diffusers/pr_14047/en/_app/immutable/nodes/165.8dcd66b7.js

import{s as fo,o as ho,n as I}from"../chunks/scheduler.53228c21.js";import{S as yo,i as bo,e as M,s as d,c as u,h as Mo,a as _,d as i,b as c,f as C,g,j as J,k as Z,l as w,m as o,n as f,t as h,o as y,p as b}from"../chunks/index.cac5d66a.js";import{C as _o}from"../chunks/CopyLLMTxtMenu.0a7e0e29.js";import{D as B}from"../chunks/Docstring.ef3c6a7f.js";import{C as j}from"../chunks/CodeBlock.606cbaf4.js";import{E as Se}from"../chunks/ExampleCodeBlock.76765dc9.js";import{H as V,E as Jo}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d66c5a2f.js";import{H as ae,a as $}from"../chunks/HfOption.6b51ddef.js";function ko(k){let n,r,t,m='Now pass all the prompts and embeddings to the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky#diffusers.KandinskyPipeline">KandinskyPipeline</a> to generate an image:',a,s,p,T,v='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png"/>',G;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyPriorPipeline, KandinskyPipeline
<span class="hljs-keyword">import</span> torch

prior_pipeline = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = KandinskyPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&quot;</span> <span class="hljs-comment"># optional to include a negative prompt, but results are usually better</span>
image_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt, guidance_scale=<span class="hljs-number">1.0</span>).to_tuple()`,lang:"py",wrap:!1}}),s=new j({props:{code:"aW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBpbWFnZV9lbWJlZHMlM0RpbWFnZV9lbWJlZHMlMkMlMjBuZWdhdGl2ZV9wcm9tcHQlM0RuZWdhdGl2ZV9wcm9tcHQlMkMlMjBuZWdhdGl2ZV9pbWFnZV9lbWJlZHMlM0RuZWdhdGl2ZV9pbWFnZV9lbWJlZHMlMkMlMjBoZWlnaHQlM0Q3NjglMkMlMjB3aWR0aCUzRDc2OCkuaW1hZ2VzJTVCMCU1RCUwQWltYWdl",highlighted:`image = pipeline(prompt, image_embeds=image_embeds, negative_prompt=negative_prompt, negative_image_embeds=negative_image_embeds, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("p"),t.innerHTML=m,a=d(),u(s.$$.fragment),p=d(),T=M("div"),T.innerHTML=v,this.h()},l(U){g(n.$$.fragment,U),r=c(U),t=_(U,"P",{"data-svelte-h":!0}),J(t)!=="svelte-onnzps"&&(t.innerHTML=m),a=c(U),g(s.$$.fragment,U),p=c(U),T=_(U,"DIV",{class:!0,"data-svelte-h":!0}),J(T)!=="svelte-i75i2v"&&(T.innerHTML=v),this.h()},h(){Z(T,"class","flex justify-center")},m(U,W){f(n,U,W),o(U,r,W),o(U,t,W),o(U,a,W),f(s,U,W),o(U,p,W),o(U,T,W),G=!0},p:I,i(U){G||(h(n.$$.fragment,U),h(s.$$.fragment,U),G=!0)},o(U){y(n.$$.fragment,U),y(s.$$.fragment,U),G=!1},d(U){U&&(i(r),i(t),i(a),i(p),i(T)),b(n,U),b(s,U)}}}function wo(k){let n,r,t,m='Pass the <code>image_embeds</code> and <code>negative_image_embeds</code> to the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22Pipeline">KandinskyV22Pipeline</a> to generate an image:',a,s,p,T,v='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-text-to-image.png"/>',G;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyV22PriorPipeline, KandinskyV22Pipeline
<span class="hljs-keyword">import</span> torch

prior_pipeline = KandinskyV22PriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-prior&quot;</span>, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = KandinskyV22Pipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder&quot;</span>, torch_dtype=torch.float16).to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&quot;</span> <span class="hljs-comment"># optional to include a negative prompt, but results are usually better</span>
image_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=<span class="hljs-number">1.0</span>).to_tuple()`,lang:"py",wrap:!1}}),s=new j({props:{code:"aW1hZ2UlMjAlM0QlMjBwaXBlbGluZShpbWFnZV9lbWJlZHMlM0RpbWFnZV9lbWJlZHMlMkMlMjBuZWdhdGl2ZV9pbWFnZV9lbWJlZHMlM0RuZWdhdGl2ZV9pbWFnZV9lbWJlZHMlMkMlMjBoZWlnaHQlM0Q3NjglMkMlMjB3aWR0aCUzRDc2OCkuaW1hZ2VzJTVCMCU1RCUwQWltYWdl",highlighted:`image = pipeline(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("p"),t.innerHTML=m,a=d(),u(s.$$.fragment),p=d(),T=M("div"),T.innerHTML=v,this.h()},l(U){g(n.$$.fragment,U),r=c(U),t=_(U,"P",{"data-svelte-h":!0}),J(t)!=="svelte-1ctutg3"&&(t.innerHTML=m),a=c(U),g(s.$$.fragment,U),p=c(U),T=_(U,"DIV",{class:!0,"data-svelte-h":!0}),J(T)!=="svelte-kkab9k"&&(T.innerHTML=v),this.h()},h(){Z(T,"class","flex justify-center")},m(U,W){f(n,U,W),o(U,r,W),o(U,t,W),o(U,a,W),f(s,U,W),o(U,p,W),o(U,T,W),G=!0},p:I,i(U){G||(h(n.$$.fragment,U),h(s.$$.fragment,U),G=!0)},o(U){y(n.$$.fragment,U),y(s.$$.fragment,U),G=!1},d(U){U&&(i(r),i(t),i(a),i(p),i(T)),b(n,U),b(s,U)}}}function To(k){let n,r='Kandinsky 3 doesn’t require a prior model so you can directly load the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky3#diffusers.Kandinsky3Pipeline">Kandinsky3Pipeline</a> and pass a prompt to generate an image:',t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> Kandinsky3Pipeline
<span class="hljs-keyword">import</span> torch

pipeline = Kandinsky3Pipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-3&quot;</span>, variant=<span class="hljs-string">&quot;fp16&quot;</span>, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting&quot;</span>
image = pipeline(prompt).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.innerHTML=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-otuj8r"&&(n.innerHTML=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function Uo(k){let n,r,t,m,a,s;return n=new $({props:{id:"text-to-image",option:"Kandinsky 2.1",$$slots:{default:[ko]},$$scope:{ctx:k}}}),t=new $({props:{id:"text-to-image",option:"Kandinsky 2.2",$$slots:{default:[wo]},$$scope:{ctx:k}}}),a=new $({props:{id:"text-to-image",option:"Kandinsky 3",$$slots:{default:[To]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment),m=d(),u(a.$$.fragment)},l(p){g(n.$$.fragment,p),r=c(p),g(t.$$.fragment,p),m=c(p),g(a.$$.fragment,p)},m(p,T){f(n,p,T),o(p,r,T),f(t,p,T),o(p,m,T),f(a,p,T),s=!0},p(p,T){const v={};T&2&&(v.$$scope={dirty:T,ctx:p}),n.$set(v);const G={};T&2&&(G.$$scope={dirty:T,ctx:p}),t.$set(G);const U={};T&2&&(U.$$scope={dirty:T,ctx:p}),a.$set(U)},i(p){s||(h(n.$$.fragment,p),h(t.$$.fragment,p),h(a.$$.fragment,p),s=!0)},o(p){y(n.$$.fragment,p),y(t.$$.fragment,p),y(a.$$.fragment,p),s=!1},d(p){p&&(i(r),i(m)),b(n,p),b(t,p),b(a,p)}}}function jo(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&quot;</span>

image = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=<span class="hljs-number">1.0</span>, guidance_scale=<span class="hljs-number">4.0</span>, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Zo(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForText2Image.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder&quot;</span>, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&quot;</span>

image = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=<span class="hljs-number">1.0</span>, guidance_scale=<span class="hljs-number">4.0</span>, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Io(k){let n,r,t,m;return n=new $({props:{id:"text-to-image",option:"Kandinsky 2.1",$$slots:{default:[jo]},$$scope:{ctx:k}}}),t=new $({props:{id:"text-to-image",option:"Kandinsky 2.2",$$slots:{default:[Zo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function Wo(k){let n,r;return n=new j({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> KandinskyImg2ImgPipeline, KandinskyPriorPipeline

prior_pipeline = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = KandinskyImg2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function $o(k){let n,r;return n=new j({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> KandinskyV22Img2ImgPipeline, KandinskyPriorPipeline

prior_pipeline = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = KandinskyV22Img2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function vo(k){let n,r="Kandinsky 3 doesn’t require a prior model so you can directly load the image-to-image pipeline:",t,m,a;return m=new j({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEthbmRpbnNreTNJbWcySW1nUGlwZWxpbmUlMEFmcm9tJTIwZGlmZnVzZXJzLnV0aWxzJTIwaW1wb3J0JTIwbG9hZF9pbWFnZSUwQWltcG9ydCUyMHRvcmNoJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBLYW5kaW5za3kzSW1nMkltZ1BpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJrYW5kaW5za3ktY29tbXVuaXR5JTJGa2FuZGluc2t5LTMlMjIlMkMlMjB2YXJpYW50JTNEJTIyZnAxNiUyMiUyQyUyMHRvcmNoX2R0eXBlJTNEdG9yY2guZmxvYXQxNiklMEFwaXBlbGluZS5lbmFibGVfbW9kZWxfY3B1X29mZmxvYWQoKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> Kandinsky3Img2ImgPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">import</span> torch

pipeline = Kandinsky3Img2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-3&quot;</span>, variant=<span class="hljs-string">&quot;fp16&quot;</span>, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-3bukbx"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function Go(k){let n,r,t,m,a,s;return n=new $({props:{id:"image-to-image",option:"Kandinsky 2.1",$$slots:{default:[Wo]},$$scope:{ctx:k}}}),t=new $({props:{id:"image-to-image",option:"Kandinsky 2.2",$$slots:{default:[$o]},$$scope:{ctx:k}}}),a=new $({props:{id:"image-to-image",option:"Kandinsky 3",$$slots:{default:[vo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment),m=d(),u(a.$$.fragment)},l(p){g(n.$$.fragment,p),r=c(p),g(t.$$.fragment,p),m=c(p),g(a.$$.fragment,p)},m(p,T){f(n,p,T),o(p,r,T),f(t,p,T),o(p,m,T),f(a,p,T),s=!0},p(p,T){const v={};T&2&&(v.$$scope={dirty:T,ctx:p}),n.$set(v);const G={};T&2&&(G.$$scope={dirty:T,ctx:p}),t.$set(G);const U={};T&2&&(U.$$scope={dirty:T,ctx:p}),a.$set(U)},i(p){s||(h(n.$$.fragment,p),h(t.$$.fragment,p),h(a.$$.fragment,p),s=!0)},o(p){y(n.$$.fragment,p),y(t.$$.fragment,p),y(a.$$.fragment,p),s=!1},d(p){p&&(i(r),i(m)),b(n,p),b(t,p),b(a,p)}}}function Co(k){let n,r,t,m='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png"/>',a;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid

image = pipeline(prompt, negative_prompt=negative_prompt, image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>, strength=<span class="hljs-number">0.3</span>).images[<span class="hljs-number">0</span>]
make_image_grid([original_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>)), image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("div"),t.innerHTML=m,this.h()},l(s){g(n.$$.fragment,s),r=c(s),t=_(s,"DIV",{class:!0,"data-svelte-h":!0}),J(t)!=="svelte-1vh4dwd"&&(t.innerHTML=m),this.h()},h(){Z(t,"class","flex justify-center")},m(s,p){f(n,s,p),o(s,r,p),o(s,t,p),a=!0},p:I,i(s){a||(h(n.$$.fragment,s),a=!0)},o(s){y(n.$$.fragment,s),a=!1},d(s){s&&(i(r),i(t)),b(n,s)}}}function Bo(k){let n,r,t,m='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-image-to-image.png"/>',a;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid

image = pipeline(image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>, strength=<span class="hljs-number">0.3</span>).images[<span class="hljs-number">0</span>]
make_image_grid([original_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>)), image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("div"),t.innerHTML=m,this.h()},l(s){g(n.$$.fragment,s),r=c(s),t=_(s,"DIV",{class:!0,"data-svelte-h":!0}),J(t)!=="svelte-1vux4a0"&&(t.innerHTML=m),this.h()},h(){Z(t,"class","flex justify-center")},m(s,p){f(n,s,p),o(s,r,p),o(s,t,p),a=!0},p:I,i(s){a||(h(n.$$.fragment,s),a=!0)},o(s){y(n.$$.fragment,s),a=!1},d(s){s&&(i(r),i(t)),b(n,s)}}}function Vo(k){let n,r;return n=new j({props:{code:"aW1hZ2UlMjAlM0QlMjBwaXBlbGluZShwcm9tcHQlMkMlMjBuZWdhdGl2ZV9wcm9tcHQlM0RuZWdhdGl2ZV9wcm9tcHQlMkMlMjBpbWFnZSUzRGltYWdlJTJDJTIwc3RyZW5ndGglM0QwLjc1JTJDJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDI1KS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2U=",highlighted:`image = pipeline(prompt, negative_prompt=negative_prompt, image=image, strength=<span class="hljs-number">0.75</span>, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function xo(k){let n,r,t,m,a,s;return n=new $({props:{id:"image-to-image",option:"Kandinsky 2.1",$$slots:{default:[Co]},$$scope:{ctx:k}}}),t=new $({props:{id:"image-to-image",option:"Kandinsky 2.2",$$slots:{default:[Bo]},$$scope:{ctx:k}}}),a=new $({props:{id:"image-to-image",option:"Kandinsky 3",$$slots:{default:[Vo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment),m=d(),u(a.$$.fragment)},l(p){g(n.$$.fragment,p),r=c(p),g(t.$$.fragment,p),m=c(p),g(a.$$.fragment,p)},m(p,T){f(n,p,T),o(p,r,T),f(t,p,T),o(p,m,T),f(a,p,T),s=!0},p(p,T){const v={};T&2&&(v.$$scope={dirty:T,ctx:p}),n.$set(v);const G={};T&2&&(G.$$scope={dirty:T,ctx:p}),t.$set(G);const U={};T&2&&(U.$$scope={dirty:T,ctx:p}),a.$set(U)},i(p){s||(h(n.$$.fragment,p),h(t.$$.fragment,p),h(a.$$.fragment,p),s=!0)},o(p){y(n.$$.fragment,p),y(t.$$.fragment,p),y(a.$$.fragment,p),s=!1},d(p){p&&(i(r),i(m)),b(n,p),b(t,p),b(a,p)}}}function Ro(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForImage2Image
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid, load_image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForImage2Image.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>)
pipeline.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A fantasy landscape, Cinematic lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&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>
original_image = load_image(url)

original_image.thumbnail((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>))

image = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=original_image, strength=<span class="hljs-number">0.3</span>).images[<span class="hljs-number">0</span>]
make_image_grid([original_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>)), image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Xo(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForImage2Image
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> make_image_grid, load_image
<span class="hljs-keyword">import</span> torch

pipeline = AutoPipelineForImage2Image.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder&quot;</span>, torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A fantasy landscape, Cinematic lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&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>
original_image = load_image(url)

original_image.thumbnail((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>))

image = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=original_image, strength=<span class="hljs-number">0.3</span>).images[<span class="hljs-number">0</span>]
make_image_grid([original_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>)), image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Po(k){let n,r,t,m;return n=new $({props:{id:"image-to-image",option:"Kandinsky 2.1",$$slots:{default:[Ro]},$$scope:{ctx:k}}}),t=new $({props:{id:"image-to-image",option:"Kandinsky 2.2",$$slots:{default:[Xo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function No(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyInpaintPipeline, KandinskyPriorPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid
<span class="hljs-keyword">import</span> torch
<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

prior_pipeline = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = KandinskyInpaintPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-inpaint&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Qo(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyV22InpaintPipeline, KandinskyV22PriorPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid
<span class="hljs-keyword">import</span> torch
<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

prior_pipeline = KandinskyV22PriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipeline = KandinskyV22InpaintPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder-inpaint&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Yo(k){let n,r,t,m;return n=new $({props:{id:"inpaint",option:"Kandinsky 2.1",$$slots:{default:[No]},$$scope:{ctx:k}}}),t=new $({props:{id:"inpaint",option:"Kandinsky 2.2",$$slots:{default:[Qo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function Fo(k){let n,r,t,m='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/inpaint_cat_hat.png"/>',a;return n=new j({props:{code:"b3V0cHV0X2ltYWdlJTIwJTNEJTIwcGlwZWxpbmUocHJvbXB0JTJDJTIwaW1hZ2UlM0Rpbml0X2ltYWdlJTJDJTIwbWFza19pbWFnZSUzRG1hc2slMkMlMjAqKnByaW9yX291dHB1dCUyQyUyMGhlaWdodCUzRDc2OCUyQyUyMHdpZHRoJTNENzY4JTJDJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDE1MCkuaW1hZ2VzJTVCMCU1RCUwQW1hc2slMjAlM0QlMjBJbWFnZS5mcm9tYXJyYXkoKG1hc2sqMjU1KS5hc3R5cGUoJ3VpbnQ4JyklMkMlMjAnTCcpJTBBbWFrZV9pbWFnZV9ncmlkKCU1QmluaXRfaW1hZ2UlMkMlMjBtYXNrJTJDJTIwb3V0cHV0X2ltYWdlJTVEJTJDJTIwcm93cyUzRDElMkMlMjBjb2xzJTNEMyk=",highlighted:`output_image = pipeline(prompt, image=init_image, mask_image=mask, **prior_output, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>, num_inference_steps=<span class="hljs-number">150</span>).images[<span class="hljs-number">0</span>]
mask = Image.fromarray((mask*<span class="hljs-number">255</span>).astype(<span class="hljs-string">&#x27;uint8&#x27;</span>), <span class="hljs-string">&#x27;L&#x27;</span>)
make_image_grid([init_image, mask, output_image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">3</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("div"),t.innerHTML=m,this.h()},l(s){g(n.$$.fragment,s),r=c(s),t=_(s,"DIV",{class:!0,"data-svelte-h":!0}),J(t)!=="svelte-1d04i1f"&&(t.innerHTML=m),this.h()},h(){Z(t,"class","flex justify-center")},m(s,p){f(n,s,p),o(s,r,p),o(s,t,p),a=!0},p:I,i(s){a||(h(n.$$.fragment,s),a=!0)},o(s){y(n.$$.fragment,s),a=!1},d(s){s&&(i(r),i(t)),b(n,s)}}}function zo(k){let n,r,t,m='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinskyv22-inpaint.png"/>',a;return n=new j({props:{code:"b3V0cHV0X2ltYWdlJTIwJTNEJTIwcGlwZWxpbmUoaW1hZ2UlM0Rpbml0X2ltYWdlJTJDJTIwbWFza19pbWFnZSUzRG1hc2slMkMlMjAqKnByaW9yX291dHB1dCUyQyUyMGhlaWdodCUzRDc2OCUyQyUyMHdpZHRoJTNENzY4JTJDJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDE1MCkuaW1hZ2VzJTVCMCU1RCUwQW1hc2slMjAlM0QlMjBJbWFnZS5mcm9tYXJyYXkoKG1hc2sqMjU1KS5hc3R5cGUoJ3VpbnQ4JyklMkMlMjAnTCcpJTBBbWFrZV9pbWFnZV9ncmlkKCU1QmluaXRfaW1hZ2UlMkMlMjBtYXNrJTJDJTIwb3V0cHV0X2ltYWdlJTVEJTJDJTIwcm93cyUzRDElMkMlMjBjb2xzJTNEMyk=",highlighted:`output_image = pipeline(image=init_image, mask_image=mask, **prior_output, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>, num_inference_steps=<span class="hljs-number">150</span>).images[<span class="hljs-number">0</span>]
mask = Image.fromarray((mask*<span class="hljs-number">255</span>).astype(<span class="hljs-string">&#x27;uint8&#x27;</span>), <span class="hljs-string">&#x27;L&#x27;</span>)
make_image_grid([init_image, mask, output_image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">3</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("div"),t.innerHTML=m,this.h()},l(s){g(n.$$.fragment,s),r=c(s),t=_(s,"DIV",{class:!0,"data-svelte-h":!0}),J(t)!=="svelte-1ajezuw"&&(t.innerHTML=m),this.h()},h(){Z(t,"class","flex justify-center")},m(s,p){f(n,s,p),o(s,r,p),o(s,t,p),a=!0},p:I,i(s){a||(h(n.$$.fragment,s),a=!0)},o(s){y(n.$$.fragment,s),a=!1},d(s){s&&(i(r),i(t)),b(n,s)}}}function Ho(k){let n,r,t,m;return n=new $({props:{id:"inpaint",option:"Kandinsky 2.1",$$slots:{default:[Fo]},$$scope:{ctx:k}}}),t=new $({props:{id:"inpaint",option:"Kandinsky 2.2",$$slots:{default:[zo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function Ko(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<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 <span class="hljs-keyword">import</span> AutoPipelineForInpainting
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

pipe = AutoPipelineForInpainting.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-inpaint&quot;</span>, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()

init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png&quot;</span>)
mask = np.zeros((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>), dtype=np.float32)
<span class="hljs-comment"># mask area above cat&#x27;s head</span>
mask[:<span class="hljs-number">250</span>, <span class="hljs-number">250</span>:-<span class="hljs-number">250</span>] = <span class="hljs-number">1</span>
prompt = <span class="hljs-string">&quot;a hat&quot;</span>

output_image = pipe(prompt=prompt, image=init_image, mask_image=mask).images[<span class="hljs-number">0</span>]
mask = Image.fromarray((mask*<span class="hljs-number">255</span>).astype(<span class="hljs-string">&#x27;uint8&#x27;</span>), <span class="hljs-string">&#x27;L&#x27;</span>)
make_image_grid([init_image, mask, output_image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">3</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function So(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<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 <span class="hljs-keyword">import</span> AutoPipelineForInpainting
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid

pipe = AutoPipelineForInpainting.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder-inpaint&quot;</span>, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()

init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png&quot;</span>)
mask = np.zeros((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>), dtype=np.float32)
<span class="hljs-comment"># mask area above cat&#x27;s head</span>
mask[:<span class="hljs-number">250</span>, <span class="hljs-number">250</span>:-<span class="hljs-number">250</span>] = <span class="hljs-number">1</span>
prompt = <span class="hljs-string">&quot;a hat&quot;</span>

output_image = pipe(prompt=prompt, image=original_image, mask_image=mask).images[<span class="hljs-number">0</span>]
mask = Image.fromarray((mask*<span class="hljs-number">255</span>).astype(<span class="hljs-string">&#x27;uint8&#x27;</span>), <span class="hljs-string">&#x27;L&#x27;</span>)
make_image_grid([init_image, mask, output_image], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">3</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Eo(k){let n,r,t,m;return n=new $({props:{id:"inpaint",option:"Kandinsky 2.1",$$slots:{default:[Ko]},$$scope:{ctx:k}}}),t=new $({props:{id:"inpaint",option:"Kandinsky 2.2",$$slots:{default:[So]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function qo(k){let n,r;return n=new j({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEthbmRpbnNreVByaW9yUGlwZWxpbmUlMkMlMjBLYW5kaW5za3lQaXBlbGluZSUwQWZyb20lMjBkaWZmdXNlcnMudXRpbHMlMjBpbXBvcnQlMjBsb2FkX2ltYWdlJTJDJTIwbWFrZV9pbWFnZV9ncmlkJTBBaW1wb3J0JTIwdG9yY2glMEElMEFwcmlvcl9waXBlbGluZSUyMCUzRCUyMEthbmRpbnNreVByaW9yUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMmthbmRpbnNreS1jb21tdW5pdHklMkZrYW5kaW5za3ktMi0xLXByaW9yJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2JTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSkudG8oJTIyY3VkYSUyMiklMEFpbWdfMSUyMCUzRCUyMGxvYWRfaW1hZ2UoJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGaGYtaW50ZXJuYWwtdGVzdGluZyUyRmRpZmZ1c2Vycy1pbWFnZXMlMkZyZXNvbHZlJTJGbWFpbiUyRmthbmRpbnNreSUyRmNhdC5wbmclMjIpJTBBaW1nXzIlMjAlM0QlMjBsb2FkX2ltYWdlKCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmhmLWludGVybmFsLXRlc3RpbmclMkZkaWZmdXNlcnMtaW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZrYW5kaW5za3klMkZzdGFycnlfbmlnaHQuanBlZyUyMiklMEFtYWtlX2ltYWdlX2dyaWQoJTVCaW1nXzEucmVzaXplKCg1MTIlMkM1MTIpKSUyQyUyMGltZ18yLnJlc2l6ZSgoNTEyJTJDNTEyKSklNUQlMkMlMjByb3dzJTNEMSUyQyUyMGNvbHMlM0QyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyPriorPipeline, KandinskyPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid
<span class="hljs-keyword">import</span> torch

prior_pipeline = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
img_1 = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png&quot;</span>)
img_2 = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg&quot;</span>)
make_image_grid([img_1.resize((<span class="hljs-number">512</span>,<span class="hljs-number">512</span>)), img_2.resize((<span class="hljs-number">512</span>,<span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Lo(k){let n,r;return n=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyV22PriorPipeline, KandinskyV22Pipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image, make_image_grid
<span class="hljs-keyword">import</span> torch

prior_pipeline = KandinskyV22PriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
img_1 = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png&quot;</span>)
img_2 = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg&quot;</span>)
make_image_grid([img_1.resize((<span class="hljs-number">512</span>,<span class="hljs-number">512</span>)), img_2.resize((<span class="hljs-number">512</span>,<span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment)},l(t){g(n.$$.fragment,t)},m(t,m){f(n,t,m),r=!0},p:I,i(t){r||(h(n.$$.fragment,t),r=!0)},o(t){y(n.$$.fragment,t),r=!1},d(t){b(n,t)}}}function Do(k){let n,r,t,m;return n=new $({props:{id:"interpolate",option:"Kandinsky 2.1",$$slots:{default:[qo]},$$scope:{ctx:k}}}),t=new $({props:{id:"interpolate",option:"Kandinsky 2.2",$$slots:{default:[Lo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function Ao(k){let n,r,t,m='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png"/>',a;return n=new j({props:{code:"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",highlighted:`<span class="hljs-comment"># prompt can be left empty</span>
prompt = <span class="hljs-string">&quot;&quot;</span>
prior_out = prior_pipeline.interpolate(images_texts, weights)

pipeline = KandinskyPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

image = pipeline(prompt, **prior_out, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("div"),t.innerHTML=m,this.h()},l(s){g(n.$$.fragment,s),r=c(s),t=_(s,"DIV",{class:!0,"data-svelte-h":!0}),J(t)!=="svelte-twgci5"&&(t.innerHTML=m),this.h()},h(){Z(t,"class","flex justify-center")},m(s,p){f(n,s,p),o(s,r,p),o(s,t,p),a=!0},p:I,i(s){a||(h(n.$$.fragment,s),a=!0)},o(s){y(n.$$.fragment,s),a=!1},d(s){s&&(i(r),i(t)),b(n,s)}}}function Oo(k){let n,r,t,m='<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinskyv22-interpolate.png"/>',a;return n=new j({props:{code:"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",highlighted:`<span class="hljs-comment"># prompt can be left empty</span>
prompt = <span class="hljs-string">&quot;&quot;</span>
prior_out = prior_pipeline.interpolate(images_texts, weights)

pipeline = KandinskyV22Pipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-decoder&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

image = pipeline(prompt, **prior_out, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),{c(){u(n.$$.fragment),r=d(),t=M("div"),t.innerHTML=m,this.h()},l(s){g(n.$$.fragment,s),r=c(s),t=_(s,"DIV",{class:!0,"data-svelte-h":!0}),J(t)!=="svelte-2ur4ou"&&(t.innerHTML=m),this.h()},h(){Z(t,"class","flex justify-center")},m(s,p){f(n,s,p),o(s,r,p),o(s,t,p),a=!0},p:I,i(s){a||(h(n.$$.fragment,s),a=!0)},o(s){y(n.$$.fragment,s),a=!1},d(s){s&&(i(r),i(t)),b(n,s)}}}function er(k){let n,r,t,m;return n=new $({props:{id:"interpolate",option:"Kandinsky 2.1",$$slots:{default:[Ao]},$$scope:{ctx:k}}}),t=new $({props:{id:"interpolate",option:"Kandinsky 2.2",$$slots:{default:[Oo]},$$scope:{ctx:k}}}),{c(){u(n.$$.fragment),r=d(),u(t.$$.fragment)},l(a){g(n.$$.fragment,a),r=c(a),g(t.$$.fragment,a)},m(a,s){f(n,a,s),o(a,r,s),f(t,a,s),m=!0},p(a,s){const p={};s&2&&(p.$$scope={dirty:s,ctx:a}),n.$set(p);const T={};s&2&&(T.$$scope={dirty:s,ctx:a}),t.$set(T)},i(a){m||(h(n.$$.fragment,a),h(t.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),y(t.$$.fragment,a),m=!1},d(a){a&&i(r),b(n,a),b(t,a)}}}function nr(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyPipeline, KandinskyPriorPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;red cat, 4k photo&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>out = pipe_prior(prompt)
<span class="hljs-meta">&gt;&gt;&gt; </span>image_emb = out.image_embeds
<span class="hljs-meta">&gt;&gt;&gt; </span>negative_image_emb = out.negative_image_embeds

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = KandinskyPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>)
<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>image = pipe(
<span class="hljs-meta">... </span>    prompt,
<span class="hljs-meta">... </span>    image_embeds=image_emb,
<span class="hljs-meta">... </span>    negative_image_embeds=negative_image_emb,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span>image[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;cat.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function tr(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyPriorPipeline, KandinskyPipeline
<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> PIL

<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> torchvision <span class="hljs-keyword">import</span> transforms

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior = KandinskyPriorPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>img1 = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;/kandinsky/cat.png&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>img2 = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;/kandinsky/starry_night.jpeg&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>images_texts = [<span class="hljs-string">&quot;a cat&quot;</span>, img1, img2]
<span class="hljs-meta">&gt;&gt;&gt; </span>weights = [<span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.4</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = KandinskyPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16)
<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>image = pipe(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&quot;</span>,
<span class="hljs-meta">... </span>    image_embeds=image_emb,
<span class="hljs-meta">... </span>    negative_image_embeds=zero_image_emb,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">150</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;starry_cat.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function sr(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyPipeline, KandinskyPriorPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior = KandinskyPriorPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/Kandinsky-2-1-prior&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;red cat, 4k photo&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>out = pipe_prior(prompt)
<span class="hljs-meta">&gt;&gt;&gt; </span>image_emb = out.image_embeds
<span class="hljs-meta">&gt;&gt;&gt; </span>negative_image_emb = out.negative_image_embeds

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = KandinskyPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>)
<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>image = pipe(
<span class="hljs-meta">... </span>    prompt,
<span class="hljs-meta">... </span>    image_embeds=image_emb,
<span class="hljs-meta">... </span>    negative_image_embeds=negative_image_emb,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span>image[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;cat.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function ar(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForText2Image
<span class="hljs-keyword">import</span> torch

pipe = AutoPipelineForText2Image.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k&quot;</span>

image = pipe(prompt=prompt, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function lr(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyImg2ImgPipeline, KandinskyPriorPipeline
<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>pipe_prior = KandinskyPriorPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A red cartoon frog, 4k&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_emb, zero_image_emb = pipe_prior(prompt, return_dict=<span class="hljs-literal">False</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = KandinskyImg2ImgPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<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>init_image = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;/kandinsky/frog.png&quot;</span>
<span class="hljs-meta">... </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>    image_embeds=image_emb,
<span class="hljs-meta">... </span>    negative_image_embeds=zero_image_emb,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span>    strength=<span class="hljs-number">0.2</span>,
<span class="hljs-meta">... </span>).images

<span class="hljs-meta">&gt;&gt;&gt; </span>image[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;red_frog.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function ir(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForImage2Image
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> os

pipe = AutoPipelineForImage2Image.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A fantasy landscape, Cinematic lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&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>

response = requests.get(url)
image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
image.thumbnail((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>))

image = pipe(prompt=prompt, image=original_image, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function or(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyInpaintPipeline, KandinskyPriorPipeline
<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><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior = KandinskyPriorPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-prior&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe_prior.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a hat&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_emb, zero_image_emb = pipe_prior(prompt, return_dict=<span class="hljs-literal">False</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = KandinskyInpaintPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-inpaint&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<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>init_image = load_image(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main&quot;</span>
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;/kandinsky/cat.png&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>mask = np.zeros((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>), dtype=np.float32)
<span class="hljs-meta">&gt;&gt;&gt; </span>mask[:<span class="hljs-number">250</span>, <span class="hljs-number">250</span>:-<span class="hljs-number">250</span>] = <span class="hljs-number">1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>out = pipe(
<span class="hljs-meta">... </span>    prompt,
<span class="hljs-meta">... </span>    image=init_image,
<span class="hljs-meta">... </span>    mask_image=mask,
<span class="hljs-meta">... </span>    image_embeds=image_emb,
<span class="hljs-meta">... </span>    negative_image_embeds=zero_image_emb,
<span class="hljs-meta">... </span>    height=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    width=<span class="hljs-number">768</span>,
<span class="hljs-meta">... </span>    num_inference_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image = out.images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;cat_with_hat.png&quot;</span>)`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function rr(k){let n,r="Examples:",t,m,a;return m=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoPipelineForInpainting
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

pipe = AutoPipelineForInpainting.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1-inpaint&quot;</span>, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;A fantasy landscape, Cinematic lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&quot;</span>

original_image = load_image(
    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main&quot;</span> <span class="hljs-string">&quot;/kandinsky/cat.png&quot;</span>
)

mask = np.zeros((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>), dtype=np.float32)
<span class="hljs-comment"># Let&#x27;s mask out an area above the cat&#x27;s head</span>
mask[:<span class="hljs-number">250</span>, <span class="hljs-number">250</span>:-<span class="hljs-number">250</span>] = <span class="hljs-number">1</span>

image = pipe(prompt=prompt, image=original_image, mask_image=mask, num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]`,lang:"py",wrap:!1}}),{c(){n=M("p"),n.textContent=r,t=d(),u(m.$$.fragment)},l(s){n=_(s,"P",{"data-svelte-h":!0}),J(n)!=="svelte-kvfsh7"&&(n.textContent=r),t=c(s),g(m.$$.fragment,s)},m(s,p){o(s,n,p),o(s,t,p),f(m,s,p),a=!0},p:I,i(s){a||(h(m.$$.fragment,s),a=!0)},o(s){y(m.$$.fragment,s),a=!1},d(s){s&&(i(n),i(t)),b(m,s)}}}function pr(k){let n,r,t,m,a,s,p,T,v,G='Kandinsky 2.1 is created by <a href="https://github.com/cene555" rel="nofollow">Arseniy Shakhmatov</a>, <a href="https://github.com/razzant" rel="nofollow">Anton Razzhigaev</a>, <a href="https://github.com/AlexWortega" rel="nofollow">Aleksandr Nikolich</a>, <a href="https://github.com/oriBetelgeuse" rel="nofollow">Vladimir Arkhipkin</a>, <a href="https://github.com/boomb0om" rel="nofollow">Igor Pavlov</a>, <a href="https://github.com/kuznetsoffandrey" rel="nofollow">Andrey Kuznetsov</a>, and <a href="https://github.com/denndimitrov" rel="nofollow">Denis Dimitrov</a>.',U,W,El="The description from it’s GitHub page is:",os,Ee,ql="<em>Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.</em>",rs,qe,Ll='The original codebase can be found at <a href="https://github.com/ai-forever/Kandinsky-2" rel="nofollow">ai-forever/Kandinsky-2</a>.',ps,ge,Dl='<p>Check out the <a href="https://huggingface.co/kandinsky-community" rel="nofollow">Kandinsky Community</a> organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.</p>',ds,fe,Al='<p>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.</p>',cs,Le,Ol="Make sure you have the following libraries installed.",ms,De,us,he,ei='<p>Kandinsky 2.1 and 2.2 usage is very similar! The only difference is Kandinsky 2.2 doesn’t accept <code>prompt</code> as an input when decoding the latents. Instead, Kandinsky 2.2 only accepts <code>image_embeds</code> during decoding.</p> <br/> <p>Kandinsky 3 has a more concise architecture and it doesn’t require a prior model. This means it’s usage is identical to other diffusion models like <a href="./stable_diffusion/stable_diffusion_xl">Stable Diffusion XL</a>.</p>',gs,Ae,fs,Oe,ni="To use the Kandinsky models for any task, you always start by setting up the prior pipeline to encode the prompt and generate the image embeddings. The prior pipeline also generates <code>negative_image_embeds</code> that correspond to the negative prompt <code>&quot;&quot;</code>. For better results, you can pass an actual <code>negative_prompt</code> to the prior pipeline, but this’ll increase the effective batch size of the prior pipeline by 2x.",hs,ye,ys,en,ti='🤗 Diffusers also provides an end-to-end API with the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky#diffusers.KandinskyCombinedPipeline">KandinskyCombinedPipeline</a> and <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22CombinedPipeline">KandinskyV22CombinedPipeline</a>, meaning you don’t have to separately load the prior and text-to-image pipeline. The combined pipeline automatically loads both the prior model and the decoder. You can still set different values for the prior pipeline with the <code>prior_guidance_scale</code> and <code>prior_num_inference_steps</code> parameters if you want.',bs,nn,si='Use the <a href="/docs/diffusers/pr_14047/en/api/pipelines/auto_pipeline#diffusers.AutoPipelineForText2Image">AutoPipelineForText2Image</a> to automatically call the combined pipelines under the hood:',Ms,be,_s,tn,Js,sn,ai="For image-to-image, pass the initial image and text prompt to condition the image to the pipeline. Start by loading the prior pipeline:",ks,Me,ws,an,li="Download an image to condition on:",Ts,ln,Us,_e,ii='<img class="rounded-xl" src="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"/>',js,on,oi="Generate the <code>image_embeds</code> and <code>negative_image_embeds</code> with the prior pipeline:",Zs,rn,Is,pn,ri="Now pass the original image, and all the prompts and embeddings to the pipeline to generate an image:",Ws,Je,$s,dn,pi='🤗 Diffusers also provides an end-to-end API with the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky#diffusers.KandinskyImg2ImgCombinedPipeline">KandinskyImg2ImgCombinedPipeline</a> and <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22Img2ImgCombinedPipeline">KandinskyV22Img2ImgCombinedPipeline</a>, meaning you don’t have to separately load the prior and image-to-image pipeline. The combined pipeline automatically loads both the prior model and the decoder. You can still set different values for the prior pipeline with the <code>prior_guidance_scale</code> and <code>prior_num_inference_steps</code> parameters if you want.',vs,cn,di='Use the <a href="/docs/diffusers/pr_14047/en/api/pipelines/auto_pipeline#diffusers.AutoPipelineForImage2Image">AutoPipelineForImage2Image</a> to automatically call the combined pipelines under the hood:',Gs,ke,Cs,mn,Bs,le,Bt,ci='⚠️ The Kandinsky models use ⬜️ <strong>white pixels</strong> to represent the masked area now instead of black pixels. If you are using <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky#diffusers.KandinskyInpaintPipeline">KandinskyInpaintPipeline</a> in production, you need to change the mask to use white pixels:',sl,un,Vs,gn,mi="For inpainting, you’ll need the original image, a mask of the area to replace in the original image, and a text prompt of what to inpaint. Load the prior pipeline:",xs,we,Rs,fn,ui="Load an initial image and create a mask:",Xs,hn,Ps,yn,gi="Generate the embeddings with the prior pipeline:",Ns,bn,Qs,Mn,fi="Now pass the initial image, mask, and prompt and embeddings to the pipeline to generate an image:",Ys,Te,Fs,_n,hi='You can also use the end-to-end <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky#diffusers.KandinskyInpaintCombinedPipeline">KandinskyInpaintCombinedPipeline</a> and <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22InpaintCombinedPipeline">KandinskyV22InpaintCombinedPipeline</a> to call the prior and decoder pipelines together under the hood. Use the <a href="/docs/diffusers/pr_14047/en/api/pipelines/auto_pipeline#diffusers.AutoPipelineForInpainting">AutoPipelineForInpainting</a> for this:',zs,Ue,Hs,Jn,Ks,kn,yi="Interpolation allows you to explore the latent space between the image and text embeddings which is a cool way to see some of the prior model’s intermediate outputs. Load the prior pipeline and two images you’d like to interpolate:",Ss,je,Es,Ze,bi='<div><img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png"/> <figcaption class="mt-2 text-center text-sm text-gray-500">a cat</figcaption></div> <div><img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg"/> <figcaption class="mt-2 text-center text-sm text-gray-500">Van Gogh&#39;s Starry Night painting</figcaption></div>',qs,wn,Mi="Specify the text or images to interpolate, and set the weights for each text or image. Experiment with the weights to see how they affect the interpolation!",Ls,Tn,Ds,Un,_i="Call the <code>interpolate</code> function to generate the embeddings, and then pass them to the pipeline to generate the image:",As,Ie,Os,jn,ea,We,Ji="<p>⚠️ ControlNet is only supported for Kandinsky 2.2!</p>",na,Zn,ki="ControlNet enables conditioning large pretrained diffusion models with additional inputs such as a depth map or edge detection. For example, you can condition Kandinsky 2.2 with a depth map so the model understands and preserves the structure of the depth image.",ta,In,wi="Let’s load an image and extract it’s depth map:",sa,Wn,aa,$e,Ti='<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png"/>',la,$n,Ui='Then you can use the <code>depth-estimation</code> <a href="https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.Pipeline" rel="nofollow">Pipeline</a> from 🤗 Transformers to process the image and retrieve the depth map:',ia,vn,oa,Gn,ra,Cn,ji='Load the prior pipeline and the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22ControlnetPipeline">KandinskyV22ControlnetPipeline</a>:',pa,Bn,da,Vn,Zi="Generate the image embeddings from a prompt and negative prompt:",ca,xn,ma,Rn,Ii='Finally, pass the image embeddings and the depth image to the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22ControlnetPipeline">KandinskyV22ControlnetPipeline</a> to generate an image:',ua,Xn,ga,ve,Wi='<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat_text2img.png"/>',fa,Pn,ha,Nn,$i="For image-to-image with ControlNet, you’ll need to use the:",ya,Qn,vi='<li><a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22PriorEmb2EmbPipeline">KandinskyV22PriorEmb2EmbPipeline</a> to generate the image embeddings from a text prompt and an image</li> <li><a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22ControlnetImg2ImgPipeline">KandinskyV22ControlnetImg2ImgPipeline</a> to generate an image from the initial image and the image embeddings</li>',ba,Yn,Gi='Process and extract a depth map of an initial image of a cat with the <code>depth-estimation</code> <a href="https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.Pipeline" rel="nofollow">Pipeline</a> from 🤗 Transformers:',Ma,Fn,_a,zn,Ci='Load the prior pipeline and the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22ControlnetImg2ImgPipeline">KandinskyV22ControlnetImg2ImgPipeline</a>:',Ja,Hn,ka,Kn,Bi="Pass a text prompt and the initial image to the prior pipeline to generate the image embeddings:",wa,Sn,Ta,En,Vi='Now you can run the <a href="/docs/diffusers/pr_14047/en/api/pipelines/kandinsky_v22#diffusers.KandinskyV22ControlnetImg2ImgPipeline">KandinskyV22ControlnetImg2ImgPipeline</a> to generate an image from the initial image and the image embeddings:',Ua,qn,ja,Ge,xi='<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat.png"/>',Za,Ln,Ia,Dn,Ri="Kandinsky is unique because it requires a prior pipeline to generate the mappings, and a second pipeline to decode the latents into an image. Optimization efforts should be focused on the second pipeline because that is where the bulk of the computation is done. Here are some tips to improve Kandinsky during inference.",Wa,An,Xi='<li>Enable <a href="../../optimization/xformers">xFormers</a> if you’re using PyTorch &lt; 2.0:</li>',$a,On,va,Ce,Pi="<li>Enable <code>torch.compile</code> if you’re using PyTorch &gt;= 2.0 to automatically use scaled dot-product attention (SDPA):</li>",Ga,et,Ca,nt,Ni='This is the same as explicitly setting the attention processor to use <a href="/docs/diffusers/pr_14047/en/api/attnprocessor#diffusers.models.attention_processor.AttnAddedKVProcessor2_0">AttnAddedKVProcessor2_0</a>:',Ba,tt,Va,Be,Qi='<li>Offload the model to the CPU with <a href="/docs/diffusers/pr_14047/en/api/pipelines/overview#diffusers.DiffusionPipeline.enable_model_cpu_offload">enable_model_cpu_offload()</a> to avoid out-of-memory errors:</li>',xa,st,Ra,Ve,Yi='<li>By default, the text-to-image pipeline uses the <a href="/docs/diffusers/pr_14047/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a> but you can replace it with another scheduler like <a href="/docs/diffusers/pr_14047/en/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a> to see how that affects the tradeoff between inference speed and image quality:</li>',Xa,at,Pa,lt,Na,x,it,al,Vt,Fi="Pipeline for generating image prior for Kandinsky",ll,xt,zi=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,il,L,ot,ol,Rt,Hi="Function invoked when calling the pipeline for generation.",rl,xe,pl,D,rt,dl,Xt,Ki="Function invoked when using the prior pipeline for interpolation.",cl,Re,Qa,pt,Ya,N,dt,ml,Pt,Si="Pipeline for text-to-image generation using Kandinsky",ul,Nt,Ei=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,gl,A,ct,fl,Qt,qi="Function invoked when calling the pipeline for generation.",hl,Xe,Fa,mt,za,R,ut,yl,Yt,Li="Combined Pipeline for text-to-image generation using Kandinsky",bl,Ft,Di=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,Ml,O,gt,_l,zt,Ai="Function invoked when calling the pipeline for generation.",Jl,Pe,kl,Ne,ft,wl,Ht,Oi=`Offloads all models (<code>unet</code>, <code>text_encoder</code>, <code>vae</code>, and <code>safety checker</code> state dicts) to CPU using 🤗
Accelerate, significantly reducing memory usage. Models are moved to a <code>torch.device(&#39;meta&#39;)</code> and loaded on a
GPU only when their specific submodule’s <code>forward</code> method is called. Offloading happens on a submodule basis.
Memory savings are higher than using <code>enable_model_cpu_offload</code>, but performance is lower.`,Ha,ht,Ka,Q,yt,Tl,Kt,eo="Pipeline for image-to-image generation using Kandinsky",Ul,St,no=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,jl,ee,bt,Zl,Et,to="Function invoked when calling the pipeline for generation.",Il,Qe,Sa,Mt,Ea,X,_t,Wl,qt,so="Combined Pipeline for image-to-image generation using Kandinsky",$l,Lt,ao=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,vl,ne,Jt,Gl,Dt,lo="Function invoked when calling the pipeline for generation.",Cl,Ye,Bl,Fe,kt,Vl,At,io=`Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
<code>torch.device(&#39;meta&#39;) and loaded to GPU only when their specific submodule has its </code>forward<code>method called. Note that offloading happens on a submodule basis. Memory savings are higher than with</code>enable_model_cpu_offload\`, but performance is lower.`,qa,wt,La,Y,Tt,xl,Ot,oo="Pipeline for text-guided image inpainting using Kandinsky2.1",Rl,es,ro=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,Xl,te,Ut,Pl,ns,po="Function invoked when calling the pipeline for generation.",Nl,ze,Da,jt,Aa,P,Zt,Ql,ts,co="Combined Pipeline for generation using Kandinsky",Yl,ss,mo=`This model inherits from <a href="/docs/diffusers/pr_14047/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.)`,Fl,se,It,zl,as,uo="Function invoked when calling the pipeline for generation.",Hl,He,Kl,Ke,Wt,Sl,ls,go=`Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
<code>torch.device(&#39;meta&#39;) and loaded to GPU only when their specific submodule has its </code>forward<code>method called. Note that offloading happens on a submodule basis. Memory savings are higher than with</code>enable_model_cpu_offload\`, but performance is lower.`,Oa,$t,el,is,nl;return a=new _o({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),p=new V({props:{title:"Kandinsky 2.1",local:"kandinsky-21",headingTag:"h1"}}),De=new j({props:{code:"JTIzJTIwdW5jb21tZW50JTIwdG8lMjBpbnN0YWxsJTIwdGhlJTIwbmVjZXNzYXJ5JTIwbGlicmFyaWVzJTIwaW4lMjBDb2xhYiUwQSUyMyFwaXAlMjBpbnN0YWxsJTIwLXElMjBkaWZmdXNlcnMlMjB0cmFuc2Zvcm1lcnMlMjBhY2NlbGVyYXRl",highlighted:`<span class="hljs-comment"># uncomment to install the necessary libraries in Colab</span>
<span class="hljs-comment">#!pip install -q diffusers transformers accelerate</span>`,lang:"py",wrap:!1}}),Ae=new V({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),ye=new ae({props:{id:"text-to-image",options:["Kandinsky 2.1","Kandinsky 2.2","Kandinsky 3"],$$slots:{default:[Uo]},$$scope:{ctx:k}}}),be=new ae({props:{id:"text-to-image",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[Io]},$$scope:{ctx:k}}}),tn=new V({props:{title:"Image-to-image",local:"image-to-image",headingTag:"h2"}}),Me=new ae({props:{id:"image-to-image",options:["Kandinsky 2.1","Kandinsky 2.2","Kandinsky 3"],$$slots:{default:[Go]},$$scope:{ctx:k}}}),ln=new j({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGxvYWRfaW1hZ2UlMEElMEElMjMlMjBkb3dubG9hZCUyMGltYWdlJTBBdXJsJTIwJTNEJTIwJTIyaHR0cHMlM0ElMkYlMkZyYXcuZ2l0aHVidXNlcmNvbnRlbnQuY29tJTJGQ29tcFZpcyUyRnN0YWJsZS1kaWZmdXNpb24lMkZtYWluJTJGYXNzZXRzJTJGc3RhYmxlLXNhbXBsZXMlMkZpbWcyaW1nJTJGc2tldGNoLW1vdW50YWlucy1pbnB1dC5qcGclMjIlMEFvcmlnaW5hbF9pbWFnZSUyMCUzRCUyMGxvYWRfaW1hZ2UodXJsKSUwQW9yaWdpbmFsX2ltYWdlJTIwJTNEJTIwb3JpZ2luYWxfaW1hZ2UucmVzaXplKCg3NjglMkMlMjA1MTIpKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

<span class="hljs-comment"># download image</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>
original_image = load_image(url)
original_image = original_image.resize((<span class="hljs-number">768</span>, <span class="hljs-number">512</span>))`,lang:"py",wrap:!1}}),rn=new j({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyQSUyMGZhbnRhc3klMjBsYW5kc2NhcGUlMkMlMjBDaW5lbWF0aWMlMjBsaWdodGluZyUyMiUwQW5lZ2F0aXZlX3Byb21wdCUyMCUzRCUyMCUyMmxvdyUyMHF1YWxpdHklMkMlMjBiYWQlMjBxdWFsaXR5JTIyJTBBJTBBaW1hZ2VfZW1iZWRzJTJDJTIwbmVnYXRpdmVfaW1hZ2VfZW1iZWRzJTIwJTNEJTIwcHJpb3JfcGlwZWxpbmUocHJvbXB0JTJDJTIwbmVnYXRpdmVfcHJvbXB0KS50b190dXBsZSgp",highlighted:`prompt = <span class="hljs-string">&quot;A fantasy landscape, Cinematic lighting&quot;</span>
negative_prompt = <span class="hljs-string">&quot;low quality, bad quality&quot;</span>

image_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt).to_tuple()`,lang:"py",wrap:!1}}),Je=new ae({props:{id:"image-to-image",options:["Kandinsky 2.1","Kandinsky 2.2","Kandinsky 3"],$$slots:{default:[xo]},$$scope:{ctx:k}}}),ke=new ae({props:{id:"image-to-image",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[Po]},$$scope:{ctx:k}}}),mn=new V({props:{title:"Inpainting",local:"inpainting",headingTag:"h2"}}),un=new j({props:{code:"JTIzJTIwRm9yJTIwUElMJTIwaW5wdXQlMEFpbXBvcnQlMjBQSUwuSW1hZ2VPcHMlMEFtYXNrJTIwJTNEJTIwUElMLkltYWdlT3BzLmludmVydChtYXNrKSUwQSUwQSUyMyUyMEZvciUyMFB5VG9yY2glMjBhbmQlMjBOdW1QeSUyMGlucHV0JTBBbWFzayUyMCUzRCUyMDElMjAtJTIwbWFzaw==",highlighted:`<span class="hljs-comment"># For PIL input</span>
<span class="hljs-keyword">import</span> PIL.ImageOps
mask = PIL.ImageOps.invert(mask)

<span class="hljs-comment"># For PyTorch and NumPy input</span>
mask = <span class="hljs-number">1</span> - mask`,lang:"py",wrap:!1}}),we=new ae({props:{id:"inpaint",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[Yo]},$$scope:{ctx:k}}}),hn=new j({props:{code:"aW5pdF9pbWFnZSUyMCUzRCUyMGxvYWRfaW1hZ2UoJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGaGYtaW50ZXJuYWwtdGVzdGluZyUyRmRpZmZ1c2Vycy1pbWFnZXMlMkZyZXNvbHZlJTJGbWFpbiUyRmthbmRpbnNreSUyRmNhdC5wbmclMjIpJTBBbWFzayUyMCUzRCUyMG5wLnplcm9zKCg3NjglMkMlMjA3NjgpJTJDJTIwZHR5cGUlM0RucC5mbG9hdDMyKSUwQSUyMyUyMG1hc2slMjBhcmVhJTIwYWJvdmUlMjBjYXQncyUyMGhlYWQlMEFtYXNrJTVCJTNBMjUwJTJDJTIwMjUwJTNBLTI1MCU1RCUyMCUzRCUyMDE=",highlighted:`init_image = load_image(<span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png&quot;</span>)
mask = np.zeros((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>), dtype=np.float32)
<span class="hljs-comment"># mask area above cat&#x27;s head</span>
mask[:<span class="hljs-number">250</span>, <span class="hljs-number">250</span>:-<span class="hljs-number">250</span>] = <span class="hljs-number">1</span>`,lang:"py",wrap:!1}}),bn=new j({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyYSUyMGhhdCUyMiUwQXByaW9yX291dHB1dCUyMCUzRCUyMHByaW9yX3BpcGVsaW5lKHByb21wdCk=",highlighted:`prompt = <span class="hljs-string">&quot;a hat&quot;</span>
prior_output = prior_pipeline(prompt)`,lang:"py",wrap:!1}}),Te=new ae({props:{id:"inpaint",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[Ho]},$$scope:{ctx:k}}}),Ue=new ae({props:{id:"inpaint",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[Eo]},$$scope:{ctx:k}}}),Jn=new V({props:{title:"Interpolation",local:"interpolation",headingTag:"h2"}}),je=new ae({props:{id:"interpolate",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[Do]},$$scope:{ctx:k}}}),Tn=new j({props:{code:"aW1hZ2VzX3RleHRzJTIwJTNEJTIwJTVCJTIyYSUyMGNhdCUyMiUyQyUyMGltZ18xJTJDJTIwaW1nXzIlNUQlMEF3ZWlnaHRzJTIwJTNEJTIwJTVCMC4zJTJDJTIwMC4zJTJDJTIwMC40JTVE",highlighted:`images_texts = [<span class="hljs-string">&quot;a cat&quot;</span>, img_1, img_2]
weights = [<span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.4</span>]`,lang:"py",wrap:!1}}),Ie=new ae({props:{id:"interpolate",options:["Kandinsky 2.1","Kandinsky 2.2"],$$slots:{default:[er]},$$scope:{ctx:k}}}),jn=new V({props:{title:"ControlNet",local:"controlnet",headingTag:"h2"}}),Wn=new j({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGxvYWRfaW1hZ2UlMEElMEFpbWclMjAlM0QlMjBsb2FkX2ltYWdlKCUwQSUyMCUyMCUyMCUyMCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmhmLWludGVybmFsLXRlc3RpbmclMkZkaWZmdXNlcnMtaW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZrYW5kaW5za3l2MjIlMkZjYXQucG5nJTIyJTBBKS5yZXNpemUoKDc2OCUyQyUyMDc2OCkpJTBBaW1n",highlighted:`<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

img = load_image(
    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png&quot;</span>
).resize((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>))
img`,lang:"py",wrap:!1}}),vn=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-keyword">def</span> <span class="hljs-title function_">make_hint</span>(<span class="hljs-params">image, depth_estimator</span>):
    image = depth_estimator(image)[<span class="hljs-string">&quot;depth&quot;</span>]
    image = np.array(image)
    image = image[:, :, <span class="hljs-literal">None</span>]
    image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
    detected_map = torch.from_numpy(image).<span class="hljs-built_in">float</span>() / <span class="hljs-number">255.0</span>
    hint = detected_map.permute(<span class="hljs-number">2</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>)
    <span class="hljs-keyword">return</span> hint

depth_estimator = pipeline(<span class="hljs-string">&quot;depth-estimation&quot;</span>)
hint = make_hint(img, depth_estimator).unsqueeze(<span class="hljs-number">0</span>).half().to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),Gn=new V({props:{title:"Text-to-image",local:"controlnet-text-to-image",headingTag:"h3"}}),Bn=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline

prior_pipeline = KandinskyV22PriorPipeline.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipeline = KandinskyV22ControlnetPipeline.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-controlnet-depth&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),xn=new j({props:{code:"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",highlighted:`prompt = <span class="hljs-string">&quot;A robot, 4k photo&quot;</span>
negative_prior_prompt = <span class="hljs-string">&quot;lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature&quot;</span>

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">43</span>)

image_emb, zero_image_emb = prior_pipeline(
    prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
).to_tuple()`,lang:"py",wrap:!1}}),Xn=new j({props:{code:"aW1hZ2UlMjAlM0QlMjBwaXBlbGluZShpbWFnZV9lbWJlZHMlM0RpbWFnZV9lbWIlMkMlMjBuZWdhdGl2ZV9pbWFnZV9lbWJlZHMlM0R6ZXJvX2ltYWdlX2VtYiUyQyUyMGhpbnQlM0RoaW50JTJDJTIwbnVtX2luZmVyZW5jZV9zdGVwcyUzRDUwJTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTJDJTIwaGVpZ2h0JTNENzY4JTJDJTIwd2lkdGglM0Q3NjgpLmltYWdlcyU1QjAlNUQlMEFpbWFnZQ==",highlighted:`image = pipeline(image_embeds=image_emb, negative_image_embeds=zero_image_emb, hint=hint, num_inference_steps=<span class="hljs-number">50</span>, generator=generator, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
image`,lang:"py",wrap:!1}}),Pn=new V({props:{title:"Image-to-image",local:"controlnet-image-to-image",headingTag:"h3"}}),Fn=new j({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjBudW1weSUyMGFzJTIwbnAlMEElMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwS2FuZGluc2t5VjIyUHJpb3JFbWIyRW1iUGlwZWxpbmUlMkMlMjBLYW5kaW5za3lWMjJDb250cm9sbmV0SW1nMkltZ1BpcGVsaW5lJTBBZnJvbSUyMGRpZmZ1c2Vycy51dGlscyUyMGltcG9ydCUyMGxvYWRfaW1hZ2UlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlMEElMEFpbWclMjAlM0QlMjBsb2FkX2ltYWdlKCUwQSUyMCUyMCUyMCUyMCUyMmh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZkYXRhc2V0cyUyRmhmLWludGVybmFsLXRlc3RpbmclMkZkaWZmdXNlcnMtaW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZrYW5kaW5za3l2MjIlMkZjYXQucG5nJTIyJTBBKS5yZXNpemUoKDc2OCUyQyUyMDc2OCkpJTBBJTBBZGVmJTIwbWFrZV9oaW50KGltYWdlJTJDJTIwZGVwdGhfZXN0aW1hdG9yKSUzQSUwQSUyMCUyMCUyMCUyMGltYWdlJTIwJTNEJTIwZGVwdGhfZXN0aW1hdG9yKGltYWdlKSU1QiUyMmRlcHRoJTIyJTVEJTBBJTIwJTIwJTIwJTIwaW1hZ2UlMjAlM0QlMjBucC5hcnJheShpbWFnZSklMEElMjAlMjAlMjAlMjBpbWFnZSUyMCUzRCUyMGltYWdlJTVCJTNBJTJDJTIwJTNBJTJDJTIwTm9uZSU1RCUwQSUyMCUyMCUyMCUyMGltYWdlJTIwJTNEJTIwbnAuY29uY2F0ZW5hdGUoJTVCaW1hZ2UlMkMlMjBpbWFnZSUyQyUyMGltYWdlJTVEJTJDJTIwYXhpcyUzRDIpJTBBJTIwJTIwJTIwJTIwZGV0ZWN0ZWRfbWFwJTIwJTNEJTIwdG9yY2guZnJvbV9udW1weShpbWFnZSkuZmxvYXQoKSUyMCUyRiUyMDI1NS4wJTBBJTIwJTIwJTIwJTIwaGludCUyMCUzRCUyMGRldGVjdGVkX21hcC5wZXJtdXRlKDIlMkMlMjAwJTJDJTIwMSklMEElMjAlMjAlMjAlMjByZXR1cm4lMjBoaW50JTBBJTBBZGVwdGhfZXN0aW1hdG9yJTIwJTNEJTIwcGlwZWxpbmUoJTIyZGVwdGgtZXN0aW1hdGlvbiUyMiklMEFoaW50JTIwJTNEJTIwbWFrZV9oaW50KGltZyUyQyUyMGRlcHRoX2VzdGltYXRvcikudW5zcXVlZXplKDApLmhhbGYoKS50byglMjJjdWRhJTIyKQ==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

img = load_image(
    <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png&quot;</span>
).resize((<span class="hljs-number">768</span>, <span class="hljs-number">768</span>))

<span class="hljs-keyword">def</span> <span class="hljs-title function_">make_hint</span>(<span class="hljs-params">image, depth_estimator</span>):
    image = depth_estimator(image)[<span class="hljs-string">&quot;depth&quot;</span>]
    image = np.array(image)
    image = image[:, :, <span class="hljs-literal">None</span>]
    image = np.concatenate([image, image, image], axis=<span class="hljs-number">2</span>)
    detected_map = torch.from_numpy(image).<span class="hljs-built_in">float</span>() / <span class="hljs-number">255.0</span>
    hint = detected_map.permute(<span class="hljs-number">2</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>)
    <span class="hljs-keyword">return</span> hint

depth_estimator = pipeline(<span class="hljs-string">&quot;depth-estimation&quot;</span>)
hint = make_hint(img, depth_estimator).unsqueeze(<span class="hljs-number">0</span>).half().to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),Hn=new j({props:{code:"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",highlighted:`prior_pipeline = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-prior&quot;</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
).to(<span class="hljs-string">&quot;cuda&quot;</span>)

pipeline = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
    <span class="hljs-string">&quot;kandinsky-community/kandinsky-2-2-controlnet-depth&quot;</span>, torch_dtype=torch.float16
).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),Sn=new j({props:{code:"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",highlighted:`prompt = <span class="hljs-string">&quot;A robot, 4k photo&quot;</span>
negative_prior_prompt = <span class="hljs-string">&quot;lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature&quot;</span>

generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">43</span>)

img_emb = prior_pipeline(prompt=prompt, image=img, strength=<span class="hljs-number">0.85</span>, generator=generator)
negative_emb = prior_pipeline(prompt=negative_prior_prompt, image=img, strength=<span class="hljs-number">1</span>, generator=generator)`,lang:"py",wrap:!1}}),qn=new j({props:{code:"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",highlighted:`image = pipeline(image=img, strength=<span class="hljs-number">0.5</span>, image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, hint=hint, num_inference_steps=<span class="hljs-number">50</span>, generator=generator, height=<span class="hljs-number">768</span>, width=<span class="hljs-number">768</span>).images[<span class="hljs-number">0</span>]
make_image_grid([img.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>)), image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))], rows=<span class="hljs-number">1</span>, cols=<span class="hljs-number">2</span>)`,lang:"py",wrap:!1}}),Ln=new V({props:{title:"Optimizations",local:"optimizations",headingTag:"h2"}}),On=new j({props:{code:"JTIwJTIwZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTIwJTIwaW1wb3J0JTIwdG9yY2glMEElMEElMjAlMjBwaXBlJTIwJTNEJTIwRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMmthbmRpbnNreS1jb21tdW5pdHklMkZrYW5kaW5za3ktMi0xJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KSUwQSUyQiUyMHBpcGUuZW5hYmxlX3hmb3JtZXJzX21lbW9yeV9lZmZpY2llbnRfYXR0ZW50aW9uKCk=",highlighted:`  from diffusers import DiffusionPipeline
  import torch

  pipe = DiffusionPipeline.from_pretrained(&quot;kandinsky-community/kandinsky-2-1&quot;, torch_dtype=torch.float16)
<span class="hljs-addition">+ pipe.enable_xformers_memory_efficient_attention()</span>`,lang:"diff",wrap:!1}}),et=new j({props:{code:"JTIwJTIwcGlwZS51bmV0LnRvKG1lbW9yeV9mb3JtYXQlM0R0b3JjaC5jaGFubmVsc19sYXN0KSUwQSUyQiUyMHBpcGUudW5ldCUyMCUzRCUyMHRvcmNoLmNvbXBpbGUocGlwZS51bmV0JTJDJTIwbW9kZSUzRCUyMnJlZHVjZS1vdmVyaGVhZCUyMiUyQyUyMGZ1bGxncmFwaCUzRFRydWUp",highlighted:`  pipe.unet.to(memory_format=torch.channels_last)
<span class="hljs-addition">+ pipe.unet = torch.compile(pipe.unet, mode=&quot;reduce-overhead&quot;, fullgraph=True)</span>`,lang:"diff",wrap:!1}}),tt=new j({props:{code:"ZnJvbSUyMGRpZmZ1c2Vycy5tb2RlbHMuYXR0ZW50aW9uX3Byb2Nlc3NvciUyMGltcG9ydCUyMEF0dG5BZGRlZEtWUHJvY2Vzc29yMl8wJTBBJTBBcGlwZS51bmV0LnNldF9hdHRuX3Byb2Nlc3NvcihBdHRuQWRkZWRLVlByb2Nlc3NvcjJfMCgpKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers.models.attention_processor <span class="hljs-keyword">import</span> AttnAddedKVProcessor2_0

pipe.unet.set_attn_processor(AttnAddedKVProcessor2_0())`,lang:"py",wrap:!1}}),st=new j({props:{code:"JTIwJTIwZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTIwJTIwaW1wb3J0JTIwdG9yY2glMEElMEElMjAlMjBwaXBlJTIwJTNEJTIwRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMmthbmRpbnNreS1jb21tdW5pdHklMkZrYW5kaW5za3ktMi0xJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5mbG9hdDE2KSUwQSUyQiUyMHBpcGUuZW5hYmxlX21vZGVsX2NwdV9vZmZsb2FkKCk=",highlighted:`  from diffusers import DiffusionPipeline
  import torch

  pipe = DiffusionPipeline.from_pretrained(&quot;kandinsky-community/kandinsky-2-1&quot;, torch_dtype=torch.float16)
<span class="hljs-addition">+ pipe.enable_model_cpu_offload()</span>`,lang:"diff",wrap:!1}}),at=new j({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMScheduler
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

scheduler = DDPMScheduler.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, subfolder=<span class="hljs-string">&quot;ddpm_scheduler&quot;</span>)
pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;kandinsky-community/kandinsky-2-1&quot;</span>, scheduler=scheduler, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"py",wrap:!1}}),lt=new V({props:{title:"KandinskyPriorPipeline",local:"diffusers.KandinskyPriorPipeline",headingTag:"h2"}}),it=new B({props:{name:"class diffusers.KandinskyPriorPipeline",anchor:"diffusers.KandinskyPriorPipeline",parameters:[{name:"prior",val:": PriorTransformer"},{name:"image_encoder",val:": CLIPVisionModelWithProjection"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"scheduler",val:": UnCLIPScheduler"},{name:"image_processor",val:": CLIPImageProcessor"}],parametersDescription:[{anchor:"diffusers.KandinskyPriorPipeline.prior",description:`<strong>prior</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/prior_transformer#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonical unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior"},{anchor:"diffusers.KandinskyPriorPipeline.image_encoder",description:`<strong>image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen image-encoder.`,name:"image_encoder"},{anchor:"diffusers.KandinskyPriorPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyPriorPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.KandinskyPriorPipeline.scheduler",description:`<strong>scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>prior</code> to generate image embedding.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py#L136"}}),ot=new B({props:{name:"__call__",anchor:"diffusers.KandinskyPriorPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"num_images_per_prompt",val:": int = 1"},{name:"num_inference_steps",val:": int = 25"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"guidance_scale",val:": float = 4.0"},{name:"output_type",val:": str | None = 'pt'"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyPriorPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.KandinskyPriorPipeline.__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. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.KandinskyPriorPipeline.__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.KandinskyPriorPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 25) &#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.KandinskyPriorPipeline.__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.KandinskyPriorPipeline.__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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyPriorPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyPriorPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pt&quot;</code>) &#x2014;
The output format of the generate image. Choose between: <code>&quot;np&quot;</code> (<code>np.array</code>) or <code>&quot;pt&quot;</code>
(<code>torch.Tensor</code>).`,name:"output_type"},{anchor:"diffusers.KandinskyPriorPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py#L405",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>KandinskyPriorPipelineOutput</code> or <code>tuple</code></p>
`}}),xe=new Se({props:{anchor:"diffusers.KandinskyPriorPipeline.__call__.example",$$slots:{default:[nr]},$$scope:{ctx:k}}}),rt=new B({props:{name:"interpolate",anchor:"diffusers.KandinskyPriorPipeline.interpolate",parameters:[{name:"images_and_prompts",val:": list"},{name:"weights",val:": list"},{name:"num_images_per_prompt",val:": int = 1"},{name:"num_inference_steps",val:": int = 25"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"negative_prior_prompt",val:": str | None = None"},{name:"negative_prompt",val:": str = ''"},{name:"guidance_scale",val:": float = 4.0"},{name:"device",val:" = None"}],parametersDescription:[{anchor:"diffusers.KandinskyPriorPipeline.interpolate.images_and_prompts",description:`<strong>images_and_prompts</strong> (<code>list[str | PIL.Image.Image | torch.Tensor]</code>) &#x2014;
list of prompts and images to guide the image generation.`,name:"images_and_prompts"},{anchor:"diffusers.KandinskyPriorPipeline.interpolate.weights",description:`<strong>weights</strong> &#x2014; (<code>list[float]</code>):
list of weights for each condition in <code>images_and_prompts</code>`,name:"weights"},{anchor:"diffusers.KandinskyPriorPipeline.interpolate.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.KandinskyPriorPipeline.interpolate.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 25) &#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.KandinskyPriorPipeline.interpolate.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.KandinskyPriorPipeline.interpolate.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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyPriorPipeline.interpolate.negative_prior_prompt",description:`<strong>negative_prior_prompt</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
<code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prior_prompt"},{anchor:"diffusers.KandinskyPriorPipeline.interpolate.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>list[str]</code>, <em>optional</em>) &#x2014;
The prompt 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:"negative_prompt"},{anchor:"diffusers.KandinskyPriorPipeline.interpolate.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py#L180",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>KandinskyPriorPipelineOutput</code> or <code>tuple</code></p>
`}}),Re=new Se({props:{anchor:"diffusers.KandinskyPriorPipeline.interpolate.example",$$slots:{default:[tr]},$$scope:{ctx:k}}}),pt=new V({props:{title:"KandinskyPipeline",local:"diffusers.KandinskyPipeline",headingTag:"h2"}}),dt=new B({props:{name:"class diffusers.KandinskyPipeline",anchor:"diffusers.KandinskyPipeline",parameters:[{name:"text_encoder",val:": MultilingualCLIP"},{name:"tokenizer",val:": XLMRobertaTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": diffusers.schedulers.scheduling_ddim.DDIMScheduler | diffusers.schedulers.scheduling_ddpm.DDPMScheduler"},{name:"movq",val:": VQModel"}],parametersDescription:[{anchor:"diffusers.KandinskyPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>MultilingualCLIP</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizer</code>) &#x2014;
Tokenizer of class`,name:"tokenizer"},{anchor:"diffusers.KandinskyPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDIMScheduler</code> | <code>DDPMScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.KandinskyPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.KandinskyPipeline.movq",description:`<strong>movq</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
MoVQ Decoder to generate the image from the latents.`,name:"movq"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py#L81"}}),ct=new B({props:{name:"__call__",anchor:"diffusers.KandinskyPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"image_embeds",val:": torch.Tensor | list[torch.Tensor]"},{name:"negative_image_embeds",val:": torch.Tensor | list[torch.Tensor]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"num_inference_steps",val:": int = 100"},{name:"guidance_scale",val:": float = 4.0"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.KandinskyPipeline.__call__.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.Tensor</code> or <code>list[torch.Tensor]</code>) &#x2014;
The clip image embeddings for text prompt, that will be used to condition the image generation.`,name:"image_embeds"},{anchor:"diffusers.KandinskyPipeline.__call__.negative_image_embeds",description:`<strong>negative_image_embeds</strong> (<code>torch.Tensor</code> or <code>list[torch.Tensor]</code>) &#x2014;
The clip image embeddings for negative text prompt, will be used to condition the image generation.`,name:"negative_image_embeds"},{anchor:"diffusers.KandinskyPipeline.__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. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.KandinskyPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.KandinskyPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.KandinskyPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.KandinskyPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyPipeline.__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.KandinskyPipeline.__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.KandinskyPipeline.__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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyPipeline.__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.KandinskyPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.Tensor)</code>.`,name:"callback"},{anchor:"diffusers.KandinskyPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.KandinskyPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py#L236",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),Xe=new Se({props:{anchor:"diffusers.KandinskyPipeline.__call__.example",$$slots:{default:[sr]},$$scope:{ctx:k}}}),mt=new V({props:{title:"KandinskyCombinedPipeline",local:"diffusers.KandinskyCombinedPipeline",headingTag:"h2"}}),ut=new B({props:{name:"class diffusers.KandinskyCombinedPipeline",anchor:"diffusers.KandinskyCombinedPipeline",parameters:[{name:"text_encoder",val:": MultilingualCLIP"},{name:"tokenizer",val:": XLMRobertaTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": diffusers.schedulers.scheduling_ddim.DDIMScheduler | diffusers.schedulers.scheduling_ddpm.DDPMScheduler"},{name:"movq",val:": VQModel"},{name:"prior_prior",val:": PriorTransformer"},{name:"prior_image_encoder",val:": CLIPVisionModelWithProjection"},{name:"prior_text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_scheduler",val:": UnCLIPScheduler"},{name:"prior_image_processor",val:": CLIPImageProcessor"}],parametersDescription:[{anchor:"diffusers.KandinskyCombinedPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>MultilingualCLIP</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyCombinedPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizer</code>) &#x2014;
Tokenizer of class`,name:"tokenizer"},{anchor:"diffusers.KandinskyCombinedPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDIMScheduler</code> | <code>DDPMScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.KandinskyCombinedPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.KandinskyCombinedPipeline.movq",description:`<strong>movq</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
MoVQ Decoder to generate the image from the latents.`,name:"movq"},{anchor:"diffusers.KandinskyCombinedPipeline.prior_prior",description:`<strong>prior_prior</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/prior_transformer#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonical unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior_prior"},{anchor:"diffusers.KandinskyCombinedPipeline.prior_image_encoder",description:`<strong>prior_image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen image-encoder.`,name:"prior_image_encoder"},{anchor:"diffusers.KandinskyCombinedPipeline.prior_text_encoder",description:`<strong>prior_text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"prior_text_encoder"},{anchor:"diffusers.KandinskyCombinedPipeline.prior_tokenizer",description:`<strong>prior_tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"prior_tokenizer"},{anchor:"diffusers.KandinskyCombinedPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>prior</code> to generate image embedding.`,name:"prior_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L113"}}),gt=new B({props:{name:"__call__",anchor:"diffusers.KandinskyCombinedPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"num_inference_steps",val:": int = 100"},{name:"guidance_scale",val:": float = 4.0"},{name:"num_images_per_prompt",val:": int = 1"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"prior_num_inference_steps",val:": int = 25"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyCombinedPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.KandinskyCombinedPipeline.__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. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.KandinskyCombinedPipeline.__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.KandinskyCombinedPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.KandinskyCombinedPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.KandinskyCombinedPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.KandinskyCombinedPipeline.__call__.prior_guidance_scale",description:`<strong>prior_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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:"prior_guidance_scale"},{anchor:"diffusers.KandinskyCombinedPipeline.__call__.prior_num_inference_steps",description:`<strong>prior_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"prior_num_inference_steps"},{anchor:"diffusers.KandinskyCombinedPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyCombinedPipeline.__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.KandinskyCombinedPipeline.__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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyCombinedPipeline.__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.KandinskyCombinedPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.Tensor)</code>.`,name:"callback"},{anchor:"diffusers.KandinskyCombinedPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.KandinskyCombinedPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L215",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),Pe=new Se({props:{anchor:"diffusers.KandinskyCombinedPipeline.__call__.example",$$slots:{default:[ar]},$$scope:{ctx:k}}}),ft=new B({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.KandinskyCombinedPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:": int | None = None"},{name:"device",val:": torch.device | str = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L196"}}),ht=new V({props:{title:"KandinskyImg2ImgPipeline",local:"diffusers.KandinskyImg2ImgPipeline",headingTag:"h2"}}),yt=new B({props:{name:"class diffusers.KandinskyImg2ImgPipeline",anchor:"diffusers.KandinskyImg2ImgPipeline",parameters:[{name:"text_encoder",val:": MultilingualCLIP"},{name:"movq",val:": VQModel"},{name:"tokenizer",val:": XLMRobertaTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": DDIMScheduler"}],parametersDescription:[{anchor:"diffusers.KandinskyImg2ImgPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>MultilingualCLIP</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyImg2ImgPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizer</code>) &#x2014;
Tokenizer of class`,name:"tokenizer"},{anchor:"diffusers.KandinskyImg2ImgPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_14047/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.KandinskyImg2ImgPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.KandinskyImg2ImgPipeline.movq",description:`<strong>movq</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
MoVQ image encoder and decoder`,name:"movq"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py#L93"}}),bt=new B({props:{name:"__call__",anchor:"diffusers.KandinskyImg2ImgPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"image",val:": torch.Tensor | PIL.Image.Image | list[torch.Tensor] | list[PIL.Image.Image]"},{name:"image_embeds",val:": Tensor"},{name:"negative_image_embeds",val:": Tensor"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"num_inference_steps",val:": int = 100"},{name:"strength",val:": float = 0.3"},{name:"guidance_scale",val:": float = 7.0"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
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<code>Image</code>, or tensor representing an image batch, that will be used as the starting point for the
process.`,name:"image"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.Tensor</code> or <code>list[torch.Tensor]</code>) &#x2014;
The clip image embeddings for text prompt, that will be used to condition the image generation.`,name:"image_embeds"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.negative_image_embeds",description:`<strong>negative_image_embeds</strong> (<code>torch.Tensor</code> or <code>list[torch.Tensor]</code>) &#x2014;
The clip image embeddings for negative text prompt, will be used to condition the image generation.`,name:"negative_image_embeds"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__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. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.KandinskyImg2ImgPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.3) &#x2014;
Conceptually, indicates how much to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code>
will be used as a starting point, adding more noise to it the larger 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 will
be maximum and the denoising process will run for the full number of iterations specified in
<code>num_inference_steps</code>. A value of 1, therefore, essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyImg2ImgPipeline.__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.KandinskyImg2ImgPipeline.__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.KandinskyImg2ImgPipeline.__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.KandinskyImg2ImgPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.Tensor)</code>.`,name:"callback"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.KandinskyImg2ImgPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py#L297",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),Qe=new Se({props:{anchor:"diffusers.KandinskyImg2ImgPipeline.__call__.example",$$slots:{default:[lr]},$$scope:{ctx:k}}}),Mt=new V({props:{title:"KandinskyImg2ImgCombinedPipeline",local:"diffusers.KandinskyImg2ImgCombinedPipeline",headingTag:"h2"}}),_t=new B({props:{name:"class diffusers.KandinskyImg2ImgCombinedPipeline",anchor:"diffusers.KandinskyImg2ImgCombinedPipeline",parameters:[{name:"text_encoder",val:": MultilingualCLIP"},{name:"tokenizer",val:": XLMRobertaTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": diffusers.schedulers.scheduling_ddim.DDIMScheduler | diffusers.schedulers.scheduling_ddpm.DDPMScheduler"},{name:"movq",val:": VQModel"},{name:"prior_prior",val:": PriorTransformer"},{name:"prior_image_encoder",val:": CLIPVisionModelWithProjection"},{name:"prior_text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_scheduler",val:": UnCLIPScheduler"},{name:"prior_image_processor",val:": CLIPImageProcessor"}],parametersDescription:[{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>MultilingualCLIP</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizer</code>) &#x2014;
Tokenizer of class`,name:"tokenizer"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDIMScheduler</code> | <code>DDPMScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.movq",description:`<strong>movq</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
MoVQ Decoder to generate the image from the latents.`,name:"movq"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.prior_prior",description:`<strong>prior_prior</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/prior_transformer#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonical unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior_prior"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.prior_image_encoder",description:`<strong>prior_image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen image-encoder.`,name:"prior_image_encoder"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.prior_text_encoder",description:`<strong>prior_text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"prior_text_encoder"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.prior_tokenizer",description:`<strong>prior_tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"prior_tokenizer"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>prior</code> to generate image embedding.`,name:"prior_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L331"}}),Jt=new B({props:{name:"__call__",anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"image",val:": torch.Tensor | PIL.Image.Image | list[torch.Tensor] | list[PIL.Image.Image]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"num_inference_steps",val:": int = 100"},{name:"guidance_scale",val:": float = 4.0"},{name:"num_images_per_prompt",val:": int = 1"},{name:"strength",val:": float = 0.3"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"prior_num_inference_steps",val:": int = 25"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__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>, or tensor representing an image batch, that will be used as the starting point for the
process. Can also accept image latents as <code>image</code>, if passing latents directly, it will not be encoded
again.`,name:"image"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__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. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__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.KandinskyImg2ImgCombinedPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.KandinskyImg2ImgCombinedPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.3) &#x2014;
Conceptually, indicates how much to transform the reference <code>image</code>. Must be between 0 and 1. <code>image</code>
will be used as a starting point, adding more noise to it the larger 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 will
be maximum and the denoising process will run for the full number of iterations specified in
<code>num_inference_steps</code>. A value of 1, therefore, essentially ignores <code>image</code>.`,name:"strength"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.prior_guidance_scale",description:`<strong>prior_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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:"prior_guidance_scale"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.prior_num_inference_steps",description:`<strong>prior_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"prior_num_inference_steps"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyImg2ImgCombinedPipeline.__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.KandinskyImg2ImgCombinedPipeline.__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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__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.KandinskyImg2ImgCombinedPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.Tensor)</code>.`,name:"callback"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L434",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),Ye=new Se({props:{anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.__call__.example",$$slots:{default:[ir]},$$scope:{ctx:k}}}),kt=new B({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.KandinskyImg2ImgCombinedPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:": int | None = None"},{name:"device",val:": torch.device | str = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L414"}}),wt=new V({props:{title:"KandinskyInpaintPipeline",local:"diffusers.KandinskyInpaintPipeline",headingTag:"h2"}}),Tt=new B({props:{name:"class diffusers.KandinskyInpaintPipeline",anchor:"diffusers.KandinskyInpaintPipeline",parameters:[{name:"text_encoder",val:": MultilingualCLIP"},{name:"movq",val:": VQModel"},{name:"tokenizer",val:": XLMRobertaTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": DDIMScheduler"}],parametersDescription:[{anchor:"diffusers.KandinskyInpaintPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>MultilingualCLIP</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyInpaintPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizer</code>) &#x2014;
Tokenizer of class`,name:"tokenizer"},{anchor:"diffusers.KandinskyInpaintPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_14047/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.KandinskyInpaintPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.KandinskyInpaintPipeline.movq",description:`<strong>movq</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
MoVQ image encoder and decoder`,name:"movq"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py#L245"}}),Ut=new B({props:{name:"__call__",anchor:"diffusers.KandinskyInpaintPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"image",val:": torch.Tensor | PIL.Image.Image"},{name:"mask_image",val:": torch.Tensor | PIL.Image.Image | numpy.ndarray"},{name:"image_embeds",val:": Tensor"},{name:"negative_image_embeds",val:": Tensor"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"num_inference_steps",val:": int = 100"},{name:"guidance_scale",val:": float = 4.0"},{name:"num_images_per_prompt",val:": int = 1"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyInpaintPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.image",description:`<strong>image</strong> (<code>torch.Tensor</code>, <code>PIL.Image.Image</code> or <code>np.ndarray</code>) &#x2014;
<code>Image</code>, or tensor representing an image batch, that will be used as the starting point for the
process.`,name:"image"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.mask_image",description:`<strong>mask_image</strong> (<code>PIL.Image.Image</code>,<code>torch.Tensor</code> or <code>np.ndarray</code>) &#x2014;
<code>Image</code>, or a tensor representing an image batch, to mask <code>image</code>. White pixels in the mask will be
repainted, while black pixels will be preserved. You can pass a pytorch tensor as mask only if the
image you passed is a pytorch tensor, and it should contain one color channel (L) instead of 3, so the
expected shape would be either <code>(B, 1, H, W,)</code>, <code>(B, H, W)</code>, <code>(1, H, W)</code> or <code>(H, W)</code> If image is an PIL
image or numpy array, mask should also be a either PIL image or numpy array. If it is a PIL image, it
will be converted to a single channel (luminance) before use. If it is a nummpy array, the expected
shape is <code>(H, W)</code>.`,name:"mask_image"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.Tensor</code> or <code>list[torch.Tensor]</code>) &#x2014;
The clip image embeddings for text prompt, that will be used to condition the image generation.`,name:"image_embeds"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.negative_image_embeds",description:`<strong>negative_image_embeds</strong> (<code>torch.Tensor</code> or <code>list[torch.Tensor]</code>) &#x2014;
The clip image embeddings for negative text prompt, will be used to condition the image generation.`,name:"negative_image_embeds"},{anchor:"diffusers.KandinskyInpaintPipeline.__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. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.KandinskyInpaintPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyInpaintPipeline.__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.KandinskyInpaintPipeline.__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.KandinskyInpaintPipeline.__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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyInpaintPipeline.__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.KandinskyInpaintPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.Tensor)</code>.`,name:"callback"},{anchor:"diffusers.KandinskyInpaintPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.KandinskyInpaintPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py#L401",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),ze=new Se({props:{anchor:"diffusers.KandinskyInpaintPipeline.__call__.example",$$slots:{default:[or]},$$scope:{ctx:k}}}),jt=new V({props:{title:"KandinskyInpaintCombinedPipeline",local:"diffusers.KandinskyInpaintCombinedPipeline",headingTag:"h2"}}),Zt=new B({props:{name:"class diffusers.KandinskyInpaintCombinedPipeline",anchor:"diffusers.KandinskyInpaintCombinedPipeline",parameters:[{name:"text_encoder",val:": MultilingualCLIP"},{name:"tokenizer",val:": XLMRobertaTokenizer"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": diffusers.schedulers.scheduling_ddim.DDIMScheduler | diffusers.schedulers.scheduling_ddpm.DDPMScheduler"},{name:"movq",val:": VQModel"},{name:"prior_prior",val:": PriorTransformer"},{name:"prior_image_encoder",val:": CLIPVisionModelWithProjection"},{name:"prior_text_encoder",val:": CLIPTextModelWithProjection"},{name:"prior_tokenizer",val:": CLIPTokenizer"},{name:"prior_scheduler",val:": UnCLIPScheduler"},{name:"prior_image_processor",val:": CLIPImageProcessor"}],parametersDescription:[{anchor:"diffusers.KandinskyInpaintCombinedPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>MultilingualCLIP</code>) &#x2014;
Frozen text-encoder.`,name:"text_encoder"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizer</code>) &#x2014;
Tokenizer of class`,name:"tokenizer"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.scheduler",description:`<strong>scheduler</strong> (<code>DDIMScheduler</code> | <code>DDPMScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to generate image latents.`,name:"scheduler"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014;
Conditional U-Net architecture to denoise the image embedding.`,name:"unet"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.movq",description:`<strong>movq</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
MoVQ Decoder to generate the image from the latents.`,name:"movq"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.prior_prior",description:`<strong>prior_prior</strong> (<a href="/docs/diffusers/pr_14047/en/api/models/prior_transformer#diffusers.PriorTransformer">PriorTransformer</a>) &#x2014;
The canonical unCLIP prior to approximate the image embedding from the text embedding.`,name:"prior_prior"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.prior_image_encoder",description:`<strong>prior_image_encoder</strong> (<code>CLIPVisionModelWithProjection</code>) &#x2014;
Frozen image-encoder.`,name:"prior_image_encoder"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.prior_text_encoder",description:`<strong>prior_text_encoder</strong> (<code>CLIPTextModelWithProjection</code>) &#x2014;
Frozen text-encoder.`,name:"prior_text_encoder"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.prior_tokenizer",description:`<strong>prior_tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"prior_tokenizer"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.prior_scheduler",description:`<strong>prior_scheduler</strong> (<code>UnCLIPScheduler</code>) &#x2014;
A scheduler to be used in combination with <code>prior</code> to generate image embedding.`,name:"prior_scheduler"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L572"}}),It=new B({props:{name:"__call__",anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__",parameters:[{name:"prompt",val:": str | list[str]"},{name:"image",val:": torch.Tensor | PIL.Image.Image | list[torch.Tensor] | list[PIL.Image.Image]"},{name:"mask_image",val:": torch.Tensor | PIL.Image.Image | list[torch.Tensor] | list[PIL.Image.Image]"},{name:"negative_prompt",val:": str | list[str] | None = None"},{name:"num_inference_steps",val:": int = 100"},{name:"guidance_scale",val:": float = 4.0"},{name:"num_images_per_prompt",val:": int = 1"},{name:"height",val:": int = 512"},{name:"width",val:": int = 512"},{name:"prior_guidance_scale",val:": float = 4.0"},{name:"prior_num_inference_steps",val:": int = 25"},{name:"generator",val:": torch._C.Generator | list[torch._C.Generator] | None = None"},{name:"latents",val:": torch.Tensor | None = None"},{name:"output_type",val:": str | None = 'pil'"},{name:"callback",val:": typing.Optional[typing.Callable[[int, int, torch.Tensor], NoneType]] = None"},{name:"callback_steps",val:": int = 1"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>list[str]</code>) &#x2014;
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<code>Image</code>, or tensor representing an image batch, that will be used as the starting point for the
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again.`,name:"image"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.mask_image",description:`<strong>mask_image</strong> (<code>np.array</code>) &#x2014;
Tensor representing an image batch, to mask <code>image</code>. White pixels in the mask will be repainted, while
black pixels will be preserved. If <code>mask_image</code> is a PIL image, it will be converted to a single
channel (luminance) before use. If it&#x2019;s a tensor, it should contain one color channel (L) instead of 3,
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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:"negative_prompt"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__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.KandinskyInpaintCombinedPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#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.KandinskyInpaintCombinedPipeline.__call__.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.prior_guidance_scale",description:`<strong>prior_guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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:"prior_guidance_scale"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.prior_num_inference_steps",description:`<strong>prior_num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 100) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"prior_num_inference_steps"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#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.KandinskyInpaintCombinedPipeline.__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.KandinskyInpaintCombinedPipeline.__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 sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__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.KandinskyInpaintCombinedPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that calls every <code>callback_steps</code> steps during inference. The function is called with the
following arguments: <code>callback(step: int, timestep: int, latents: torch.Tensor)</code>.`,name:"callback"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function is called. If not specified, the callback is called at
every step.`,name:"callback_steps"},{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__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/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput">ImagePipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L675",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/pr_14047/en/api/pipelines/ddim#diffusers.ImagePipelineOutput"
>ImagePipelineOutput</a> or <code>tuple</code></p>
`}}),He=new Se({props:{anchor:"diffusers.KandinskyInpaintCombinedPipeline.__call__.example",$$slots:{default:[rr]},$$scope:{ctx:k}}}),Wt=new B({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.KandinskyInpaintCombinedPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:": int | None = None"},{name:"device",val:": torch.device | str = None"}],source:"https://github.com/huggingface/diffusers/blob/vr_14047/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py#L655"}}),$t=new 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