Create convert.py

convert.py

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+import torch
+import safetensors.torch
+from transformers import T5Tokenizer, T5EncoderModel
+
+#https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2/blob/main/t2i/model/pytorch_model_ema.pt
+input_diffusion = "pytorch_model_ema.pt"
+
+#https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2/tree/main/t2i/clip_text_encoder
+input_bert = "./clip_text_encoder/pytorch_model.bin"
+
+#https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors
+# or
+#https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors
+input_vae = "sdxl_vae.safetensors"
+
+output = "hunyuan_dit_1.2.safetensors"
+
+bert_sd = torch.load(input_bert, weights_only=True)
+
+mt5 = T5EncoderModel.from_pretrained("google/mt5-xl")
+tokenizer = T5Tokenizer.from_pretrained("google/mt5-xl")
+
+sp_model = torch.ByteTensor(list(tokenizer.sp_model.serialized_model_proto()))
+t5_sd = mt5.state_dict()
+
+out_sd = {}
+
+out_sd["text_encoders.mt5xl.spiece_model"] = sp_model
+
+for k in t5_sd:
+    out_sd["text_encoders.mt5xl.transformer.{}".format(k)] = t5_sd[k].half()
+
+for k in bert_sd:
+    if not k.startswith("visual."):
+        out_sd["text_encoders.hydit_clip.transformer.{}".format(k)] = bert_sd[k].half()
+
+hydit = torch.load(input_diffusion, weights_only=True)
+for k in hydit:
+    out_sd["model.{}".format(k)] = hydit[k].half()
+
+
+vae_sd = safetensors.torch.load_file(input_vae)
+
+for k in vae_sd:
+    out_sd["vae.{}".format(k)] = vae_sd[k].half()
+
+safetensors.torch.save_file(out_sd, output)