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obsxrver/wan2.2-t2v-scat
wan2.2-t2v-scat is a text-to-video model from obsxrver. Use it when you need video from a text prompt. It is set up for diffusers. The card lists the license as apache-2.0.
UPDATE: 03-25-2026 New Version: ScatMaster-V2 Released: Fix random text appearing, better control and character adherence
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.safetensors614 MB · 100%
From the Hugging Face model README
UPDATE: 03-25-2026 New Version: ScatMaster-V2 Released: Fix random text appearing, better control and character adherence
Download ./config.json for the workflow file.
pip install huggingface_hub
hf download obsxrver/wan2.2-scat-xviii-PooMaestro --local-dir ./wan2.2-scat-xviii-PooMaestro
she defecates
covered in feces
A woman, she is nude, lying on her back, looking at the viewer, legs up, covered in feces, she is rubbing her pussy
nude woman, squatting over a bed, she defecates on the bed, looking at the viewer, moaning
Q: Why did you do this?
A: Because I can, next.
Q: Dataset?
A: My dataset consisted of 33 high-quality videos and 16 images, some of which were stills from the videos. For the videos, I trained with a size of 298px^2 (0.0888MP), and the pictures at 960^2 (0.92MP). The dataset needs to be high quality and free of noise or artifacts. Otherwise, these will translate into artifacts in the final LoRA, which is no bueno.
Q: Captions?
A: I captioned these using Gemini 2.5 Pro, each caption consisted of "[Setting], [Subject], [Action], [Lighting]" The exact system prompt is available in the repo. I manually added "still-frame" to every image(not sure if this helps), and I also blurred out the actor's faces in many of the videos, and added "her face is blurred out", to the caption. This helps with negating the undesired effect of baking-in the shape and visual characteristics of any given actor's face from the dataset into the LoRA's output.
Example: A room, a woman wearing a grey shirt, her face is blurred out, squatting on the floor and spreading her ass, a plain wall, the woman defecates, bright, direct lighting.
Q: Training?
A: I used Musubi-Tuner. You can find the training commands outlined at https://github.com/obsxrver/wan22-lora-training. I use these training commands for every LoRA I make and almost always have good results.