![]() al explores sampling-based strategies for image generation that leverage pretrained multimodal discriminative models. Other work incorporates additional sources of supervision during training to improve image quality. This is interesting to compare to our reranking with CLIP, which is done offline. AttnGAN incorporates attention between the text and image features, and proposes a contrastive text-image feature matching loss as an auxiliary objective. StackGAN and StackGAN++ use multi-scale GANs to scale up the image resolution and improve visual fidelity. The embeddings are produced by an encoder pretrained using a contrastive loss, not unlike CLIP. al, whose approach uses a GAN conditioned on text embeddings. Text-to-image synthesis has been an active area of research since the pioneering work of Reed et. We provide more details about the architecture and training procedure in our paper. DALL♾ uses the standard causal mask for the text tokens, and sparse attention for the image tokens with either a row, column, or convolutional attention pattern, depending on the layer. The attention mask at each of its 64 self-attention layers allows each image token to attend to all text tokens. Finally, the transformations “a sketch of the animal” and “a cell phone case with the animal” explore the use of this capability for illustrations and product design.ĭALL♾ is a simple decoder-only transformer that receives both the text and the image as a single stream of 1280 tokens-256 for the text and 1024 for the image-and models all of them autoregressively. Those that only change the color of the animal, such as “animal colored pink,” are less reliable, but show that DALL♾ is sometimes capable of segmenting the animal from the background. Other transformations, such as “animal with sunglasses” and “animal wearing a bow tie,” require placing the accessory on the correct part of the animal’s body. This works less reliably, and for several of the photos, DALL♾ only generates plausible completions in one or two instances. The transformation “animal in extreme close-up view” requires DALL♾ to recognize the breed of the animal in the photo, and render it up close with the appropriate details. The most straightforward ones, such as “photo colored pink” and “photo reflected upside-down,” also tend to be the most reliable, although the photo is often not copied or reflected exactly. Each transparent GIF can be instantly downloaded by clicking the "Save as" and "Download" buttons.We find that DALL♾ is able to apply several kinds of image transformations to photos of animals, with varying degrees of reliability. To see all the transparent pixels at a glance, you can turn on the black and white filter, which displays transparent areas in black color and all opaque areas in white. In this case, you can use the "Show One Frame" option that will pause the GIF player and display only the requested frame. Sometimes, you need to see how one particular frame looks like. You can also turn on the built-in GIF player and view the transparent GIF frame by frame. To make all frames transparent, enter the "*" symbol. Frames that will be made transparent can be listed as "1, 2, 6" or specified as a range "2-5". By default, the application makes all GIF frames transparent but if necessary, you can remove the color only in the specified frames. By increasing or decreasing the threshold value, you can control the transparent color's tint, tone, and shade. The color can be entered as a name ("blue"), hex or RGB code ("#0000FF" or "RGB(0, 0, 255)"), or selected via the attached color palette in options. ![]() When you specify the color that should change to the transparent color, then it matches this color everywhere in the GIF. As a bonus, our app also allows making any GIF region transparent (not just the background). For example, if your GIF has a red background, then you can enter "red" or hex code "#FF0000" in the transparent color field, and this red background will be assigned the transparent color index, which will make it disappear from the output GIF. This is a browser-based program that creates GIF animations with a transparent background. ![]()
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