Dalle2 free

Simply import OpenAIClipAdapter and pass it into the DiffusionPrior or Decoder like so```pythonimport torchfrom dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder, OpenAIClipAdapteropenai pretrained clip - defaults to ViT/B-32clip = OpenAIClipAdapter()mock datatext = torch.randint(0, 49408, (4, 256)).cuda()images = torch.randn(4, 3, 256, 256).cuda()prior networks (with transformer)priornetwork = DiffusionPriorNetwork( dim = 512, depth = 6, dimhead = 64, heads = 8).cuda()diffusionprior = DiffusionPrior( net = priornetwork, clip = clip, timesteps = 100, conddropprob = 0.2).cuda()loss = diffusion_prior(text, images)loss.backward()do above for many steps ...decoder (with unet)unet1 = Unet( dim = 128, imageembeddim = 512, conddim = 128, channels = 3, dimmults=(1, 2, 4, 8)).cuda()unet2 = Unet( dim = 16, imageembeddim = 512, conddim = 128, channels = 3, dimmults = (1, 2, 4, 8, 16)).cuda()decoder = Decoder( unet = (unet1, unet2), imagesizes = (128, 256), clip = clip, timesteps = 100, imageconddropprob = 0.1, textconddropprob = 0.5, conditionontextencodings = False # set this to True if you wish to condition on text during training and sampling).cuda()for unetnumber in (1, 2): loss = decoder(images, unetnumber = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much loss.backward()do above for many stepsdalle2 = DALLE2( prior = diffusion_prior, decoder = decoder)images = dalle2( ['a butterfly trying to escape a tornado'], cond_scale = 2. # classifier free guidance strength (> 1 would strengthen the condition))save your image (in this example, of size 256x256)```Now you'll just have to worry about training the Prior and the Decoder!ExperimentalDALL-E2 with Latent DiffusionThis repository decides to take the next step and offer DALL-E v2 combined with latent diffusion, from Rombach et al.You can use it as follows. Latent diffusion can be limited to just the first U-Net in the cascade, or to any

HOME Past Mazes Free Printable Mazes Blog Maze Books About April 6, 2023 Welcome to the 2nd in a series of posts where I will test AI image generators and see how they handle making maze art. I will be asking 10 prompts and seeing what gets generated. My goal is to evaluate different AI image sites against each other to see how they perform. In my first post I discussed the project, and today we start with the first site evaluation: DallE2. Making Maze Art with DallE2You can access the website here. You must sign in. As of this post there is no limit to the number of prompts you can ask for, and the site is free to use. Each prompt, or generation with DallE2 will generate 4 image options. For this exercise I chose the one closest to what I had asked for, or the most interesting. Let’s get started.Prompt 1 - Make a medium difficulty maze of the Eiffel Tower in black and white with arrows at the start and finish I see the Eiffel Tower. In fact I see 2 of it for some reason. I see a maze like structure. It is black and white. But I see no arrows, and the maze is not really a maze.Prompt 2 - Draw a medium difficulty large maze of the Empire State Building with the start and goal embedded in the structure Not sure what this is. I changed the prompt vs. #1 to get rid of the arrows…I like the mazelike structure on the top of the page. And there looks to be a small Death Star under it. I think maybe DallE2 can’t make mazes.. just mazelike drawings ?Prompt 3 - Draw a difficult maze of the White House pixel art style It is pixel art style. Despite having no start and goal it looks the most mazelike of anything generated yet. And there is a house, although it is grey and looks like a dirty igloo. Interesting. Not winning any awards.Prompt 4 - Draw a difficult maze that looks like a drawing of

= 0.5).cuda()mock images (get a lot of this)images = torch.randn(4, 3, 512, 512).cuda()feed images into decoder, specifying which unet you want to traineach unet can be trained separately, which is one of the benefits of the cascading DDPM schemeloss = decoder(images, unet_number = 1)loss.backward()loss = decoder(images, unet_number = 2)loss.backward()do the above for many steps for both unets```Finally, to generate the DALL-E2 images from text. Insert the trained DiffusionPrior as well as the Decoder (which wraps CLIP, the causal transformer, and unet(s))```pythonfrom dalle2_pytorch import DALLE2dalle2 = DALLE2( prior = diffusion_prior, decoder = decoder)send the text as a string if you want to use the simple tokenizer from DALLE v1or you can do it as token ids, if you have your own tokenizertexts = ['glistening morning dew on a flower petal']images = dalle2(texts) # (1, 3, 256, 256)```That's it!Let's see the whole script below```pythonimport torchfrom dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder, CLIPclip = CLIP( dimtext = 512, dimimage = 512, dimlatent = 512, numtexttokens = 49408, textencdepth = 6, textseqlen = 256, textheads = 8, visualencdepth = 6, visualimagesize = 256, visualpatchsize = 32, visual_heads = 8).cuda()mock datatext = torch.randint(0, 49408, (4, 256)).cuda()images = torch.randn(4, 3, 256, 256).cuda()trainloss = clip( text, images, return_loss = True)loss.backward()do above for many steps ...prior networks (with transformer)priornetwork = DiffusionPriorNetwork( dim = 512, depth = 6, dimhead = 64, heads = 8).cuda()diffusionprior = DiffusionPrior( net = priornetwork, clip = clip, timesteps = 100, conddropprob = 0.2).cuda()loss = diffusion_prior(text, images)loss.backward()do above for many steps ...decoder (with unet)unet1 = Unet( dim = 128, imageembeddim = 512, conddim = 128, channels = 3, dimmults=(1, 2, 4, 8)).cuda()unet2 = Unet( dim = 16, imageembeddim = 512, conddim = 128, channels = 3, dimmults = (1, 2, 4, 8, 16)).cuda()decoder = Decoder( unet = (unet1, unet2), imagesizes = (128, 256), clip

= clip, timesteps = 100, imageconddropprob = 0.1, textconddropprob = 0.5, conditionontextencodings = False # set this to True if you wish to condition on text during training and sampling).cuda()for unetnumber in (1, 2): loss = decoder(images, unetnumber = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much loss.backward()do above for many stepsdalle2 = DALLE2( prior = diffusion_prior, decoder = decoder)images = dalle2( ['cute puppy chasing after a squirrel'], cond_scale = 2. # classifier free guidance strength (> 1 would strengthen the condition))save your image (in this example, of size 256x256)```Everything in this readme should run without errorYou can also train the decoder on images of greater than the size (say 512x512) at which CLIP was trained (256x256). The images will be resized to CLIP image resolution for the image embeddingsFor the layperson, no worries, training will all be automated into a CLI tool, at least for small scale training.Training on Preprocessed CLIP EmbeddingsIt is likely, when scaling up, that you would first preprocess your images and text into corresponding embeddings before training the prior network. You can do so easily by simply passing in image_embed, text_embed, and optionally text_encodings and text_maskWorking example below```pythonimport torchfrom dalle2_pytorch import DiffusionPriorNetwork, DiffusionPrior, CLIPget trained CLIP from step oneclip = CLIP( dimtext = 512, dimimage = 512, dimlatent = 512, numtexttokens = 49408, textencdepth = 6, textseqlen = 256, textheads = 8, visualencdepth = 6, visualimagesize = 256, visualpatchsize = 32, visual_heads = 8,).cuda()setup prior network, which contains an autoregressive transformerpriornetwork = DiffusionPriorNetwork( dim = 512, depth = 6, dimhead = 64, heads = 8).cuda()diffusion prior network, which contains the CLIP and network (with transformer) abovediffusionprior = DiffusionPrior( net = priornetwork, clip =