EDIT: (currently using the 1ksampler workflow with this new version of the acceleration lora: <a target="_blank" rel="ugc" href="https://civarchive.com/models/1585622?modelVersionId=2014449">https://civarchive.com/models/1585622?modelVersionId=2014449</a>)2in1 - Includes an updated version of the 1ksampler workflow and a new 2ksampler workflow.fun-reward mps lora<a target="_blank" rel="ugc" href="https://huggingface.co/alibaba-pai/Wan2.1-Fun-Reward-LoRAs/blob/main/Wan2.1-Fun-14B-InP-MPS.safetensors">https://huggingface.co/alibaba-pai/Wan2.1-Fun-Reward-LoRAs/blob/main/Wan2.1-Fun-14B-InP-MPS.safetensors</a>Self forcing accelerator lora:<a target="_blank" rel="ugc" href="https://civarchive.com/models/1713337?modelVersionId=1938875">https://civarchive.com/models/1713337?modelVersionId=1938875</a>still has my custom comfyui themes in the zip file.DISCLAIMER! You need the fun-reward-mps and the self forcing acceleration lora for these to work properly.Update: with the 2ksampler workflow i managed to squeeze it to a total of 4 steps:1st Ksampler - total steps 4 - steps 0 to 1 - cfg 3.5 - return with leftover noise DISABLED2nd Ksampler - total steps 4 - steps 1 to 4 - cfg 1 - add noise ENABLED<hr />V4.1 - Removed florence, it's kinda useless with this setup. Moved shift before sage attention, i think it's supposed to be like that, atleast it feels better now. Still contains my custom comfyui themes as an extra.<hr />Wan gguf workflow using one of the self forcing loras to use low step counts.Use whatever gguf model you want, i'm using the Q5_K_M.Self forcing accelerator lora:<a target="_blank" rel="ugc" href="https://civarchive.com/models/1713337?modelVersionId=1938875">https://civarchive.com/models/1713337?modelVersionId=1938875 </a>(i'm using this one)<a target="_blank" rel="ugc" href="https://civarchive.com/models/1585622?modelVersionId=1909719">https://civarchive.com/models/1585622?modelVersionId=1909719</a>Also includes my custom themes for comfyui, to install it click the kogwheel (settings) - Appearance - color palette - import: (Astraali_RedLINE.json / Astraali_DarkLINE_1.json / Astraali_BlueGOO_4.json)

Files with SHA256: 47f18ff7d6a1770314663319c820a6f94069f46d9bbf1a270ce3acc84b68a487