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
File size: 504 Bytes
fc7eaf0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"status": "passed",
"gpu_name": "NVIDIA H100 80GB HBM3",
"standalone_diffusers_load": true,
"all_merged_tensor_hashes_match": 1160,
"native_sampler_latent_max_abs": 0.0,
"native_text_encoder_parity": true,
"scheduler_parity": true,
"transformer_calls": 4,
"cpu_offload": "passed",
"height": 1024,
"width": 1024,
"vae_dtype": "torch.float32",
"guidance_embedding": 3.5,
"external_cfg": 1.0,
"inference_seconds": 12.483246803283691,
"elapsed_seconds": 74.13123774528503
}
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