Instructions to use carlofkl/DreamLite-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use carlofkl/DreamLite-base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("carlofkl/DreamLite-base", 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
DreamLite
ByteDance's UNet-based text-to-image and image-edit diffusion model. 3-branch dual-CFG design, runs at 1024ร1024.
import torch
from diffusers import DreamLitePipeline
pipe = DreamLitePipeline.from_pretrained(
"carlofkl/DreamLite-base", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe("a corgi astronaut", num_inference_steps=28).images[0]
License: CC BY-NC 4.0 (non-commercial). A full model card will be added once the diffusers integration PR is merged.
- Downloads last month
- 497