Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionImg2ImgPipeline
stable-diffusion
stable-diffusion-diffusers
image-to-image
upgc
unfiltered
nsfw-allowed
Instructions to use Skybox-Studios/UPGC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Skybox-Studios/UPGC with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Skybox-Studios/UPGC", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
UPGC: Unfiltered Prompt Generation Control
UPGC is a specialized Stable Diffusion v1.5 model configuration optimized for maximum creative freedom and unrestricted prompt adherence.
Key Features:
- No Safety Filters: The internal safety checker and feature extractor are completely disabled.
- Unrestricted Content: Designed to follow user prompts without internal censorship or smoothing.
- Image-to-Image Support: Full compatibility for transforming existing images into unrestricted variations.
- High Guidance Optimization: Best results at
guidance_scaleof 12.0-15.0 for strict adherence.
Usage:
from diffusers import StableDiffusionImg2ImgPipeline
import torch
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Skybox-Studios/UPGC", torch_dtype=torch.float16).to("cuda")
Disclaimer:
This model is unfiltered. Users are responsible for the content generated and must adhere to local laws and the CreativeML Open RAIL-M license.
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Model tree for Skybox-Studios/UPGC
Base model
runwayml/stable-diffusion-v1-5