Text-to-Image
Diffusers
Safetensors
PyTorch
StableDiffusionPipeline
Stable-Diffusion-Model
diffusion-models-class
Instructions to use MADZaMeR/stable_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MADZaMeR/stable_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MADZaMeR/stable_diffusion", 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
| license: creativeml-openrail-m | |
| tags: | |
| - pytorch | |
| - diffusers | |
| - Stable-Diffusion-Model | |
| - diffusion-models-class | |
| This model is a diffusion model for text to image generation. | |
| ## Usage | |
| ```python | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| pipeline = StableDiffusionPipeline.from_pretrained('MADZaMeR/stable_diffusion', torch_dtype=torch.float16 | |
| ).to("cuda" if torch.cuda.is_available() else "cpu") | |
| prompt = "a photograph of an astronaut riding a horse" | |
| image = pipeline(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] | |
| image | |
| ``` | |