--- license: other license_name: minimax-h3-community license_link: LICENSE pipeline_tag: text-to-video library_name: radiance tags: - radiance - rocm - amd - rdna4 - fp8 - text-to-video - text-to-audio-video - distillation - few-step - minimax-h3 - fasth3 - latent-upscaler base_model: - FastVideo/FastVideo-FastH3-8-Step-V2 - LBH-123-AI/Minimax_h3_latent_Upscaler --- # FastH3 8-Step V2, FP8 — radiance container FastVideo's [FastVideo-FastH3-8-Step-V2](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2) — MiniMax-H3 distilled to eight steps with video sparse attention: video with a synchronized stereo soundtrack from text — as a single `.rad` container for the [radiance](https://codeberg.org/StillDeadcode/radiance) inference engine (AMD RDNA4, ROCm). The MiniMax H3 Community License does not cover the European Union, the United Kingdom, the Republic of Korea or the United States of America; see [License](#license). | | | |---|---| | File | `fasth3-8-step-v2-fp8.rad` — 62.30 GiB | | Weights | every linear of the text encoder (the 50 layers of Qwen3-VL-32B the model reads), the token refiner and the DiT's 50 blocks, and the modulation projections, FP8 E4M3 per 128×128 block (bf16 scales); the video decoder bf16; the audio decoder f32 | | Steps | 8, the schedule the model was trained on | | Output | MP4: H.264 at 24 fps, up to 1344x768, 5.17 to 14.4 s; AAC at 32 kHz stereo | | Latent upscaler | [LBH-123-AI/Minimax_h3_latent_Upscaler](https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler) v1 (3D conv, 345M parameters, bf16; Apache-2.0) | | Needs | radiance 1.8.1 or newer (the upscaler, and prompts over 16 tokens, need 1.8.1); two 32 GB cards and about 39 GiB of pinned host memory | ## Serve ```sh radiance --model fasth3-8-step-v2-fp8.rad --tp 2 --vision-tower-placement ram --host 0.0.0.0 --port 8000 curl -s http://localhost:8000/v1/videos -H 'Content-Type: application/json' -d '{ "prompt": "A red fox trotting through a snowy pine forest, snow crunching underfoot", "seconds": "5", "size": "1344x768", "seed": 7}' # -> {"id": "video_...", "status": "queued", ...} curl -s http://localhost:8000/v1/videos/video_... # poll until "status": "completed" curl -s http://localhost:8000/v1/videos/video_.../content -o fox.mp4 ``` The model is served as `fastvideo-fasth3-8-step-v2` on OpenAI's video jobs API (`POST /v1/videos`, `GET /v1/videos/{id}`, `GET /v1/videos/{id}/content`, list and delete), with SGLang's `target` field: - `prompt` is read as written, up to 2048 tokens. `seconds` is rounded up to the model's length steps (17 frames): 5.17 to 14.4 s. `size` is `WIDTHxHEIGHT`, multiples of 32, at most 1344x768 pixels of area. `seed` makes a video repeatable on the same deployment. - The model runs only the schedule it was trained on: `num_inference_steps` may be left out or be 9 (grid points, as diffusers counts them); any other count is refused. - Text to video and audio only. FastVideo notes that difficult motion, fine detail and some audio may fall below the base model's. On two Radeon AI PRO R9700s (gfx1201) a 5.17 s clip takes about 68 s at 1344x768, 38 s at 1024x576 and 17 s at 672x384; MiniMax-H3 at its default 50 steps takes about 8.8 minutes at 1344x768 ([StillDeadcode/minimax-h3-fp8](https://huggingface.co/StillDeadcode/minimax-h3-fp8)). Attention skips 80% of the 64-token video tiles, as the model was trained. The text encoder and the modulation projections are read from pinned host memory, which `--vision-tower-placement ram` selects. At 256x256, frames match FastVideo's reference pipeline by eye. ## Latent upscaling The container carries LBH-123-AI's latent upscaler. A request with `"latent_upscale": 2` runs its first steps at half the size (672x384 for 1344x768), the upscaler enlarges the clean latent, and the last `upscale_refine_steps` (3 of the 8 by default) run at the full size from a re-noised copy of it: ```sh curl -s http://localhost:8000/v1/videos -H 'Content-Type: application/json' -d '{ "prompt": "...", "seconds": "5", "size": "1344x768", "seed": 7, "latent_upscale": 2}' ``` | 5.17 s at 1344x768, 2x R9700 | time | |---|---| | native, 8 steps | 68 s | | `latent_upscale` 2, 3 refine steps (the default) | 41 s | | `latent_upscale` 2, 4 refine steps | 46 s | | `latent_upscale` 4, 3 refine steps | 35 s | The factor goes up to 4. At 1344x768 a factor of 4 starts from 352x192; FastH3 still lays the scene out there, a little softer than at 2. The video object of an upscaled video reports `latent_upscale`, `upscale_refine_steps` and `upscale_from` (the small size). ## How this file was made ```sh rad-convert FastVideo/FastVideo-FastH3-8-Step-V2 --recipe minimax-h3-fp8.recipe -o fasth3-8-step-v2-fp8.rad ``` `FastVideo/FastVideo-FastH3-8-Step-V2` is the downloaded pipeline directory; its text encoder and decoders are MiniMax-H3's own files. The recipe (`minimax-h3-fp8.recipe` in this repository, the same as MiniMax-H3's) names what is quantised; everything it does not name is the checkpoint's own dtype: ``` te.blk.*.attn_qkv.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 te.blk.*.attn_output.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 te.blk.*.ffn_gate_up.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 te.blk.*.ffn_down.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 te.blk.*.attn_norm.weight cast dtype=f32 te.blk.*.ffn_norm.weight cast dtype=f32 ref.blk.*.attn_qkv.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 ref.blk.*.attn_output.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 ref.blk.*.ffn_gate_up.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 ref.blk.*.ffn_down.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 dit.blk.*.attn_qkv.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 dit.blk.*.attn_output.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 dit.blk.*.ffn_gate_up.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 dit.blk.*.ffn_down.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 dit.blk.*.ada*.weight rtn codes=fp8_e4m3 block=128x128 scale=bf16 vae.blk.*.attn_qkv.weight cast dtype=bf16 vae.blk.*.attn_out.weight cast dtype=bf16 vae.blk.*.ffn_up.weight cast dtype=bf16 vae.blk.*.ffn_down.weight cast dtype=bf16 vae.proj_out.* cast dtype=bf16 aud.*.weight cast dtype=f32 ups.embed.* cast dtype=f32 ups.*.emb_layers.* cast dtype=f32 ``` The upscaler is the companion folder `latent_upscaler/` of the pipeline directory (its `minimax_h3_latent_upscaler_3d_conv_v1/config.json` and bf16 weights, renamed `diffusion_pytorch_model.safetensors`); this file had it added to the 1.8.0 container in place (`rad-convert ... --reuse fasth3-8-step-v2-fp8.rad --in-place`), which leaves every other weight as it was. ## License FastH3 8-Step V2 is a modified MiniMax H3 model from the FastVideo team, and MiniMax H3 is licensed under the MiniMax H3 Community License Agreement, Copyright © 2026 MiniMax (`LICENSE` in this repository; `NOTICE` carries its notice and FastVideo's). The agreement grants its rights only within its Applicable Territory: worldwide except the European Union, the United Kingdom, the Republic of Korea and the United States of America, where it does not license using, reproducing or distributing the model or this file. Its use restrictions and Acceptable Use Policy (Exhibit A of `LICENSE`) apply to everyone who downloads or uses this file, and to anyone it is passed on to. This container is a modified copy of FastVideo-FastH3-8-Step-V2: the linears named above are re-encoded as FP8, the video decoder's linears cast from f32 to bf16, the audio decoder's weight norms folded, the text encoder's last 14 layers (which the model does not read) left out, and everything repacked into one `.rad` file. The text encoder is Qwen3-VL-32B, licensed under the Apache License 2.0 (`LICENSE-Qwen3-VL`). The latent upscaler is LBH-123-AI's Minimax_h3_latent_Upscaler v1, licensed under the Apache License 2.0 (`LICENSE-latent-upscaler`); its weights are included as published, its embedding MLP cast from bf16 to f32.