12 ms·

Like diffusion but faster: The Paella model for fast image generation

• gcanyon 3y ago
  Way back when, it was pretty easy to recognize diagrams created with MacDraw. There was a particular visual style to the primitives it included that flowed through to the final product. This was of course easier to notice because there were so few alternatives at the time. Given that Paella uses tokens instead of the source image, I wonder if the results will have a (human- or machine-) detectable "style" to them.
  • sbierwagen 3y ago
    The usual answer to "all AI art looks the same" is https://i.redd.it/jvwyyqn7776a1.jpg https://i.redd.it/jvwyyqn7776a1.jpg
    • omnicognate 3y ago
      Perhaps if you ask an 11 year old.
• RobotToaster 3y ago
  Github for those looking for the code https://github.com/dome272/Paella https://github.com/dome272/Paella
• FloatArtifact 3y ago
  A question for curiosity. Why can't it train on 256x256 pixels yet generate any size image? So if it was trained on multiple sizes of images, could you also generate of a larger size without upscaling?
  • wincy 3y ago
    As someone who doesn’t know the “why” but uses Stable diffusion a lot and has an intuitive feel for the “what” of what happens, it’s like trying to use a low res pattern for your wallpaper on Windows. It’ll either just repeat over and over so you’ll end up with weird multi headed people with heads on top of their heads, or you just upscale which hallucinates details in a totally different way that doesn’t really add new interesting details. With automatic1111 you can get around this by upscaling then inpainting the spots you want more detail and specifying a specific prompt for that particular area.
  • kmeisthax 3y ago
    Well, it's complicated. The model can't just work on arbitrary image sizes because the model was trained with a fixed number of input and output neurons. For example, 512x512 is Stable Diffusion's "native size." However, there are tricks to work around this. Diffusion models work by predicting image noise, which is then subtracted from the image iteratively until you get a result that matches the prompt. Stable Diffusion specifically has the following architectural features: - A Variational Autoencoder (VAE) layer that encodes the 512x512 input into a 128x128 latent space[0] - Three cross-attention blocks that take the encoded text prompt and input latent-space image, and output a downscaled image to the next layer - A simpler downscaling block that just has a linear and convolutional layer - Skip connections between the last four downscaling blocks and corresponding upscaling blocks that do the opposite, in the opposite order (e.g. simple upscale, then three cross-attention blocks). - The aforementioned opposite blocks (upscale + cross-attn upscale) - VAE decoder that goes from latent space back to a 512x512 output At the end of this process you get what the combined model thinks is noise in the image according to the prompt you gave it. You then subtract the noise and repeat for a certain number of iterations until done. So obviously, if you wanted a smaller image, you could crop the input and output at each iteration so that the model can only draw in the 'center'. Larger images are a bit trickier, you have to feed the image through in halves and then merge the noise predictions together before subtracting. This of course has limitations: since the model is looking at only half the image, there's nothing to steer the overall process, so it will draw things that look locally coherent but make no sense globally[1]. I suspect - as in, I'm totally guessing here - that we might be able to fix that by also running the diffusion process on a downscaled version of the image and then scaling the noise prediction back up to average with the other outputs. As far as I'm aware no SD frontends do this. But if that worked you could build up a resolution pyramid of models at different sizes taking fragments of the image and working together to denoise the image. If you were training from scratch you could even add scale and position information to the condition vector so the model can learn what image features should exist at what sizes. [0] Think of this like if every pixel of the latent-space image was, instead of RGB, four different channels worth of information about the distribution of pixels in the color-space image. This compresses the image so that the U-Net part of the model can be architecturally simpler - in fact, lots of machine learning research is finding new ways to compress data into a smaller amount of input neurons. [1] Moreso than diffusion models normally do
  • dplavery92 3y ago
    Presumably a transformer model or similar that uses positional encodings for the tokens could do that, but the U-Net decoder here uses a fixed-shape output and learns relationships between tokens (and sizes of image features) based on the positions of those tokens in a fixed-size vector. You could still apply this process convolutionally and slide the entire network around to generate an image that is an arbitrary multiple of the token size, but image content in one area of the image will only be "aware" of image content at a fixed-size neighborhood (e.g. 256x256).
    • GaggiX 3y ago
      This is not how it works, there is no sliding window, the model has no restrictions on the W and H dimensions, only on the C dim, so the UNet can actually be used directly on images with different aspect ratio and resolution; the attention layers pay attention to the entire image.
      • dplavery92 3y ago
        From Sections 3 and 4 of the VQGAN paper[1] upon this work is built: "To generate images in the megapixel regime, we ... have to work patch-wise and crop images to restrict the length of [the quantized encoding vector] s to a maximally feasible size during training. To sample images, we then use the transformer in a sliding-window manner as illustrated in Fig.3." ... "The sliding window approach introduced in Sec.3.2 enables image synthesis beyond a resolution of 256×256pixels." From the Paella paper[2]: "Our proposal builds on the two-stage paradigm introduced by Esser et al. and consists of a Vector-quantized Generative Adversarial Network (VQGAN) for projecting the high dimensional images into a lower-dimensional latent space... [w]e use a pretrained VQGAN with an f=4 compression and a base resolution of 256×256×3, mapping the image to a latent resolution of 64×64indices." After training, in describing their token predictor architecture: "Our architecture consists of a U-Net-style encoder-decoder structure based on residual blocks,employing convolutional[sic] and attention in both, the encoder and decoder pathways." U-Net, of course, is a convolutional neural network architecture. [3]. The "down" and "up" encoder/decoder blocks in the Paella code are batch-normed CNN layers. [4] [1] https://arxiv.org/pdf/2012.09841.pdf https://arxiv.org/pdf/2012.09841.pdf [2] https://arxiv.org/pdf/2211.07292.pdf https://arxiv.org/pdf/2211.07292.pdf [3] https://arxiv.org/abs/1505.04597 https://arxiv.org/abs/1505.04597 [4] https://github.com/dome272/Paella/blob/main/src/modules.py#L148 https://github.com/dome272/Paella/blob/main/src/modules.py#L...
  • dome271 3y ago
    Yea you can. It's the same as with any other CNN based model that is not forced to have a specific shape (like transformers if they use specific positional embeddings). You can also look at the blog post where different resolution images are generated https://laion.ai/blog/paella/ https://laion.ai/blog/paella/
    • dome271 3y ago
      I also explain more in the video I made about Paella (https://youtu.be/zdE1I6kYKYc https://youtu.be/zdE1I6kYKYc). Maybe that clarifies things more.
    • dplavery92 3y ago
      Transformers are not forced to use a specific input (or output) shape; the original ViT paper demonstrates interpolating positional embeddings to inference with arbitrary image shapes.
• x3874 3y ago
  [flagged]
  • marcus0x62 3y ago
    I don’t understand your parenthetical reference - paella and patella are very different things… In any case, I took Paella to be a play on infusion (used in making Paella vs diffusion. Or maybe these guys just really like rice.
  • eutropia 3y ago
    I think it's an analogy. paella is a Spanish rice dish. Rice cooked with various other ingredients like chicken, seafood, peppers, tomatoes, etc... If an image (a 2-d array of pixels) is a plate of rice, standard diffusion models denoise starting from each grain of rice (pixel). If you've ever seen a step-by-step output from a diffusion model, you know what I'm talking about. This model makes use of a CNN (convolutional neural net) to decode tokens from the image. A CNN takes m-by-m sections of elements (i.e. a square of pixels) as input, and translates them into 1d vectors as part of the input to the NN. Taking "chunks" of the image like this allows the net to learn things like edges, shapes, groups of color, etc. You could consider the convolutional samples as "chunks" in the paella: the meat, vegetables, and other goodies that make the dish beloved by many.
  • dang 3y ago
    "Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting." https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
  • GaggiX 3y ago
    Paella is spanish food, one of the researchers is spanish and among the first things the model was good at was food.
    • x3874 3y ago
      [flagged]
• ben_w 3y ago
  Half a second. Can't even type a good prompt in that fast. One of my dad's preferred anecdotes about how much computers sped up in his career, was the number of digits of pi that the company mainframe could compute. He was born in '39. And now I can generate images from descriptions faster than I can give those descriptions. At this rate, websites will be replaced with image generators and LLMs, and the loading speed won't change.
  • interroboink 3y ago
    Just to be clear, it's half a second for 256x256, where Stable Diffusion takes 3.2 seconds. Still a great speed-up, but not producing the big hi-res images people might be thinking of.
    • barrkel 3y ago
      Stable Diffusion takes 3.9 seconds to produce 8x 256x256 images (so about 0.5 seconds each), or 2 seconds to produce 1x 256x256 images. That's with DPM++ 2M Karras sampling with 20 iterations on a 3090.
      • interroboink 3y ago
        I was just referring to the article with those numbers. See the "results" section, where they talk about their hardware and such, for apples-to-apples comparison.
    • nkozyra 3y ago
      > Just to be clear, it's half a second for 256x256 But to continue their story, a lot of us used to play games at a 256x256 resolution. So in the grand scheme of "things improve quickly, by a lot" it very much applies.
  • Turskarama 3y ago
    The load speed for modern websites is mostly due to all the trackers rather than the actual UI so I don't think load speed would actually be affected at all.
• tehsauce 3y ago
  Their main claim of “faster” unfortunately is false. > Running on an Nvidia A100 GPU, Paella took 0.5 seconds to produce a 256x256-pixel image in eight steps, while Stable Diffusion took 3.2 seconds Using the latest methods (torch 2.0 compile, improved schedulers) stable diffusion only takes about 1 second to generate a 512x512 image on an a100 gpu. A 256x256 image 1/4 the size presumably takes less than half that time. So the corrected title is “Like diffusion but slightly slower and lower quality.”
  • jeron 3y ago
    to add, there's a finetuned version of Stable Diffusion 1.5 that can output 5 fps for 256x256 (0.2 seconds per image)[0]. So over 2x faster than Paella at 256x256 [0]:https://www.reddit.com/r/StableDiffusion/comments/z3m97e/minisd_a_256x256_finetune_of_stable_diffusion_15/ https://www.reddit.com/r/StableDiffusion/comments/z3m97e/min...
  • dome271 3y ago
    Hey, (one of the authors here). First of all the blog post is talking about the v1 of the paper, which was extremely fast, but not comparable to SD in any way. The v2 in arxiv is slower and does not achieve 0.5 seconds, but performs much better and closer to SD. So no doubt on this. But I just want to mention that you should not compare apples with oranges. Torch.compile also makes Paella much faster and using an optimized sampling pipeline it would always be faster than SD at 256x256 if you keep the conditions the same. Of course you could talk about distilling SD and then you can achieve maybe 1 step predictions etc. But you could probably do the same to Paella. I think it's important to stick with the main improvement from the paper that naive sampling can be done with much less steps when sticking to the original method, while being simple in its theory and implementation. But hey, way to go and improve on Paella in the future maybe :D
    • tehsauce 3y ago
      Hi! I really appreciate folks like you conducting and publishing real research. There have been a ton of companies recently which have been very rosily promoting their new models. My criticism was only to push back on overly optimistic marketing, and regret that some of it was directed at you. If you have a link to the v2 paper, would love to take a look!
• nodja 3y ago
  Note that Paella is a bit old in image model terms (Nov 2022) and modern stable diffusion tools have access to optimized workflows. My 3060 can generate a 256x256 8 step image in 0.5 seconds, no A100 needed. A 3090 is double the performance of a 3060 at 512x512, and an A100 is 50% faster than a 3090... If you have access to an high end consumer GPU (4090) you can generate 512x512 images in less than a second, it's reached the point that you can increase the batch size and have it show 2-4 images per prompt without adversely affecting your workflow. Too bad SD1.5 is too small* and we'll require models with more parameters if we want a true general purpose image model. If SD1.5 was the end-game, we'd have truly instant high res image generation in just a couple more generations of GPUs, think generating images in real time as you type the prompt, or have sliders that affect the strength of certain tokens and see the effects in real time, etc. Tho I heard that SDXL is actually faster for higher resolutions (>1024x1024) due to removing attention on the first layer, making it scale better with resolution even tho SDXL has 4x the parameter size. * Current SD1.5 models that can generate consistent high quality images have been fine-tuned and merged so many times that a lot of general knowledge has been lost, e.g. they can be great at generating landscapes, but lacking in generating humans, or they can be very good at a certain style like comics but can do comic style only and lose the ability to generate more dynamic face variations, etc.
  • dome271 3y ago
    Fully correct, also the v2 of the paper introduced a model that is bigger and slower, however generates better images. So the 500ms was only for the first model we introduced in v1. I also want to mention our new work as it is very much related to this whole topic of "speeding up models" -> either training or sampling: Würstchen: https://github.com/dome272/wuerstchen/ https://github.com/dome272/wuerstchen/ With a current version we are training at the moment we can sample (using torch.compile) 4 1024x1024 images in 4 seconds. Also the training of this kind of model is very fast due to spatially encoding images much much more -> 3x512x512 images -> 16x12x12 latents => 42x spatial compression, whereas StableDiffusion has an 8x compression (3x512x512 -> 4x64x64).
  • PheonixPharts 3y ago
    Personally I think the SD1.5 trade off, loss of general knowledge for surprisingly high quality images on consumer hardware, is worth it. It's fairly impressive to me what the community has made possible with SD1.5. Sure on a vanilla task something like Dall-E 2 generally performs better, but with some tweaking you can easily beat out Dall-E on a home gaming PC. The fact that you can fine-tune SD1.5 on a 4090 is incredible to me. Given how much powerful AI is locked behind fees and private APIs it's refreshing to see so much cool stuff coming out of the OSS world again. Best of all is it's not being driven exclusively by people with an ML background, but moreso curious amateurs. It really brings me back to a time when playing around with software/the web felt exciting.