Instructions to use Cosmo-Hug/FeverDream with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cosmo-Hug/FeverDream with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cosmo-Hug/FeverDream", 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
Commit History
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This model was designed as a general purpose fine tuning of SD1.5 on high quality photographs and traditional artworks upscaled and denoised before training to get the sharpest cleanest results possible. It's trained at 576 resolution using the offset noise fix so generations are sharp and detailed with vibrant colors, deep blacks, and a well balanced contrast. This model was not trained with A.I. generated images or merged with any other models which means your images won't have that green/aqua color cast seen in so many models today. The largest portion of the dataset consists of photographs of women, men, gorgeous landscapes, and luxurious home/cabin/hotel interiors, some abandoned buildings and cityscapes, followed by a few unique art styles that can most easily be discovered by prompting with the commonly used words below. Some tips for realistic images: Don't use the word "realistic" part of the dataset contains "realistic porcelain dolls" and it will likely reference thos…a75529b