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50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import torch
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from diffsynth import ModelManager, FluxImagePipeline, download_models
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from diffsynth.controlnets.processors import Annotator
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import numpy as np
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from PIL import Image
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download_models(["FLUX.1-dev"])
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda")
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model_manager.load_models([
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"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
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"models/FLUX/FLUX.1-dev/text_encoder_2",
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"models/FLUX/FLUX.1-dev/ae.safetensors",
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"models/ostris/Flex.2-preview/Flex.2-preview.safetensors"
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])
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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image = pipe(
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prompt="portrait of a beautiful Asian girl, long hair, red t-shirt, sunshine, beach",
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num_inference_steps=50, embedded_guidance=3.5,
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seed=0
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)
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image.save("image_1.jpg")
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mask = np.zeros((1024, 1024, 3), dtype=np.uint8)
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mask[200:400, 400:700] = 255
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mask = Image.fromarray(mask)
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mask.save("image_mask.jpg")
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inpaint_image = image
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image = pipe(
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prompt="portrait of a beautiful Asian girl with sunglasses, long hair, red t-shirt, sunshine, beach",
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num_inference_steps=50, embedded_guidance=3.5,
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flex_inpaint_image=inpaint_image, flex_inpaint_mask=mask,
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seed=4
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)
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image.save("image_2.jpg")
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control_image = Annotator("canny")(image)
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control_image.save("image_control.jpg")
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image = pipe(
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prompt="portrait of a beautiful Asian girl with sunglasses, long hair, yellow t-shirt, sunshine, beach",
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num_inference_steps=50, embedded_guidance=3.5,
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flex_control_image=control_image,
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seed=4
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)
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image.save("image_3.jpg")
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