Instructions to use cuio/DreamLite-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuio/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("cuio/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 Settings
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "DreamLitePipeline", | |
| "_diffusers_version": "0.37.0", | |
| "processor": [ | |
| "transformers", | |
| "Qwen3VLProcessor" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Qwen3VLForConditionalGeneration" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "Qwen2TokenizerFast" | |
| ], | |
| "unet": [ | |
| "dreamlite.models.unets.unet_2d_condition_mobile", | |
| "DreamLiteUNetModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderTiny" | |
| ] | |
| } | |