Text-to-Image
Diffusers
Safetensors
English
ZImageAnyFlow2Pipeline
z-image
anyflow2
distillation
custom-pipeline
Instructions to use kimi000/ZImage-AnyFlow2-4Step-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/ZImage-AnyFlow2-4Step-1000 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/ZImage-AnyFlow2-4Step-1000", 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 validation.json from kimi000/ZImage-AnyFlow2-4Step-1000: direct link, hf CLI and curl.
- Browser
- Download file 237 Bytes
-
https://huggingface.co/kimi000/ZImage-AnyFlow2-4Step-1000/resolve/main/validation.json
- Command line
-
hf download hf://kimi000/ZImage-AnyFlow2-4Step-1000/validation.json
-
curl -L -o validation.json https://huggingface.co/kimi000/ZImage-AnyFlow2-4Step-1000/resolve/main/validation.json
237 Bytes
| { | |
| "real_ema_merge": "bitwise_exact_peft_bf16_projection", | |
| "pipeline_load": "passed", | |
| "four_step_end_to_end_cpu_smoke": "passed", | |
| "smoke_resolution": 64, | |
| "training_and_demo_resolution": 1024, | |
| "pixel_std": 100.10801277562224 | |
| } | |