7221eef457
* docs: fix latent -> latents in LTX latent upsampler docstrings __call__ takes 'latents' (the docstring said 'latent', and its own description says 'Input latents to normalize'). * docs: fix 'promt' typo in kandinsky5 default prompt templates The default system template sent to the Qwen text encoder misspelled 'prompt engineer' as 'promt engineer' (4 pipelines). Note: this changes the token sequence of the default template, so default outputs will differ numerically from before / from the upstream Kandinsky5 reference. If exact parity with the reference repo is preferred, the reference itself would need the same fix. * docs: fix docstring parameter names that do not match signatures - flux controlnet encode_prompt documented a clip_skip parameter that does not exist (copy-paste from SD-style docs; flux has no CLIP layer-skipping) - hunyuan_video1_5 pipelines documented num_images_per_prompt while the parameter is num_videos_per_prompt - kandinsky5 _encode_prompt_qwen/_encode_prompt_clip documented num_videos_per_prompt, which neither method accepts * docs(zh): fix English anchors pointing at translated Chinese headings speed-memory-optims.md linked ./memory#model-offloading and #group-offloading while the zh memory page translates those headings (模型卸载 / 组卸载); using_peft_for_inference.md linked #hotswapping while the zh heading is 热切换. Repointed at the Chinese slugs. * docs: fix PAG anchor and the StableDiffusionPipelineSafe row in the SD overview - pag.md linked #pagappliedlayers; the heading '### pag_applied_layers' keeps its underscores (#pag_applied_layers) - the SD overview table linked ./stable_diffusion_safe, a doc page that no longer exists anywhere under docs/source; the row now names the deprecated pipeline as plain text instead of a dead link * docs: fix dead Masked Im2Im TOC anchors in community pipelines README The TOC rows linked #stable-diffusion-masked-im2im / #stable-diffusion-xl-masked-im2im; the actual headings are 'Masked Im2Im Stable Diffusion Pipeline' and 'Masked Im2Im Stable Diffusion Pipeline XL' (lines 3184/3219). * docs(examples): drop duplicated 'with' in T5/Gemma sequence-length help strings 16 example scripts' --max_sequence_length help read 'to use with with the T5 text encoder' (or the Gemma variant); surfaced in --help output. * fix(links): keep the pag_applied_layers anchor in doc-builder form doc-builder slugifies headings by dropping underscores, so the heading '### pag_applied_layers' anchors as #pagappliedlayers — the form the check-links CI expects. GitHub-style underscores broke it. Signed-off-by: simpleqt <89645338+simpleqt@users.noreply.github.com> * fix(kandinsky5): recompute prompt_template_encode_start_idx after the promt fix Correcting 'promt' -> 'prompt' in the system templates shifts each template's token count by one (verified with the Qwen2.5-VL tokenizer: 41->40, 55->54, 129->128, 129->128). Without this, the hardcoded start indices slice the encoded prompt one token off. --------- Signed-off-by: simpleqt <89645338+simpleqt@users.noreply.github.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