Instructions to use Abiray/Minimax-H3-nvfp4-INT4-INT8-Convrot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Abiray/Minimax-H3-nvfp4-INT4-INT8-Convrot with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Abiray/Minimax-H3-nvfp4-INT4-INT8-Convrot", 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
smaller text encoder?
is that possible to have TE in a smaller quantization like int4😂 it's even bigger than the diffusion model
Yeah, give me some time
Yeah, give me some time
oh, sorry but I saw one here😂nearly as big as nvfp4 https://huggingface.co/Merserk/MiniMax-H3-INT4-ConvRot/blob/main/qwen3vl_32b_minimax_h3_int4_convrot.safetensors
I tested a Qwen3-VL-8B encoder with MiniMax H3 using an experimental projection adapter, and it works locally.
Setup:
- Put
qwen3vl_8b_nvfp4.safetensorsinComfyUI/models/text_encoders/. - Put
mmh3-8b-ClipProj-celeb-mlp.safetensorsinComfyUI/models/clip_projections/. - Load
qwen3vl_8b_nvfp4.safetensorswithCLIPLoaderand settypetoboogu. - Connect the
CLIPLoaderoutput toClipProjApply, and selectmmh3-8b-ClipProj-celeb-mlp.safetensors. - Connect the
ClipProjApplyoutput to the H3 prompt/reference-image conditioning path. The rest of the H3 workflow can remain unchanged.
The 8B encoder should not be connected directly to H3. Qwen3-VL-8B produces 4096-dimensional features, while H3 expects 5120-dimensional conditioning. ClipProjApply performs the required 4096 -> 5120 projection.
This worked in my local testing, but it is a community/experimental adapter rather than official native H3 support for Qwen3-VL-8B. I would still compare identity preservation, fine details, and prompt understanding against the original encoder.