update conversion script: remove dtype, always keep same precision as original checkpoint

scripts/convert_hunyuan_video1_5_to_diffusers.py

@@ -1,19 +1,17 @@
 """
 python scripts/convert_hunyuan_video1_5_to_diffusers.py \
     --original_state_dict_repo_id tencent/HunyuanVideo-1.5\
-    --output_path /fsx/yiyi/hy15/480p_i2v\
-    --transformer_type 480p_i2v \
-    --dtype fp32
+    --output_path /fsx/yiyi/HunyuanVideo-1.5-Diffusers/transformer\
+    --transformer_type 480p_t2v
 """
 
 """
 python scripts/convert_hunyuan_video1_5_to_diffusers.py \
     --original_state_dict_repo_id tencent/HunyuanVideo-1.5\
     --output_path /fsx/yiyi/HunyuanVideo-1.5-Diffusers \
-    --dtype bf16 \
     --save_pipeline \
     --byt5_path /fsx/yiyi/hy15/text_encoder/Glyph-SDXL-v2\
-    --transformer_type 480p_i2v
+    --transformer_type 480p_t2v
 """
 
 import argparse
@@ -51,12 +49,33 @@ TRANSFORMER_CONFIGS = {
         "rope_axes_dim": (16, 56, 56),
         "use_meanflow": False,
     },
+    "480p_t2v": {
+        "in_channels": 65,
+        "out_channels": 32,
+        "num_attention_heads": 16,
+        "attention_head_dim": 128,
+        "num_layers": 54,
+        "num_refiner_layers": 2,
+        "mlp_ratio": 4.0,
+        "patch_size": 1,
+        "patch_size_t": 1,
+        "qk_norm": "rms_norm",
+        "text_embed_dim": 3584,
+        "text_embed_2_dim": 1472,
+        "image_embed_dim": 1152,
+        "rope_theta": 256.0,
+        "rope_axes_dim": (16, 56, 56),
+        "use_meanflow": False,
+    },
 }
 
 SCHEDULER_CONFIGS = {
     "480p_i2v": {
         "shift": 5.0,
     },
+    "480p_t2v": {
+        "shift": 5.0,
+    },
 }
 
 GUIDANCE_CONFIGS = {
@@ -64,6 +83,10 @@ GUIDANCE_CONFIGS = {
         "guidance_scale": 6.0,
         "embedded_guidance_scale": None,
     },
+    "480p_t2v": {
+        "guidance_scale": 6.0,
+        "embedded_guidance_scale": None,
+    },
 
     }
 
@@ -555,6 +578,7 @@ def load_original_transformer_state_dict(args):
     model_dir = model_dir / "transformer" / args.transformer_type
     return load_sharded_safetensors(model_dir)
 
+
 def load_original_vae_state_dict(args):
     if args.original_state_dict_repo_id is not None:
         ckpt_path = hf_hub_download(
@@ -570,6 +594,7 @@ def load_original_vae_state_dict(args):
     original_state_dict = load_file(ckpt_path)
     return original_state_dict
 
+
 def convert_transformer(args):
     original_state_dict = load_original_transformer_state_dict(args)
 
@@ -581,6 +606,7 @@ def convert_transformer(args):
 
     return transformer
 
+
 def convert_vae(args):
     original_state_dict = load_original_vae_state_dict(args)
     with init_empty_weights():
@@ -591,7 +617,7 @@ def convert_vae(args):
 
 def load_mllm():
     print(f" loading from Qwen/Qwen2.5-VL-7B-Instruct")
-    text_encoder = AutoModel.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", low_cpu_mem_usage=True)
+    text_encoder = AutoModel.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype=torch.bfloat16,low_cpu_mem_usage=True)
     if hasattr(text_encoder, 'language_model'):
         text_encoder = text_encoder.language_model
     tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", padding_side="right")
@@ -641,8 +667,6 @@ def add_special_token(
     text_encoder.resize_token_embeddings(len(tokenizer), mean_resizing=False)
 
 
-
-
 def load_byt5(args):
     """
     Load ByT5 encoder with Glyph-SDXL-v2 weights and save in HuggingFace format.
@@ -714,7 +738,6 @@ def get_args():
     )
     parser.add_argument("--original_state_dict_folder", type=str, default=None, help="Local folder name of the original state dict")
     parser.add_argument("--output_path", type=str, required=True, help="Path where converted model(s) should be saved")
-    parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.")
     parser.add_argument(
         "--transformer_type", type=str, default="480p_i2v", choices=list(TRANSFORMER_CONFIGS.keys())
     )
@@ -736,13 +759,6 @@ def get_args():
     return parser.parse_args()
 
 
-DTYPE_MAPPING = {
-    "fp32": torch.float32,
-    "fp16": torch.float16,
-    "bf16": torch.bfloat16,
-}
-
-
 if __name__ == "__main__":
     args = get_args()
 
@@ -750,22 +766,16 @@ if __name__ == "__main__":
         raise ValueError("Please provide --byt5_path when saving pipeline")
 
     transformer = None
-    dtype = DTYPE_MAPPING[args.dtype]
 
     transformer = convert_transformer(args)
-    transformer = transformer.to(dtype=dtype)
     if not args.save_pipeline:
         transformer.save_pretrained(args.output_path, safe_serialization=True)
     else:
-
         vae = convert_vae(args)
-        vae = vae.to(dtype=dtype)
 
 
         text_encoder, tokenizer = load_mllm()
         text_encoder_2, tokenizer_2 = load_byt5(args)
-        text_encoder = text_encoder.to(dtype=dtype)
-        text_encoder_2 = text_encoder_2.to(dtype=dtype)
 
         flow_shift = SCHEDULER_CONFIGS[args.transformer_type]["shift"]
         scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift)