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48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig, ControlNetInput
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import numpy as np
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from PIL import Image
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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="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_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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image_1 = pipe(
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prompt="a cat sitting on a chair",
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height=1024, width=1024,
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seed=8, rand_device="cuda",
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)
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image_1.save("image_1.jpg")
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mask = np.zeros((1024, 1024, 3), dtype=np.uint8)
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mask[100:350, 350: -300] = 255
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mask = Image.fromarray(mask)
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mask.save("mask.jpg")
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image_2 = pipe(
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prompt="a cat sitting on a chair, wearing sunglasses",
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controlnet_inputs=[ControlNetInput(image=image_1, inpaint_mask=mask, scale=0.9)],
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height=1024, width=1024,
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seed=9, rand_device="cuda",
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)
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image_2.save("image_2.jpg") |