diffusers
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π€ Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
### Describe the bug When using the SD3 ControlNet training script, the training dataset embeddings are precomputed and the results are given a fingerprint based on the input script arguments,...
Bumps [torch](https://github.com/pytorch/pytorch) from 2.2.0 to 2.7.1. Release notes Sourced from torch's releases. PyTorch 2.7.1 Release, bug fix release This release is meant to fix the following issues (regressions / silent...
### Describe the bug If a Hidream lora is trained using OneTrainer, some keys of the checkpoint have the ".alpha" in them instead of all of them including 'lora'. So...
# What does this PR do? ### What is TeaCache? [TeaCache](https://github.com/LiewFeng/TeaCache) (Timestep Embedding Aware Cache) is a training-free caching technique that speeds up diffusion model inference by **1.5x-2.6x** by reusing...
Hello folks π ## Whatβ Weβre excited to bring a contributor-focused program to you! In this program, we want to work and collaborate with serious contributors to Diffusers and reward...
# [Feature Request] Implement Selective Attention for Memory-Efficient Inference **Is your feature request related to a problem? Please describe.** Yes, it is hard to generate longer videos or process high-resolution...
# What does this PR do? This is a draft implementation of the Unified SP attention approach. - Implements `_all_to_all_dim_exchange` with custom scatter and gather indices - Implements `TemplatedUnifiedAttention` Core...
This PR is a continuation of the following discussion https://github.com/huggingface/diffusers/issues/4679 https://github.com/huggingface/diffusers/pull/4899, and it addresses the following issues: 1. Loading SAI's control-lora files and enabling controlled image generation. 2. Building a...
# What does this PR do? This PR introduces the LongCat-Image Series into the diffusers library. The LongCat-Image Series comprises powerful and highly efficient 6B-parameter models, featuring both a state-of-the-art...
In diffusion denoising model, we usually use `classifier free guidance` to control qualities. With [State Manager](https://github.com/huggingface/diffusers/blob/152f7ca357c066c4af3d1a58cdf17662ef5a2f87/src/diffusers/hooks/hooks.py#L42), we want each context hold different `state`, so we have to set which context...