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https://github.com/modelscope/DiffSynth-Studio.git
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49 lines
2.2 KiB
Python
49 lines
2.2 KiB
Python
import importlib
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import torch
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from PIL import Image
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig
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from modelscope import dataset_snapshot_download
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if importlib.util.find_spec("transformers") is None:
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raise ImportError("You are using Nexus-GenV2. It depends on transformers, which is not installed. Please install it with `pip install transformers==4.49.0`.")
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else:
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import transformers
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assert transformers.__version__ == "4.49.0", "Nexus-GenV2 requires transformers==4.49.0, please install it with `pip install transformers==4.49.0`."
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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="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors", **vram_config),
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ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin", **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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],
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nexus_gen_processor_config=ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="processor/"),
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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(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/nexusgen/cat.jpg")
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ref_image = Image.open("data/examples/nexusgen/cat.jpg").convert("RGB")
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prompt = "Add a crown."
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image = pipe(
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prompt=prompt, negative_prompt="",
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seed=42, cfg_scale=2.0, num_inference_steps=50,
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nexus_gen_reference_image=ref_image,
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height=512, width=512,
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)
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image.save("cat_crown.jpg")
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