Switch to AutoencoderTiny in Diffusers Examples
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Description
I'm trying to switch to AutoencoderTiny from AutoencoderKL in demo_txt2img_xl with Turbo model. After some attempts to changing models.py it finally works, but images comes with artifacts.
python demo_txt2img_xl.py "Einstein" --version xl-turbo --onnx-dir onnx-sdxl-turbo --engine-dir engine-sdxl-turbo --denoising-steps 1 --scheduler EulerA --guidance-scale 0 --width 512 --height 512
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with demo/Diffusion/models.py and demo_txt2img_xl.py, then run the provided xl-turbo command to reproduce the artifacts. Trace the AutoencoderTiny switch in the example and compare its generated output with the shown result. Done means the example uses AutoencoderTiny with the Turbo model and produces images without the reported artifacts.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100