Instructions to use QuantFunc/Minimax-H3-Quantfunc-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QuantFunc/Minimax-H3-Quantfunc-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QuantFunc/Minimax-H3-Quantfunc-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
是否支持Comfyui直接使用?模型什么时候发布?
是否支持Comfyui直接使用?模型什么时候发布?
yes, i will share the workflow tomorrow
thanks
Is the model open source? Why does the current workflow require API keys? Will it be needed in the future? Will there still be updates to versions that do not require keys?
Get your own API key and use QuantFunc in ComfyUI completely free—or integrate the engine into your code with one of our SDK plans for performance that’s 50% faster than the ComfyUI plugin. See our pricing plans.
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thx for remained,
Which model were you using before? Also, you can try setting block_cache to 0.0.3.
btw you can turn the pinned_memory on
it will speed up the hold process when works with low vram like 8G~20G
