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2246d2c7c4
* 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: Γlvaro Somoza <asomoza@users.noreply.github.com>
67 lines
2.3 KiB
Markdown
67 lines
2.3 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Z-Image
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</div>
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[Z-Image](https://huggingface.co/papers/2511.22699) is a powerful and highly efficient image generation model with 6B parameters. Currently there's only one model with two more to be released:
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|Model|Hugging Face|
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|---|---|
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|Z-Image-Turbo|https://huggingface.co/Tongyi-MAI/Z-Image-Turbo|
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## Z-Image-Turbo
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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.
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## Image-to-image
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Use [`ZImageImg2ImgPipeline`] to transform an existing image based on a text prompt.
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```python
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import torch
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from diffusers import ZImageImg2ImgPipeline
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from diffusers.utils import load_image
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pipe = ZImageImg2ImgPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
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init_image = load_image(url).resize((1024, 1024))
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prompt = "A fantasy landscape with mountains and a river, detailed, vibrant colors"
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image = pipe(
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prompt,
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image=init_image,
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strength=0.6,
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num_inference_steps=9,
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guidance_scale=0.0,
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generator=torch.Generator("cuda").manual_seed(42),
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).images[0]
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image.save("zimage_img2img.png")
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```
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## ZImagePipeline
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[[autodoc]] ZImagePipeline
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- all
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- __call__
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## ZImageImg2ImgPipeline
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[[autodoc]] ZImageImg2ImgPipeline
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- all
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- __call__
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