Qwen + Wan 2.2 Low Noise T2I (2K GGUF Workflow Included)

Workflow : https://pastebin.com/f32CAsS7

Hardware : RTX 3090 24GB

Models : Qwen Q4 GGUF + Wan 2.2 Low GGUF

Elapsed Time E2E (2k Upscale) : 300s cold start, 80-130s (0.5MP – 1MP)

**Main Takeaway – Qwen Latents are compatible with Wan 2.2 Sampler**

Got a bit fed up with the cryptic responses posters gave whenever asked for workflows. This workflow is the effort piecing together information from random responses.

There are two stages:

1stage: (42s-77s). Qwen sampling at 0.75/1.0/1.5MP

2stage: (~110s): Wan 2.2 4 step

__1st stage can go to VERY low resolutions. Haven’t test 512×512 YET but 0.75MP works__

* Text – text gets lost at 1.5 upscale , appears to be restored with 2.0x upscale. I’ve included a prompt from the Comfy Qwen blog

* Landscapes (Not tested)

* Cityscapes (Not tested)

* Interiors *(untested)

* Portraits – Closeups Not great (male older subjects fare better). Okay with full body, mid length. Ironically use 0.75 MP to smooth out features. It’s obsessed with freckles. Avoid. This may be fixed by https://www.reddit.com/r/StableDiffusion/comments/1mjys5b/18_qwenimage_realism_lora_samples_first_attempt/ by the never sleeping u/AI_Characters

Next:

– Experiment with leftover noise

– Obvious question – Does Wan2.2 upscale work well on __any__ compatible vae encoded image ?

– What happens at 4K ?

– Can we get away with lower steps in Stage 1