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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")