Text-to-Image
Diffusion Single File
MiniMax H3
Pruna AI
English
comfyui
image-to-image
image-generation
image-editing
prompt-upsampling
lora
int8
4-bit precision
Instructions to use PrunaAI/P-H3-Image-Comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use PrunaAI/P-H3-Image-Comfy 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
- Pruna AI
How to use PrunaAI/P-H3-Image-Comfy with Pruna AI:
from pruna import PrunaModel model = PrunaModel.from_pretrained("PrunaAI/P-H3-Image-Comfy") - Notebooks
- Google Colab
- Kaggle
text encoder of choice in prompt upsampler
#11
by squallelf - opened
I am not really familiar with LLM quantization. but for image generation, 4 bits model seems too bad to the quality.
But in the world of LLM, i seems commonly encounter Q4 model. Is using Q4 model in LLM still good to go?
Actually, what i would like to ask is, if i use the W4A8 prompt upsampler and text encoder, will it still a good choice without much quality loss?
And also i would like to know if the vision tower in W4A8 CLIP also 4 bits quantized? or fully preserved? since we almost always have image input, so the vision of CLIP is somehow quite important.