Instructions to use valhalla/t2i-style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use valhalla/t2i-style with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("valhalla/t2i-style", 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
t2iadapter-valhalla/t2i-style
These are t2iadapter weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning.
You can find some example images below.
prompt: a picture of a cat, 4k photo, highly detailed
prompt: a jungle, 4k photo, highly detailed
prompt: a truck, 4k photo, highly detailed
prompt: a digital painting of a lion, highly detailed

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Model tree for valhalla/t2i-style
Base model
stabilityai/stable-diffusion-xl-base-1.0