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41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth.utils.controlnet import Annotator
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from modelscope import snapshot_download
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snapshot_download("sd_lora/Annotators", allow_file_pattern="dpt_hybrid-midas-501f0c75.pt", local_dir="models/Annotators")
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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="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"),
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],
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)
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image_1 = pipe(
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prompt="a beautiful Asian girl, full body, red dress, summer",
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height=1024, width=1024,
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seed=6, rand_device="cuda",
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)
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image_1.save("image_1.jpg")
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image_canny = Annotator("canny")(image_1)
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image_depth = Annotator("depth")(image_1)
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image_2 = pipe(
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prompt="a beautiful Asian girl, full body, red dress, winter",
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controlnet_inputs=[
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ControlNetInput(image=image_canny, scale=0.3, processor_id="canny"),
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ControlNetInput(image=image_depth, scale=0.3, processor_id="depth"),
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],
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height=1024, width=1024,
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seed=7, rand_device="cuda",
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)
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image_2.save("image_2.jpg")
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