Instructions to use nitrosocke/redshift-diffusion-768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitrosocke/redshift-diffusion-768 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nitrosocke/redshift-diffusion-768", 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
- Draw Things
- DiffusionBee
Future Diffusion
This is the fine-tuned Stable Diffusion 2.0 model trained on high quality 3D images with a 768x768 pixel resolution.
Use the tokensredshift style
in your prompts for the effect.
Trained on Stability.ai's Stable Diffusion 2.0 with 768x768 resolution.
If you enjoy my work and want to test new models before release, please consider supporting me
- The weights are now available! You can download them here: redshift-diffusion-768.ckpt
- You can try out the model online here: Diffusion Space Demo
- or try out this model with my local Diffusers based Gradio WebUI
Characters rendered with the model:
Cars and Animals rendered with the model:
Landscapes rendered with the model:

Prompt and settings for the Characters:
redshift style portrait black female cyberpunk hacker tattoos colorful short hair wearing a crop top redshift style Negative Prompt: mutated body double head bad anatomy long face long neck long body text watermark signature
Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 768x1024
Prompt and settings for the Landscapes:
redshift style beautiful fjord at sunrise Negative Prompt: fog blurry soft
Steps: 20, Sampler: Euler a, CFG scale: 7, Size: 1536x768
This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the train-text-encoder flag in 7.500 steps.
License
This model is open access and available to all, with a CreativeML Open RAIL++-M License further specifying rights and usage. Please read the full license here
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