이미지 기반 이미지 생성은 ComfyUI에서 사용자가 이미지를 입력하면 이를 기반으로 새로운 이미지를 생성할 수 있는 워크플로우입니다.이미지 기반 이미지 생성은 다음과 같은 시나리오에서 활용될 수 있습니다:
원본 이미지 스타일 변환, 예를 들어 사실적인 사진을 예술적 스타일로 변환하기
선화를 사실적인 이미지로 변환하기
이미지 복원
오래된 사진에 색칠하기
그 외 다양한 시나리오
비유적으로 설명하자면:
이는 마치 예술가에게 참조 이미지를 바탕으로 특정 작품을 만들어달라고 요청하는 것과 같습니다.이 튜토리얼을 텍스트 기반 이미지 생성 튜토리얼과 자세히 비교해보면,
이미지 기반 이미지 생성 과정이 텍스트 기반 이미지 생성과 매우 유사하다는 것을 알 수 있습니다.
다만 추가적인 참조 이미지를 조건으로 입력받는 차이가 있을 뿐입니다. 텍스트 기반 이미지 생성에서는 예술가(이미지 모델)가 우리의 프롬프트를 바탕으로 자유롭게 창작하도록 하지만,
이미지 기반 이미지 생성에서는 참조 이미지와 프롬프트를 모두 고려하여 예술가가 창작하도록 합니다.
이미지 기반 이미지 생성 워크플로우의 핵심은 KSampler 노드의 denoise 파라미터이며, 이 값은 1보다 작아야 합니다.denoise 파라미터를 조정하고 이미지를 생성해보면 다음과 같은 특징을 발견할 수 있습니다:
denoise 값이 작을수록 생성된 이미지와 참조 이미지 간의 차이가 줄어듭니다.
denoise 값이 클수록 생성된 이미지와 참조 이미지 간의 차이가 커집니다.
이는 denoise가 참조 이미지를 변환한 후 잠재 공간 이미지에 추가되는 노이즈의 강도를 결정하기 때문입니다. 만약 denoise가 1이라면 잠재 공간 이미지는 완전히 무작위 노이즈가 되어, empty latent image 노드에서 생성된 잠재 공간과 동일하게 되며 참조 이미지의 특성을 모두 잃게 됩니다.
denoise를 낮춰도 한 번의 실행에서 수행되는 샘플링 스텝 수는 줄지 않습니다. 샘플링이 완전히 멈추는 경우는 denoise가 0일 때뿐입니다. ComfyUI는 steps / denoise 스텝(소수점 버림)에 해당하는 스케줄을 만든 뒤 마지막 steps + 1개의 sigma만 사용하므로, steps 20과 denoise 0.5의 조합은 40 스텝 스케줄의 저노이즈 절반 구간을 20 스텝으로 실행하며, 스텝 밀도는 그 긴 스케줄과 같습니다. 노이즈 범위의 일부만 전체 실행과 같은 스텝 밀도로 샘플링하려면 steps에 denoise를 곱한 값으로 설정합니다. denoise 0.5일 때 steps 10은 20 스텝 실행의 후반부가 사용하는 sigma를 그대로 사용하므로 모델 평가 횟수는 절반이 됩니다.
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https://mintcdn.com/dripart/OltlUSVBSNcJsDMs/images/tutorial/basic/img2img/image-to-image-02-guide.jpg?w=840\u0026fit=max\u0026auto=format\u0026n=OltlUSVBSNcJsDMs\u0026q=85\u0026s=4935b3f629d63f2bf0c8a79fa864eb16 840w, https://mintcdn.com/dripart/OltlUSVBSNcJsDMs/images/tutorial/basic/img2img/image-to-image-02-guide.jpg?w=1100\u0026fit=max\u0026auto=format\u0026n=OltlUSVBSNcJsDMs\u0026q=85\u0026s=ba9c51655e26715be1b316c307c3a761 1100w, https://mintcdn.com/dripart/OltlUSVBSNcJsDMs/images/tutorial/basic/img2img/image-to-image-02-guide.jpg?w=1650\u0026fit=max\u0026auto=format\u0026n=OltlUSVBSNcJsDMs\u0026q=85\u0026s=90406adf554c73a31ffe9b86cfea06ed 1650w, https://mintcdn.com/dripart/OltlUSVBSNcJsDMs/images/tutorial/basic/img2img/image-to-image-02-guide.jpg?w=2500\u0026fit=max\u0026auto=format\u0026n=OltlUSVBSNcJsDMs\u0026q=85\u0026s=d813a71613511b97258f4b01317f565a 2500w`}),`\n`,_jsxs(_components.ol,{children:[`\n`,_jsxs(_components.li,{children:[_jsx(_components.code,{children:`Load Checkpoint`}),`가 `,_jsx(_components.strong,{children:`v1-5-pruned-emaonly-fp16.safetensors`}),`를 로드하도록 확인하세요.`]}),`\n`,_jsxs(_components.li,{children:[`입력 이미지를 `,_jsx(_components.code,{children:`Load Image`}),` 노드에 업로드하세요.`]}),`\n`,_jsxs(_components.li,{children:[_jsx(_components.code,{children:`Queue`}),`를 클릭하거나 `,_jsx(_components.code,{children:`Ctrl/Cmd + Enter`}),`를 눌러 생성하세요.`]}),`\n`]}),`\n`,_jsx(Heading,{level:`2`,id:`이미지-기반-이미지-생성-워크플로우의-핵심-포인트`,children:`이미지 기반 이미지 생성 워크플로우의 핵심 포인트`}),`\n`,_jsxs(_components.p,{children:[`이미지 기반 이미지 생성 워크플로우의 핵심은 `,_jsx(_components.code,{children:`KSampler`}),` 노드의 `,_jsx(_components.code,{children:`denoise`}),` 파라미터이며, 이 값은 `,_jsx(_components.strong,{children:`1보다 작아야 합니다`}),`.`]}),`\n`,_jsxs(_components.p,{children:[_jsx(_components.code,{children:`denoise`}),` 파라미터를 조정하고 이미지를 생성해보면 다음과 같은 특징을 발견할 수 있습니다:`]}),`\n`,_jsxs(_components.ul,{children:[`\n`,_jsxs(_components.li,{children:[_jsx(_components.code,{children:`denoise`}),` 값이 작을수록 생성된 이미지와 참조 이미지 간의 차이가 줄어듭니다.`]}),`\n`,_jsxs(_components.li,{children:[_jsx(_components.code,{children:`denoise`}),` 값이 클수록 생성된 이미지와 참조 이미지 간의 차이가 커집니다.`]}),`\n`]}),`\n`,_jsxs(_components.p,{children:[`이는 `,_jsx(_components.code,{children:`denoise`}),`가 참조 이미지를 변환한 후 잠재 공간 이미지에 추가되는 노이즈의 강도를 결정하기 때문입니다. 만약 `,_jsx(_components.code,{children:`denoise`}),`가 1이라면 잠재 공간 이미지는 완전히 무작위 노이즈가 되어, `,_jsx(_components.code,{children:`empty latent image`}),` 노드에서 생성된 잠재 공간과 동일하게 되며 참조 이미지의 특성을 모두 잃게 됩니다.`]}),`\n`,_jsx(Note,{children:_jsxs(_components.p,{children:[_jsx(_components.code,{children:`denoise`}),`를 낮춰도 한 번의 실행에서 수행되는 샘플링 스텝 수는 줄지 않습니다. 샘플링이 완전히 멈추는 경우는 `,_jsx(_components.code,{children:`denoise`}),`가 0일 때뿐입니다. 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