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25 lines
1.2 KiB
Python
25 lines
1.2 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig
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from diffsynth import load_state_dict
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLUX.1-dev-LoRA-Encoder_full/epoch-0.safetensors")
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pipe.lora_encoder.load_state_dict(state_dict)
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lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors")
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pipe.load_lora(pipe.dit, lora) # Use `pipe.clear_lora()` to drop the loaded LoRA.
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image = pipe(prompt="", seed=0, lora_encoder_inputs=lora)
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image.save("image_FLUX.1-dev-LoRA-Encoder_full.jpg")
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