How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("kimi000/soft-horizon-53", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Z-Image Prompt Rubric v4 75% Multicap - Step 500 EMA

This is a complete native Diffusers Z-Image pipeline exported from zimage_base_diffusionnft_dvreward_prompt_rubric_v4_75pct_multicap_512px_10step_cw at Step 500. The EMA LoRA (rank 256, alpha 256) is merged into BF16 base weights, so FAR and PEFT are not required for inference.

python demo.py --prompt "A red cube beside a blue glass sphere."

The helper uses CFG 4, fixed scheduler shift 6, dynamic shifting disabled, and an explicit 50-update sigma schedule ending at sigma 0.

Downloads last month
-
Safetensors
Model size
6B params
Tensor type
BF16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support