mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig, ControlNetInput
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from modelscope import dataset_snapshot_download
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from modelscope import snapshot_download
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from PIL import Image
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import numpy as np
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# This model has additional requirements.
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# Please install the following packages.
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# pip install facexlib insightface onnxruntime
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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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snapshot_download(
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"ByteDance/InfiniteYou",
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allow_file_pattern="supports/insightface/models/antelopev2/*",
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local_dir="models/ByteDance/InfiniteYou",
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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="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin", **vram_config),
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ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.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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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/examples_in_diffsynth",
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local_dir="./",
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allow_file_pattern=f"data/examples/infiniteyou/*",
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)
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height, width = 1024, 1024
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controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8))
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controlnet_inputs = [ControlNetInput(image=controlnet_image, scale=1.0, processor_id="None")]
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prompt = "A man, portrait, cinematic"
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id_image = "data/examples/infiniteyou/man.jpg"
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id_image = Image.open(id_image).convert('RGB')
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image = pipe(
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prompt=prompt, seed=1,
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infinityou_id_image=id_image, infinityou_guidance=1.0,
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controlnet_inputs=controlnet_inputs,
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num_inference_steps=50, embedded_guidance=3.5,
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height=height, width=width,
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)
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image.save("man.jpg")
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prompt = "A woman, portrait, cinematic"
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id_image = "data/examples/infiniteyou/woman.jpg"
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id_image = Image.open(id_image).convert('RGB')
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image = pipe(
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prompt=prompt, seed=1,
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infinityou_id_image=id_image, infinityou_guidance=1.0,
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controlnet_inputs=controlnet_inputs,
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num_inference_steps=50, embedded_guidance=3.5,
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height=height, width=width,
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)
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image.save("woman.jpg") |