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DownFlow/Z-Image-Turbo-Fuli
Z-Image-Turbo-Fuli is a text-to-image model from DownFlow. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as apache-2.0.
Z-Image Turbo with Fuliji artist LoRA baked in. The LoRA weights have been permanently merged into the base transformer via mergeandunload(), so no PEFT dependency is needed at inference time.
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.safetensors20.5 GB · 100%
From the Hugging Face model README
Z-Image Turbo with Fuliji artist LoRA baked in. The LoRA weights have been permanently merged into the base transformer via merge_and_unload(), so no PEFT dependency is needed at inference time.
Want the standalone LoRA adapter instead? Use DownFlow/Z-Image-Turbo-Fuli-LoRA to apply the adapter on top of any Z-Image-Turbo checkpoint.
This model is Tongyi-MAI/Z-Image-Turbo (an 8-step flow-matching image generation model) fine-tuned with a LoRA trained on art from 8 Chinese anime/illustration artists in the DownFlow/fuliji dataset.
Trigger the artist style by prepending by <artist>, to your prompt.
pip install diffusers transformers accelerate safetensors
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"DownFlow/Z-Image-Turbo-Fuli",
torch_dtype=torch.bfloat16,
).to("cuda")
image = pipe(
prompt="by 蠢沫沫, 1girl, solo, smile, soft lighting",
num_inference_steps=8,
guidance_scale=0.0, # Z-Image Turbo uses CFG=0
height=512,
width=512,
).images[0]
image.save("output.png")
vLLM (≥ 0.8) can serve this model via an OpenAI-compatible /v1/images/generations endpoint.
pip install "vllm>=0.8.0"
vllm serve DownFlow/Z-Image-Turbo-Fuli \
--task generate \
--dtype bfloat16 \
--max-model-len 512 \
--port 8000
curl http://localhost:8000/v1/images/generations \
-H "Content-Type: application/json" \
-d '{
"model": "DownFlow/Z-Image-Turbo-Fuli",
"prompt": "by 蠢沫沫, 1girl, smile, soft watercolour style",
"n": 1,
"size": "512x512"
}'
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
response = client.images.generate(
model="DownFlow/Z-Image-Turbo-Fuli",
prompt="by 年年, 1girl, white dress, cherry blossoms",
n=1,
size="512x512",
)
print(response.data[0].url)
Prepend by <artist>, at the start of your prompt.
| Token | Training images |
|---|---|
萌芽儿o0 | 30 |
年年 | 26 |
封疆疆v | 26 |
焖焖碳 | 26 |
星之迟迟 | 25 |
蠢沫沫 | 23 |
雨波HaneAme | 23 |
清水由乃 | 21 |
| Property | Value |
|---|---|
| Base model | Tongyi-MAI/Z-Image-Turbo |
| Fine-tuning method | LoRA rank=32, alpha=32 — merged into weights |
| Target modules | to_q, to_k, to_v, w1, w2, w3 |
| Training steps | 5 000 (3 000 at lr=1e-4 + 2 000 continued at lr=5e-5, EMA decay=0.9999) |
| Training resolution | 512 × 512 |
| Inference steps | 8 |
| CFG scale | 0.0 (CFG-free) |
| Precision | bfloat16 |
| Dataset | DownFlow/fuliji (8 artists, ~200 images) |