ComfyUI Workflow Summary ComfyUI 예시
The workflow integrates various nodes for image generation and enhancement with face detection, segmentation, and text conditioning. It starts with model loading for Stable Diffusion (SDXL柔美\fudukiMix_v20.safetensors), object detection (bbox/face_yolov8m.pt), SAM modeling, and UNet stages. Text descriptions are encoded via CLIP to provide positive and negative conditioning. The detection, segmentation, and masking processes are used to focus on specific image areas. The KSampler uses models and conditioning for generating latent images, followed by VAEDecode to convert latents to images. Several integrations of empty latent images, samplers, decoders, and loaders for VAE and CLIP contribute to the detailed image generation process. Complex nodes like DetailerForEach apply multiple conditioning phrases to enhance specific image segments. The final output involves comparing images and saving them with configurable settings.
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The workflow integrates various nodes for image generation and enhancement with face detection, segmentation, and text conditioning. It starts with model loading for Stable Diffusion (SDXL柔美\fudukiMix_v20.safetensors), object detection (bbox/face_yolov8m.pt), SAM modeling, and UNet stages. Text descriptions are encoded via CLIP to provide positive and negative conditioning. The detection, segmentation, and masking processes are used to focus on specific image areas. The KSampler uses models and conditioning for generating latent images, followed by VAEDecode to convert latents to images. Several integrations of empty latent images, samplers, decoders, and loaders for VAE and CLIP contribute to the detailed image generation process. Complex nodes like DetailerForEach apply multiple conditioning phrases to enhance specific image segments. The final output involves comparing images and saving them with configurable settings.
