Duplicate from lilcheaty/MiniMax-H3-NVFP4

Co-authored-by: Safzan Pirani <lilcheaty@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+license: other
+license_name: minimax-h3-community-license-agreement
+license_link: https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE
+tags:
+- comfyui
+- nvfp4
+- quantized
+- video
+- text-to-video
+base_model: MiniMaxAI/MiniMax-H3
+base_model_relation: quantized
+---
+
+# MiniMax H3 — NVFP4
+
+NVFP4 quantizations of the **MiniMax-H3** ref2va diffusion transformer for ComfyUI.
+
+- Original model: https://huggingface.co/MiniMaxAI/MiniMax-H3
+- ComfyUI repackage these derive from: https://huggingface.co/Comfy-Org/MiniMax-H3
+
+**NVFP4 requires an NVIDIA Blackwell GPU** (RTX 50-series, RTX PRO 6000, B200). On Ada,
+Hopper or older the NVFP4 path is emulated — use Comfy-Org's `int8_convrot` files instead.
+
+## Which file do I want?
+
+| file | size | s/it | VRAM (DiT) | notes |
+|---|---|---|---|---|
+| **`minimax_h3_ref2va_pruned_nvfp4.safetensors`** | **12.5 GB** | **1.90** | 11.9 GB | **recommended** |
+| `minimax_h3_ref2va_nvfp4_mixed.safetensors` | 24.4 GB | 1.92 | ~20 GB | from unpruned bf16 |
+| `minimax_h3_ref2va_nvfp4_full.safetensors` | 18.7 GB | 1.91 | ~16 GB | experimental |
+
+Take **`pruned_nvfp4`** unless you have a specific reason not to: half the size of the
+alternatives at identical speed, and it leaves the modulation path at full precision.
+
+## Why the pruned base is the right one to quantize
+
+Comfy-Org's `pruned` checkpoint is not lossily pruned — it is a **structural refactor of
+AdaLN**, and understanding it explains the whole table above.
+
+In the bf16 model, AdaLN modulation dominates the parameter count:
+
+| group | bf16 | pruned |
+|---|---|---|
+| `adaln_proj` | **13.04B (39.4%)** | **0.04B (0.2%)** |
+| `mlp` | 12.02B | 11.56B |
+| `attn` | 8.02B | 7.71B |
+| token_refiner / norms / embedders | 0.05B | 0.80B |
+| **total** | **33.12B** | **20.11B** |
+
+The bf16 model projects a 5376-dim conditioning vector into modulation parameters per block.
+The pruned model replaces this with an 8-dim timestep table (`adaln_t_table`, shape
+`[1025, 8]`) feeding `adaln_proj.linear` of shape `[96768, 8]`. Because modulation depends
+only on the timestep, that 5376-wide projection was almost entirely redundant — 13.04B
+parameters collapse to 0.04B, a ~326x reduction.
+
+This matters for quantization because AdaLN is the part you least want to quantize: it emits
+the scale and shift applied to every residual stream, so error there is multiplicative and
+compounds across all 50 blocks and every sampling step. In the bf16 model you face a bad
+choice — protect AdaLN and produce a ~36 GB file (larger than the 34 GB int8 it should beat),
+or quantize 39% of the model and hope. **In the pruned model the problem disappears**: AdaLN
+is already tiny, so you keep it at full precision for free and quantize only attn+mlp, which
+are error-tolerant.
+
+Comfy-Org's `pruned_int8_convrot` quantizes exactly those 200 attn/mlp layers to
+int8_convrot and leaves everything else alone. `pruned_nvfp4` takes that same set to NVFP4.
+
+## Measured
+
+RTX PRO 6000 Blackwell (96 GB), ComfyUI 0.30.0, ref2va, 864x480, 39 frames, 20 steps,
+`res_multistep` / `beta`, three matched seeds:
+
+| model | size | staged VRAM | s/it |
+|---|---|---|---|
+| `pruned_int8_convrot` (Comfy-Org) | 21.0 GB | 19,995 MB | 2.17 |
+| **`pruned_nvfp4` (this repo)** | **12.5 GB** | **11,944 MB** | **1.90** |
+
+**-40% file size, -8.0 GB VRAM, -12.4% sampling time.**
+
+At ~12 GB for the DiT, a 32 GB card (RTX 5090) becomes viable if the text encoder is
+offloaded to CPU after encoding — it runs once per prompt, not once per sampling step.
+
+## How `pruned_nvfp4` was built
+
+The obvious tool does **not** work. The StarNodes model converter passes non-floating-point
+tensors through untouched:
+
+```python
+if not tensor.dtype.is_floating_point:
+    return tensor          # already-int8 weights are copied verbatim
+```
+
+so running it on an int8 checkpoint silently produces a byte-identical file. A real
+dequantize -> requantize is required. `comfy-kitchen` exposes both halves:
+
+```python
+params = ckt.TensorWiseINT8Layout.Params(
+    scale=weight_scale, orig_dtype=torch.bfloat16, orig_shape=qdata.shape,
+    is_weight=True, convrot=True, convrot_groupsize=256)
+deq = ckt.TensorWiseINT8Layout.dequantize(qdata, params)              # -> bf16
+nq, nparams = ckt.TensorCoreNVFP4Layout.quantize(deq.contiguous())    # -> NVFP4
+tensors = ckt.TensorCoreNVFP4Layout.state_dict_tensors(nq, nparams)
+```
+
+Per-layer config lives in a `comfy_quant` uint8 tensor holding JSON, e.g.
+`{"format": "int8_tensorwise", "convrot": true, "convrot_groupsize": 256}`, rewritten to
+`{"format": "nvfp4"}` on output. Note `quantize()` requires bf16/fp16 — float32 raises
+`Unsupported dtype code`.
+
+Full script: `pruned_to_nvfp4.py` in this repo. 200 layers, ~6 seconds on one GPU.
+
+## Prompting: H3 wants a structured IR, not prose
+
+**Read this before blaming the weights for bad output.** H3 was trained on the structured
+output of **H3-Context-IR**, a preprocessing model that rewrites a plain request into
+labelled sections; MiniMax's model card calls it "critical to the quality of the final
+output". ComfyUI passes your raw string straight to the DiT, so you must write that
+structure yourself.
+
+Official guides:
+[base](https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_base_en.md) ·
+[ref](https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_ref_en.md)
+
+```text
+For the target video, at 0.00 seconds into the target video, <Picture 1> (from [Shot 1]) is fully referenced.
+
+integrated_multimodal_description: [Shot 1] Live-action, cinematic, the young woman shown in
+<Picture 1> remains beside the rain-covered train window, preserving her appearance and the
+carriage layout. The camera trucks right with small amplitude at slow speed as she lifts her
+gaze toward the passing city lights. The quiet, breathy young woman (S1) says:
+<d>[English] I get off at the next station.</d> She folds the letter along its existing crease.
+
+overall_soundscape: The train wheels produce a steady metallic rhythm beneath a low
+ventilation hum. Rain ticks against the window while paper rustles softly in her hands.
+
+non_diegetic_music: Sustained cello notes at a slow tempo with widely spaced piano tones.
+```
+
+**Dialogue must be explicit or you get gibberish.** Speech is generated jointly with video,
+so saying *that* someone speaks without giving the words yields correct prosody and mouth
+shapes with no lexical content. Speaker identity, action and delivery go *outside* `<d>`;
+only the language tag and verbatim words go *inside*. Use stable IDs `(S1)`, `(S2)`, and
+`(S1,S2)` for simultaneous speech.
+
+Other essentials: `[Shot 1]` carries no timestamp, later shots use `[Shot N] At MM:SS.mmm`;
+aim for 350-500 words of description; write camera motion as type + amplitude + speed;
+reference tags must appear in the order the inputs were connected. ref2va accepts up to 9
+reference images, and 3-4 varied shots hold identity far better than one.
+
+## Companion files (mirrored, not ours)
+
+| file | precision | origin |
+|---|---|---|
+| `text_encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors` | NVFP4-AWQ | **Comfy-Org, unmodified** |
+| `vae/minimax_h3_video_vae_fp16.safetensors` | FP16 | **Comfy-Org, unmodified** |
+| `vae/minimax_h3_audio_vae_fp32.safetensors` | FP32 | **Comfy-Org, unmodified** |
+
+**The NVFP4 text encoder is Comfy-Org's work, not ours.** Only the
+`minimax_h3_ref2va_*nvfp4*.safetensors` diffusion models here are new.
+
+VAEs are deliberately **not** quantized: they are small, run once per generation rather than
+per step, and decode straight to pixels and audio samples where error is immediately
+visible. The text encoder being NVFP4 buys VRAM, not speed — it also runs once per prompt.
+
+## Usage
+
+```
+📂 ComfyUI/models/
+├── 📂 diffusion_models/
+│   └── minimax_h3_ref2va_pruned_nvfp4.safetensors
+├── 📂 text_encoders/
+│   └── qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
+└── 📂 vae/
+    ├── minimax_h3_video_vae_fp16.safetensors
+    └── minimax_h3_audio_vae_fp32.safetensors
+```
+
+Recommended stack total: **33.7 GB**. Use the official
+[R2V template](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json),
+swapping the diffusion model. Requires ComfyUI >= 0.30.0 (native H3 support in
+`comfy/ldm/minimax/`). `CLIPLoader` type must be `minimax`; sampler `res_multistep`;
+frame `length` must satisfy 17n+5.
+
+## Honest limitations
+
+- Quality was compared against `pruned_int8_convrot` at three matched seeds with no visible
+  degradation, but this is **not a rigorous evaluation** — no FVD, no human study, no
+  long-duration or 2K testing.
+- `pruned_nvfp4` is **doubly quantized** (bf16 -> int8_convrot by Comfy-Org -> NVFP4 here).
+  Error from both passes compounds. It held up in testing, but that is a real caveat.
+- Only **ref2va** is converted. `fl2va` is not included.
+- Benchmarks are single-GPU, one card, one resolution.
+
+Failure cases are welcome in the discussions tab — concrete artifacts beat aggregate scores.
+
+## License
+
+Inherits the MiniMax-H3 Community License Agreement from the original model.

make_profile.py

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+"""Generate StarNodes converter profiles for MiniMax-H3 from the bf16 safetensors header.
+
+Two variants:
+  minimax_h3_nvfp4_mixed.json  - AdaLN kept at FP8, attn/mlp at NVFP4  (recommended)
+  minimax_h3_nvfp4_full.json   - everything incl. AdaLN at NVFP4       (aggressive)
+
+Kept at BF16 in both: all biases, all norms, rope freqs, patch/time/condition
+embedders and the final output heads (~0.04B params total, 0.1% of the model).
+"""
+import json, struct, sys, os, datetime
+
+SRC = "/workspace/ComfyUI/models/diffusion_models/minimax_h3_ref2va_bf16.safetensors"
+OUT_DIR = "/workspace/ComfyUI/custom_nodes/comfyui-starnodes-modelconverter/profiles"
+
+with open(SRC, "rb") as f:
+    n = struct.unpack("<Q", f.read(8))[0]
+    hdr = json.loads(f.read(n))
+keys = sorted(k for k in hdr if k != "__metadata__")
+
+
+def classify(key, adaln_fmt):
+    # non-weight tensors and everything tiny stays bf16
+    if not key.endswith(".weight"):
+        return "BF16"
+    if "norm" in key or key.endswith("inv_freq"):
+        return "BF16"
+    if any(t in key for t in ("patch_proj", "time_embedder", "condition_proj", "final_layer")):
+        return "BF16"
+    if "adaln" in key:
+        return adaln_fmt
+    if ".attn." in key or ".mlp." in key:
+        return "NVFP4"
+    return "BF16"
+
+
+def build(adaln_fmt, name):
+    layers, counts = {}, {}
+    for k in keys:
+        fmt = classify(k, adaln_fmt)
+        layers[k] = fmt
+        counts[fmt] = counts.get(fmt, 0) + 1
+        if fmt != "BF16":
+            base = k[: -len(".weight")]
+            layers[f"{base}.weight_scale"] = "FP32_SCALE"
+            layers[f"{base}.comfy_quant"] = "METADATA"
+    prof = {
+        "__metadata__": {
+            "original_model_name": name,
+            "original_model_path": SRC,
+            "timestamp": datetime.datetime.now().isoformat(),
+            "total_layers": len(layers),
+            "created_by": "hand-authored for MiniMax-H3 (33.12B: adaln 39.4%, mlp 36.3%, attn 24.2%)",
+        },
+        "layers": layers,
+    }
+    path = os.path.join(OUT_DIR, f"{name}.json")
+    with open(path, "w") as f:
+        json.dump(prof, f, indent=1)
+    print(f"{name}: {counts}  -> {path}")
+
+
+build("FP8_E4M3FN + SCALE", "minimax_h3_nvfp4_mixed")
+build("NVFP4", "minimax_h3_nvfp4_full")

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profiles/minimax_h3_nvfp4_full.json

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+{
+ "__metadata__": {
+  "original_model_name": "minimax_h3_nvfp4_full",
+  "original_model_path": "/workspace/ComfyUI/models/diffusion_models/minimax_h3_ref2va_bf16.safetensors",
+  "timestamp": "2026-08-03T06:06:50.561944",
+  "total_layers": 1051,
+  "created_by": "hand-authored for MiniMax-H3 (33.12B: adaln 39.4%, mlp 36.3%, attn 24.2%)"
+ },
+ "layers": {
+  "audio_patch_proj.bias": "BF16",
+  "audio_patch_proj.weight": "BF16",
+  "blocks.0.adaln_proj.linear.bias": "BF16",
+  "blocks.0.adaln_proj.linear.weight": "NVFP4",
+  "blocks.0.adaln_proj.linear.weight_scale": "FP32_SCALE",
+  "blocks.0.adaln_proj.linear.comfy_quant": "METADATA",
+  "blocks.0.attn.k_norm.weight": "BF16",
+  "blocks.0.attn.out_proj.weight": "NVFP4",
+  "blocks.0.attn.out_proj.weight_scale": "FP32_SCALE",
+  "blocks.0.attn.out_proj.comfy_quant": "METADATA",
+  "blocks.0.attn.q_norm.weight": "BF16",
+  "blocks.0.attn.qkv_proj.weight": "NVFP4",
+  "blocks.0.attn.qkv_proj.weight_scale": "FP32_SCALE",
+  "blocks.0.attn.qkv_proj.comfy_quant": "METADATA",
+  "blocks.0.mlp.fc1.weight": "NVFP4",
+  "blocks.0.mlp.fc1.weight_scale": "FP32_SCALE",
+  "blocks.0.mlp.fc1.comfy_quant": "METADATA",
+  "blocks.0.mlp.fc2.weight": "NVFP4",
+  "blocks.0.mlp.fc2.weight_scale": "FP32_SCALE",
+  "blocks.0.mlp.fc2.comfy_quant": "METADATA",
+  "blocks.0.norm1.weight": "BF16",
+  "blocks.0.norm2.weight": "BF16",
+  "blocks.1.adaln_proj.linear.bias": "BF16",
+  "blocks.1.adaln_proj.linear.weight": "NVFP4",
+  "blocks.1.adaln_proj.linear.weight_scale": "FP32_SCALE",
+  "blocks.1.adaln_proj.linear.comfy_quant": "METADATA",
+  "blocks.1.attn.k_norm.weight": "BF16",
+  "blocks.1.attn.out_proj.weight": "NVFP4",
+  "blocks.1.attn.out_proj.weight_scale": "FP32_SCALE",
+  "blocks.1.attn.out_proj.comfy_quant": "METADATA",
+  "blocks.1.attn.q_norm.weight": "BF16",
+  "blocks.1.attn.qkv_proj.weight": "NVFP4",
+  "blocks.1.attn.qkv_proj.weight_scale": "FP32_SCALE",
+  "blocks.1.attn.qkv_proj.comfy_quant": "METADATA",
+  "blocks.1.mlp.fc1.weight": "NVFP4",
+  "blocks.1.mlp.fc1.weight_scale": "FP32_SCALE",
+  "blocks.1.mlp.fc1.comfy_quant": "METADATA",
+  "blocks.1.mlp.fc2.weight": "NVFP4",
+  "blocks.1.mlp.fc2.weight_scale": "FP32_SCALE",
+  "blocks.1.mlp.fc2.comfy_quant": "METADATA",
+  "blocks.1.norm1.weight": "BF16",
+  "blocks.1.norm2.weight": "BF16",
+  "blocks.10.adaln_proj.linear.bias": "BF16",
+  "blocks.10.adaln_proj.linear.weight": "NVFP4",
+  "blocks.10.adaln_proj.linear.weight_scale": "FP32_SCALE",
+  "blocks.10.adaln_proj.linear.comfy_quant": "METADATA",
+  "blocks.10.attn.k_norm.weight": "BF16",
+  "blocks.10.attn.out_proj.weight": "NVFP4",
+  "blocks.10.attn.out_proj.weight_scale": "FP32_SCALE",
+  "blocks.10.attn.out_proj.comfy_quant": "METADATA",
+  "blocks.10.attn.q_norm.weight": "BF16",
+  "blocks.10.attn.qkv_proj.weight": "NVFP4",
+  "blocks.10.attn.qkv_proj.weight_scale": "FP32_SCALE",
+  "blocks.10.attn.qkv_proj.comfy_quant": "METADATA",
+  "blocks.10.mlp.fc1.weight": "NVFP4",
+  "blocks.10.mlp.fc1.weight_scale": "FP32_SCALE",
+  "blocks.10.mlp.fc1.comfy_quant": "METADATA",
+  "blocks.10.mlp.fc2.weight": "NVFP4",
+  "blocks.10.mlp.fc2.weight_scale": "FP32_SCALE",
+  "blocks.10.mlp.fc2.comfy_quant": "METADATA",
+  "blocks.10.norm1.weight": "BF16",
+  "blocks.10.norm2.weight": "BF16",
+  "blocks.11.adaln_proj.linear.bias": "BF16",
+  "blocks.11.adaln_proj.linear.weight": "NVFP4",
+  "blocks.11.adaln_proj.linear.weight_scale": "FP32_SCALE",
+  "blocks.11.adaln_proj.linear.comfy_quant": "METADATA",
+  "blocks.11.attn.k_norm.weight": "BF16",
+  "blocks.11.attn.out_proj.weight": "NVFP4",
+  "blocks.11.attn.out_proj.weight_scale": "FP32_SCALE",
+  "blocks.11.attn.out_proj.comfy_quant": "METADATA",
+  "blocks.11.attn.q_norm.weight": "BF16",
+  "blocks.11.attn.qkv_proj.weight": "NVFP4",
+  "blocks.11.attn.qkv_proj.weight_scale": "FP32_SCALE",
+  "blocks.11.attn.qkv_proj.comfy_quant": "METADATA",
+  "blocks.11.mlp.fc1.weight": "NVFP4",
+  "blocks.11.mlp.fc1.weight_scale": "FP32_SCALE",
+  "blocks.11.mlp.fc1.comfy_quant": "METADATA",
+  "blocks.11.mlp.fc2.weight": "NVFP4",
+  "blocks.11.mlp.fc2.weight_scale": "FP32_SCALE",
+  "blocks.11.mlp.fc2.comfy_quant": "METADATA",
+  "blocks.11.norm1.weight": "BF16",
+  "blocks.11.norm2.weight": "BF16",
+  "blocks.12.adaln_proj.linear.bias": "BF16",
+  "blocks.12.adaln_proj.linear.weight": "NVFP4",
+  "blocks.12.adaln_proj.linear.weight_scale": "FP32_SCALE",
+  "blocks.12.adaln_proj.linear.comfy_quant": "METADATA",
+  "blocks.12.attn.k_norm.weight": "BF16",
+  "blocks.12.attn.out_proj.weight": "NVFP4",
+  "blocks.12.attn.out_proj.weight_scale": "FP32_SCALE",
+  "blocks.12.attn.out_proj.comfy_quant": "METADATA",
+  "blocks.12.attn.q_norm.weight": "BF16",
+  "blocks.12.attn.qkv_proj.weight": "NVFP4",
+  "blocks.12.attn.qkv_proj.weight_scale": "FP32_SCALE",
+  "blocks.12.attn.qkv_proj.comfy_quant": "METADATA",
+  "blocks.12.mlp.fc1.weight": "NVFP4",
+  "blocks.12.mlp.fc1.weight_scale": "FP32_SCALE",
+  "blocks.12.mlp.fc1.comfy_quant": "METADATA",
+  "blocks.12.mlp.fc2.weight": "NVFP4",
+  "blocks.12.mlp.fc2.weight_scale": "FP32_SCALE",
+  "blocks.12.mlp.fc2.comfy_quant": "METADATA",
+  "blocks.12.norm1.weight": "BF16",
+  "blocks.12.norm2.weight": "BF16",
+  "blocks.13.adaln_proj.linear.bias": "BF16",
+  "blocks.13.adaln_proj.linear.weight": "NVFP4",
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+}

profiles/minimax_h3_nvfp4_mixed.json

@@ -0,0 +1,1062 @@
+{
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+  "final_layer.adaln_proj.linear.weight": "BF16",
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+  "final_layer.norm.weight": "BF16",
+  "final_layer.video_out.bias": "BF16",
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+  "rope.inv_freq": "BF16",
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+  "time_embedder.proj_in.weight": "BF16",
+  "time_embedder.proj_out.bias": "BF16",
+  "time_embedder.proj_out.weight": "BF16",
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+  "token_refiner.blocks.1.mlp.fc1.weight": "NVFP4",
+  "token_refiner.blocks.1.mlp.fc1.weight_scale": "FP32_SCALE",
+  "token_refiner.blocks.1.mlp.fc1.comfy_quant": "METADATA",
+  "token_refiner.blocks.1.mlp.fc2.weight": "NVFP4",
+  "token_refiner.blocks.1.mlp.fc2.weight_scale": "FP32_SCALE",
+  "token_refiner.blocks.1.mlp.fc2.comfy_quant": "METADATA",
+  "token_refiner.blocks.1.norm1.weight": "BF16",
+  "token_refiner.blocks.1.norm2.weight": "BF16",
+  "token_refiner.final_norm.weight": "BF16",
+  "video_patch_proj.bias": "BF16",
+  "video_patch_proj.weight": "BF16"
+ }
+}

pruned_to_nvfp4.py

@@ -0,0 +1,90 @@
+"""Requantize MiniMax-H3 pruned_int8_convrot -> NVFP4.
+
+The StarNodes converter cannot do this: it passes non-floating-point tensors through
+untouched, so an already-int8 model comes out byte-identical. This walks the state dict
+and does a real dequantize -> requantize on the 200 attn/mlp layers, leaving AdaLN, the
+norms, embedders and token_refiner exactly as they are.
+
+Per-layer input:   .weight (I8), .weight_scale (F32), .comfy_quant (U8 json bytes)
+Per-layer output:  .weight + NVFP4 layout suffixes, .comfy_quant = {"format": "nvfp4"}
+"""
+import json, os, sys, time, collections
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+from comfy_kitchen import tensor as ckt
+
+SRC = "/ComfyUI/models/diffusion_models/minimax_h3_ref2va_pruned_int8_convrot.safetensors"
+DST = "/ComfyUI/models/diffusion_models/minimax_h3_ref2va_pruned_nvfp4.safetensors"
+DEV = "cuda"
+FP8_DTYPES = (torch.float8_e4m3fn, torch.float8_e5m2)
+
+f = safe_open(SRC, "pt")
+keys = list(f.keys())
+
+# layers that carry a comfy_quant blob are the quantized ones
+quant_bases = sorted({k[: -len(".comfy_quant")] for k in keys if k.endswith(".comfy_quant")})
+print(f"quantized layers found: {len(quant_bases)}", flush=True)
+
+out, stats = {}, collections.Counter()
+consumed = set()
+t0 = time.time()
+
+for i, base in enumerate(quant_bases):
+    wk, sk, ck_ = f"{base}.weight", f"{base}.weight_scale", f"{base}.comfy_quant"
+    cfg = json.loads(bytes(f.get_tensor(ck_).tolist()).decode())
+    if cfg.get("format") != "int8_tensorwise":
+        print(f"  SKIP {base}: unexpected format {cfg}", flush=True)
+        stats["skipped_unknown_format"] += 1
+        continue
+
+    qdata = f.get_tensor(wk).to(DEV)
+    scale = f.get_tensor(sk).to(DEV)
+
+    params = ckt.TensorWiseINT8Layout.Params(
+        scale=scale,
+        orig_dtype=torch.bfloat16,
+        orig_shape=tuple(qdata.shape),
+        is_weight=True,
+        convrot=cfg.get("convrot", False),
+        convrot_groupsize=cfg.get("convrot_groupsize", 256),
+    )
+    deq = ckt.TensorWiseINT8Layout.dequantize(qdata, params)          # -> bf16
+
+    nq, nparams = ckt.TensorCoreNVFP4Layout.quantize(deq.float().contiguous())
+    tensors = ckt.TensorCoreNVFP4Layout.state_dict_tensors(nq, nparams)
+
+    for suffix, t in tensors.items():
+        name = f"{base}.weight{suffix}"
+        if t.dtype == torch.float8_e8m0fnu:
+            out[name] = t.view(torch.uint8).cpu()
+        elif t.dtype in FP8_DTYPES:
+            out[name] = t.view(torch.uint8).cpu().view(t.dtype)
+        else:
+            out[name] = t.cpu()
+
+    blob = json.dumps({"format": "nvfp4"}).encode()
+    out[ck_] = torch.tensor(list(blob), dtype=torch.uint8)
+
+    consumed.update({wk, sk, ck_})
+    stats["nvfp4"] += 1
+    del qdata, scale, deq, nq
+    if (i + 1) % 25 == 0:
+        torch.cuda.empty_cache()
+        print(f"  {i+1}/{len(quant_bases)}  {time.time()-t0:.0f}s", flush=True)
+
+# copy everything untouched
+for k in keys:
+    if k in consumed or k in out:
+        continue
+    out[k] = f.get_tensor(k)
+    stats["copied"] += 1
+
+print(f"stats: {dict(stats)}", flush=True)
+print(f"writing {DST} ...", flush=True)
+save_file(out, DST)
+gb = os.path.getsize(DST) / 1e9
+print(f"DONE {gb:.2f} GB in {time.time()-t0:.0f}s", flush=True)
+
+c = collections.Counter(str(v.dtype) for v in out.values())
+print("dtype census:", dict(c), flush=True)

text_encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors

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vae/minimax_h3_audio_vae_fp32.safetensors

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vae/minimax_h3_video_vae_fp16.safetensors

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