github huggingface/diffusers v0.4.0
v0.4.0 Better, faster, stronger!
on GitHub

latest releases: v0.30.3, v0.30.2, v0.30.1...
24 months ago

🚗 Faster

We have thoroughly profiled our codebase and applied a number of incremental improvements that, when combined, provide a speed improvement of almost 3x.

On top of that, we now default to using the float16 format. It's much faster than float32 and, according to our tests, produces images with no discernible difference in quality. This beats the use of autocast, so the resulting code is cleaner!

🔑 use_auth_token no more

The recently released version of huggingface-hub automatically uses your access token if you are logged in, so you don't need to put it everywhere in your code. All you need to do is authenticate once using huggingface-cli login in your terminal and you're all set. 

- pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)
+ pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")

We bumped huggingface-hub version to 0.10.0 in our dependencies to achieve this.

🎈More flexible APIs

  • Schedulers now use a common, simpler unified API design. This has allowed us to remove many conditionals and special cases in the rest of the code, including the pipelines. This is very important for us and for the users of 🧨 diffusers: we all gain clarity and a solid abstraction for schedulers. See the description in #719 for more details

Please update any custom Stable Diffusion pipelines accordingly: 

- if isinstance(self.scheduler, LMSDiscreteScheduler):
-    latents = latents * self.scheduler.sigmas[0]
+ latents = latents * self.scheduler.init_noise_sigma
- if isinstance(self.scheduler, LMSDiscreteScheduler):
-     sigma = self.scheduler.sigmas[i]
-     latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
- if isinstance(self.scheduler, LMSDiscreteScheduler):
-     latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
- else:
-     latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
  • Pipeline callbacks. As a community project (h/t @jamestiotio!), diffusers pipelines can now invoke a callback function during generation, providing the latents at each step of the process. This makes it easier to perform tasks such as visualization, inspection, explainability and others the community may invent.

🛠️ More tasks

Building on top of the previous foundations, this release incorporates several new tasks that have been adapted from research papers or community projects. These include:

  • Textual inversion. Makes it possible to quickly train a new concept or style and incorporate it into the vocabulary of Stable Diffusion. Hundreds of people have already created theirs, and they can be shared and combined together. See the training Colab to get started.
  • Dreambooth. Similar goal to textual inversion, but instead of creating a new item in the vocabulary it fine-tunes the model to make it learn a new concept. Training Colab.
  • Negative prompts. Another community effort led by @shirayu. The Stable Diffusion pipeline can now receive both a positive prompt (the one you want to create), and a negative prompt (something you want to drive the model away from). This opens up a lot of creative possibilities!

🏃‍♀️ Under the hood changes to support better fine-tuning

Gradient checkpointing and 8-bit optimizers have been successfully applied to achieve Dreambooth fine-tuning in a Colab notebook! These updates will make it easier for diffusers to support general-purpose fine-tuning (coming soon!).

⚠️ Experimental: community pipelines

This is big, but it's still an experimental feature that may change in the future.

We are constantly amazed at the amount of imagination and creativity in the diffusers community, so we've made it easy to create custom pipelines and share them with others. You can write your own pipeline code, store it in 🤗 Hub, GitHub or your local filesystem and StableDiffusionPipeline.from_pretrained will be able to load and run it. Read more in the documentation.

We can't wait to see what new tasks the community creates!

💪 Quality of life fixes

Bug fixing, improved documentation, better tests are all important to ensure diffusers is a high-quality codebase, and we always spend a lot of effort working on them. Several first-time contributors have helped here, and we are very grateful for their efforts!

🙏 Significant community contributions

The following people have made significant contributions to the library over the last release:

  • @Victarry – Add training example for DreamBooth (#554)
  • @jamestiotio – Add callback parameters for Stable Diffusion pipelines (#521)
  • @jachiam – Allow resolutions that are not multiples of 64 (#505)
  • @johnowhitaker – Adding pred_original_sample to SchedulerOutput for some samplers (#614).
  • @keturn – Interesting discussions and insights on many topics.

✏️ Change list

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