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50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig
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vram_config = {
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"offload_dtype": torch.float8_e4m3fn,
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"offload_device": "cpu",
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"onload_dtype": torch.float8_e4m3fn,
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"onload_device": "cpu",
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"preparing_dtype": torch.float8_e4m3fn,
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"preparing_device": "cuda",
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"computation_dtype": torch.bfloat16,
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"computation_device": "cuda",
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}
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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", **vram_config),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors", **vram_config),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", **vram_config),
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ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors", **vram_config),
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],
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vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
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)
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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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# Empty prompt can automatically activate LoRA capabilities.
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image = pipe(prompt="", seed=0, lora_encoder_inputs=lora)
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image.save("image_1.jpg")
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image = pipe(prompt="", seed=0)
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image.save("image_1_origin.jpg")
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# Prompt without trigger words can also activate LoRA capabilities.
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image = pipe(prompt="a car", seed=0, lora_encoder_inputs=lora)
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image.save("image_2.jpg")
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image = pipe(prompt="a car", seed=0,)
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image.save("image_2_origin.jpg")
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# Adjust the activation intensity through the scale parameter.
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image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=1.0)
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image.save("image_3.jpg")
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image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=0.5)
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image.save("image_3_scale.jpg")
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