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convert_original_to_comfy.py

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+#!/usr/bin/env python3
+"""
+Convert LongCat-Image transformer weights from HuggingFace Diffusers format
+to ComfyUI format.
+
+Usage:
+    python conversion.py input.safetensors output.safetensors
+
+The input file is the Diffusers-format transformer, typically:
+    meituan-longcat/LongCat-Image/transformer/diffusion_pytorch_model.safetensors
+
+The output file will contain ComfyUI-format keys with fused QKV tensors,
+ready for zero-copy loading via UNETLoader.
+"""
+
+import argparse
+import torch
+import logging
+from safetensors.torch import load_file, save_file
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def convert_longcat_image(state_dict):
+    out_sd = {}
+    double_q, double_k, double_v = {}, {}, {}
+    double_tq, double_tk, double_tv = {}, {}, {}
+    single_q, single_k, single_v, single_mlp = {}, {}, {}, {}
+
+    for k, v in state_dict.items():
+        if k.startswith("transformer_blocks."):
+            idx = k.split(".")[1]
+            rest = ".".join(k.split(".")[2:])
+            prefix = "double_blocks.{}.".format(idx)
+
+            if rest.startswith("norm1.linear."):
+                out_sd[prefix + "img_mod.lin." + rest.split(".")[-1]] = v
+            elif rest.startswith("norm1_context.linear."):
+                out_sd[prefix + "txt_mod.lin." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.to_q."):
+                double_q[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.to_k."):
+                double_k[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.to_v."):
+                double_v[idx + "." + rest.split(".")[-1]] = v
+            elif rest == "attn.norm_q.weight":
+                out_sd[prefix + "img_attn.norm.query_norm.weight"] = v
+            elif rest == "attn.norm_k.weight":
+                out_sd[prefix + "img_attn.norm.key_norm.weight"] = v
+            elif rest.startswith("attn.to_out.0."):
+                out_sd[prefix + "img_attn.proj." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.add_q_proj."):
+                double_tq[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.add_k_proj."):
+                double_tk[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.add_v_proj."):
+                double_tv[idx + "." + rest.split(".")[-1]] = v
+            elif rest == "attn.norm_added_q.weight":
+                out_sd[prefix + "txt_attn.norm.query_norm.weight"] = v
+            elif rest == "attn.norm_added_k.weight":
+                out_sd[prefix + "txt_attn.norm.key_norm.weight"] = v
+            elif rest.startswith("attn.to_add_out."):
+                out_sd[prefix + "txt_attn.proj." + rest.split(".")[-1]] = v
+            elif rest.startswith("ff.net.0.proj."):
+                out_sd[prefix + "img_mlp.0." + rest.split(".")[-1]] = v
+            elif rest.startswith("ff.net.2."):
+                out_sd[prefix + "img_mlp.2." + rest.split(".")[-1]] = v
+            elif rest.startswith("ff_context.net.0.proj."):
+                out_sd[prefix + "txt_mlp.0." + rest.split(".")[-1]] = v
+            elif rest.startswith("ff_context.net.2."):
+                out_sd[prefix + "txt_mlp.2." + rest.split(".")[-1]] = v
+            else:
+                out_sd["double_blocks.{}.{}".format(idx, rest)] = v
+
+        elif k.startswith("single_transformer_blocks."):
+            idx = k.split(".")[1]
+            rest = ".".join(k.split(".")[2:])
+            prefix = "single_blocks.{}.".format(idx)
+
+            if rest.startswith("norm.linear."):
+                out_sd[prefix + "modulation.lin." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.to_q."):
+                single_q[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.to_k."):
+                single_k[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("attn.to_v."):
+                single_v[idx + "." + rest.split(".")[-1]] = v
+            elif rest == "attn.norm_q.weight":
+                out_sd[prefix + "norm.query_norm.weight"] = v
+            elif rest == "attn.norm_k.weight":
+                out_sd[prefix + "norm.key_norm.weight"] = v
+            elif rest.startswith("proj_mlp."):
+                single_mlp[idx + "." + rest.split(".")[-1]] = v
+            elif rest.startswith("proj_out."):
+                out_sd[prefix + "linear2." + rest.split(".")[-1]] = v
+            else:
+                out_sd["single_blocks.{}.{}".format(idx, rest)] = v
+
+        elif k == "x_embedder.weight" or k == "x_embedder.bias":
+            out_sd["img_in." + k.split(".")[-1]] = v
+        elif k == "context_embedder.weight" or k == "context_embedder.bias":
+            out_sd["txt_in." + k.split(".")[-1]] = v
+        elif k.startswith("time_embed.timestep_embedder.linear_1."):
+            out_sd["time_in.in_layer." + k.split(".")[-1]] = v
+        elif k.startswith("time_embed.timestep_embedder.linear_2."):
+            out_sd["time_in.out_layer." + k.split(".")[-1]] = v
+        elif k.startswith("norm_out.linear."):
+            # HF AdaLayerNormContinuous stores [scale | shift] but ComfyUI
+            # LastLayer expects [shift | scale], so swap the two halves.
+            half = v.shape[0] // 2
+            v = torch.cat([v[half:], v[:half]], dim=0)
+            out_sd["final_layer.adaLN_modulation.1." + k.split(".")[-1]] = v
+        elif k == "proj_out.weight" or k == "proj_out.bias":
+            out_sd["final_layer.linear." + k.split(".")[-1]] = v
+        else:
+            out_sd[k] = v
+
+    for suffix in ["weight", "bias"]:
+        for idx in sorted(set(x.split(".")[0] for x in double_q)):
+            qk = idx + "." + suffix
+            if qk in double_q and qk in double_k and qk in double_v:
+                out_sd["double_blocks.{}.img_attn.qkv.{}".format(idx, suffix)] = torch.cat([double_q[qk], double_k[qk], double_v[qk]], dim=0)
+            if qk in double_tq and qk in double_tk and qk in double_tv:
+                out_sd["double_blocks.{}.txt_attn.qkv.{}".format(idx, suffix)] = torch.cat([double_tq[qk], double_tk[qk], double_tv[qk]], dim=0)
+
+        for idx in sorted(set(x.split(".")[0] for x in single_q)):
+            qk = idx + "." + suffix
+            if qk in single_q and qk in single_k and qk in single_v and qk in single_mlp:
+                out_sd["single_blocks.{}.linear1.{}".format(idx, suffix)] = torch.cat([single_q[qk], single_k[qk], single_v[qk], single_mlp[qk]], dim=0)
+
+    return out_sd
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert LongCat-Image weights from Diffusers to ComfyUI format"
+    )
+    parser.add_argument("input", help="Path to Diffusers-format safetensors file")
+    parser.add_argument("output", help="Path to write ComfyUI-format safetensors file")
+    args = parser.parse_args()
+
+    logger.info(f"Loading {args.input}...")
+    sd = load_file(args.input)
+
+    logger.info(f"Converting {len(sd)} keys...")
+    converted = convert_longcat_image(sd)
+
+    logger.info(f"Saving {len(converted)} keys to {args.output}...")
+    save_file(converted, args.output)
+
+    logger.info("Done.")
+
+
+if __name__ == "__main__":
+    main()

download_original.sh

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+#!/bin/bash
+curl -OL https://huggingface.co/meituan-longcat/LongCat-Image/resolve/main/transformer/diffusion_pytorch_model.safetensors

split_files/diffusion_models/longcat_image_bf16.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c83c314a3d879d43e5700072033256000f46a56900ae48b209a77ac1921488b
+size 12541383144