9a7f824645
* add ostris trainer to README & add cache latents of vae
* add ostris trainer to README & add cache latents of vae
* style
* readme
* add test for latent caching
* add ostris noise scheduler
9ee1ef2a0a/toolkit/samplers/custom_flowmatch_sampler.py (L95)
* style
* fix import
* style
* fix tests
* style
* --change upcasting of transformer?
* update readme according to main
* add pivotal tuning for CLIP
* fix imports, encode_prompt call,add TextualInversionLoaderMixin to FluxPipeline for inference
* TextualInversionLoaderMixin support for FluxPipeline for inference
* move changes to advanced flux script, revert canonical
* add latent caching to canonical script
* revert changes to canonical script to keep it separate from https://github.com/huggingface/diffusers/pull/9160
* revert changes to canonical script to keep it separate from https://github.com/huggingface/diffusers/pull/9160
* style
* remove redundant line and change code block placement to align with logic
* add initializer_token arg
* add transformer frac for range support from pure textual inversion to the orig pivotal tuning
* support pure textual inversion - wip
* adjustments to support pure textual inversion and transformer optimization in only part of the epochs
* fix logic when using initializer token
* fix pure_textual_inversion_condition
* fix ti/pivotal loading of last validation run
* remove embeddings loading for ti in final training run (to avoid adding huggingface hub dependency)
* support pivotal for t5
* adapt pivotal for T5 encoder
* adapt pivotal for T5 encoder and support in flux pipeline
* t5 pivotal support + support fo pivotal for clip only or both
* fix param chaining
* fix param chaining
* README first draft
* readme
* readme
* readme
* style
* fix import
* style
* add fix from https://github.com/huggingface/diffusers/pull/9419
* add to readme, change function names
* te lr changes
* readme
* change concept tokens logic
* fix indices
* change arg name
* style
* dummy test
* revert dummy test
* reorder pivoting
* add warning in case the token abstraction is not the instance prompt
* experimental - wip - specific block training
* fix documentation and token abstraction processing
* remove transformer block specification feature (for now)
* style
* fix copies
* fix indexing issue when --initializer_concept has different amounts
* add if TextualInversionLoaderMixin to all flux pipelines
* style
* fix import
* fix imports
* address review comments - remove necessary prints & comments, use pin_memory=True, use free_memory utils, unify warning and prints
* style
* logger info fix
* make lora target modules configurable and change the default
* make lora target modules configurable and change the default
* style
* make lora target modules configurable and change the default, add notes to readme
* style
* add tests
* style
* fix repo id
* add updated requirements for advanced flux
* fix indices of t5 pivotal tuning embeddings
* fix path in test
* remove `pin_memory`
* fix filename of embedding
* fix filename of embedding
---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.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 | ✅ | ✅ | - |
| InstructPix2Pix | ✅ | ✅ | - |
| Reinforcement Learning for Control | - | - | coming soon. |
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