as many use cases as possible, the example scripts are just that - examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data and the training loop to allow you to tweak and edit them as required.
- Beginner-friendly: We do not aim for providing state-of-the-art training scripts for the newest models, but rather examples that can be used as a way to better understand diffusion models and how to use them with the
diffuserslibrary. We often purposefully leave out certain state-of-the-art methods if we consider them too complex for beginners. - One-purpose-only: Examples should show one task and one task only. Even if a task is from a modeling point of view very similar, e.g. image super-resolution and image modification tend to use the same model and training method, we want examples to showcase only one task to keep them as readable and easy-to-understand as possible.
We provide official examples that cover the most popular tasks of diffusion models.
Official examples are actively maintained by the diffusers maintainers and we try to rigorously follow our example philosophy as defined above.
If you feel like another important example should exist, we are more than happy to welcome a Feature Request or directly a Pull Request from you!
Training examples show how to pretrain or fine-tune diffusion models for a variety of tasks. Currently we support:
| Task | 🤗 Accelerate | 🤗 Datasets | Colab |
|---|---|---|---|
| Unconditional Image Generation | ✅ | ✅ |  |
| Text-to-Image fine-tuning | ✅ | ✅ | |
| Textual Inversion | ✅ | - | |
| Dreambooth | ✅ | - | |
| ControlNet | ✅ | ✅ | Notebook |
| InstructPix2Pix | ✅ | ✅ | Notebook |
| Reinforcement Learning for Control | - | - | Notebook1, Notebook2 |
Community
In addition, we provide community examples, which are examples added and maintained by our community.
Community examples can consist of both training examples or inference pipelines.
For such examples, we are more lenient regarding the philosophy defined above and also cannot guarantee to provide maintenance for every issue.
Examples that are useful for the community, but are either not yet deemed popular or not yet following our above philosophy should go into the community examples folder. The community folder therefore includes training examples and inference pipelines.
Note: Community examples can be a great first contribution to show to the community how you like to use diffusers 🪄.
Research Projects
We also provide research_projects examples that are maintained by the community as defined in the respective research project folders. These examples are useful and offer the extended capabilities which are complementary to the official examples. You may refer to research_projects for details.
Important note
To make sure you can successfully run the latest versions of the example scripts, you have to install the library from source and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
Then cd in the example folder of your choice and run
pip install -r requirements.txt