Instructions to use Comfy-Org/Qwen-Image-2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use Comfy-Org/Qwen-Image-2.1 with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Where are the fp8 quants?
No more fp8 quants? This is starting to being ridiculous, now even the text encoder is unnecessary quantized as Int8 ConvRot. It would be great to have at least a fp8 version, because Int8 ConvRot is significantly slower on non blackwell or enterprise GPUs.
Comfyui team already tested int8 is the best for all GPU, old and new GPU. Please check previous blog. Or ask AI to verify / find this blog for you.
Comfyui team already tested int8 is the best for all GPU, old and new GPU. Please check previous blog. Or ask AI to verify / find this blog for you.
Yes, all GPUs, except mine (and others)... How can you simply ignore this whole suggestion and say "Oh, they said it is better, so it's better! π§ π₯". That's crazy... I have the same issue of these users: "https://huggingface.co/Comfy-Org/Qwen-Image-2.1/discussions/8#6ab172c779e657da3a84337b" and "https://github.com/Comfy-Org/ComfyUI/issues/16470". GGUF being significantly fast and better in output quality, so what do you really want to say? And plus, why even the text encoder is also in Int8 ConvRot? I searched for a good reason but this doesn't make sense, considering the fact that you need cu130 to archive the best of this quant "https://github.com/Comfy-Org/ComfyUI/issues/16470#issuecomment-5779280565".