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+fields.+However%252C+substantial+computational+complexity+poses+limitations+for+their+application+in+long-context+tasks%252C+such+as+high-resolution+image+generation.+This+paper+introduces+a+series+of+architectures+adapted+from+the+RWKV+model+used+in+the+NLP%252C+with+requisite+modifications+tailored+for+diffusion+model+applied+to+image+generation+tasks%252C+referred+to+as+Diffusion-RWKV.+Similar+to+the+diffusion+with+Transformers%252C+our+model+is+designed+to+efficiently+handle+patchnified+inputs+in+a+sequence+with+extra+conditions%252C+while+also+scaling+up+effectively%252C+accommodating+both+large-scale+parameters+and+extensive+datasets.+Its+distinctive+advantage+manifests+in+its+reduced+spatial+aggregation+complexity%252C+rendering+it+exceptionally+adept+at+processing+high-resolution+images%252C+thereby+eliminating+the+necessity+for+windowing+or+group+cached+operations.+Experimental+results+on+both+condition+and+unconditional+image+generation+tasks+demonstrate+that+Diffison-RWKV+achieves+performance+on+par+with+or+surpasses+existing+CNN+or+Transformer-based+diffusion+models+in+FID+and+IS+metrics+while+significantly+reducing+total+computation+FLOP+usage.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.43df65f35be2c70d0c4989658ab5ce4c%3frik%3dgL%252f30TTnwE0fww%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Diffusion+Model+Qkv&simid=608030631395990357&FORM=IRPRST&ck=3D8A4E482988E52887F233A1C91FBA1E&selectedIndex=7&itb=0)
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