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CalamitousFelicitousness 2246d2c7c4 Add ZImageImg2ImgPipeline (#12751 )

* Add ZImageImg2ImgPipeline

Updated the pipeline structure to include ZImageImg2ImgPipeline
    alongside ZImagePipeline.
Implemented the ZImageImg2ImgPipeline class for image-to-image
    transformations, including necessary methods for
    encoding prompts, preparing latents, and denoising.
Enhanced the auto_pipeline to map the new ZImageImg2ImgPipeline
    for image generation tasks.
Added unit tests for ZImageImg2ImgPipeline to ensure
    functionality and performance.
Updated dummy objects to include ZImageImg2ImgPipeline for
    testing purposes.

* Address review comments for ZImageImg2ImgPipeline

- Add `# Copied from` annotations to encode_prompt and _encode_prompt
- Add ZImagePipeline to auto_pipeline.py for AutoPipeline support

* Add ZImage pipeline documentation

---------

Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Álvaro Somoza <asomoza@users.noreply.github.com>

2025-12-07 22:06:23 -10:00

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Z-Image

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:

Model	Hugging Face
Z-Image-Turbo	https://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.

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")

ZImagePipeline

autodoc ZImagePipeline - all - call

ZImageImg2ImgPipeline

autodoc ZImageImg2ImgPipeline - all - call

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