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
File size: 1,157 Bytes
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license: cc-by-nc-4.0
library_name: diffusers
pipeline_tag: text-to-image
---
# Requirements
This pipeline relies on `Qwen3VLForConditionalGeneration` / `Qwen3VLProcessor`. Due to upstream changes in `transformers >= 5.0`, you must pin:
```bash
pip install "transformers==4.57.3"
```
Using `transformers >= 5.0` will produce visible block-pattern artifacts in the generated image.
# DreamLite
ByteDance's UNet-based text-to-image and image-edit diffusion model.
3-branch dual-CFG design, runs at 1024×1024.
```python
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. |