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39 lines
1.5 KiB
Python
39 lines
1.5 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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"onload_dtype": torch.float8_e4m3fn,
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"onload_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/LoRAFusion-preview-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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pipe.enable_lora_merger()
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pipe.load_lora(
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pipe.dit,
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ModelConfig(model_id="cancel13/cxsk", origin_file_pattern="30.safetensors"),
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)
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pipe.load_lora(
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pipe.dit,
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ModelConfig(model_id="DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1", origin_file_pattern="merged_lora.safetensors"),
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)
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image = pipe(prompt="a cat", seed=0)
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image.save("image_fused.jpg")
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