a AduH @s0ddlmZddlZddlZddlmZddlmmZGdddej Z Gdddej Z Gdddej Z Gd d d ej Z d5d d ZGdddej ZGdddej ZddZd6ddZd7ddZdd Zd8d#d$ZGd%d&d&ej Zd'd(Zd)d*Zd+d,ZGd-d.d.ej Zd/d0Zd9d3d4ZdS):) OrderedDictNc s(eZdZdfd d Zdd d ZZS)RRDBNet N leakyreluCNAupconvConv2DFc s~tt|tt|d}|dkr*d}d|_|ddkrDd|_n|dkr^|ddkr^d|_t|dddd}fdd t|D}tdd| d }| d krt n| d krt nt d | d|dkrdd}nfdd t|D}tddd}t|dddd}| rDt | nd}t |tt g||Rg||||R|_dS)Nrrr) kernel_size norm_typeact_typeconvtypecs.g|]&}tddddddd qS)rrr zeror) rgcstridebiaspad_typerrmodergaussian_noiseplus)RRDB.0_)rrrnfrnrrA/var/www/html/stable-diffusion-webui/modules/esrgan_model_arch.py s z$RRDBNet.__init__..)rrrrrr pixelshufflezupsample mode [] is not foundrrcsg|]}dqS)r%r r)rrrupsample_blockr r!r",)superr__init__intmathlog resrgan_scale conv_blockrange upconv_blockpixelshuffle_blockNotImplementedErroract sequential ShortcutBlockmodel)selfin_ncout_ncrnbrrupscalerrrZ upsample_moderZfinalactrrZ n_upscaleZfea_convZ rb_blocksZLR_conv upsamplerZHR_conv0ZHR_conv1Zoutact __class__)rrrrrrrr&r!r)sD zRRDBNet.__init__cCs>|jdkrt|dd}n|jdkr0t|dd}n|}||S)Nr r)scaler )r-pixel_unshuffler6)r7xZoutmfeatr r r!forward5s   zRRDBNet.forward) rrrNrrr r NFF)N__name__ __module__ __qualname__r)rC __classcell__r r r=r!rs &rc s*eZdZdZdfd d Zd dZZS)rzr Residual in Residual Dense Block (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) rrr rNrrr Fcstt||dkrt    d |_t    d |_t    d |_n: f ddt|D}tj ||_ dS)Nr spectral_normrrcs.g|]&}t    d qS)rI)ResidualDenseBlock_5Cr rrrrrrrrrrrrJrr r!r"Vs  z!RRDB.__init__..) r(rr)rKRDB1RDB2RDB3r/nn SequentialRDBs)r7rrrrrrrrrrrrJrrZRDB_listr=rLr!r)Fs$    "z RRDB.__init__cCs@t|dr*||}||}||}n ||}|d|S)NrM皙?)hasattrrMrNrOrR)r7rAoutr r r!rC[s      z RRDB.forward) rrrr r rNrrr FFFrErFrG__doc__r)rCrHr r r=r!r@s rc s*eZdZdZdfd d ZddZZS)rKa Residual Dense Block The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) Modified options that can be used: - "Partial Convolution based Padding" arXiv:1811.11718 - "Spectral normalization" arXiv:1802.05957 - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. {Rakotonirina} and A. {Rasoanaivo} @rrr rNrrr Fc stt|| rtnd|_| r,t||nd|_t||||||||| | | d |_t|||||||||| | | d |_t|d||||||||| | | d |_ t|d||||||||| | | d |_ | dkrd}n|}t|d||d|||||| | | d |_ dS)N)rrrrrrrJr rrr) r(rKr) GaussianNoisenoiseconv1x1r.conv1conv2conv3conv4conv5)r7rrrrrrrrrrrJrrZlast_actr=r r!r)ps4zResidualDenseBlock_5C.__init__c Cs||}|t||fd}|jr4|||}|t|||fd}|t||||fd}|jrt||}|t|||||fd}|jr|| d|S|d|SdS)Nr rS) r\r]torchcatr[r^r_r`rZmul)r7rAx1x2x3x4x5r r r!rCs zResidualDenseBlock_5C.forward) rXrrr r rNrrr FFFrVr r r=r!rKes  rKcs&eZdZdfdd ZddZZS)rY皙?Fcs,t||_||_tjdtjd|_dS)Nr)dtype)r(r)sigmais_relative_detachratensorfloatrZ)r7rkrlr=r r!r)s zGaussianNoise.__init__cCsb|jr^|jdkr^|j|j|_|jr4|j|n|j|}|jj| |}||}|S)Nr) trainingrkrZtodevicerldetachrepeatsizenormal_)r7rAr?Z sampled_noiser r r!rCs zGaussianNoise.forward)riFrDr r r=r!rYsrYr cCstj||d|ddS)Nr F)rrr)rPConv2d) in_planes out_planesrr r r!r[sr[cs*eZdZdZd fdd Zd d ZZS) SRVGGNetCompactzA compact VGG-style network structure for super-resolution. This class is copied from https://github.com/xinntao/Real-ESRGAN rrXr rpreluc sNtt|||_||_||_||_||_||_t |_ |j t ||ddd|dkrlt jdd}n,|dkrt j|d}n|dkrt jd dd }|j |t|D]n}|j t ||ddd|dkrt jdd}n.|dkrt j|d}n|dkrt jd dd }|j |q|j t ||||dddt ||_dS) Nrr reluTinplacerz)num_parametersrri)negative_sloper})r(ryr) num_in_ch num_out_chnum_featnum_convr;rrP ModuleListbodyappendrvReLUPReLU LeakyReLUr/ PixelShuffler<) r7rrrrr;r activationrr=r r!r)s6    "zSRVGGNetCompact.__init__cCsP|}tdt|jD]}|j||}q||}tj||jdd}||7}|S)Nrnearest scale_factorr)r/lenrr<F interpolater;)r7rArUibaser r r!rCs zSRVGGNetCompact.forward)rrrXr rrzrVr r r=r!rys&rycs2eZdZdZd fdd ZddZdd ZZS) UpsamplezUpsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data. The input data is assumed to be of the form `minibatch x channels x [optional depth] x [optional height] x width`. NrcsVtt|t|tr.tdd|D|_n|r:t|nd|_||_||_||_ dS)Ncss|]}t|VqdSN)rn)rfactorr r r! r'z$Upsample.__init__..) r(rr) isinstancetuplerrnrrt align_corners)r7rtrrrr=r r!r)s zUpsample.__init__cCstjj||j|j|j|jdS)N)rtrrr)rP functionalrrtrrr)r7rAr r r!rCszUpsample.forwardcCs8|jdurd|j}n d|j}|d|j7}|S)Nz scale_factor=zsize=z, mode=)rrtr)r7infor r r! extra_reprs   zUpsample.extra_repr)NNrN)rErFrGrWr)rCrrHr r r=r!rs rc Cs||\}}}}||d}||dkr4||dks8J||}||}|||||||} | dddddd||||S)z Pixel unshuffle. Args: x (Tensor): Input feature with shape (b, c, hh, hw). scale (int): Downsample ratio. Returns: Tensor: the pixel unshuffled feature. r rr rr)rtviewpermutereshape) rAr?bchhhw out_channelhwx_viewr r r!r@s r@r rTrr{r c  Cs\t|||d||||dd| d } t|} |r:t||nd} |rJt|nd} t| | | | S)z Pixel shuffle layer (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, CVPR17) r Nrrrrr)r.rPrnormr3r4)r8r9upscale_factorrrrrrrrconv pixel_shufflenar r r!r1s r1rc CsF| dkrd||fn|}t|| d} t||||||||| d } t| | S)z Upconv layer Conv3Dr rr)rr.r4) r8r9rrrrrrrrrupsamplerr r r!r0/s   r0cKs0g}t|D]}||fi|q tj|S)aMake layers by stacking the same blocks. Args: basic_block (nn.module): nn.module class for basic block. (block) num_basic_block (int): number of blocks. (n_layers) Returns: nn.Sequential: Stacked blocks in nn.Sequential. )r/rrPrQ) basic_blocknum_basic_blockkwarglayersrr r r! make_layerDs rrS?cCs|}|dkrt|}nb|dvr2t||}nL|dkrJtj||d}n4|dkr\t}n"|dkrnt}ntd|d|S) z activation helper r{)rlrelurz)r~inittanhsigmoidzactivation layer [r$)lowerrPrrrTanhSigmoidr2)rr}Z neg_slopeZn_prelubetalayerr r r!r3Rs   r3cs$eZdZfddZddZZS)Identitycstt|dSr)r(rr))r7kwargsr=r r!r)eszIdentity.__init__cGs|Srr )r7rArr r r!rChszIdentity.forwardrDr r r=r!rds rcCs^|}|dkr tj|dd}n:|dkr8tj|dd}n"|dkrJdd}ntd |d |S) z Return a normalization layer batchT)affineinstanceFnonecSstSr)r)rAr r r! norm_layertr'znorm..norm_layerznormalization layer [r$)rrP BatchNorm2dInstanceNorm2dr2)rncrrr r r!rls rcCsd|}|dkrdS|dkr(t|}n8|dkr| d krtj||||| |||d }ntj||||| |||d }| rtj |}| rt | nd}d | vr4|r"t ||nd}t ||||S| dkr|dur^| dur^t | dd}|rnt ||nd}t ||||SdS)z4 Conv layer with padding, normalization, activation )rNACZCNACzWrong conv mode []rNrZ PartialConv2D) PartialConv2d)rrrrrgroupsZ DeformConv2D) DeformConv2drrrFr|) rrZtorchvision.opsrrrPConv3drvutilsrJr3rr4)r8r9rrrrrrrrrrrJrprrrrrr r r!r.s@         r.)r )r rr TrNr{r ) r rr TrNr{rr )TrSr r) r r r TrNr{rr F) collectionsrr+ratorch.nnrPZtorch.nn.functionalrrrrrrKrYr[ryrr@r1r0rr3rrrrr5r4r.r r r r!s<  2%; ;