import{S as ys,i as Rs,s as Ms,e as n,k as p,w as v,t as i,M as Ts,c as s,d as t,m as h,a as r,x as b,h as l,b as c,N as $s,G as o,g as u,y,q as R,o as M,B as T,v as js,L as En}from"../../chunks/vendor-hf-doc-builder.js";import{T as bs}from"../../chunks/Tip-hf-doc-builder.js";import{D as ze}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Un}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Be}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Cn}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Is(I){let m,S,w,f,_;return f=new Un({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Swin2SRConfig, Swin2SRModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = Swin2SRConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swin2SRModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){m=n("p"),S=i("Example:"),w=p(),v(f.$$.fragment)},l(a){m=s(a,"P",{});var g=r(m);S=l(g,"Example:"),g.forEach(t),w=h(a),b(f.$$.fragment,a)},m(a,g){u(a,m,g),o(m,S),u(a,w,g),y(f,a,g),_=!0},p:En,i(a){_||(R(f.$$.fragment,a),_=!0)},o(a){M(f.$$.fragment,a),_=!1},d(a){a&&t(m),a&&t(w),T(f,a)}}}function Cs(I){let m,S,w,f,_;return{c(){m=n("p"),S=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),w=n("code"),f=i("Module"),_=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(a){m=s(a,"P",{});var g=r(m);S=l(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),w=s(g,"CODE",{});var x=r(w);f=l(x,"Module"),x.forEach(t),_=l(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(a,g){u(a,m,g),o(m,S),o(m,w),o(w,f),o(m,_)},d(a){a&&t(m)}}}function Es(I){let m,S,w,f,_;return f=new Un({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, Swin2SRModel
<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> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;caidas/swin2SR-classical-sr-x2-64&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swin2SRModel.from_pretrained(<span class="hljs-string">&quot;caidas/swin2SR-classical-sr-x2-64&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">180</span>, <span class="hljs-number">488</span>, <span class="hljs-number">648</span>]`}}),{c(){m=n("p"),S=i("Example:"),w=p(),v(f.$$.fragment)},l(a){m=s(a,"P",{});var g=r(m);S=l(g,"Example:"),g.forEach(t),w=h(a),b(f.$$.fragment,a)},m(a,g){u(a,m,g),o(m,S),u(a,w,g),y(f,a,g),_=!0},p:En,i(a){_||(R(f.$$.fragment,a),_=!0)},o(a){M(f.$$.fragment,a),_=!1},d(a){a&&t(m),a&&t(w),T(f,a)}}}function Us(I){let m,S,w,f,_;return{c(){m=n("p"),S=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),w=n("code"),f=i("Module"),_=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(a){m=s(a,"P",{});var g=r(m);S=l(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),w=s(g,"CODE",{});var x=r(w);f=l(x,"Module"),x.forEach(t),_=l(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(a,g){u(a,m,g),o(m,S),o(m,w),o(w,f),o(m,_)},d(a){a&&t(m)}}}function xs(I){let m,S,w,f,_;return f=new Un({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, Swin2SRForImageSuperResolution

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;caidas/swin2SR-classical-sr-x2-64&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swin2SRForImageSuperResolution.from_pretrained(<span class="hljs-string">&quot;caidas/swin2SR-classical-sr-x2-64&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare image for the model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>output = outputs.reconstruction.data.squeeze().<span class="hljs-built_in">float</span>().cpu().clamp_(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>).numpy()
<span class="hljs-meta">&gt;&gt;&gt; </span>output = np.moveaxis(output, source=<span class="hljs-number">0</span>, destination=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>output = (output * <span class="hljs-number">255.0</span>).<span class="hljs-built_in">round</span>().astype(np.uint8)  <span class="hljs-comment"># float32 to uint8</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># you can visualize \`output\` with \`Image.fromarray\`</span>`}}),{c(){m=n("p"),S=i("Example:"),w=p(),v(f.$$.fragment)},l(a){m=s(a,"P",{});var g=r(m);S=l(g,"Example:"),g.forEach(t),w=h(a),b(f.$$.fragment,a)},m(a,g){u(a,m,g),o(m,S),u(a,w,g),y(f,a,g),_=!0},p:En,i(a){_||(R(f.$$.fragment,a),_=!0)},o(a){M(f.$$.fragment,a),_=!1},d(a){a&&t(m),a&&t(w),T(f,a)}}}function ks(I){let m,S,w,f,_,a,g,x,et,To,N,V,no,pe,ot,so,tt,$o,C,nt,he,st,rt,ue,at,it,Ze,lt,ct,jo,Ae,dt,Io,Ge,ro,mt,Co,Y,xn,Eo,fe,pt,Xe,ht,Uo,F,ut,ge,ft,gt,we,wt,_t,xo,W,H,ao,_e,St,io,vt,ko,Q,bt,Se,yt,Rt,Jo,L,Mt,ve,Tt,$t,Fo,P,D,lo,be,jt,co,It,No,k,ye,Ct,mo,Et,Ut,O,Re,xt,po,kt,Wo,z,K,ho,Me,Jt,uo,Ft,Po,$,Te,Nt,B,Wt,qe,Pt,zt,$e,Bt,Zt,At,Z,Gt,Ve,Xt,qt,Ye,Vt,Yt,Ht,ee,zo,A,oe,fo,je,Qt,go,Lt,Bo,J,Ie,Dt,Ce,Ot,Ee,Kt,en,on,E,Ue,tn,G,nn,He,sn,rn,wo,an,ln,cn,te,dn,ne,Zo,X,se,_o,xe,mn,So,pn,Ao,j,ke,hn,vo,un,fn,Je,gn,Fe,wn,_n,Sn,U,Ne,vn,q,bn,Qe,yn,Rn,bo,Mn,Tn,$n,re,jn,ae,Go;return a=new Be({}),pe=new Be({}),_e=new Be({}),be=new Be({}),ye=new ze({props:{name:"class transformers.Swin2SRImageProcessor",anchor:"transformers.Swin2SRImageProcessor",parameters:[{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_pad",val:": bool = True"},{name:"pad_size",val:": int = 8"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.Swin2SRImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the <code>do_rescale</code>
parameter in the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.Swin2SRImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
<code>preprocess</code> method.`,name:"rescale_factor"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swin2sr/image_processing_swin2sr.py#L30"}}),Re=new ze({props:{name:"preprocess",anchor:"transformers.Swin2SRImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"rescale_factor",val:": typing.Optional[float] = None"},{name:"do_pad",val:": typing.Optional[bool] = None"},{name:"pad_size",val:": typing.Optional[int] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.Swin2SRImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,name:"images"},{anchor:"transformers.Swin2SRImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image values between [0 - 1].`,name:"do_rescale"},{anchor:"transformers.Swin2SRImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.Swin2SRImageProcessor.preprocess.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to pad the image to make the height and width divisible by <code>window_size</code>.`,name:"do_pad"},{anchor:"transformers.Swin2SRImageProcessor.preprocess.pad_size",description:`<strong>pad_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>32</code>) &#x2014;
The size of the sliding window for the local attention.`,name:"pad_size"},{anchor:"transformers.Swin2SRImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.Swin2SRImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li>Unset: Use the channel dimension format of the input image.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swin2sr/image_processing_swin2sr.py#L84"}}),Me=new Be({}),Te=new ze({props:{name:"class transformers.Swin2SRConfig",anchor:"transformers.Swin2SRConfig",parameters:[{name:"image_size",val:" = 64"},{name:"patch_size",val:" = 1"},{name:"num_channels",val:" = 3"},{name:"embed_dim",val:" = 180"},{name:"depths",val:" = [6, 6, 6, 6, 6, 6]"},{name:"num_heads",val:" = [6, 6, 6, 6, 6, 6]"},{name:"window_size",val:" = 8"},{name:"mlp_ratio",val:" = 2.0"},{name:"qkv_bias",val:" = True"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"drop_path_rate",val:" = 0.1"},{name:"hidden_act",val:" = 'gelu'"},{name:"use_absolute_embeddings",val:" = False"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"upscale",val:" = 2"},{name:"img_range",val:" = 1.0"},{name:"resi_connection",val:" = '1conv'"},{name:"upsampler",val:" = 'pixelshuffle'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.Swin2SRConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.Swin2SRConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.Swin2SRConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.Swin2SRConfig.embed_dim",description:`<strong>embed_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 180) &#x2014;
Dimensionality of patch embedding.`,name:"embed_dim"},{anchor:"transformers.Swin2SRConfig.depths",description:`<strong>depths</strong> (<code>list(int)</code>, <em>optional</em>, defaults to <code>[6, 6, 6, 6, 6, 6]</code>) &#x2014;
Depth of each layer in the Transformer encoder.`,name:"depths"},{anchor:"transformers.Swin2SRConfig.num_heads",description:`<strong>num_heads</strong> (<code>list(int)</code>, <em>optional</em>, defaults to <code>[6, 6, 6, 6, 6, 6]</code>) &#x2014;
Number of attention heads in each layer of the Transformer encoder.`,name:"num_heads"},{anchor:"transformers.Swin2SRConfig.window_size",description:`<strong>window_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Size of windows.`,name:"window_size"},{anchor:"transformers.Swin2SRConfig.mlp_ratio",description:`<strong>mlp_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 2.0) &#x2014;
Ratio of MLP hidden dimensionality to embedding dimensionality.`,name:"mlp_ratio"},{anchor:"transformers.Swin2SRConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not a learnable bias should be added to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.Swin2SRConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings and encoder.`,name:"hidden_dropout_prob"},{anchor:"transformers.Swin2SRConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.Swin2SRConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
Stochastic depth rate.`,name:"drop_path_rate"},{anchor:"transformers.Swin2SRConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder. If string, <code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>,
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Whether or not to add absolute position embeddings to the patch embeddings.`,name:"use_absolute_embeddings"},{anchor:"transformers.Swin2SRConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
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The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.Swin2SRConfig.upscale",description:`<strong>upscale</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
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The convolutional block to use before the residual connection in each stage.`,name:"resi_connection"},{anchor:"transformers.Swin2SRConfig.upsampler",description:`<strong>upsampler</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pixelshuffle&quot;</code>) &#x2014;
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Initializing with a config file does not load the weights associated with the model, only the
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
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Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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elements depending on the configuration (<a
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>Swin2SRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
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`}}),te=new bs({props:{$$slots:{default:[Cs]},$$scope:{ctx:I}}}),ne=new Cn({props:{anchor:"transformers.Swin2SRModel.forward.example",$$slots:{default:[Es]},$$scope:{ctx:I}}}),xe=new Be({}),ke=new ze({props:{name:"class transformers.Swin2SRForImageSuperResolution",anchor:"transformers.Swin2SRForImageSuperResolution",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.Swin2SRForImageSuperResolution.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.Swin2SRForImageSuperResolution.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.Swin2SRForImageSuperResolution.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/swin2sr#transformers.Swin2SRConfig"
>Swin2SRConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Reconstruction loss.</p>
</li>
<li>
<p><strong>reconstruction</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 Reconstructed images, possibly upscaled.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states
(also called feature maps) of the model at the output of each stage.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.ImageSuperResolutionOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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Swin2R improves the `),ue=s(ie,"A",{href:!0,rel:!0});var Wn=r(ue);at=l(Wn,"SwinIR"),Wn.forEach(t),it=l(ie," model by incorporating "),Ze=s(ie,"A",{href:!0});var Pn=r(Ze);lt=l(Pn,"Swin Transformer v2"),Pn.forEach(t),ct=l(ie,` layers which mitigates issues such as training instability, resolution gaps between pre-training
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In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the \u201CAIM 2022 Challenge on Super-Resolution of Compressed Image and Video\u201D.`),Zn.forEach(t),Bn.forEach(t),Co=h(e),Y=s(e,"IMG",{src:!0,alt:!0,width:!0}),Eo=h(e),fe=s(e,"SMALL",{});var In=r(fe);pt=l(In,"Swin2SR architecture. Taken from the "),Xe=s(In,"A",{href:!0});var An=r(Xe);ht=l(An,"original paper."),An.forEach(t),In.forEach(t),Uo=h(e),F=s(e,"P",{});var Le=r(F);ut=l(Le,"This model was contributed by "),ge=s(Le,"A",{href:!0,rel:!0});var Gn=r(ge);ft=l(Gn,"nielsr"),Gn.forEach(t),gt=l(Le,`.
The original code can be found `),we=s(Le,"A",{href:!0,rel:!0});var Xn=r(we);wt=l(Xn,"here"),Xn.forEach(t),_t=l(Le,"."),Le.forEach(t),xo=h(e),W=s(e,"H2",{class:!0});var Xo=r(W);H=s(Xo,"A",{id:!0,class:!0,href:!0});var qn=r(H);ao=s(qn,"SPAN",{});var Vn=r(ao);b(_e.$$.fragment,Vn),Vn.forEach(t),qn.forEach(t),St=h(Xo),io=s(Xo,"SPAN",{});var Yn=r(io);vt=l(Yn,"Resources"),Yn.forEach(t),Xo.forEach(t),ko=h(e),Q=s(e,"P",{});var qo=r(Q);bt=l(qo,"Demo notebooks for Swin2SR can be found "),Se=s(qo,"A",{href:!0,rel:!0});var Hn=r(Se);yt=l(Hn,"here"),Hn.forEach(t),Rt=l(qo,"."),qo.forEach(t),Jo=h(e),L=s(e,"P",{});var Vo=r(L);Mt=l(Vo,"A demo Space for image super-resolution with SwinSR can be found "),ve=s(Vo,"A",{href:!0,rel:!0});var Qn=r(ve);Tt=l(Qn,"here"),Qn.forEach(t),$t=l(Vo,"."),Vo.forEach(t),Fo=h(e),P=s(e,"H2",{class:!0});var Yo=r(P);D=s(Yo,"A",{id:!0,class:!0,href:!0});var Ln=r(D);lo=s(Ln,"SPAN",{});var Dn=r(lo);b(be.$$.fragment,Dn),Dn.forEach(t),Ln.forEach(t),jt=h(Yo),co=s(Yo,"SPAN",{});var On=r(co);It=l(On,"Swin2SRImageProcessor"),On.forEach(t),Yo.forEach(t),No=h(e),k=s(e,"DIV",{class:!0});var De=r(k);b(ye.$$.fragment,De),Ct=h(De),mo=s(De,"P",{});var Kn=r(mo);Et=l(Kn,"Constructs a Swin2SR image processor."),Kn.forEach(t),Ut=h(De),O=s(De,"DIV",{class:!0});var Ho=r(O);b(Re.$$.fragment,Ho),xt=h(Ho),po=s(Ho,"P",{});var es=r(po);kt=l(es,"Preprocess an image or batch of images."),es.forEach(t),Ho.forEach(t),De.forEach(t),Wo=h(e),z=s(e,"H2",{class:!0});var Qo=r(z);K=s(Qo,"A",{id:!0,class:!0,href:!0});var os=r(K);ho=s(os,"SPAN",{});var ts=r(ho);b(Me.$$.fragment,ts),ts.forEach(t),os.forEach(t),Jt=h(Qo),uo=s(Qo,"SPAN",{});var ns=r(uo);Ft=l(ns,"Swin2SRConfig"),ns.forEach(t),Qo.forEach(t),Po=h(e),$=s(e,"DIV",{class:!0});var le=r($);b(Te.$$.fragment,le),Nt=h(le),B=s(le,"P",{});var Oe=r(B);Wt=l(Oe,"This is the configuration class to store the configuration of a "),qe=s(Oe,"A",{href:!0});var ss=r(qe);Pt=l(ss,"Swin2SRModel"),ss.forEach(t),zt=l(Oe,`. It is used to instantiate a Swin
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configuration with the defaults will yield a similar configuration to that of the Swin Transformer v2
`),$e=s(Oe,"A",{href:!0,rel:!0});var rs=r($e);Bt=l(rs,"caidas/swin2sr-classicalsr-x2-64"),rs.forEach(t),Zt=l(Oe," architecture."),Oe.forEach(t),At=h(le),Z=s(le,"P",{});var Ke=r(Z);Gt=l(Ke,"Configuration objects inherit from "),Ve=s(Ke,"A",{href:!0});var as=r(Ve);Xt=l(as,"PretrainedConfig"),as.forEach(t),qt=l(Ke,` and can be used to control the model outputs. Read the
documentation from `),Ye=s(Ke,"A",{href:!0});var is=r(Ye);Vt=l(is,"PretrainedConfig"),is.forEach(t),Yt=l(Ke," for more information."),Ke.forEach(t),Ht=h(le),b(ee.$$.fragment,le),le.forEach(t),zo=h(e),A=s(e,"H2",{class:!0});var Lo=r(A);oe=s(Lo,"A",{id:!0,class:!0,href:!0});var ls=r(oe);fo=s(ls,"SPAN",{});var cs=r(fo);b(je.$$.fragment,cs),cs.forEach(t),ls.forEach(t),Qt=h(Lo),go=s(Lo,"SPAN",{});var ds=r(go);Lt=l(ds,"Swin2SRModel"),ds.forEach(t),Lo.forEach(t),Bo=h(e),J=s(e,"DIV",{class:!0});var eo=r(J);b(Ie.$$.fragment,eo),Dt=h(eo),Ce=s(eo,"P",{});var Do=r(Ce);Ot=l(Do,`The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch `),Ee=s(Do,"A",{href:!0,rel:!0});var ms=r(Ee);Kt=l(ms,"torch.nn.Module"),ms.forEach(t),en=l(Do,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Do.forEach(t),on=h(eo),E=s(eo,"DIV",{class:!0});var ce=r(E);b(Ue.$$.fragment,ce),tn=h(ce),G=s(ce,"P",{});var oo=r(G);nn=l(oo,"The "),He=s(oo,"A",{href:!0});var ps=r(He);sn=l(ps,"Swin2SRModel"),ps.forEach(t),rn=l(oo," forward method, overrides the "),wo=s(oo,"CODE",{});var hs=r(wo);an=l(hs,"__call__"),hs.forEach(t),ln=l(oo," special method."),oo.forEach(t),cn=h(ce),b(te.$$.fragment,ce),dn=h(ce),b(ne.$$.fragment,ce),ce.forEach(t),eo.forEach(t),Zo=h(e),X=s(e,"H2",{class:!0});var Oo=r(X);se=s(Oo,"A",{id:!0,class:!0,href:!0});var us=r(se);_o=s(us,"SPAN",{});var fs=r(_o);b(xe.$$.fragment,fs),fs.forEach(t),us.forEach(t),mn=h(Oo),So=s(Oo,"SPAN",{});var gs=r(So);pn=l(gs,"Swin2SRForImageSuperResolution"),gs.forEach(t),Oo.forEach(t),Ao=h(e),j=s(e,"DIV",{class:!0});var de=r(j);b(ke.$$.fragment,de),hn=h(de),vo=s(de,"P",{});var ws=r(vo);un=l(ws,"Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration."),ws.forEach(t),fn=h(de),Je=s(de,"P",{});var Ko=r(Je);gn=l(Ko,"This model is a PyTorch "),Fe=s(Ko,"A",{href:!0,rel:!0});var _s=r(Fe);wn=l(_s,"torch.nn.Module"),_s.forEach(t),_n=l(Ko,` sub-class. Use
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