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https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-09 06:45:09 +00:00
change method name, add comments
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@@ -136,7 +136,8 @@ def convert_sdxl_text_encoder_2_checkpoint(checkpoint, max_length):
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return new_sd, logit_scale
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return new_sd, logit_scale
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def _load_state_dict(model, state_dict, device, dtype=None):
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# load state_dict without allocating new tensors
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def _load_state_dict_on_device(model, state_dict, device, dtype=None):
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# dtype will use fp32 as default
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# dtype will use fp32 as default
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missing_keys = list(model.state_dict().keys() - state_dict.keys())
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missing_keys = list(model.state_dict().keys() - state_dict.keys())
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unexpected_keys = list(state_dict.keys() - model.state_dict().keys())
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unexpected_keys = list(state_dict.keys() - model.state_dict().keys())
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@@ -145,26 +146,21 @@ def _load_state_dict(model, state_dict, device, dtype=None):
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if not missing_keys and not unexpected_keys:
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if not missing_keys and not unexpected_keys:
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for k in list(state_dict.keys()):
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for k in list(state_dict.keys()):
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set_module_tensor_to_device(model, k, device, value=state_dict.pop(k), dtype=dtype)
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set_module_tensor_to_device(model, k, device, value=state_dict.pop(k), dtype=dtype)
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return '<All keys matched successfully>'
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return "<All keys matched successfully>"
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# error_msgs
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# error_msgs
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error_msgs: List[str] = []
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error_msgs: List[str] = []
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if missing_keys:
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if missing_keys:
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error_msgs.insert(
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error_msgs.insert(0, "Missing key(s) in state_dict: {}. ".format(", ".join('"{}"'.format(k) for k in missing_keys)))
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0, 'Missing key(s) in state_dict: {}. '.format(
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', '.join('"{}"'.format(k) for k in missing_keys)))
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if unexpected_keys:
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if unexpected_keys:
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error_msgs.insert(
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error_msgs.insert(0, "Unexpected key(s) in state_dict: {}. ".format(", ".join('"{}"'.format(k) for k in unexpected_keys)))
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0, 'Unexpected key(s) in state_dict: {}. '.format(
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', '.join('"{}"'.format(k) for k in unexpected_keys)))
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raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
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raise RuntimeError("Error(s) in loading state_dict for {}:\n\t{}".format(model.__class__.__name__, "\n\t".join(error_msgs)))
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model.__class__.__name__, "\n\t".join(error_msgs)))
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def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None):
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def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dtype=None):
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# model_version is reserved for future use
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# model_version is reserved for future use
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# dtype is reserved for full_fp16/bf16 integration
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# dtype is reserved for full_fp16/bf16 integration. Text Encoder will remain fp32, because it runs on CPU when caching
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# Load the state dict
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# Load the state dict
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if model_util.is_safetensors(ckpt_path):
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if model_util.is_safetensors(ckpt_path):
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@@ -197,7 +193,7 @@ def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dty
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for k in list(state_dict.keys()):
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for k in list(state_dict.keys()):
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if k.startswith("model.diffusion_model."):
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if k.startswith("model.diffusion_model."):
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unet_sd[k.replace("model.diffusion_model.", "")] = state_dict.pop(k)
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unet_sd[k.replace("model.diffusion_model.", "")] = state_dict.pop(k)
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info = _load_state_dict(unet, unet_sd, device=map_location)
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info = _load_state_dict_on_device(unet, unet_sd, device=map_location)
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print("U-Net: ", info)
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print("U-Net: ", info)
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# Text Encoders
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# Text Encoders
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@@ -98,8 +98,8 @@ def _load_target_model(name_or_path: str, vae_path: Optional[str], model_version
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# Diffusers U-Net to original U-Net
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# Diffusers U-Net to original U-Net
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state_dict = sdxl_model_util.convert_diffusers_unet_state_dict_to_sdxl(unet.state_dict())
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state_dict = sdxl_model_util.convert_diffusers_unet_state_dict_to_sdxl(unet.state_dict())
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with init_empty_weights():
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with init_empty_weights():
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unet = sdxl_original_unet.SdxlUNet2DConditionModel()
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unet = sdxl_original_unet.SdxlUNet2DConditionModel() # overwrite unet
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sdxl_model_util._load_state_dict(unet, state_dict, device=device)
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sdxl_model_util._load_state_dict_on_device(unet, state_dict, device=device)
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print("U-Net converted to original U-Net")
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print("U-Net converted to original U-Net")
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logit_scale = None
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logit_scale = None
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