import{s as Be,o as We,n as $e}from"../chunks/scheduler.53228c21.js";import{S as Se,i as Pe,e as m,s as o,c as d,h as Ge,a as c,d as n,b as i,f as K,g,j as J,k as Y,l as b,m as s,n as u,t as f,o as h,p as _}from"../chunks/index.100fac89.js";import{C as Re}from"../chunks/CopyLLMTxtMenu.20a72e0e.js";import{D as _e}from"../chunks/Docstring.49101787.js";import{C as Je}from"../chunks/CodeBlock.d30a6509.js";import{E as Fe}from"../chunks/ExampleCodeBlock.4a9effb9.js";import{H as te,E as ze}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.4d82655d.js";function Qe(A){let l,M="Examples:",y,r,p;return r=new Je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ZImagePipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = ZImagePipeline.from_pretrained(<span class="hljs-string">&quot;Z-a-o/Z-Image-Turbo&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># (1) Use flash attention 2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># pipe.transformer.set_attention_backend(&quot;flash&quot;)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># (2) Use flash attention 3</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># pipe.transformer.set_attention_backend(&quot;_flash_3&quot;)</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;一幅为名为“造相「Z-IMAGE-TURBO」”的项目设计的创意海报。画面巧妙地将文字概念视觉化：一辆复古蒸汽小火车化身为巨大的拉链头，正拉开厚厚的冬日积雪，展露出一个生机盎然的春天。&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(
<span class="hljs-meta">... </span>    prompt,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">1024</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">1024</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">9</span>,
<span class="hljs-meta">... </span>    guidance_scale=<span class="hljs-number">0.0</span>,
<span class="hljs-meta">... </span>    generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">42</span>),
<span class="hljs-meta">... </span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;zimage.png&quot;</span>)`,wrap:!1}}),{c(){l=m("p"),l.textContent=M,y=o(),d(r.$$.fragment)},l(a){l=c(a,"P",{"data-svelte-h":!0}),J(l)!=="svelte-kvfsh7"&&(l.textContent=M),y=i(a),g(r.$$.fragment,a)},m(a,I){s(a,l,I),s(a,y,I),u(r,a,I),p=!0},p:$e,i(a){p||(f(r.$$.fragment,a),p=!0)},o(a){h(r.$$.fragment,a),p=!1},d(a){a&&(n(l),n(y)),_(r,a)}}}function Ee(A){let l,M="Examples:",y,r,p;return r=new Je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ZImageImg2ImgPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = ZImageImg2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;Z-a-o/Z-Image-Turbo&quot;</span>, torch_dtype=torch.bfloat16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>init_image = load_image(url).resize((<span class="hljs-number">1024</span>, <span class="hljs-number">1024</span>))

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A fantasy landscape with mountains and a river, detailed, vibrant colors&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(
<span class="hljs-meta">... </span>    prompt,
<span class="hljs-meta">... </span>    image=init_image,
<span class="hljs-meta">... </span>    strength=<span class="hljs-number">0.6</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">9</span>,
<span class="hljs-meta">... </span>    guidance_scale=<span class="hljs-number">0.0</span>,
<span class="hljs-meta">... </span>    generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">42</span>),
<span class="hljs-meta">... </span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;zimage_img2img.png&quot;</span>)`,wrap:!1}}),{c(){l=m("p"),l.textContent=M,y=o(),d(r.$$.fragment)},l(a){l=c(a,"P",{"data-svelte-h":!0}),J(l)!=="svelte-kvfsh7"&&(l.textContent=M),y=i(a),g(r.$$.fragment,a)},m(a,I){s(a,l,I),s(a,y,I),u(r,a,I),p=!0},p:$e,i(a){p||(f(r.$$.fragment,a),p=!0)},o(a){h(r.$$.fragment,a),p=!1},d(a){a&&(n(l),n(y)),_(r,a)}}}function Le(A){let l,M,y,r,p,a,I,ne,C,Ze='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',ae,x,Ve='<a href="https://huggingface.co/papers/2511.22699" rel="nofollow">Z-Image</a> is a powerful and highly efficient image generation model with 6B parameters. Currently there’s only one model with two more to be released:',se,N,Ce='<thead><tr><th>Model</th> <th>Hugging Face</th></tr></thead> <tbody><tr><td>Z-Image-Turbo</td> <td><a href="https://huggingface.co/Tongyi-MAI/Z-Image-Turbo" rel="nofollow">https://huggingface.co/Tongyi-MAI/Z-Image-Turbo</a></td></tr></tbody>',oe,k,ie,F,ve="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.",le,$,re,B,je='Use <a href="/docs/diffusers/main/en/api/pipelines/z_image#diffusers.ZImageImg2ImgPipeline">ZImageImg2ImgPipeline</a> to transform an existing image based on a text prompt.',pe,W,me,S,ce,Z,P,ye,T,G,Ie,q,xe="Function invoked when calling the pipeline for generation.",be,v,de,R,ge,w,z,we,X,Ne="The ZImage pipeline for image-to-image generation.",Me,U,Q,Te,O,ke="Function invoked when calling the pipeline for image-to-image generation.",Ue,j,ue,E,fe,ee,he;return p=new Re({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),I=new te({props:{title:"Z-Image",local:"z-image",headingTag:"h1"}}),k=new te({props:{title:"Z-Image-Turbo",local:"z-image-turbo",headingTag:"h2"}}),$=new te({props:{title:"Image-to-image",local:"image-to-image",headingTag:"h2"}}),W=new Je({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ZImageImg2ImgPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

pipe = ZImageImg2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;Tongyi-MAI/Z-Image-Turbo&quot;</span>, torch_dtype=torch.bfloat16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>
init_image = load_image(url).resize((<span class="hljs-number">1024</span>, <span class="hljs-number">1024</span>))

prompt = <span class="hljs-string">&quot;A fantasy landscape with mountains and a river, detailed, vibrant colors&quot;</span>
image = pipe(
    prompt,
    image=init_image,
    strength=<span class="hljs-number">0.6</span>,
    num_inference_steps=<span class="hljs-number">9</span>,
    guidance_scale=<span class="hljs-number">0.0</span>,
    generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">42</span>),
).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;zimage_img2img.png&quot;</span>)`,wrap:!1}}),S=new te({props:{title:"ZImagePipeline",local:"diffusers.ZImagePipeline",headingTag:"h2"}}),P=new _e({props:{name:"class diffusers.ZImagePipeline",anchor:"diffusers.ZImagePipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": PreTrainedModel"},{name:"tokenizer",val:": AutoTokenizer"},{name:"transformer",val:": ZImageTransformer2DModel"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/z_image/pipeline_z_image.py#L137"}}),G=new _e({props:{name:"__call__",anchor:"diffusers.ZImagePipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"sigmas",val:": typing.Optional[typing.List[float]] = None"},{name:"guidance_scale",val:": float = 5.0"},{name:"cfg_normalization",val:": bool = False"},{name:"cfg_truncation",val:": float = 1.0"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"joint_attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 512"}],parametersDescription:[{anchor:"diffusers.ZImagePipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.ZImagePipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.ZImagePipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.ZImagePipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.ZImagePipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
Custom sigmas to use for the denoising process with schedulers which support a <code>sigmas</code> argument in
their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is passed
will be used.`,name:"sigmas"},{anchor:"diffusers.ZImagePipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 5.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.ZImagePipeline.__call__.cfg_normalization",description:`<strong>cfg_normalization</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
Whether to apply configuration normalization.`,name:"cfg_normalization"},{anchor:"diffusers.ZImagePipeline.__call__.cfg_truncation",description:`<strong>cfg_truncation</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
The truncation value for configuration.`,name:"cfg_truncation"},{anchor:"diffusers.ZImagePipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.ZImagePipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.ZImagePipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.ZImagePipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will be generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.ZImagePipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.ZImagePipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.ZImagePipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.ZImagePipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <code>~pipelines.stable_diffusion.ZImagePipelineOutput</code> instead of a plain
tuple.`,name:"return_dict"},{anchor:"diffusers.ZImagePipeline.__call__.joint_attention_kwargs",description:`<strong>joint_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow">diffusers.models.attention_processor</a>.`,name:"joint_attention_kwargs"},{anchor:"diffusers.ZImagePipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.ZImagePipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.ZImagePipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/z_image/pipeline_z_image.py#L293",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ZImagePipelineOutput</code> if
<code>return_dict</code> is True, otherwise a <code>tuple</code>. When returning a tuple, the first element is a list with the
generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ZImagePipelineOutput</code> or <code>tuple</code></p>
`}}),v=new Fe({props:{anchor:"diffusers.ZImagePipeline.__call__.example",$$slots:{default:[Qe]},$$scope:{ctx:A}}}),R=new te({props:{title:"ZImageImg2ImgPipeline",local:"diffusers.ZImageImg2ImgPipeline",headingTag:"h2"}}),z=new _e({props:{name:"class diffusers.ZImageImg2ImgPipeline",anchor:"diffusers.ZImageImg2ImgPipeline",parameters:[{name:"scheduler",val:": FlowMatchEulerDiscreteScheduler"},{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": PreTrainedModel"},{name:"tokenizer",val:": AutoTokenizer"},{name:"transformer",val:": ZImageTransformer2DModel"}],parametersDescription:[{anchor:"diffusers.ZImageImg2ImgPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler">FlowMatchEulerDiscreteScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>transformer</code> to denoise the encoded image latents.`,name:"scheduler"},{anchor:"diffusers.ZImageImg2ImgPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.ZImageImg2ImgPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>PreTrainedModel</code>) &#x2014;
A text encoder model to encode text prompts.`,name:"text_encoder"},{anchor:"diffusers.ZImageImg2ImgPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>AutoTokenizer</code>) &#x2014;
A tokenizer to tokenize text prompts.`,name:"tokenizer"},{anchor:"diffusers.ZImageImg2ImgPipeline.transformer",description:`<strong>transformer</strong> (<a href="/docs/diffusers/main/en/api/models/z_image_transformer2d#diffusers.ZImageTransformer2DModel">ZImageTransformer2DModel</a>) &#x2014;
A ZImage transformer model to denoise the encoded image latents.`,name:"transformer"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/z_image/pipeline_z_image_img2img.py#L149"}}),Q=new _e({props:{name:"__call__",anchor:"diffusers.ZImageImg2ImgPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"image",val:": typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None"},{name:"strength",val:": float = 0.6"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"sigmas",val:": typing.Optional[typing.List[float]] = None"},{name:"guidance_scale",val:": float = 5.0"},{name:"cfg_normalization",val:": bool = False"},{name:"cfg_truncation",val:": float = 1.0"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"joint_attention_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"callback_on_step_end",val:": typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None"},{name:"callback_on_step_end_tensor_inputs",val:": typing.List[str] = ['latents']"},{name:"max_sequence_length",val:": int = 512"}],parametersDescription:[{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the image generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.Tensor</code>, <code>PIL.Image.Image</code>, <code>np.ndarray</code>, <code>List[torch.Tensor]</code>, <code>List[PIL.Image.Image]</code>, or <code>List[np.ndarray]</code>) &#x2014;
<code>Image</code>, numpy array or tensor representing an image batch to be used as the starting point. For both
numpy array and pytorch tensor, the expected value range is between <code>[0, 1]</code>. If it&#x2019;s a tensor or a
list of tensors, the expected shape should be <code>(B, C, H, W)</code> or <code>(C, H, W)</code>. If it is a numpy array or
a list of arrays, the expected shape should be <code>(B, H, W, C)</code> or <code>(H, W, C)</code>.`,name:"image"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.6) &#x2014;
Indicates extent to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code> is used as a
starting point and more noise is added the higher the <code>strength</code>. The number of denoising steps depends
on the amount of noise initially added. When <code>strength</code> is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in <code>num_inference_steps</code>. A value of 1
essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The height in pixels of the generated image. If not provided, uses the input image height.`,name:"height"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
The width in pixels of the generated image. If not provided, uses the input image width.`,name:"width"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.sigmas",description:`<strong>sigmas</strong> (<code>List[float]</code>, <em>optional</em>) &#x2014;
Custom sigmas to use for the denoising process with schedulers which support a <code>sigmas</code> argument in
their <code>set_timesteps</code> method. If not defined, the default behavior when <code>num_inference_steps</code> is passed
will be used.`,name:"sigmas"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 5.0) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.cfg_normalization",description:`<strong>cfg_normalization</strong> (<code>bool</code>, <em>optional</em>, defaults to False) &#x2014;
Whether to apply configuration normalization.`,name:"cfg_normalization"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.cfg_truncation",description:`<strong>cfg_truncation</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
The truncation value for configuration.`,name:"cfg_truncation"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will be generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>List[torch.FloatTensor]</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <code>~pipelines.stable_diffusion.ZImagePipelineOutput</code> instead of a plain
tuple.`,name:"return_dict"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.joint_attention_kwargs",description:`<strong>joint_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttentionProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py" rel="nofollow">diffusers.models.attention_processor</a>.`,name:"joint_attention_kwargs"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.callback_on_step_end",description:`<strong>callback_on_step_end</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: <code>callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)</code>. <code>callback_kwargs</code> will include a list of all tensors as specified by
<code>callback_on_step_end_tensor_inputs</code>.`,name:"callback_on_step_end"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.callback_on_step_end_tensor_inputs",description:`<strong>callback_on_step_end_tensor_inputs</strong> (<code>List</code>, <em>optional</em>) &#x2014;
The list of tensor inputs for the <code>callback_on_step_end</code> function. The tensors specified in the list
will be passed as <code>callback_kwargs</code> argument. You will only be able to include variables listed in the
<code>._callback_tensor_inputs</code> attribute of your pipeline class.`,name:"callback_on_step_end_tensor_inputs"},{anchor:"diffusers.ZImageImg2ImgPipeline.__call__.max_sequence_length",description:`<strong>max_sequence_length</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Maximum sequence length to use with the <code>prompt</code>.`,name:"max_sequence_length"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/z_image/pipeline_z_image_img2img.py#L364",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ZImagePipelineOutput</code> if
<code>return_dict</code> is True, otherwise a <code>tuple</code>. When returning a tuple, the first element is a list with the
generated images.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ZImagePipelineOutput</code> or <code>tuple</code></p>
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