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Fix: HunyuanImage-2.1
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‎diffsynth/utils/state_dict_converters/hunyuan_dit_converter.py‎

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Original file line numberDiff line numberDiff line change
@@ -1,52 +1,3 @@
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# def HunyuanDiTStateDictConverter(state_dict):
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# new_dict = {}
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# for k, w in state_dict.items():
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# if k.startswith("double_blocks") and "attn_qkv.weight" in k:
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# hidden_size = w.shape[1]
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# k1 = k.replace("attn_qkv.weight", "attn_q.weight")
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# w1 = w[:hidden_size, :]
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# new_dict[k1] = w1
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# k2 = k.replace("attn_qkv.weight", "attn_k.weight")
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# w2 = w[hidden_size : 2 * hidden_size, :]
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# new_dict[k2] = w2
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# k3 = k.replace("attn_qkv.weight", "attn_v.weight")
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# w3 = w[-hidden_size:, :]
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# new_dict[k3] = w3
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# elif k.startswith("double_blocks") and "attn_qkv.bias" in k:
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# hidden_size = w.shape[0] // 3
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# k1 = k.replace("attn_qkv.bias", "attn_q.bias")
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# w1 = w[:hidden_size]
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# new_dict[k1] = w1
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# k2 = k.replace("attn_qkv.bias", "attn_k.bias")
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# w2 = w[hidden_size : 2 * hidden_size]
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# new_dict[k2] = w2
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# k3 = k.replace("attn_qkv.bias", "attn_v.bias")
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# w3 = w[-hidden_size:]
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# new_dict[k3] = w3
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# elif k.startswith("single_blocks") and "linear1" in k:
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# hidden_size = state_dict[k.replace("linear1", "linear2")].shape[0]
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# k1 = k.replace("linear1", "linear1_q")
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# w1 = w[:hidden_size]
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# new_dict[k1] = w1
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# k2 = k.replace("linear1", "linear1_k")
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# w2 = w[hidden_size : 2 * hidden_size]
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# new_dict[k2] = w2
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# k3 = k.replace("linear1", "linear1_v")
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# w3 = w[2 * hidden_size : 3 * hidden_size]
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# new_dict[k3] = w3
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# k4 = k.replace("linear1", "linear1_mlp")
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# w4 = w[3 * hidden_size :]
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# new_dict[k4] = w4
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# elif k.startswith("single_blocks") and "linear2" in k:
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# k1 = k.replace("linear2", "linear2.fc")
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# new_dict[k1] = w
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# else:
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# new_dict[k] = w
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# return new_dict
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def HunyuanDiTStateDictConverter(state_dict):
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new_dict = {}
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‎diffsynth/utils/state_dict_converters/hunyuan_t5_converter.py‎

Lines changed: 0 additions & 51 deletions
Original file line numberDiff line numberDiff line change
@@ -1,54 +1,3 @@
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# import torch
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# def HunyuanT5StateDictConverter(state_dict: dict) -> dict:
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# if 'state_dict' in state_dict:
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# sd = state_dict["state_dict"]
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# else:
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# sd = state_dict
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# prefix = 'module.text_tower.encoder.'
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# first_key = next(iter(sd.keys()), None)
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# if first_key is None or not first_key.startswith(prefix):
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# return sd
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# newsd = {}
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# for k, v in sd.items():
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# if k.startswith(prefix):
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# newsd[k[len(prefix):]] = v
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# else:
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# newsd[k] = v
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# return newsd
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# import torch
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# def HunyuanT5StateDictConverter(state_dict: dict) -> dict:
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# if 'state_dict' in state_dict_dict:
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# sd = state_dict_dict["state_dict"]
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# elif 'model' in state_dict_dict:
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# sd = state_dict_dict["model"]
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# else:
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# sd = state_dict_dict
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# prefix = 'module.text_tower.encoder.'
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# first_key = next(iter(sd.keys()), None)
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# if first_key is None or not first_key.startswith(prefix):
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# return sd
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# newsd = {}
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# for k, v in sd.items():
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# if k.startswith(prefix):
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# newsd[k[len(prefix):]] = v
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# else:
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# newsd[k] = v
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# return newsd
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import torch
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def HunyuanT5StateDictConverter(state_dict: dict) -> dict:

‎diffsynth/utils/state_dict_converters/hunyuan_vae_converter.py‎

Lines changed: 0 additions & 57 deletions
Original file line numberDiff line numberDiff line change
@@ -1,60 +1,3 @@
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# import torch
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# def HunyuanVAEStateDictConverter(state_dict: dict) -> dict:
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# if 'state_dict' in state_dict:
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# sd = state_dict["state_dict"]
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# elif 'model' in state_dict:
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# sd = state_dict["model"]
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# else:
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# sd = state_dict
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# converted_state_dict = {}
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# for key, value in sd.items():
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# if 'weight' in key:
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# if len(value.shape) == 5 and value.shape[2] == 1:
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# converted_state_dict[key] = value.squeeze(2)
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# else:
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# converted_state_dict[key] = value
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# else:
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# converted_state_dict[key] = value
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# return converted_state_dict
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# import torch
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# def HunyuanVAEStateDictConverter(state_dict: dict) -> dict:
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# if 'state_dict' in state_dict:
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# sd = state_dict["state_dict"]
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# elif 'model' in state_dict:
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# sd = state_dict["model"]
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# else:
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# sd = state_dict
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# first_key = next(iter(sd.keys()), None)
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# prefix = None
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# if first_key is not None:
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# if first_key.startswith("model."):
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# prefix = "model."
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# elif first_key.startswith("vae."):
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# prefix = "vae."
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# converted_state_dict = {}
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# for key, value in sd.items():
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# if prefix is not None and key.startswith(prefix):
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# key = key[len(prefix):]
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# if 'weight' in key:
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# if len(value.shape) == 5 and value.shape[2] == 1:
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# converted_state_dict[key] = value.squeeze(2)
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# else:
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# converted_state_dict[key] = value
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# else:
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# converted_state_dict[key] = value
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# return converted_state_dict
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import torch
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def HunyuanVAEStateDictConverter(state_dict: dict) -> dict:

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