Jib Mix Wan - v2 WAN 2.2 Merge
Recommended Prompts
highly detailed
Recommended Negative Prompts
use LightX lora with a negative weight for more realism
(bright:1.2), (overexposed:1.3), (high key:1.2), (blurry:1.2), (out of focus:1.2), (deformed:1.2), (ugly:1.2), (disfigured:1.2), (mutated:1.2), (extra limbs:1.3), (missing limbs:1.3), (bad anatomy:1.3), (malformed hands:1.3), (fewer digits:1.3), (extra digits:1.3), (low quality:1.2), (bad quality:1.2), (poorly drawn:1.2), (render:1.2), (cgi:1.2), (illustration:1.2), (drawing:1.2), (painting:1.2), (sketch:1.2), (cartoon:1.2), (anime:1.2), (watermark:1.2), (text:1.2), (signature:1.2)
Recommended Parameters
samplers
steps
cfg
Tips
Use the Res_3s sampler with Bong_Tangent scheduler from the RES4LYF custom nodes pack for best results.
Try low steps generation from 2 to 8 steps depending on quality needs.
For more realism, apply LightX lora with a negative weight and increase steps slightly.
Version Highlights
A merge of Wan 2.2 and My V1 mix
Creator Sponsors
Check out the RES4LYF custom nodes pack: https://github.com/ClownsharkBatwing/RES4LYF
Try the free txt2img workflow by AItrepreneur: https://pastebin.com/GPYQjUrx
Support AItrepreneur on Patreon: https://www.patreon.com/c/aitrepreneur/posts
Merge of my favourite Wan image enhancing loras, makes it much faster to use than when adding the lora separately.
Samplers
In my testing/research, I have found the best Sampler is Res_3s with Bong_Tangent Scheduler both from the RES4LYF custom nodes pack: https://github.com/ClownsharkBatwing/RES4LYF
I am using this great free txt2img workflow: https://pastebin.com/GPYQjUrx from AItrepreneur check out his Patreon https://www.patreon.com/c/aitrepreneur/posts
V2 is WAN 2.2. Merge that can do incredibly low steps down to 2 steps.
4 steps look better and 8 steps is great quality.
If you want a bit more realism, you can use the LightX lora with a negative weight, but you might have to then increase steps.
Can make decent images/videos in as low as 6 steps as it has speed loras mixed in.
Contributor
Model Details
Model type
Base model
Model version
Model hash
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Discussion
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