Instructions to use akanshpatel/LGM-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use akanshpatel/LGM-Test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("akanshpatel/LGM-Test", 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": "LGMFullPipeline", | |
| "_diffusers_version": "0.25.0", | |
| "feature_extractor": ["transformers", "CLIPImageProcessor"], | |
| "image_encoder": ["transformers", "CLIPVisionModel"], | |
| "requires_safety_checker": false, | |
| "scheduler": ["diffusers", "DDIMScheduler"], | |
| "text_encoder": ["transformers", "CLIPTextModel"], | |
| "tokenizer": ["transformers", "CLIPTokenizer"], | |
| "unet": ["mv_unet", "MultiViewUNetModel"], | |
| "vae": ["diffusers", "AutoencoderKL"], | |
| "lgm": ["lgm", "LGM"] | |
| } | |