wehub-resource-sync
57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from abc import ABC
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from diffusers.loaders import StableDiffusionLoraLoaderMixin, FluxLoraLoaderMixin
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class LoraLoader(ABC):
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def __init__(self, paths, weights, scale):
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self.paths = paths
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self.weights = weights
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self.scale = scale
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class SDLoraLoader(LoraLoader, StableDiffusionLoraLoaderMixin):
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def __init__(self, paths, weights, scale):
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super().__init__(paths, weights, scale)
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class FLUXLoraLoader(LoraLoader, FluxLoraLoaderMixin):
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def __init__(self, paths, weights, scale):
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super().__init__(paths, weights, scale)
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def merge_loras(model, lora_loader):
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paths, weights, scale = lora_loader.paths, lora_loader.weights, lora_loader.scale
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for i, path in enumerate(paths):
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print(f"[I] Loading LoRA: {path}, weight {weights[i]}")
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if isinstance(lora_loader, SDLoraLoader):
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state_dict, network_alphas = lora_loader.lora_state_dict(path, unet_config=model.config)
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lora_loader.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=model, adapter_name=path)
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elif isinstance(lora_loader, FLUXLoraLoader):
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state_dict, network_alphas = lora_loader.lora_state_dict(path, return_alphas=True)
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lora_loader.load_lora_into_transformer(state_dict, network_alphas=network_alphas, transformer=model, adapter_name=path)
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else:
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raise ValueError(f"Unsupported LoRA loader: {lora_loader}")
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model.set_adapters(paths, weights=weights)
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# NOTE: fuse_lora an experimental API in Diffusers
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model.fuse_lora(adapter_names=paths, lora_scale=scale)
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model.unload_lora()
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return model
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