5ffb73d4ae
* add vae * Initial commit for Flux 2 Transformer implementation * add pipeline part * small edits to the pipeline and conversion * update conversion script * fix * up up * finish pipeline * Remove Flux IP Adapter logic for now * Remove deprecated 3D id logic * Remove ControlNet logic for now * Add link to ViT-22B paper as reference for parallel transformer blocks such as the Flux 2 single stream block * update pipeline * Don't use biases for input projs and output AdaNorm * up * Remove bias for double stream block text QKV projections * Add script to convert Flux 2 transformer to diffusers * make style and make quality * fix a few things. * allow sft files to go. * fix image processor * fix batch * style a bit * Fix some bugs in Flux 2 transformer implementation * Fix dummy input preparation and fix some test bugs * fix dtype casting in timestep guidance module. * resolve conflicts., * remove ip adapter stuff. * Fix Flux 2 transformer consistency test * Fix bug in Flux2TransformerBlock (double stream block) * Get remaining Flux 2 transformer tests passing * make style; make quality; make fix-copies * remove stuff. * fix type annotaton. * remove unneeded stuff from tests * tests * up * up * add sf support * Remove unused IP Adapter and ControlNet logic from transformer (#9) * copied from * Apply suggestions from code review Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: apolinário <joaopaulo.passos@gmail.com> * up * up * up * up * up * Refactor Flux2Attention into separate classes for double stream and single stream attention * Add _supports_qkv_fusion to AttentionModuleMixin to allow subclasses to disable QKV fusion * Have Flux2ParallelSelfAttention inherit from AttentionModuleMixin with _supports_qkv_fusion=False * Log debug message when calling fuse_projections on a AttentionModuleMixin subclass that does not support QKV fusion * Address review comments * Update src/diffusers/pipelines/flux2/pipeline_flux2.py Co-authored-by: YiYi Xu <yixu310@gmail.com> * up * Remove maybe_allow_in_graph decorators for Flux 2 transformer blocks (#12) * up * support ostris loras. (#13) * up * update schdule * up * up (#17) * add training scripts (#16) * add training scripts Co-authored-by: Linoy Tsaban <linoytsaban@gmail.com> * model cpu offload in validation. * add flux.2 readme * add img2img and tests * cpu offload in log validation * Apply suggestions from code review * fix * up * fixes * remove i2i training tests for now. --------- Co-authored-by: Linoy Tsaban <linoytsaban@gmail.com> Co-authored-by: linoytsaban <linoy@huggingface.co> * up --------- Co-authored-by: yiyixuxu <yixu310@gmail.com> Co-authored-by: Daniel Gu <dgu8957@gmail.com> Co-authored-by: yiyi@huggingface.co <yiyi@ip-10-53-87-203.ec2.internal> Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com> Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> Co-authored-by: apolinário <joaopaulo.passos@gmail.com> Co-authored-by: yiyi@huggingface.co <yiyi@ip-26-0-160-103.ec2.internal> Co-authored-by: Linoy Tsaban <linoytsaban@gmail.com> Co-authored-by: linoytsaban <linoy@huggingface.co>
🧨 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