fda1531d8a
* CheckIn - created DownSubBlocks * Added extra channels, implemented subblock fwd * Fixed connection sizes * checkin * Removed iter, next in forward * Models for SD21 & SDXL run through * Added back pipelines, cleared up connections * Cleaned up connection creation * added debug logs * updated logs * logs: added input loading * Update umer_debug_logger.py * log: Loading hint * Update umer_debug_logger.py * added logs * Changed debug logging * debug: added more logs * Fixed num_norm_groups * Debug: Logging all of SDXL input * Update umer_debug_logger.py * debug: updated logs * checkim * Readded tests * Removed debug logs * Fixed Slow Tests * Added value ckecks | Updated model_cpu_offload_seq * accelerate-offloading works ; fast tests work * Made unet & addon explicit in controlnet * Updated slow tests * Added dtype/device to ControlNetXS * Filled in test model paths * Added image_encoder/feature_extractor to XL pipe * Fixed fast tests * Added comments and docstrings * Fixed copies * Added docs ; Updates slow tests * Moved changes to UNetMidBlock2DCrossAttn * tiny cleanups * Removed stray prints * Removed ip adapters + freeU - Removed ip adapters + freeU as they don't make sense for ControlNet-XS - Fixed imports of UNet components * Fixed test_save_load_float16 * Make style, quality, fix-copies * Changed loading/saving API for ControlNetXS - Changed loading/saving API for ControlNetXS - other small fixes * Removed ControlNet-XS from research examples * Make style, quality, fix-copies * Small fixes - deleted ControlNetXSModel.init_original - added time_embedding_mix to StableDiffusionControlNetXSPipeline .from_pretrained / StableDiffusionXLControlNetXSPipeline.from_pretrained - fixed copy hints * checkin May 11 '23 * CheckIn Mar 12 '24 * Fixed tests for SD * Added tests for UNetControlNetXSModel * Fixed SDXL tests * cleanup * Delete Pipfile * CheckIn Mar 20 Started replacing sub blocks by `ControlNetXSCrossAttnDownBlock2D` and `ControlNetXSCrossAttnUplock2D` * check-in Mar 23 * checkin 24 Mar * Created init for UNetCnxs and CnxsAddon * CheckIn * Made from_modules, from_unet and no_control work * make style,quality,fix-copies & small changes * Fixed freezing * Added gradient ckpt'ing; fixed tests * Fix slow tests(+compile) ; clear naming confusion * Don't create UNet in init ; removed class_emb * Incorporated review feedback - Deleted get_base_pipeline / get_controlnet_addon for pipes - Pipes inherit from StableDiffusionXLPipeline - Made module dicts for cnxs-addon's down/mid/up classes - Added support for qkv fusion and freeU * Make style, quality, fix-copies * Implemented review feedback * Removed compatibility check for vae/ctrl embedding * make style, quality, fix-copies * Delete Pipfile * Integrated review feedback - Importing ControlNetConditioningEmbedding now - get_down/mid/up_block_addon now outside class - renamed `do_control` to `apply_control` * Reduced size of test tensors For this, added `norm_num_groups` as parameter everywhere * Renamed cnxs-`Addon` to cnxs-`Adapter` - `ControlNetXSAddon` -> `ControlNetXSAdapter` - `ControlNetXSAddonDownBlockComponents` -> `DownBlockControlNetXSAdapter`, and similarly for mid/up - `get_mid_block_addon` -> `get_mid_block_adapter`, and similarly for mid/up * Fixed save_pretrained/from_pretrained bug * Removed redundant code --------- Co-authored-by: Dhruv Nair <dhruv.nair@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