import{s as we,n as Ue,o as Ze}from"../chunks/scheduler.53228c21.js";import{S as be,i as je,e as i,s as n,c as d,h as Be,a as p,d as a,b as s,f as ge,g as c,j as M,k as fe,w as g,l as We,m as l,n as y,t as h,o as J,p as u}from"../chunks/index.100fac89.js";import{C as Ge}from"../chunks/CopyLLMTxtMenu.20a72e0e.js";import{C as x}from"../chunks/CodeBlock.d30a6509.js";import{H as Te,E as Ce}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.4d82655d.js";function Ie(pe){let r,F,X,Q,f,_,T,V,w,oe='<a href="https://huggingface.co/papers/2302.08453" rel="nofollow">T2I-Adapter</a> is an adapter that enables controllable generation like <a href="./controlnet">ControlNet</a>. A T2I-Adapter works by learning a <em>mapping</em> between a control signal (for example, a depth map) and a pretrained model’s internal knowledge. The adapter is plugged in to the base model to provide extra guidance based on the control signal during generation.',$,U,me='Load a T2I-Adapter conditioned on a specific control, such as canny edge, and pass it to the pipeline in <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a>.',N,Z,H,b,Me='Generate a canny image with <a href="https://github.com/opencv/opencv-python" rel="nofollow">opencv-python</a>.',S,j,L,B,re="Pass the canny image to the pipeline to generate an image.",z,W,q,o,de='<figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png" width="300" alt="Generated image (prompt only)"/> <figcaption style="text-align: center;">original image</figcaption></figure> <figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/canny-cat.png" width="300" alt="Control image (Canny edges)"/> <figcaption style="text-align: center;">canny image</figcaption></figure> <figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2i-canny-cat-generated.png" width="300" alt="Generated image (ControlNet + prompt)"/> <figcaption style="text-align: center;">generated image</figcaption></figure>',D,G,P,C,ce="You can compose multiple controls, such as canny image and a depth map, with the <code>MultiAdapter</code> class.",K,I,ye="The example below composes a canny image and depth map.",O,k,he="Load the control images and T2I-Adapters as a list.",ee,v,te,R,Je='Pass the adapters, prompt, and control images to <a href="/docs/diffusers/main/en/api/pipelines/stable_diffusion/adapter#diffusers.StableDiffusionXLAdapterPipeline">StableDiffusionXLAdapterPipeline</a>. Use the <code>adapter_conditioning_scale</code> parameter to determine how much weight to assign to each control.',ae,A,le,m,ue='<figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/canny-cat.png" width="300" alt="Generated image (prompt only)"/> <figcaption style="text-align: center;">canny image</figcaption></figure> <figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_depth_image.png" width="300" alt="Control image (Canny edges)"/> <figcaption style="text-align: center;">depth map</figcaption></figure> <figure><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2i-multi-rabbit.png" width="300" alt="Generated image (ControlNet + prompt)"/> <figcaption style="text-align: center;">generated image</figcaption></figure>',ne,Y,se,E,ie;return f=new Ge({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new Te({props:{title:"T2I-Adapter",local:"t2i-adapter",headingTag:"h1"}}),Z=new x({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwVDJJQWRhcHRlciUyQyUyMFN0YWJsZURpZmZ1c2lvblhMQWRhcHRlclBpcGVsaW5lJTJDJTIwQXV0b2VuY29kZXJLTCUwQSUwQXQyaV9hZGFwdGVyJTIwJTNEJTIwVDJJQWRhcHRlci5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyVGVuY2VudEFSQyUyRnQyaS1hZGFwdGVyLWNhbm55LXNkeGwtMS4wJTIyJTJDJTBBJTIwJTIwJTIwJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTJDJTBBKQ==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> T2IAdapter, StableDiffusionXLAdapterPipeline, AutoencoderKL

t2i_adapter = T2IAdapter.from_pretrained(
    <span class="hljs-string">&quot;TencentARC/t2i-adapter-canny-sdxl-1.0&quot;</span>,
    torch_dtype=torch.float16,
)`,wrap:!1}}),j=new x({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> cv2
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

original_image = load_image(
    <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png&quot;</span>
)

image = np.array(original_image)

low_threshold = <span class="hljs-number">100</span>
high_threshold = <span class="hljs-number">200</span>

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, <span class="hljs-literal">None</span>]
image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
canny_image = Image.fromarray(image)`,wrap:!1}}),W=new x({props:{code:"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",highlighted:`vae = AutoencoderKL.from_pretrained(<span class="hljs-string">&quot;madebyollin/sdxl-vae-fp16-fix&quot;</span>, torch_dtype=torch.float16)
pipeline = StableDiffusionXLAdapterPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
    adapter=t2i_adapter,
    vae=vae,
    torch_dtype=torch.float16,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;&quot;&quot;
A photorealistic overhead image of a cat reclining sideways in a flamingo pool floatie holding a margarita. 
The cat is floating leisurely in the pool and completely relaxed and happy.
&quot;&quot;&quot;</span>

pipeline(
    prompt, 
    image=canny_image,
    num_inference_steps=<span class="hljs-number">100</span>, 
    guidance_scale=<span class="hljs-number">10</span>,
).images[<span class="hljs-number">0</span>]`,wrap:!1}}),G=new Te({props:{title:"MultiAdapter",local:"multiadapter",headingTag:"h2"}}),v=new x({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionXLAdapterPipeline, AutoencoderKL, MultiAdapter, T2IAdapter

canny_image = load_image(
    <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/canny-cat.png&quot;</span>
)
depth_image = load_image(
    <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_depth_image.png&quot;</span>
)
controls = [canny_image, depth_image]
prompt = [<span class="hljs-string">&quot;&quot;&quot;
a relaxed rabbit sitting on a striped towel next to a pool with a tropical drink nearby, 
bright sunny day, vacation scene, 35mm photograph, film, professional, 4k, highly detailed
&quot;&quot;&quot;</span>]

adapters = MultiAdapter(
    [
        T2IAdapter.from_pretrained(<span class="hljs-string">&quot;TencentARC/t2i-adapter-canny-sdxl-1.0&quot;</span>, torch_dtype=torch.float16),
        T2IAdapter.from_pretrained(<span class="hljs-string">&quot;TencentARC/t2i-adapter-depth-midas-sdxl-1.0&quot;</span>, torch_dtype=torch.float16),
    ]
)`,wrap:!1}}),A=new x({props:{code:"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",highlighted:`vae = AutoencoderKL.from_pretrained(<span class="hljs-string">&quot;madebyollin/sdxl-vae-fp16-fix&quot;</span>, torch_dtype=torch.float16)
pipeline = StableDiffusionXLAdapterPipeline.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-xl-base-1.0&quot;</span>,
    torch_dtype=torch.float16,
    vae=vae,
    adapter=adapters,
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipeline(
    prompt,
    image=controls,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    adapter_conditioning_scale=[<span class="hljs-number">0.7</span>, <span class="hljs-number">0.7</span>]
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