This workflow extends the prompt adherence of Wan 2.2 by using qwen in the first stage at a low resolution. Brings remarkably good detail to low resolution outputs.
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
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 512x512 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
Description
FAQ
Comments (15)
You confuse when you have upscale in the title- you really want to make it clear this is an image generation workflow.
The real magic is using only the Wan2.2 stage for video upscaling- and I don't understand why no-one seems to focus on that type of workflow, given how many users are desperate for an excellent local video upscaling option.
Thought I'd put T2I, good catch. Fixed the title
Are there any Wan2.2 video upscaling workflows yet though?
I hear this a lot, but so far... it seems that Wan2.2 can't do a decent video upscale.
Undoriel y can use seedvr 3b and 7b models, slow but amazing, download the nodes and models https://huggingface.co/ByteDance-Seed/SeedVR2-3B. workflow in this post : https://www.reddit.com/r/StableDiffusion/comments/1momnvw/an_experiment_with_wan_22_and_seedvr2_upscale/?share_id=ypS6aCZ-C3iwf85ONZjo9&utm_content=2&utm_medium=android_app&utm_name=androidcss&utm_source=share&utm_term=3
@Undoriel I get decent results using USDU. Denoise only needs to be at 0.1 and 2 steps.
Thanks for sharing, what this json code text box does? I have to change it every time?
Wow! Elegantly simple with outstanding results. The fact that the latents are compatible is a game changer. I've struck out trying that with many other model pairs, so I'd stopped testing it. I also didn't realize that the Clownshark sampler let you use such a low denoising strength with a latent upscale. Great work!
What is the full filename of the LoRA you use in your workflow? I can only see wan/realism/Wan2.2_LowNoise_
That's odd. You should see the whole lora, at least in the json. It's the smartphone lora found here
https://civitai.com/models/1834338?modelVersionId=2079658
Nm, I downloaded it and opened the json. Thanks anyway. Nice job btw.
ClownsharKSampler_Beta
hello !, do you know how can I install this one ?
thanks in advanced ! your workflow looks neat !
Hi, thanks for sharing. I've noticed this ghosting artifact, visible also on your first example when you zoom in. Any ideas on how this could be eliminated ?
This is a problem with upscaling latents with too low a denoise. Try increasing the ClownsharKSampler's denoise to 0.4 and it should resolve the shadow.
can you make this img to img thanks dude