Instructions to use Spectrallic/MiniMax-H3-LightX2V-int8-convrot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use Spectrallic/MiniMax-H3-LightX2V-int8-convrot with Diffusion Single File:
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- Notebooks
- Google Colab
- Kaggle
MiniMax-H3 FL2VA and Ref2VA (pruned) 4-step and 8-step LightX2V Turbo: INT8 ConvRot
These are the pruned MiniMax-H3 FL2VA and Ref2VA transformers from Comfy-Org/MiniMax-H3 with a full-rank LightX2V Turbo LoRA merged in, quantized to INT8 ConvRot. All four were made the same way and differ only in the base model and the LoRA:
| File | Base model | Steps | Same layout as (Comfy-Org diffusion_models/) |
|---|---|---|---|
minimax_h3_fl2va_pruned_turbo_4step_v1.3_int8_convrot.safetensors |
FL2VA | 4 | minimax_h3_fl2va_pruned_int8_convrot.safetensors |
minimax_h3_ref2va_pruned_turbo_4step_v1.0_int8_convrot.safetensors |
Ref2VA | 4 | minimax_h3_ref2va_pruned_int8_convrot.safetensors |
minimax_h3_fl2va_pruned_turbo_8step_v1.1_int8_convrot.safetensors |
FL2VA | 8 | minimax_h3_fl2va_pruned_int8_convrot.safetensors |
minimax_h3_ref2va_pruned_turbo_8step_v1.1_int8_convrot.safetensors |
Ref2VA | 8 | minimax_h3_ref2va_pruned_int8_convrot.safetensors |
The tensor names and order, dtypes, shapes, data offsets and .comfy_quant descriptors all match the Comfy-Org file. The
difference is that the distillation is baked in, so you don't need a LoRA loader.
Modified files: the weights differ from the MiniMax-H3 release, as described under How they were made.
Layout: every block linear (blocks.N.attn.{qkv_proj,out_proj}, blocks.N.mlp.{fc1,fc2}) is INT8 and has its own
.comfy_quant tensor ({"format": "int8_tensorwise", "convrot": true, "convrot_groupsize": 256}) and a per-row F32
weight_scale. Everything else keeps the Comfy-Org dtype.
Examples
FL2VA, 4 steps
| Steps | CFG | Size | Frames | Seed | Sampler | Shift (video / audio) |
|---|---|---|---|---|---|---|
| 4 | 1.0 | 1344×768 | 124 at 24 fps | 42 | LightX2V Turbo | 6 / 3 |
A cinematic view of an ancient floating city suspended above a sea of cumulus clouds. The camera begins in a wide establishing shot and slowly dollies in toward the city, gliding forward through the soft pastel cloudscape as its intricate, hand-drawn architecture comes into view. A waterfall cascades from the city's edge into the clouds, while shafts of golden sunlight break through the cumulus clouds and illuminate the ultra-detailed digital painting.
This render used SageAttention with sparse attention at density 0.15 (first step dense) as described here.
Ref2VA, 4 steps
| Steps | CFG | Size | Frames | Seed | Sampler | Shift (video / audio) |
|---|---|---|---|---|---|---|
| 4 | 1.0 | 1344×768 | 124 at 24 fps | 43 | LightX2V Turbo | 6 / 3 |
A close-up of a small wooden fishing boat in a stormy green sea at night, rain lashing the deck, while the floating city from <Picture 1> hangs in the sky far above and lightning flashes behind it. Handheld camera. Loud thunder and crashing waves.
<Picture 1> is the first frame of the FL2VA example above, scaled to match the output size. This render used the same attention setup as the FL2VA example: SageAttention with sparse attention at density 0.15 (first step dense).
FL2VA, 8 steps
| Steps | CFG | Size | Frames | Seed | Sampler | Shift (video / audio) |
|---|---|---|---|---|---|---|
| 8 | 1.0 | 1344×768 | 124 at 24 fps | 42 | LightX2V Turbo | 6 / 3 |
Same prompt and render setup as the 4-step FL2VA example.
Ref2VA, 8 steps
| Steps | CFG | Size | Frames | Seed | Sampler | Shift (video / audio) |
|---|---|---|---|---|---|---|
| 8 | 1.0 | 1344×768 | 124 at 24 fps | 43 | LightX2V Turbo | 6 / 3 |
Same prompt and render setup as the 4-step Ref2VA example. <Picture 1> is the first frame of the 8-step FL2VA example above.
Sampling
Use each LightX2V Turbo student's own operating point: 4 steps for the 4-step files and 8 steps for the 8-step files, CFG 1 (no classifier-free guidance), video time shift 6 and audio shift 3. Workflows are in the LightX2V Turbo repository.
Both examples were rendered in Nox with the LightX2V Turbo sampler (Euler). For Ref2VA, refer to references in the prompt as <Picture i>,
<Video k> and <Audio j>.
How they were made
<variant> below is fl2va or ref2va, <lora_variant> is fl2v or ref2v, and <steps> is 4step or 8step.
<version> is 1.3 for FL2VA 4-step, 1.0 for Ref2VA 4-step, and 1.1 for both 8-step files.
- Source:
- Base:
diffusion_models/minimax_h3_<variant>_pruned_bf16.safetensorsfrom Comfy-Org/MiniMax-H3 (Ref2VA: revision912b6e38). - LoRA:
minimax_h3_<lora_variant>_turbo_<steps>_v<version>_768p_comfyui_bf16.safetensorsfrom lightx2v/Minimax-h3-Turbo (revision30f485ed). These are the authors' ComfyUI conversions, with no rank reduction: rank 128, with q/k/v fused intoattn.qkv_projadapters of rank 384. - Layout reference:
diffusion_models/minimax_h3_<variant>_pruned_int8_convrot.safetensorsfrom Comfy-Org/MiniMax-H3 (FL2VA: revisiond6cfb4e5; Ref2VA: revision912b6e38).
- Base:
- Merge: the LoRA is merged at strength 1.0 into its 208 targets:
attn.{qkv_proj,out_proj}andmlp.{fc1,fc2}in all 50 blocks and bothtoken_refinerblocks. Each merge computesW' = W + (alpha/rank) · up @ downin FP32. - Quantize: the 200 block linears are rotated with a group-256 Hadamard ConvRot, then quantized to INT8 with
per-row scales, directly from the FP32 merge result. The 8 merged
token_refinerweights go back to BF16, because that layer stays unquantized in the Comfy-Org file. - Copy everything else: tensors the LoRA doesn't touch are copied bit for bit from the layout reference.
Checks
- Against the Comfy-Org INT8 file, byte for byte (all four files): all 932 tensors match in name, order, dtype, shape and offset. Only
408 tensors have different bytes: the 200 INT8 weights, their 200 scales and the 8
token_refinerweights. The other 524 are identical, including all 200.comfy_quanttensors. - LoRA presence: this projects the change from the base onto the LoRA delta, where 1 means fully
present. 4 steps: the overall result is 0.9993 for FL2VA and 0.9995 for Ref2VA. Of the 208 layers, 200 are within
1 ± 0.1 in each file. The lowest is
token_refiner.blocks.0.mlp.fc2(0.0271 for FL2VA, 0.0429 for Ref2VA). 8 steps: the overall result is 0.9990 for FL2VA and 0.9997 for Ref2VA. Of the 208 layers, 199 are within 1 ± 0.1 for FL2VA and 197 for Ref2VA. The lowest istoken_refiner.blocks.0.mlp.fc2(0.0276 for FL2VA, 0.0277 for Ref2VA).
Credits & license
- Base model: MiniMaxAI/MiniMax-H3, repackaged by Comfy-Org/MiniMax-H3
- Distillation: lightx2v/Minimax-h3-Turbo (code)
This derivative is released under the MiniMax H3 Community License Agreement, which includes territorial
restrictions. See LICENSE and NOTICE.
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