SDXL 1.0: Image-to-Image Refinement in ComfyUI
Blueprint for restyling an input image at 0.45–0.65 denoise with SDXL 1.0 (8 GB+ VRAM) using Load Image → VAE Encode → KSampler (denoise 0.55) → VAE Decode.
Updated Oct 17, 2026 · 1 min read
- Model Architecture
- SDXL 1.0
- Workflow Task
- Image-to-Image Refinement
- VRAM Requirement
- 8 GB+
- Primary Loader
- Load Checkpoint
- Model Directory
- models/checkpoints/
Node graph blueprint: SDXL 1.0 × Image-to-Image Refinement
This route pairs **SDXL 1.0** with a **Image-to-Image Refinement** setup designed for restyling an input image at 0.45–0.65 denoise. Place your model weights in `models/checkpoints/` and start the graph with **Load Checkpoint**.
Core wiring chain: `Load Image → VAE Encode → KSampler (denoise 0.55) → VAE Decode`. Connect your positive and negative conditioning from CLIP Text Encode into KSampler and decode to RGB pixels.
VRAM tuning and execution tips on Windows
- **Memory target (8 GB+):** If your GPU sits at 8 GB or 12 GB, use FP8 or GGUF Q4/Q5 weights and swap in VAE Decode (Tiled) to avoid end-of-pass memory spikes.
- **Workflow persistence:** Every image saved by this route embeds the full JSON graph inside the PNG metadata — drag the output back onto ComfyUI Studio anytime to resume.
- **Get the app:** Download the Windows desktop or portable build from /download.
FAQs
Which loader node do I use for SDXL 1.0 in a Image-to-Image Refinement?
Use `Load Checkpoint` with weights stored in `models/checkpoints/`, then wire the output into `Load Image → VAE Encode → KSampler (denoise 0.55) → VAE Decode`.
How much VRAM does SDXL 1.0 need for Image-to-Image Refinement?
Target VRAM is 8 GB+. On 8 GB cards, load quantized FP8 or GGUF weights and enable tiled VAE decoding.
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