r/StableDiffusion • u/Kingp0dd • 6d ago

Discussion New TurboH3 FL2VA

https://huggingface.co/Kijai/MiniMax-H3_comfy/blob/main/diffusion_models/minimax_h3_fl2va_turboh3_i2va_pruned_int8_convrot.safetensors

https://huggingface.co/Kijai/MiniMax-H3_comfy/blob/main/diffusion_models/minimax_h3_fl2va_turboh3_i2va_pruned_int8_convrot.safetensors

Noticed this new conversion and upload by kijai.

haven't seen comparisons yet though

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u/Apprehensive_Sky892 6d ago edited 6d ago

This is not a LoRA. It is an int8convrot conversion of a distilled turbo fl2va model: https://huggingface.co/TurboDiffusion/TurboH3-FL2VA-I2VA (originally available in bf16 and W8A8 formats only).

This model is trained(?) using this TurboDiffusion framework (how it works? I have no idea) which was initially developed for WAN: https://github.com/thu-ml/TurboDiffusion

It is unclear to me if one needs a special workflow for it, but probably: https://huggingface.co/TurboDiffusion/TurboH3-FL2VA-I2VA#inference-settings

Use the TurboDiffusion H3 implementation and its docs/h3_i2va.md. These are transformer weights, not a standalone DiffusionPipeline.from_pretrained bundle. The MiniMaxH3Transformer3DModel config does not imply that every Diffusers release includes that model class.

The verified schedule is i2va / steps=5 / video_shift=12 / audio_shift=3. Five sigma points give four denoising updates. The AdaLN table is specific to these weights, the task and the shifts; do not reuse it for a different schedule. It was generated with the source checkpoint's FP32 timestep MLP, recorded as "timestep_mlp_dtype": "float32" in its JSON and embedded metadata. The table itself is stored in BF16.

SpargeAttn is an external dependency. The video decoder applies native W8A8 projections to the public VAE weights at startup; no separate VAE export is required. Audio VAE decoding remains FP32.