Text-to-Image
Diffusers
Safetensors
French
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use Acadys/PointConImageModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Acadys/PointConImageModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Acadys/PointConImageModel", 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
- Xet hash:
- f294e67c86de94dd20e50aab9a823bbae1e92e20b42e4200844bc3332eb3fd51
- Size of remote file:
- 3.44 GB
- SHA256:
- 15be1b597e3999651fc385fb689dd6e62fcce26e6042605ba232ed8db62df47c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.