I had good success with already positioned initial frames and combined with "Self Forcing"; 3-5 steps usually enough when using with it.
As usual I just picked some generated images randomly to animate from related models, kudos to creators.
No trigger word used directly but tongue job is common in all. I recommend focusing on the action describing the subject with the concept related words. Some nsfw words included in the train set such as: cum, blowjob, tongue job etc.
Here’s an example prompt I used in some of these generations—feel free to alter it heavily:
A woman is performing a tongue job on a circumcised, erect penis. She is licking the head of the penis.Wan 2.2 Experimental Update:
I start exploring the new Wan version, the two model approach seems to be adding better movements and prompt adherence but causing some other problems such as rapid movements or little artifacts here and there.
I have trained a lora for each version, a high noise lora for movements and low noise lora for finer details. You can also use low noise lora directly like wan 2.1 if you want.
Note: If you have any concept requests or want to support my experiments, feel free to message me. Testing different settings and learning new training recipes for wan 2.2 seems to be expensive for my taste, I am open for runpod gift vouchers for that purpose... (Begging part)
I use total steps of 6, 2 for high 4 for low part using self reinforcing lora (for 2.1) on 2.0 strength, rest is same as official comfy workflow.
This is just early access and experiment version, use it at your own risk xD
As usual:
Please use the model responsibly. No real person faces were used in the training.
Description
FAQ
Comments (6)
IMPORTANT: INFORM ALL WAN USERS! I DISCOVERED HOW TO USE LORAS 'NSFW/SFW' IN WAN 2.2 T2V, GETTING THE FULL POTENTIAL OF "HIGH NOISE" AND "LOW NOISE." THE RESULTS WERE IMPRESSIVE!! I ran it on an RTX 3060 6GB, 32GB RAM, 480x480, length: 41, generation time: 5 minutes to generate each video. The quality is fantastic! For those of you with more powerful computers, the results will be even more incredible. I used the models: WAN 2.2 - Q4_1.gguf FOLLOW THE STEPS BELOW: 1: Don't use acceleration/enhancement LoRas (such as Lightx2v or other) on "high noise"; use only on "low noise" with strength 1. (I'm using this version of Lightx2v LoRa): lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16 2: Use 'NSFW/SFW' LoRas on both "high noise" and "low noise" models. On "high noise," use strength 2, and on "low noise," use half the strength, with strength 1. You can vary the strengths as you like, but always keep the "low noise" strength at half the strength of the "high noise." 3: Use these settings in KSamplers: "high noise": steps: 9 / cfg: 3.5 / euler/simple / start_at_step: 0 / end_at_step: 4 "low noise": steps: 9 / cfg: 1.0 / euler/simple / start_at_step: 4 / end_at_step: 10000 Note: To improve speed, I set the Windows system to "best performance," disable unnecessary background programs and applications, and lower the desktop resolution. This significantly speeds up video generation. I hope this helps.
Wan 2.2 version seems to run a bit fast. I hope this means I can interpolate frames and get longer, normal speed videos. It's rough when a lora makes slow motion videos, and you have to generate for twice as long to get a normal length video. I love when loras are on the fast motion side of things.
t2v version?
hi, is this an update or the same thing as published some days ago?
Good job.
By the way, it seems the base model label is incorrect, so it’s not being filtered as 2.2 lora.
im using steps 4 config 1. shoudl i change any of these?
Details
Files
wan2_2_tongue_job_exp_high_noise.safetensors
Mirrors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
POV-Tongue-Job_wan2_2_tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
tongue_job_exp_high_noise.safetensors
wan2_2_tongue_job_exp_high_noise.safetensors
tongue-job_wan22-HN.safetensors