huggingface / huggingface/diffusers
AnimateDiff SparseCtrl RGB does not work as expected
- 主要言語
- Python
- スター
- 34.5k
- フォーク
- 7.3k
- 平均マージ
- 3日 3時間
- マージ済み PR(30日)
- 91
説明
Relevant comments are [this](https://github.com/huggingface/diffusers/pull/8897#issuecomment-2255416318) and [this](https://github.com/huggingface/diffusers/pull/8897#issuecomment-2255478105).
AnimateDiff SparseCtrl RGB does not work similar to other implementations and cannot replicate their outputs. This makes me believe that there is something incorrect with our SparseControlNet or MotionAdapter implementation.
When comparing the results of the [original](https://github.com/guoyww/AnimateDiff)/[Comfy](https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved) implementation to Diffusers implementation, one can notice that if an image is used with an unrelated prompt, the Diffusers implementation ignores the image and just follows the prompt whereas the other implementations try to incorporate both.
Since the original and Comfy implementations produce this behaviour consistently, this seems more like a problem with Diffusers implementation. However, I've not been able to spot differences in implementation just by comparing the code visually. I also tried matching outputs layerwise and it seemed to be alright (although I didn't investigate this as deeply as I should have due to other priorities).
If someone from the community actively following/using the AnimateDiff implementations can help determine the cause of this bug, it would be really awesome and helpful.
コントリビューションガイド
調査の方向性
Start with the linked comments on PR 8897 and reproduce the AnimateDiff SparseCtrl RGB comparison using the original and Comfy implementations as references. Investigate the Diffusers SparseControlNet and MotionAdapter behavior with an unrelated prompt, and compare the conditioning results. Done means identifying and correcting the cause so the image influences the output rather than being ignored.
索引モデルが issue の本文から書いたものです。
評価
- 技術スタック
- python, pytorch
- 領域
- computer-vision, machine-learning
- issue の種類
- バグ
- 難易度
- 4/5
- 見積もり時間
- 3〜5日
- 活発さ
- 停滞
- 明瞭さ
- 説明が足りない
- 初心者へのやさしさ
- 35/100