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
File size: 646 Bytes
91627db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"global_step": 1000,
"weights": "ema",
"format": "diffusers",
"serialization": "safetensors",
"lora_merged": true,
"source_checkpoint": "step_1000_lora.pt",
"source_sha256": "87e13b102ee3f3e79bdbab00d5027a84a3c576ebd94d2ac9ff3dfe90807ef9c6",
"base_model": "Tongyi-MAI/Z-Image",
"global_batch_size": 128,
"training_resolution": 1024,
"num_horizon_basis": 128,
"coordinate_mode": "global",
"shift": 6.0,
"final_step_size_scale": 0.5,
"distillation_teacher_cfg": 4.0,
"inference_cfg": "none",
"inference_steps": 4,
"prompt_mix": {
"MJHQ": 0.5,
"TextAtlas": 0.25,
"reward_prompt_dataset": 0.25
}
}
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