Qwen Image + Z Turbo

First Pass — Base Image (Qwen Image Model)

• Loads the primary diffusion model, CLIP, and VAE.

• Uses a Qwen Master Prompt to generate the initial semantic prompt.

• Converts the text prompt + negative prompt into CLIP conditioning.

• Sets the image size via an Empty Latent node.

• Runs a KSampler to generate the first-pass image.

• Decodes the latent to an actual image.

• Sends this image to a preview and to the comparison block.

2. Second Pass — Refinement (Z-Turbo Model)

• Loads a second, faster refinement model (Z-Turbo) along with matching CLIP and VAE.

• Reuses the first pass image by re-encoding it back to latent form.

• Adds additional positive/negative text conditioning.

• Runs a second KSampler tuned for enhancement.

• Decodes the refined latent to the final image.

• Previews the refined image and compares it with the first-pass output.

3. overall goal

• Generate an initial image using a high-quality Qwen model.

• Feed that image into a second model (Z-Turbo) for cleanup, sharpening, and stylistic improvement.

• Allow visual comparison between the initial and refined outputs.