minimax_h3_fl2va_fp8_e4m3fn

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What minimax_h3_fl2va_fp8_e4m3fn is

MiniMax H3 MXFP8 MXFP8 quantization of the MiniMax H3 fl2va (first-last-frame to video+audio) diffusion checkpoint. better audio at 5 (talking heads, less motions) & 8-steps (shift 8) Your browser does not support the video tag. MiniMax H3 FP16attn Balanced quality and performance fp16attn 8-steps | shift: 8.0 Your browser does not support the video tag. MiniMax H3 FP8 (E4M3FN) Quantized Based on testing (this model and prunedfp8), the minimal steps to get results similar to Lightx2v using 4-step LoRAs is 5step & 5steps. The sweet spot for quality and speed is 8step and 8steps. This quantization requires a minimum of 8step. - Sampler: dpmpp2m - Scheduler: sgmuniform FP8 quantization of the MiniMax H3 fl2va (first-last-frame to video+audio) diffusion checkpoint. Your browser does not support the video tag. Original model: MiniMaxAI/MiniMax-H3 File sizes Simple Upscalers Old but gold upscale models. Lower image resolution for faster inference and more steps. 736x416 | ~56 sec | 1472x832 60fps Your browser does not support the video tag. Left at native precision (quality-critical) ComfyUI-MiniMaxH3-Text-Enhancer node What this is: the result of looking into why male anatomy comes out misshaped in text-to-video scenes or when a nude male appears in the I2V scene. Along the way we learned some things worth knowing about how this model really works, read README.md and notes.md in case of you are interested. Q: Is it a text-encoder problem? Would an abliterated Qwen3-VL solve it? - A: No. The stock encoder doesn't refuse NSFW prompts, the tokenizer and embedding table handle them normally. Its only strong divergence from stock is on harmful-instruction content (~11% embedding shift); sexual content shifts only ~1–3%. Abliteration targets the wrong thing here. Q: Is the base model censored? Would a domain-specific LoRA be ineffective? - A: No, the base is uncensored. The misshape comes from weak text conditioning: only ~3–5% of the embedding actually carries the prompt, so the DiT's generic body prior wins over the anatomy request. Modifying internal values (conditionproj ×1.2 + 12,658 FFN columns ×1.2) improves the result slightly and a properly trained LoRA would refi

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys minimax-h3-fl2va-fp8-e4m3fn for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minimax-h3-fl2va-fp8-e4m3fn below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"minimax-h3-fl2va-fp8-e4m3fn","prompt":"a drone shot over a forest"}'

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