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Context: Make-A-Video’s final T2V inference scheme (depicted in Fig. 2) can be formulated as:yt^=SRh∘SRlt∘↑F∘Dt∘P∘(x^,Cx(x)),\hat{y_{t}}=\operatorname{SR}_{h}\circ\operatorname{SR}_{l}^{t}\circ\uparrow_{F}\circ\operatorname{D}^{t}\circ\operatorname{P}\circ(\hat{x},\operatorname{C}_{x}(x)),over^ start_ARG italic_y start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT end_ARG = roman_SR start_POSTSUBSCRIPT italic_h end_POSTSUBSCRIPT ∘ roman_SR start_POSTSUBSCRIPT italic_l end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT ∘ ↑ start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT ∘ roman_D start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT ∘ roman_P ∘ ( over^ start_ARG italic_x end_ARG , roman_C start_POSTSUBSCRIPT italic_x end_POSTSUBSCRIPT ( italic_x ) ) ,(1)where \hat{y_{t}} is the generated video, \operatorname{SR}_{h},\operatorname{SR}_{l} are the spatial and spatiotemporal super-resolution networks (Sec. 3.2), \uparrow_{F} is a frame interpolation network (Sec. 3.3), \operatorname{D}^{t} is the spatiotemporal decoder (Sec. 3.2), \operatorname{P} is the prior (Sec. 3.1), \hat{x} is the BPE-encoded text, \operatorname{C}_{x} is the CLIP text encoder (Radford et al., 2021), and x is the input text. The three main components are described in detail in the following sections. We use the following networks to produce high-resolution images from text:(i) A prior network \operatorname{\textbf{P}}, that during inference generates image embeddings y_{e} given text embeddings x_{e} and BPE encoded text tokens \hat{x}, (ii) a decoder network \operatorname{\textbf{D}} that generates a low-resolution 64\times 64 RGB image \hat{y}_{l}, conditioned on the image embeddings y_{e}, and (iii) two super-resolution networks \operatorname{\textbf{SR}}_{\textbf{l}},\operatorname{\textbf{SR}}_{\textbf{h}} that increase the generated image \hat{y}_{l} resolution to 256\times 256 and 768\times 768 pixels respectively, resulting in the final222We then downsample to 512 using bicubic interpolation for a cleaner aesthetic. Maintaining a clean aesthetic for high definition videos is part of future work. generated image \hat{y}.

Question: How is this x converted to y using which network?
First, a prior network \operatorname{\textbf{P}}, that during inference generates image embeddings y_{e} given text embeddings x_{e} and BPE encoded text tokens \hat{x}. Second, a decoder network \operatorname{\textbf{D}} that generates a low-resolution 64\times 64 RGB image \hat{y}_{l}, conditioned on the image embeddings y_{e}. Finally, two super-resolution networks \operatorname{\textbf{SR}}_{\textbf{l}},\operatorname{\textbf{SR}}_{\textbf{h}} that increase the generated image \hat{y}_{l} resolution to 256\times 256 and 768\times 768 pixels respectively, resulting in the final222We then downsample to 512 using bicubic interpolation for a cleaner aesthetic. Maintaining a clean aesthetic for high definition videos is part of future work. generated image \hat{y}.