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38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
import torch
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from diffsynth.core import ModelConfig
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from diffsynth.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline
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vram_config = {
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"offload_dtype": torch.float32,
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"offload_device": "cpu",
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"onload_dtype": torch.float32,
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"onload_device": "cpu",
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"preparing_dtype": torch.float32,
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"preparing_device": "cuda",
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"computation_dtype": torch.float32,
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"computation_device": "cuda",
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}
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pipe = StableDiffusionXLPipeline.from_pretrained(
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torch_dtype=torch.float32,
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model_configs=[
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ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
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ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="text_encoder_2/model.safetensors", **vram_config),
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ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="unet/diffusion_pytorch_model.safetensors", **vram_config),
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ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
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],
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tokenizer_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer/"),
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tokenizer_2_config=ModelConfig(model_id="stabilityai/stable-diffusion-xl-base-1.0", origin_file_pattern="tokenizer_2/"),
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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 = pipe(
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prompt="a photo of an astronaut riding a horse on mars",
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negative_prompt="",
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cfg_scale=5.0,
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height=1024,
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width=1024,
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seed=42,
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num_inference_steps=50,
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)
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image.save("image.jpg")
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