This two-stage ComfyUI workflow turns a low- or mid-resolution image into a high-fidelity result using the Wan2.2 diffusion model in controlled image-to-image mode. After you LoadImage, GeminiNodeV3 analyzes the picture and generates a concise descriptive prompt that conditions Wan2.2 during upscaling. The pipeline then runs in two passes inside the Image Controlled Upscale group: a coarse first pass to establish structure at a larger size, followed by a fine second pass to restore and enhance details while keeping the original look.

Stage 1 and Stage 2 are implemented with the custom upscaler nodes (8c62a455-6744-43c8-9352-543905ed4db7 and 058e194e-5740-4f04-a9ca-7c741daecfcc), which accept your resize factor, denoise strength, and sampler steps. Author notes provide practical ranges: use 0.20–0.40 denoise for a more creative, coarse pass and 0.10–0.20 for detail-preserving refinement; 25 steps for quick results, or 50+ for maximum quality. PreviewAny gives you live feedback, ImageCompare shows side-by-side before/after in the Image Compare group, and SaveImage exports the final output. A MarkdownNote inside the workflow summarizes the guidance so you can quickly dial in the right settings for different images.

This iterative approach is especially effective for AI-generated images or stylized art that benefit from prompt-guided reconstruction. By pairing auto-prompting (GeminiNodeV3) with Wan2.2’s image-to-image strengths, the workflow preserves identity while recovering edges, textures, and fine details that simple pixel upscalers can miss.