Instructions to use blanchon/nitro_e_512_lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use blanchon/nitro_e_512_lite with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("blanchon/nitro_e_512_lite", 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
Download model_index.json from blanchon/nitro_e_512_lite: direct link, hf CLI and curl.
- Browser
- Download file 419 Bytes
-
https://huggingface.co/blanchon/nitro_e_512_lite/resolve/main/model_index.json
- Command line
-
hf download hf://blanchon/nitro_e_512_lite/model_index.json
-
curl -L -o model_index.json https://huggingface.co/blanchon/nitro_e_512_lite/resolve/main/model_index.json
419 Bytes
| { | |
| "_class_name": "NitroEPipeline", | |
| "_diffusers_version": "0.36.0.dev0", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "LlamaForCausalLM" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "PreTrainedTokenizerFast" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "NitroETransformer2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderDC" | |
| ] | |
| } | |