Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
diffusers-training
Instructions to use Sajid121/OUtput_result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Sajid121/OUtput_result with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Sajid121/OUtput_result", 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
- Xet hash:
- f40532b009fd4e4e89b63631e328318d172f6ee64cdf6bc10f49b3b54080e5b7
- Size of remote file:
- 6.88 GB
- SHA256:
- 0a32c209ed1c3ee0d0bd76dde8db5bee7224580f7f45ac36d4fe2d70f3b5f388
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.