import{s as Ve,o as Le,n as Ce}from"../chunks/scheduler.8c3d61f6.js";import{S as qe,i as Ye,g as p,s as a,r as y,A as Se,h as c,f as n,c as l,j as te,x as u,u as b,k as F,y as m,a as i,v as M,d as w,t as T,w as J}from"../chunks/index.da70eac4.js";import{T as Fe}from"../chunks/Tip.6f698f24.js";import{D as he}from"../chunks/Docstring.367be9ed.js";import{C as Re}from"../chunks/CodeBlock.a9c4becf.js";import{E as Qe}from"../chunks/ExampleCodeBlock.dd9ee828.js";import{H as ve,E as Ee}from"../chunks/getInferenceSnippets.cb466318.js";function He(I){let s,g='Make sure to check out the Schedulers <a href="../../using-diffusers/schedulers">guide</a> to learn how to explore the tradeoff between scheduler speed and quality, and see the <a href="../../using-diffusers/loading#reuse-a-pipeline">reuse components across pipelines</a> section to learn how to efficiently load the same components into multiple pipelines.';return{c(){s=p("p"),s.innerHTML=g},l(d){s=c(d,"P",{"data-svelte-h":!0}),u(s)!=="svelte-1qn15hi"&&(s.innerHTML=g)},m(d,r){i(d,s,r)},p:Ce,d(d){d&&n(s)}}}function ze(I){let s,g="Examples:",d,r,f;return r=new Re({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.pipelines <span class="hljs-keyword">import</span> BlipDiffusionPipeline
<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><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>blip_diffusion_pipe = BlipDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Salesforce/blipdiffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span>cond_subject = <span class="hljs-string">&quot;dog&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tgt_subject = <span class="hljs-string">&quot;dog&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text_prompt_input = <span class="hljs-string">&quot;swimming underwater&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>cond_image = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/dog.jpg&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>guidance_scale = <span class="hljs-number">7.5</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_inference_steps = <span class="hljs-number">25</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>negative_prompt = <span class="hljs-string">&quot;over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate&quot;</span>


<span class="hljs-meta">&gt;&gt;&gt; </span>output = blip_diffusion_pipe(
<span class="hljs-meta">... </span>    text_prompt_input,
<span class="hljs-meta">... </span>    cond_image,
<span class="hljs-meta">... </span>    cond_subject,
<span class="hljs-meta">... </span>    tgt_subject,
<span class="hljs-meta">... </span>    guidance_scale=guidance_scale,
<span class="hljs-meta">... </span>    num_inference_steps=num_inference_steps,
<span class="hljs-meta">... </span>    neg_prompt=negative_prompt,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>).images
<span class="hljs-meta">&gt;&gt;&gt; </span>output[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;image.png&quot;</span>)`,wrap:!1}}),{c(){s=p("p"),s.textContent=g,d=a(),y(r.$$.fragment)},l(o){s=c(o,"P",{"data-svelte-h":!0}),u(s)!=="svelte-kvfsh7"&&(s.textContent=g),d=l(o),b(r.$$.fragment,o)},m(o,j){i(o,s,j),i(o,d,j),M(r,o,j),f=!0},p:Ce,i(o){f||(w(r.$$.fragment,o),f=!0)},o(o){T(r.$$.fragment,o),f=!1},d(o){o&&(n(s),n(d)),J(r,o)}}}function Ae(I){let s,g="Examples:",d,r,f;return r=new Re({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers.pipelines <span class="hljs-keyword">import</span> BlipDiffusionControlNetPipeline
<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><span class="hljs-keyword">from</span> controlnet_aux <span class="hljs-keyword">import</span> CannyDetector
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>blip_diffusion_pipe = BlipDiffusionControlNetPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Salesforce/blipdiffusion-controlnet&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>style_subject = <span class="hljs-string">&quot;flower&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tgt_subject = <span class="hljs-string">&quot;teapot&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text_prompt = <span class="hljs-string">&quot;on a marble table&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>cldm_cond_image = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/kettle.jpg&quot;</span>
<span class="hljs-meta">... </span>).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>canny = CannyDetector()
<span class="hljs-meta">&gt;&gt;&gt; </span>cldm_cond_image = canny(cldm_cond_image, <span class="hljs-number">30</span>, <span class="hljs-number">70</span>, output_type=<span class="hljs-string">&quot;pil&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>style_image = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/ayushtues/blipdiffusion_images/resolve/main/flower.jpg&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>guidance_scale = <span class="hljs-number">7.5</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_inference_steps = <span class="hljs-number">50</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>negative_prompt = <span class="hljs-string">&quot;over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, out of frame, ugly, bad anatomy, bad proportions, deformed, blurry, duplicate&quot;</span>


<span class="hljs-meta">&gt;&gt;&gt; </span>output = blip_diffusion_pipe(
<span class="hljs-meta">... </span>    text_prompt,
<span class="hljs-meta">... </span>    style_image,
<span class="hljs-meta">... </span>    cldm_cond_image,
<span class="hljs-meta">... </span>    style_subject,
<span class="hljs-meta">... </span>    tgt_subject,
<span class="hljs-meta">... </span>    guidance_scale=guidance_scale,
<span class="hljs-meta">... </span>    num_inference_steps=num_inference_steps,
<span class="hljs-meta">... </span>    neg_prompt=negative_prompt,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span>).images
<span class="hljs-meta">&gt;&gt;&gt; </span>output[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;image.png&quot;</span>)`,wrap:!1}}),{c(){s=p("p"),s.textContent=g,d=a(),y(r.$$.fragment)},l(o){s=c(o,"P",{"data-svelte-h":!0}),u(s)!=="svelte-kvfsh7"&&(s.textContent=g),d=l(o),b(r.$$.fragment,o)},m(o,j){i(o,s,j),i(o,d,j),M(r,o,j),f=!0},p:Ce,i(o){f||(w(r.$$.fragment,o),f=!0)},o(o){T(r.$$.fragment,o),f=!1},d(o){o&&(n(s),n(d)),J(r,o)}}}function Ke(I){let s,g,d,r,f,o="<p>This pipeline is deprecated but it can still be used. However, we won’t test the pipeline anymore and won’t accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.</p>",j,N,se,$,xe='BLIP-Diffusion was proposed in <a href="https://huggingface.co/papers/2305.14720" rel="nofollow">BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing</a>. It enables zero-shot subject-driven generation and control-guided zero-shot generation.',oe,G,Ie="The abstract from the paper is:",ie,k,Ze='<em>Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts. Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text. Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Project page at <a href="https://dxli94.github.io/BLIP-Diffusion-website/" rel="nofollow">this https URL</a>.</em>',ae,X,Pe='The original codebase can be found at <a href="https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion" rel="nofollow">salesforce/LAVIS</a>. You can find the official BLIP-Diffusion checkpoints under the <a href="https://hf.co/SalesForce" rel="nofollow">hf.co/SalesForce</a> organization.',le,W,De='<code>BlipDiffusionPipeline</code> and <code>BlipDiffusionControlNetPipeline</code> were contributed by <a href="https://github.com/ayushtues/" rel="nofollow"><code>ayushtues</code></a>.',re,Z,pe,Q,ce,h,R,_e,E,Ne="Pipeline for Zero-Shot Subject Driven Generation using Blip Diffusion.",ye,H,$e=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,be,v,V,Me,z,Ge="Function invoked when calling the pipeline for generation.",we,P,de,L,me,_,q,Te,A,ke="Pipeline for Canny Edge based Controlled subject-driven generation using Blip Diffusion.",Je,K,Xe=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`,je,C,Y,Be,O,We="Function invoked when calling the pipeline for generation.",Ue,D,fe,S,ue,ne,ge;return N=new ve({props:{title:"BLIP-Diffusion",local:"blip-diffusion",headingTag:"h1"}}),Z=new Fe({props:{$$slots:{default:[He]},$$scope:{ctx:I}}}),Q=new ve({props:{title:"BlipDiffusionPipeline",local:"diffusers.BlipDiffusionPipeline",headingTag:"h2"}}),R=new he({props:{name:"class diffusers.BlipDiffusionPipeline",anchor:"diffusers.BlipDiffusionPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": ContextCLIPTextModel"},{name:"vae",val:": AutoencoderKL"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": PNDMScheduler"},{name:"qformer",val:": Blip2QFormerModel"},{name:"image_processor",val:": BlipImageProcessor"},{name:"ctx_begin_pos",val:": int = 2"},{name:"mean",val:": typing.List[float] = None"},{name:"std",val:": typing.List[float] = None"}],parametersDescription:[{anchor:"diffusers.BlipDiffusionPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer for the text encoder`,name:"tokenizer"},{anchor:"diffusers.BlipDiffusionPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>ContextCLIPTextModel</code>) &#x2014;
Text encoder to encode the text prompt`,name:"text_encoder"},{anchor:"diffusers.BlipDiffusionPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
VAE model to map the latents to the image`,name:"vae"},{anchor:"diffusers.BlipDiffusionPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.BlipDiffusionPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.BlipDiffusionPipeline.qformer",description:`<strong>qformer</strong> (<code>Blip2QFormerModel</code>) &#x2014;
QFormer model to get multi-modal embeddings from the text and image.`,name:"qformer"},{anchor:"diffusers.BlipDiffusionPipeline.image_processor",description:`<strong>image_processor</strong> (<code>BlipImageProcessor</code>) &#x2014;
Image Processor to preprocess and postprocess the image.`,name:"image_processor"},{anchor:"diffusers.BlipDiffusionPipeline.ctx_begin_pos",description:`<strong>ctx_begin_pos</strong> (int, <code>optional</code>, defaults to 2) &#x2014;
Position of the context token in the text encoder.`,name:"ctx_begin_pos"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py#L80"}}),V=new he({props:{name:"__call__",anchor:"diffusers.BlipDiffusionPipeline.__call__",parameters:[{name:"prompt",val:": typing.List[str]"},{name:"reference_image",val:": Image"},{name:"source_subject_category",val:": typing.List[str]"},{name:"target_subject_category",val:": typing.List[str]"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"guidance_scale",val:": float = 7.5"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"num_inference_steps",val:": int = 50"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"neg_prompt",val:": typing.Optional[str] = ''"},{name:"prompt_strength",val:": float = 1.0"},{name:"prompt_reps",val:": int = 20"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.BlipDiffusionPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.reference_image",description:`<strong>reference_image</strong> (<code>PIL.Image.Image</code>) &#x2014;
The reference image to condition the generation on.`,name:"reference_image"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.source_subject_category",description:`<strong>source_subject_category</strong> (<code>List[str]</code>) &#x2014;
The source subject category.`,name:"source_subject_category"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.target_subject_category",description:`<strong>target_subject_category</strong> (<code>List[str]</code>) &#x2014;
The target subject category.`,name:"target_subject_category"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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 random sampling.`,name:"latents"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#x2014;
Guidance scale as defined in <a href="https://huggingface.co/papers/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://huggingface.co/papers/2205.11487" 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.BlipDiffusionPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height of the generated image.`,name:"height"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width of the generated image.`,name:"width"},{anchor:"diffusers.BlipDiffusionPipeline.__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.BlipDiffusionPipeline.__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.BlipDiffusionPipeline.__call__.neg_prompt",description:`<strong>neg_prompt</strong> (<code>str</code>, <em>optional</em>, defaults to &quot;&quot;) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"neg_prompt"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.prompt_strength",description:`<strong>prompt_strength</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
The strength of the prompt. Specifies the number of times the prompt is repeated along with prompt_reps
to amplify the prompt.`,name:"prompt_strength"},{anchor:"diffusers.BlipDiffusionPipeline.__call__.prompt_reps",description:`<strong>prompt_reps</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014;
The number of times the prompt is repeated along with prompt_strength to amplify the prompt.`,name:"prompt_reps"},{anchor:"diffusers.BlipDiffusionPipeline.__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: <code>&quot;pil&quot;</code> (<code>PIL.Image.Image</code>), <code>&quot;np&quot;</code>
(<code>np.array</code>) or <code>&quot;pt&quot;</code> (<code>torch.Tensor</code>).`,name:"output_type"},{anchor:"diffusers.BlipDiffusionPipeline.__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 <a href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/blip_diffusion/pipeline_blip_diffusion.py#L192",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),P=new Qe({props:{anchor:"diffusers.BlipDiffusionPipeline.__call__.example",$$slots:{default:[ze]},$$scope:{ctx:I}}}),L=new ve({props:{title:"BlipDiffusionControlNetPipeline",local:"diffusers.BlipDiffusionControlNetPipeline",headingTag:"h2"}}),q=new he({props:{name:"class diffusers.BlipDiffusionControlNetPipeline",anchor:"diffusers.BlipDiffusionControlNetPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"text_encoder",val:": ContextCLIPTextModel"},{name:"vae",val:": AutoencoderKL"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": PNDMScheduler"},{name:"qformer",val:": Blip2QFormerModel"},{name:"controlnet",val:": ControlNetModel"},{name:"image_processor",val:": BlipImageProcessor"},{name:"ctx_begin_pos",val:": int = 2"},{name:"mean",val:": typing.List[float] = None"},{name:"std",val:": typing.List[float] = None"}],parametersDescription:[{anchor:"diffusers.BlipDiffusionControlNetPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer for the text encoder`,name:"tokenizer"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>ContextCLIPTextModel</code>) &#x2014;
Text encoder to encode the text prompt`,name:"text_encoder"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/main/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
VAE model to map the latents to the image`,name:"vae"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.qformer",description:`<strong>qformer</strong> (<code>Blip2QFormerModel</code>) &#x2014;
QFormer model to get multi-modal embeddings from the text and image.`,name:"qformer"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.controlnet",description:`<strong>controlnet</strong> (<a href="/docs/diffusers/main/en/api/models/controlnet#diffusers.ControlNetModel">ControlNetModel</a>) &#x2014;
ControlNet model to get the conditioning image embedding.`,name:"controlnet"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.image_processor",description:`<strong>image_processor</strong> (<code>BlipImageProcessor</code>) &#x2014;
Image Processor to preprocess and postprocess the image.`,name:"image_processor"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.ctx_begin_pos",description:`<strong>ctx_begin_pos</strong> (int, <code>optional</code>, defaults to 2) &#x2014;
Position of the context token in the text encoder.`,name:"ctx_begin_pos"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py#L87"}}),Y=new he({props:{name:"__call__",anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__",parameters:[{name:"prompt",val:": typing.List[str]"},{name:"reference_image",val:": Image"},{name:"condtioning_image",val:": Image"},{name:"source_subject_category",val:": typing.List[str]"},{name:"target_subject_category",val:": typing.List[str]"},{name:"latents",val:": typing.Optional[torch.Tensor] = None"},{name:"guidance_scale",val:": float = 7.5"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"num_inference_steps",val:": int = 50"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"neg_prompt",val:": typing.Optional[str] = ''"},{name:"prompt_strength",val:": float = 1.0"},{name:"prompt_reps",val:": int = 20"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.reference_image",description:`<strong>reference_image</strong> (<code>PIL.Image.Image</code>) &#x2014;
The reference image to condition the generation on.`,name:"reference_image"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.condtioning_image",description:`<strong>condtioning_image</strong> (<code>PIL.Image.Image</code>) &#x2014;
The conditioning canny edge image to condition the generation on.`,name:"condtioning_image"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.source_subject_category",description:`<strong>source_subject_category</strong> (<code>List[str]</code>) &#x2014;
The source subject category.`,name:"source_subject_category"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.target_subject_category",description:`<strong>target_subject_category</strong> (<code>List[str]</code>) &#x2014;
The target subject category.`,name:"target_subject_category"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.Tensor</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 random sampling.`,name:"latents"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#x2014;
Guidance scale as defined in <a href="https://huggingface.co/papers/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://huggingface.co/papers/2205.11487" 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.BlipDiffusionControlNetPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height of the generated image.`,name:"height"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width of the generated image.`,name:"width"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.seed",description:`<strong>seed</strong> (<code>int</code>, <em>optional</em>, defaults to 42) &#x2014;
The seed to use for random generation.`,name:"seed"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__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.BlipDiffusionControlNetPipeline.__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.BlipDiffusionControlNetPipeline.__call__.neg_prompt",description:`<strong>neg_prompt</strong> (<code>str</code>, <em>optional</em>, defaults to &quot;&quot;) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"neg_prompt"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.prompt_strength",description:`<strong>prompt_strength</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
The strength of the prompt. Specifies the number of times the prompt is repeated along with prompt_reps
to amplify the prompt.`,name:"prompt_strength"},{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.prompt_reps",description:`<strong>prompt_reps</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014;
The number of times the prompt is repeated along with prompt_strength to amplify the prompt.`,name:"prompt_reps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py#L240",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/dit#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),D=new Qe({props:{anchor:"diffusers.BlipDiffusionControlNetPipeline.__call__.example",$$slots:{default:[Ae]},$$scope:{ctx:I}}}),S=new Ee({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/blip_diffusion.md"}}),{c(){s=p("meta"),g=a(),d=p("p"),r=a(),f=p("blockquote"),f.innerHTML=o,j=a(),y(N.$$.fragment),se=a(),$=p("p"),$.innerHTML=xe,oe=a(),G=p("p"),G.textContent=Ie,ie=a(),k=p("p"),k.innerHTML=Ze,ae=a(),X=p("p"),X.innerHTML=Pe,le=a(),W=p("p"),W.innerHTML=De,re=a(),y(Z.$$.fragment),pe=a(),y(Q.$$.fragment),ce=a(),h=p("div"),y(R.$$.fragment),_e=a(),E=p("p"),E.textContent=Ne,ye=a(),H=p("p"),H.innerHTML=$e,be=a(),v=p("div"),y(V.$$.fragment),Me=a(),z=p("p"),z.textContent=Ge,we=a(),y(P.$$.fragment),de=a(),y(L.$$.fragment),me=a(),_=p("div"),y(q.$$.fragment),Te=a(),A=p("p"),A.textContent=ke,Je=a(),K=p("p"),K.innerHTML=Xe,je=a(),C=p("div"),y(Y.$$.fragment),Be=a(),O=p("p"),O.textContent=We,Ue=a(),y(D.$$.fragment),fe=a(),y(S.$$.fragment),ue=a(),ne=p("p"),this.h()},l(e){const 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Oe='{"title":"BLIP-Diffusion","local":"blip-diffusion","sections":[{"title":"BlipDiffusionPipeline","local":"diffusers.BlipDiffusionPipeline","sections":[],"depth":2},{"title":"BlipDiffusionControlNetPipeline","local":"diffusers.BlipDiffusionControlNetPipeline","sections":[],"depth":2}],"depth":1}';function et(I){return Le(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rt extends qe{constructor(s){super(),Ye(this,s,et,Ke,Ve,{})}}export{rt as component};
