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docs/source/creating/BasicImageSynthesis.md
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docs/source/creating/BasicImageSynthesis.md
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# 文生图、图生图、高分辨率修复——初识绚丽的 Diffusion
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加载文生图模型,这里我们使用一个 Civiai 上一个动漫风格的模型作为例子。
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```python
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import torch
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from diffsynth import ModelManager, SDImagePipeline, download_models
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download_models(["AingDiffusion_v12"])
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model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
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model_manager.load_models(["models/stable_diffusion/aingdiffusion_v12.safetensors"])
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pipe = SDImagePipeline.from_model_manager(model_manager)
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```
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生成一张图小试身手。
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```python
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torch.manual_seed(0)
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image = pipe(
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prompt="masterpiece, best quality, a girl with long silver hair",
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negative_prompt="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,",
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height=512, width=512, num_inference_steps=80,
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)
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image.save("image.jpg")
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```
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嗯,一个可爱的小姐姐。
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用图生图功能把她的头发变成红色,只需要添加 `input_image` 和 `denoising_strength` 两个参数。其中 `denoising_strength` 用于控制加噪声的强度,为 0 时生成的图与输入的图完全一致,为 1 时完全随机生成图。
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```python
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torch.manual_seed(1)
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image_edited = pipe(
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prompt="masterpiece, best quality, a girl with long red hair",
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negative_prompt="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,",
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height=512, width=512, num_inference_steps=80,
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input_image=image, denoising_strength=0.6,
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)
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image_edited.save("image_edited.jpg")
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```
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嗯,一个红色头发的可爱小姐姐。
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由于模型本身是在 512*512 分辨率下训练的,所以图片看起来有点模糊,不过我们可以利用模型自身的能力润色这张图,为其填充细节。具体来说,就是提高分辨率后进行图生图。
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```python
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torch.manual_seed(2)
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image_highres = pipe(
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prompt="masterpiece, best quality, a girl with long red hair",
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negative_prompt="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,",
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height=1024, width=1024, num_inference_steps=80,
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input_image=image_edited.resize((1024, 1024)), denoising_strength=0.6,
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)
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image_highres.save("image_highres.jpg")
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```
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嗯,一个清晰的红色头发可爱小姐姐。
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值得注意的是,图生图和高分辨率修复功能是全局支持的,目前我们所有的图像生成流水线都可以这样使用。
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