Hugging Face 热门模型第 30 名：Efficient-Large-Model/Open-H3-Regenerate-2K，140 赞，近期下载 0 次。
标签：diffusers, safetensors, minimax-h3, lora, video-refinement, sol-engine, video-to-video, base_model:MiniMaxAI/MiniMax-H3, base_model:adapter:MiniMaxAI/MiniMax-H3, license:other, region:us
README：
Open-H3-Regenerate-2K
An open replacement for the closed H3-Regenerate-2K API: a three-step MiniMax-H3 refinement pass that regenerates a
768P H3 draft (1344×768, 121 frames) at 2560×1440.
Project page ·
Code
Speed on one B200
One 5 s clip, 768P latent → 2560×1440 video: latent upscale + 3-step refine + VAE decode, warm, median of 3 runs:
| Forward |
VAE decoder |
Total |
Speedup |
standard_fwd (dense attention, BF16) |
released H3 VAE |
87.26 s |
1.00× |
sol_engine_fwd (Sol-Attn, MXFP8, CUDA graphs) |
released H3 VAE |
37.39 s |
2.33× |
sol_engine_fwd (Sol-Attn, MXFP8, CUDA graphs) |
fast H3 VAE decoder |
22.45 s |
3.89× |
The fast H3 VAE decoder is the non-causal 24-layer decoder of
StreamRender-H3, distilled from the released H3 VAE;
it decodes the 2560×1440 clip in 1.6 s instead of 15 s. Latent upscale + refine alone go from 73.11 s to 20.46 s (3.57×).
Per-module numbers are in the code README.
Files
| Path |
Content |
lora/open_h3_regenerate_2k_lora.safetensors |
One merged LoRA (FP32, rank 576 on 312 linears, scale 1) |
lora/sharp_curve_branches.safetensors |
The 50 LMS Sharp curve AdaLN branches (FP32) |
lora/lora_config.json, lora/module_ranks.json |
Sources, hashes, per-module ranks |
lora/MERGE_VERIFY.json |
Merge check against the three separate adapters |
latent_upsampler/ |
Author 3D-conv latent upscaler, BF16 |
vae_fast/fast_h3_vae_noncausal_24l.safetensors |
Fast H3 VAE decoder from StreamRender-H3 (non-causal, 24 layers, 1.62B parameters) |
base_model/ |
MiniMax-H3 Diffusers components (transformer, VAE, audio VAE, text encoder, tokenizer, processor, schedulers), unmodified copy of revision 6818f6c |
base_model/ mirrors MiniMaxAI/MiniMax-H3 @ 6818f6c
without changes (the native FL2VA/, Ref2VA/ and transformer_ref/ folders are left out). download.py in the code
folder fetches everything here plus the optional HyperFlow 8-step adapter and Sharp's 1025×8 adaln_t_table
(a base-derived tensor, read by byte range from Comfy-Org/MiniMax-H3).
What the LoRA contains
The reference recipe applies three adapters to the MiniMax-H3 transformer:
| Adapter |
Rank / alpha |
| Open-H3 refiner, step 5000 EMA |
384 / 384 |
| LightX2V FL2VA Turbo v1.2 |
128 / 8 |
| LMS Sharp v1.0 |
64 |
They are merged by concatenating the factors along the rank axis with the strengths (and Turbo's alpha/rank) folded
into B, so B @ A equals the sum of the three scaled deltas exactly. Over all 312 linears the merged delta
matches the separate adapters to a relative error of 2.2e-8 (MERGE_VERIFY.json). Sharp's curve branches take an
8-D timestep curve instead of the layer input, so they are kept as separate modules.
Sampling: three Euler steps on sigmas [0.909375, 0.857143, 0.666667, 0], CFG 1, initial state
(1 - 0.909375) * upscaled + 0.909375 * noise, model time 1 - sigma.
License
The refiner LoRA, the fast H3 VAE decoder and base_model/ follow the
MiniMax H3 Community License,
including its territory, use and redistribution terms. The components we did not train follow their own licenses:
the latent upscaler, and the
LightX2V FL2VA Turbo and
LMS Sharp
LoRAs merged into our LoRA. This bundle is not
covered by a single blanket license. The runtime code's license does not replace
model licenses.
The original belongs to Hugging Face 热门模型 and its authors and is reproduced here for reading. To have it removed, write to support@aisa.one.
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