Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", 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
| { | |
| "_class_name":"MarigoldPipeline", | |
| "_diffusers_version":"0.24.0", | |
| "scale_invariant": true, | |
| "shift_invariant": true, | |
| "default_denoising_steps": 10, | |
| "default_processing_resolution": 768, | |
| "unet":[ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae":[ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ], | |
| "scheduler":[ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder":[ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer":[ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ] | |
| } |