Repository: huggingface/diffusers
File: src/diffusers/models/model_loading_utils.py (Python)

Code before:

    is_quantized = hf_quantizer is not None
    empty_state_dict = model.state_dict()

    for param_name, param in state_dict.items():
        if param_name not in empty_state_dict:
            continue

        set_module_kwargs = {}
        # We convert floating dtypes to the `dtype` passed. We also want to keep the buffers/params
        # in int/uint/bool and not cast them.
        # TODO: revisit cases when param.dtype == torch.float8_e4m3fn
        if dtype is not None and torch.is_floating_point(param):
            if keep_in_fp32_modules is not None and any(
                module_to_keep_in_fp32 in param_name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules
            ):
                param = param.to(torch.float32)
                set_module_kwargs["dtype"] = torch.float32
            # For quantizers have save weights using torch.float8_e4m3fn
            elif hf_quantizer is not None and param.dtype == getattr(torch, "float8_e4m3fn", None):
                pass
            else:
                param = param.to(dtype)
                set_module_kwargs["dtype"] = dtype

        if is_accelerate_version(">=", "1.9.0.dev0"):
            set_module_kwargs["non_blocking"] = True
            set_module_kwargs["clear_cache"] = False

        # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model, and which
        # uses `param.copy_(input_param)` that preserves the contiguity of the parameter in the model.
        # Reference: https://github.com/pytorch/pytorch/blob/db79ceb110f6646523019a59bbd7b838f43d4a86/torch/nn/modules/module.py#L2040C29-L2040C29
        old_param = model
        splits = param_name.split(".")
        for split in splits:
            old_param = getattr(old_param, split)

        if not isinstance(old_param, (torch.nn.Parameter, torch.Tensor)):
            old_param = None

        if old_param is not None:
            if dtype is None:
                param = param.to(old_param.dtype)

            if old_param.is_contiguous():
                param = param.contiguous()

        param_device = _determine_param_device(param_name, device_map)

        # bnb params are flattened.
        # gguf quants have a different shape based on the type of quantization applied

Review comment (suggestion):
```suggestion
        if is_accelerate_version(">", "1.8.0"):
```
This way we don't need to put dev. I'll also do a accelerate release next week ! 

Code after:

    is_quantized = hf_quantizer is not None
    empty_state_dict = model.state_dict()

    for param_name, param in state_dict.items():
        if param_name not in empty_state_dict:
            continue

        set_module_kwargs = {}
        # We convert floating dtypes to the `dtype` passed. We also want to keep the buffers/params
        # in int/uint/bool and not cast them.
        # TODO: revisit cases when param.dtype == torch.float8_e4m3fn
        if dtype is not None and torch.is_floating_point(param):
            if keep_in_fp32_modules is not None and any(
                module_to_keep_in_fp32 in param_name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules
            ):
                param = param.to(torch.float32)
                set_module_kwargs["dtype"] = torch.float32
            # For quantizers have save weights using torch.float8_e4m3fn
            elif hf_quantizer is not None and param.dtype == getattr(torch, "float8_e4m3fn", None):
                pass
            else:
                param = param.to(dtype)
                set_module_kwargs["dtype"] = dtype

        if is_accelerate_version(">", "1.8.1"):
            set_module_kwargs["non_blocking"] = True
            set_module_kwargs["clear_cache"] = False

        # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model, and which
        # uses `param.copy_(input_param)` that preserves the contiguity of the parameter in the model.
        # Reference: https://github.com/pytorch/pytorch/blob/db79ceb110f6646523019a59bbd7b838f43d4a86/torch/nn/modules/module.py#L2040C29-L2040C29
        old_param = model
        splits = param_name.split(".")
        for split in splits:
            old_param = getattr(old_param, split)

        if not isinstance(old_param, (torch.nn.Parameter, torch.Tensor)):
            old_param = None

        if old_param is not None:
            if dtype is None:
                param = param.to(old_param.dtype)

            if old_param.is_contiguous():
                param = param.contiguous()

        param_device = _determine_param_device(param_name, device_map)

        # bnb params are flattened.
        # gguf quants have a different shape based on the type of quantization applied