Instructions to use kimi000/Flux1-Dev-4Step-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/Flux1-Dev-4Step-2000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kimi000/Flux1-Dev-4Step-2000", 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 kimi000/Flux1-Dev-4Step-2000: direct link, hf CLI and curl.
- Browser
- Download file 532 Bytes
-
https://huggingface.co/kimi000/Flux1-Dev-4Step-2000/resolve/main/model_index.json
- Command line
-
hf download hf://kimi000/Flux1-Dev-4Step-2000/model_index.json
-
curl -L -o model_index.json https://huggingface.co/kimi000/Flux1-Dev-4Step-2000/resolve/main/model_index.json
532 Bytes
| { | |
| "_class_name": "Flux1FourStepPipeline", | |
| "_diffusers_version": "0.39.0", | |
| "transformer": [ | |
| "transformer", | |
| "Flux1IntervalTransformer" | |
| ], | |
| "vae": [ | |
| "vae", | |
| "Flux1Autoencoder" | |
| ], | |
| "scheduler": [ | |
| "scheduler", | |
| "Flux1FourStepScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "text_encoder_2": [ | |
| "transformers", | |
| "T5EncoderModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "tokenizer_2": [ | |
| "transformers", | |
| "T5TokenizerFast" | |
| ] | |
| } | |