Instructions to use Comfy-Org/MOSS-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use Comfy-Org/MOSS-Image 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
GGUF Versions
It would be helpful if GGUF versions like the ones from https://huggingface.co/realrebelai/MOSS-Image_GGUFs were included in this package. That way, people using lower-VRAM GPUs could run the model more easily.
What is low VRAM in this case? The model is already so small and int8 is going to be a lot faster for most GPUs. GGUFs aren't and won't be supported, but 4bit quant can be added if there's actual use for it.
4bit quants make it possible for people with 6gb vram to run it with TE offload to CPU
It would be helpful if GGUF versions like the ones from https://huggingface.co/realrebelai/MOSS-Image_GGUFs were included in this package. That way, people using lower-VRAM GPUs could run the model more easily.
Of course this would be helpful, they will not support this because they hate anything that isn't Int8 ConvRot 😂 (I'm being ironic, they just don't want to waste their time, and yes, they don't like GGUF too).
Fairly certain the int8 version runs faster and with higher quality even with 6GB, at least when the GPU has int8 support on hardware level. GGUF is a needlessly slow format for diffusion models.
a 5.41 GB file is not gonna fit into 6GB VRAM since the weights arent the only thing eating up VRAM. Thats exactly why int4 convrot would be useful.