import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("shindi/realistic-skin-style", dtype=torch.bfloat16, device_map="cuda")
prompt = "jqkz"
image = pipe(prompt).images[0]Realistic-Skin- Style Dreambooth model trained by shindi with Hugging Face Dreambooth Training Space with the v2-1-768 base model
You run your new concept via diffusers Colab Notebook for Inference. Don't forget to use the concept prompts!
RealisticSkinStyle
This is my (Saleh) experiment with training on detailed photos of people featured textured skin. I noticed typically in my generations, skin looks airbrushed and smoonth which gives away that it's AI. Hoping to use this as a base model for further fine tunes so that models can look more realistic.
🚨 This is V1 of this exploration. Ideas for further exploration is annotating the training data as described here: https://www.reddit.com/r/StableDiffusion/comments/zcr644/make_better_dreambooth_style_models_by_using/
Note that you can use parenthesis (like this) to increase attention on a word or phrase. For example "A woman with ((blue eyes)) wearing a (((Hijab))), jqkz". More parentheses means more attention.
Sample prompts:
"A smiling black woman, jqkz"
"A white woman wearing a white shirt, jqkz style"
"A photograph of an old man wearing a black shirt, jqkz style"
"A young man with a cigarette, jqkz"
"A woman with ((blue eyes)) wearing a (((Hijab))), jqkz"
Example Outputs
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