Diffusers
Safetensors
MiniMax H3
video-generation
reference-to-video
text-to-audio-video
fastvideo
fasth3-omni
nvfp4
qad
Instructions to use FastVideo/FastH3-Omni-NVFP4-QAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastH3-Omni-NVFP4-QAD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/FastH3-Omni-NVFP4-QAD", 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
Download vae_decoder_nvfp4_fast/config.json from FastVideo/FastH3-Omni-NVFP4-QAD: direct link, hf CLI and curl.
- Browser
- Download file 1.8 kB
-
https://huggingface.co/FastVideo/FastH3-Omni-NVFP4-QAD/resolve/main/vae_decoder_nvfp4_fast/config.json
- Command line
-
hf download hf://FastVideo/FastH3-Omni-NVFP4-QAD/vae_decoder_nvfp4_fast/config.json
-
curl -L -o config.json https://huggingface.co/FastVideo/FastH3-Omni-NVFP4-QAD/resolve/main/vae_decoder_nvfp4_fast/config.json
1.8 kB
| { | |
| "name": "FastH3 NVFP4 VAE decoder (fast-8L)", | |
| "format": "fastvideo_h3_decoder_nvfp4_deploy_v1", | |
| "architecture": "MiniMaxH3VideoViTDecoder3d", | |
| "decoder_blocks": 8, | |
| "hidden_size": 2048, | |
| "nvfp4": { | |
| "scheme": "W4A4 NVFP4 (E2M1 values, E4M3 per-16 block scales, FP32 per-tensor scale)", | |
| "block_size": 16, | |
| "activation_global_scale": "unit", | |
| "rotation_group": null, | |
| "quantized_linears": 48, | |
| "quantized_layers": "transformer_blocks.*.{attn.to_q,attn.to_k,attn.to_v,attn.to_out.0,ff.net.0.proj,ff.net.2}", | |
| "high_precision": "proj_in, proj_out, norms, register tokens, post_quant_conv" | |
| }, | |
| "compute_dtype": "bfloat16 autocast", | |
| "host_vae": "any MiniMax-H3 video VAE with >= 8 decoder blocks (e.g. the 26-block light VAE of the FastH3 V2/Trim stacks); the loader keeps 8", | |
| "tensor_count_by_dtype": { | |
| "float32": 89, | |
| "bfloat16": 96 | |
| }, | |
| "nvfp4_linear_parameters": 537083904, | |
| "source_step": 5000, | |
| "files": { | |
| "decoder.safetensors": { | |
| "format": "fastvideo_h3_decoder_nvfp4_deploy_v1", | |
| "contents": "NVFP4 linears as bf16 master weights, packed to NVFP4 at load; other tensors in their trained dtype", | |
| "bytes": 1099886160, | |
| "use": "reference weights, re-quantization, further training" | |
| }, | |
| "decoder-nvfp4.safetensors": { | |
| "format": "fastvideo_h3_decoder_nvfp4_packed_v1", | |
| "contents": "pre-packed NVFP4: per linear packed_weight (uint8, two E2M1 codes per byte), weight_inv_scale (E4M3 per-16 block scales, FlashInfer SfLayout.layout_128x4 swizzle), weight_global_sf (fp32), bias (bf16), input_amax (fp32); other tensors in their trained dtype (fp32)", | |
| "bytes": 328147232, | |
| "use": "smallest download and lowest load memory; Blackwell only; decodes bit-identically to decoder.safetensors" | |
| } | |
| } | |
| } | |