Sexiam Img2Img 2.0 — v1.0

Sexiam’s IMG2IMG + Upscale Workflow Guide (ComfyUI)

A clean, practical walkthrough for using the workflow effectively.

🧱 1. Load Your Checkpoint

Start here:

Load your SDXL checkpoint

(Optional) Load an external VAE

SDXL behaves differently depending on its VAE, so pick whichever looks best for your model.

🔧 2. Set Your VAE Mode

If you want to use the checkpoint’s built-in VAE, set:

“Use Checkpoint VAE?” = True

If you want to use your external VAE, set:

“Use Checkpoint VAE?” = False

🖼️ 3. Load Your Input Image

Upload your source image using the Load Image node.

Orientation Check

Set the “Image is Portrait?” toggle correctly:

Portrait → True

Landscape → False

Load an Upscale Model

Pick the upscaler you want (Remacri, SwinIR, ESRGAN, etc.). You’re not upscaling the final output here — you’re shaping a clean latent size for SDXL.

📏 Why the Workflow Uses ‘Upscale by Model’ Before Sampling

You might ask: “Why am I using ‘Upscale by Model’ for image-to-image?”

Here’s the short answer:

➡️ You’re not actually upscaling to output — you’re resizing the latent. SDXL was trained on ~ 1MP images , so pushing too high causes:

Generation errors

Warped structure

Model collapse

This workflow uses a safe baseline:

832×1216 (portrait)

1216×832 (landscape)

Multiplied by 1.5× (50% larger)

This is the maximum size SDXL samplers can reliably handle before breaking.

If you're having issues, reset the “Scale to Target Ratio” nodes to whatever size works for your system.

This workflow is ideal for using the input as a loose generational base , meaning:

Great for creative reinterpretations

Fine for refinement

Some proportions/details may change (normal for IMG2IMG)

✍️ 4. Enter Your Prompts

Fill in your:

Positive prompt

Negative prompt

These control the details, style, and adherence to the original image.

🎛️ 5. Adjust KSampler Settings (Critical for IMG2IMG)

For IMG2IMG, denoise strength is the most important setting:

0.7 → Loose interpretation of the input

Model only reuses ~30% of the original

0.5 → Preserves composition + color

Allows detail refinement

Great for keeping the structure mostly intact

Use lower values when you want accuracy, higher when you want creativity.

🔍 6. Optional: Final Model Upscaling (Use Only at Low Denoise)

This section lets you upscale the final output if your denoise is 0.4 or lower .

Why this matters: Low denoise keeps most of the latent structure, so upscaling is stable.

Results vary based on:

GPU VRAM

System RAM

How large you upscale

Steps:

Load the final image

Set the upscale multiplier

1.5× → 50% bigger

2× → double the resolution

Choose an upscale model

Realistic outputs → clean general ESRGAN models

Stylized/anime outputs → anime-optimized upscale models

📦 Required Custom Nodes

These are the only node packs used in the workflow. Install them through ComfyUI-Manager or manually via GitHub.

rgthree-comfy

comfyui-mixlab-nodes

Masquerade Nodes

ComfyLiterals