Text-to-Image
Diffusers
Safetensors
English
ZImageAnyFlow2Pipeline
z-image
anyflow2
distillation
custom-pipeline
Instructions to use kimi000/ZImage-4Step-3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/ZImage-4Step-3000 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-4Step-3000", 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: 385 Bytes
a7509ea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"real_ema_merge": "bitwise_exact_peft_bf16_projection",
"pipeline_load": "passed",
"smoke_resolution": 1024,
"training_and_demo_resolution": 1024,
"pixel_std": 85.7381439388038,
"global_step": 3000,
"weights": "ema",
"device": "cuda",
"transformer_calls": 4,
"inference_seconds": 2.988361120223999,
"cpu_offload": "passed",
"four_step_end_to_end": "passed"
}
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