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z_image_convert_original_to_comfy.py

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+import safetensors.torch
+import torch
+import sys
+
+# Usage: python z_image_convert_original_to_comfy.py output.safetensors diffusion_model*.safetensors
+
+cast_to = None
+if "fp8_e4m3fn" in sys.argv[1]:
+    cast_to = torch.float8_e4m3fn
+elif "fp16" in sys.argv[1]:
+    cast_to = torch.float16
+elif "bf16" in sys.argv[1]:
+    cast_to = torch.bfloat16
+
+replace_keys = {"all_final_layer.2-1.": "final_layer.",
+                "all_x_embedder.2-1.": "x_embedder.",
+                ".attention.to_out.0.bias": ".attention.out.bias",
+                ".attention.norm_k.weight": ".attention.k_norm.weight",
+                ".attention.norm_q.weight": ".attention.q_norm.weight",
+                ".attention.to_out.0.weight": ".attention.out.weight"
+                }
+
+out_sd = {}
+for f in sys.argv[2:]:
+    sd = safetensors.torch.load_file(f)
+    cc = None
+    for k in sd:
+        w = sd[k]
+
+        if cast_to is not None:
+            w = w.to(cast_to)
+        k_out = k
+        if k_out.endswith(".attention.to_out.0.bias"):
+            continue
+        if k_out.endswith(".attention.to_k.weight"):
+            cc = [w]
+            continue
+        if k_out.endswith(".attention.to_q.weight"):
+            cc = [w] + cc
+            continue
+        if k_out.endswith(".attention.to_v.weight"):
+            cc = cc + [w]
+            w = torch.cat(cc, dim=0)
+            k_out = k_out.replace(".attention.to_v.weight", ".attention.qkv.weight")
+
+        for r, rr in replace_keys.items():
+            k_out = k_out.replace(r, rr)
+        out_sd[k_out] = w
+
+
+
+safetensors.torch.save_file(out_sd, sys.argv[1])