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26 lines
952 B
Python
26 lines
952 B
Python
import torch
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from diffsynth import ModelManager, FluxImagePipeline, download_models, load_state_dict
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from diffsynth.models.flux_reference_embedder import FluxReferenceEmbedder
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from PIL import Image
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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/FLUX/FLUX.1-dev/flux1-dev.safetensors"
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])
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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pipe.reference_embedder = FluxReferenceEmbedder().to(dtype=torch.bfloat16, device="cuda")
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pipe.reference_embedder.init()
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for i in range(4):
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image = pipe(
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prompt="a girl.",
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num_inference_steps=30, embedded_guidance=3.5,
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height=512, width=512,
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reference_images=[Image.open("data/example4.jpg").resize((512, 512))]
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)
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image.save(f"image_{i}.jpg") |