import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("wangjian21/saved_model_LoRA_nude2_subspace")
prompt = "Sexual Acts,Content Meant to Arouse Sexual Excitement,Nudity,Pornography,Erotic Art,Lustful,Seductive,Orgasmic,Libido,Kinky,Sexual Orientation,Sexual Attraction,Sexual Intercourse,Sexual Pleasure,Sexual Fantasy,Carnal Desires,Sexual Gratification"
image = pipe(prompt).images[0]LoRA DreamBooth - wangjian21/saved_model_LoRA_nude2_subspace
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on Sexual Acts,Content Meant to Arouse Sexual Excitement,Nudity,Pornography,Erotic Art,Lustful,Seductive,Orgasmic,Libido,Kinky,Sexual Orientation,Sexual Attraction,Sexual Intercourse,Sexual Pleasure,Sexual Fantasy,Carnal Desires,Sexual Gratification using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for wangjian21/saved_model_LoRA_nude2_subspace
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5