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Z-Image

docs/source/en/api/pipelines/z_image.md

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Z-Image

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Z-Image is a powerful and highly efficient image generation model with 6B parameters. Currently there's only one model with two more to be released:

ModelHugging Face
Z-Image-Turbohttps://huggingface.co/Tongyi-MAI/Z-Image-Turbo

Z-Image-Turbo

Z-Image-Turbo is a distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers sub-second inference latency on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.

Image-to-image

Use [ZImageImg2ImgPipeline] to transform an existing image based on a text prompt.

python
import torch
from diffusers import ZImageImg2ImgPipeline
from diffusers.utils import load_image

pipe = ZImageImg2ImgPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
pipe.to("cuda")

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
init_image = load_image(url).resize((1024, 1024))

prompt = "A fantasy landscape with mountains and a river, detailed, vibrant colors"
image = pipe(
    prompt,
    image=init_image,
    strength=0.6,
    num_inference_steps=9,
    guidance_scale=0.0,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("zimage_img2img.png")

Inpainting

Use [ZImageInpaintPipeline] to inpaint specific regions of an image based on a text prompt and mask.

python
import torch
import numpy as np
from PIL import Image
from diffusers import ZImageInpaintPipeline
from diffusers.utils import load_image

pipe = ZImageInpaintPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
pipe.to("cuda")

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
init_image = load_image(url).resize((1024, 1024))

# Create a mask (white = inpaint, black = preserve)
mask = np.zeros((1024, 1024), dtype=np.uint8)
mask[256:768, 256:768] = 255  # Inpaint center region
mask_image = Image.fromarray(mask)

prompt = "A beautiful lake with mountains in the background"
image = pipe(
    prompt,
    image=init_image,
    mask_image=mask_image,
    strength=1.0,
    num_inference_steps=9,
    guidance_scale=0.0,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("zimage_inpaint.png")

ZImagePipeline

[[autodoc]] ZImagePipeline - all - call

ZImageImg2ImgPipeline

[[autodoc]] ZImageImg2ImgPipeline - all - call

ZImageInpaintPipeline

[[autodoc]] ZImageInpaintPipeline - all - call