import{s as fe,n as Te,o as we}from"../chunks/scheduler.8c3d61f6.js";import{S as Ue,i as Ze,g as i,s as n,r as d,A as be,h as p,f as a,c as s,j as Je,u as c,x as M,k as ue,l as y,y as je,a as l,v as h,d as J,t as u,w as g}from"../chunks/index.da70eac4.js";import{C as E}from"../chunks/CodeBlock.a9c4becf.js";import{H as ge,E as Be}from"../chunks/index.ef90fe87.js";function We(se){let r,F,Y,x,f,Q,T,ie='<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.',_,w,pe='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>.',V,U,N,Z,oe='Generate a canny image with <a href="https://github.com/opencv/opencv-python" rel="nofollow">opencv-python</a>.',H,b,S,j,me="Pass the canny image to the pipeline to generate an image.",$,B,L,o,Me='<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>',z,W,q,G,re="You can compose multiple controls, such as canny image and a depth map, with the <code>MultiAdapter</code> class.",D,C,de="The example below composes a canny image and depth map.",P,I,ce="Load the control images and T2I-Adapters as a list.",K,k,O,v,ye='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.',ee,R,te,m,he='<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>',ae,A,le,X,ne;return f=new ge({props:{title:"T2I-Adapter",local:"t2i-adapter",headingTag:"h1"}}),U=new E({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}}),b=new E({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}}),B=new E({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}}),W=new ge({props:{title:"MultiAdapter",local:"multiadapter",headingTag:"h2"}}),k=new E({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}}),R=new E({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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