SoL-Refiner
Collection
Original LTX-2.3 refiners and the LTX-2.5 extension for MiniMax-H3. https://nvlabs.github.io/Sana/Sol-Refiner/ • 4 items • Updated • 6
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Efficient-Large-Model/Open-H3-Regenerate-2K")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]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.
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.
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