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)