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1106:, argues that " peoples' responsibility as to whether they are ethical, moral, and legal in how they operate this technology", and that putting the capabilities of Stable Diffusion into the hands of the public would result in the technology providing a net benefit, in spite of the potential negative consequences. In addition, Mostaque argues that the intention behind the open availability of Stable Diffusion is to end corporate control and dominance over such technologies, who have previously only developed closed AI systems for image synthesis. This is reflected by the fact that any restrictions Stability AI places on the content that users may generate can easily be bypassed due to the availability of the source code.
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512×512 resolution; the version 2.0 update of the Stable
Diffusion model later introduced the ability to natively generate images at 768×768 resolution. Another challenge is in generating human limbs due to poor data quality of limbs in the LAION database. The model is insufficiently trained to understand human limbs and faces due to the lack of representative features in the database, and prompting the model to generate images of such type can confound the model. Stable Diffusion XL (SDXL) version 1.0, released in July 2023, introduced native 1024x1024 resolution and improved generation for limbs and text.
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the original model. This approach ensures that training with small datasets of image pairs does not compromise the integrity of production-ready diffusion models. The "zero convolution" is a 1×1 convolution with both weight and bias initialized to zero. Before training, all zero convolutions produce zero output, preventing any distortion caused by
ControlNet. No layer is trained from scratch; the process is still fine-tuning, keeping the original model secure. This method enables training on small-scale or even personal devices.
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prompts". Negative prompts are a feature included in some front-end implementations, including
Stability AI's own DreamStudio cloud service, and allow the user to specify prompts which the model should avoid during image generation. The specified prompts may be undesirable image features that would otherwise be present within image outputs due to the positive prompts provided by the user, or due to how the model was originally trained, with mangled human hands being a common example.
569:. However, this fine-tuning process is sensitive to the quality of new data; low resolution images or different resolutions from the original data can not only fail to learn the new task but degrade the overall performance of the model. Even when the model is additionally trained on high quality images, it is difficult for individuals to run models in consumer electronics. For example, the training process for waifu-diffusion requires a minimum 30 GB of
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588:, as the model was primarily trained on images with English descriptions. As a result, generated images reinforce social biases and are from a western perspective, as the creators note that the model lacks data from other communities and cultures. The model gives more accurate results for prompts that are written in English in comparison to those written in other languages, with western or white cultures often being the default representation.
494:, a German non-profit which receives funding from Stability AI. The Stable Diffusion model was trained on three subsets of LAION-5B: laion2B-en, laion-high-resolution, and laion-aesthetics v2 5+. A third-party analysis of the model's training data identified that out of a smaller subset of 12 million images taken from the original wider dataset used, approximately 47% of the sample size of images came from 100 different domains, with
840:, which fills the masked space with newly generated content based on the provided prompt. A dedicated model specifically fine-tuned for inpainting use-cases was created by Stability AI alongside the release of Stable Diffusion 2.0. Conversely, outpainting extends an image beyond its original dimensions, filling the previously empty space with content generated based on the provided prompt.
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a longer duration of time, however a smaller value may result in visual defects. Another configurable option, the classifier-free guidance scale value, allows the user to adjust how closely the output image adheres to the prompt. More experimentative use cases may opt for a lower scale value, while use cases aiming for more specific outputs may use a higher value.
628:. Hypernetworks steer results towards a particular direction, allowing Stable Diffusion-based models to imitate the art style of specific artists, even if the artist is not recognised by the original model; they process the image by finding key areas of importance such as hair and eyes, and then patch these areas in secondary latent space.
824:, in which the visual features of image data are changed and anonymized. The same process may also be useful for image upscaling, in which the resolution of an image is increased, with more detail potentially being added to the image. Additionally, Stable Diffusion has been experimented with as a tool for image compression. Compared to
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Stable
Diffusion has issues with degradation and inaccuracies in certain scenarios. Initial releases of the model were trained on a dataset that consists of 512×512 resolution images, meaning that the quality of generated images noticeably degrades when user specifications deviate from its "expected"
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ControlNet is a neural network architecture designed to manage diffusion models by incorporating additional conditions. It duplicates the weights of neural network blocks into a "locked" copy and a "trainable" copy. The "trainable" copy learns the desired condition, while the "locked" copy preserves
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which affects the output image. Users may opt to randomize the seed in order to explore different generated outputs, or use the same seed to obtain the same image output as a previously generated image. Users are also able to adjust the number of inference steps for the sampler; a higher value takes
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More traditional visual artists have expressed concern that widespread usage of image synthesis software such as Stable
Diffusion may eventually lead to human artists, along with photographers, models, cinematographers, and actors, gradually losing commercial viability against AI-based competitors.
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The Stable
Diffusion model supports the ability to generate new images from scratch through the use of a text prompt describing elements to be included or omitted from the output. Existing images can be re-drawn by the model to incorporate new elements described by a text prompt (a process known as
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The model was initially trained on the laion2B-en and laion-high-resolution subsets, with the last few rounds of training done on LAION-Aesthetics v2 5+, a subset of 600 million captioned images which the LAION-Aesthetics
Predictor V2 predicted that humans would, on average, give a score of at
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implementations of Stable
Diffusion, which allow users to modify the weight given to specific parts of the text prompt. Emphasis markers allow users to add or reduce emphasis to keywords by enclosing them with brackets. An alternative method of adjusting weight to parts of the prompt are "negative
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Stable
Diffusion also includes another sampling script, "img2img", which consumes a text prompt, path to an existing image, and strength value between 0.0 and 1.0. The script outputs a new image based on the original image that also features elements provided within the text prompt. The strength
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Stable
Diffusion is notably more permissive in the types of content users may generate, such as violent or sexually explicit imagery, in comparison to other commercial products based on generative AI. Addressing the concerns that the model may be used for abusive purposes, CEO of Stability AI,
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The text to image sampling script within Stable Diffusion, known as "txt2img", consumes a text prompt in addition to assorted option parameters covering sampling types, output image dimensions, and seed values. The script outputs an image file based on the model's interpretation of the prompt.
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data scraped from the web, where 5 billion image-text pairs were classified based on language and filtered into separate datasets by resolution, a predicted likelihood of containing a watermark, and predicted "aesthetic" score (e.g. subjective visual quality). The dataset was created by
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An "embedding" can be trained from a collection of user-provided images, and allows the model to generate visually similar images whenever the name of the embedding is used within a generation prompt. Embeddings are based on the "textual inversion" concept developed by researchers from
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The architecture is named "multimodal diffusion transformer (MMDiT), where the "multimodal" means that it mixes text and image encodings inside its operations. This differs from previous versions of DiT, where the text encoding affects the image encoding, but not vice versa.
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The XL version uses the same LDM architecture as previous versions, except larger: larger UNet backbone, larger cross-attention context, two text encoders instead of one, and trained on multiple aspect ratios (not just the square aspect ratio like previous versions).
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The Transformer architecture used for SD 3.0 has three "tracks", for original text encoding, transformed text encoding, and image encoding (in latent space). The transformed text encoding and image encoding are mixed during each transformer block.
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Additional use-cases for image modification via img2img are offered by numerous front-end implementations of the Stable Diffusion model. Inpainting involves selectively modifying a portion of an existing image delineated by a user-provided
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Radford, Alec; Kim, Jong Wook; Hallacy, Chris; Ramesh, Aditya; Goh, Gabriel; Agarwal, Sandhini; Sastry, Girish; Askell, Amanda; Mishkin, Pamela (February 26, 2021). "Learning Transferable Visual Models From Natural Language Supervision".
1152:, claiming that these companies have infringed the rights of millions of artists by training AI tools on five billion images scraped from the web without the consent of the original artists. The same month, Stability AI was also sued by
1195:", giving medical advice, automatically creating legal obligations, producing legal evidence, and "discriminating against or harming individuals or groups based on ... social behavior or ... personal or personality characteristics ...
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of the provided input image, and generates a new output image based on both the text prompt and the depth information, which allows the coherence and depth of the original input image to be maintained in the generated output.
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Gal, Rinon; Alaluf, Yuval; Atzmon, Yuval; Patashnik, Or; Bermano, Amit H.; Chechik, Gal; Cohen-Or, Daniel (August 2, 2022). "An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion".
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Podell, Dustin; English, Zion; Lacey, Kyle; Blattmann, Andreas; Dockhorn, Tim; Müller, Jonas; Penna, Joe; Rombach, Robin (July 4, 2023). "SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis".
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value denotes the amount of noise added to the output image. A higher strength value produces more variation within the image but may produce an image that is not semantically consistent with the prompt provided.
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Stable Diffusion claims no rights on generated images and freely gives users the rights of usage to any generated images from the model provided that the image content is not illegal or harmful to individuals.
1179:, along with the model (pretrained weights). It applies the Creative ML OpenRAIL-M license, a form of Responsible AI License (RAIL), to the model (M). The license prohibits certain use cases, including crime,
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617:, where vector representations for specific tokens used by the model's text encoder are linked to new pseudo-words. Embeddings can be used to reduce biases within the original model, or mimic visual styles.
1124:, a user interface for Stable Diffusion, took place, with the hackers claiming they targeted users who committed "one of our sins", which included AI-art generation, art theft, promoting cryptocurrency.
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least 5 out of 10 when asked to rate how much they liked them. The LAION-Aesthetics v2 5+ subset also excluded low-resolution images and images which LAION-5B-WatermarkDetection identified as carrying a
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The images Stable Diffusion was trained on have been filtered without human input, leading to some harmful images and large amounts of private and sensitive information appearing in the training data.
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Meng, Chenlin; He, Yutong; Song, Yang; Song, Jiaming; Wu, Jiajun; Zhu, Jun-Yan; Ermon, Stefano (January 4, 2022). "SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations".
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Meng, Chenlin; He, Yutong; Song, Yang; Song, Jiaming; Wu, Jiajun; Zhu, Jun-Yan; Ermon, Stefano (August 2, 2021). "SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations".
379:, capturing a more fundamental semantic meaning of the image. Gaussian noise is iteratively applied to the compressed latent representation during forward diffusion. The U-Net block, composed of a
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1037:(2021). This paper describes the CLIP method for training text encoders, which convert text into floating point vectors. Such text encodings are used by the diffusion model to create images.
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ViT-L/14 text encoder is used to transform text prompts to an embedding space. Researchers point to increased computational efficiency for training and generation as an advantage of LDMs.
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the output from forward diffusion backwards to obtain a latent representation. Finally, the VAE decoder generates the final image by converting the representation back into pixel space.
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Chambon, Pierre; Bluethgen, Christian; Langlotz, Curtis P.; Chaudhari, Akshay (October 9, 2022). "Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains".
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285:. Four of the original 5 authors (Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz) later joined Stability AI and released subsequent versions of Stable Diffusion.
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Luzi, Lorenzo; Siahkoohi, Ali; Mayer, Paul M.; Casco-Rodriguez, Josue; Baraniuk, Richard (October 21, 2022). "Boomerang: Local sampling on image manifolds using diffusion models".
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and Robin Rombach of CompVis, who were among the researchers who had earlier invented the latent diffusion model architecture used by Stable Diffusion. Stability AI also credited
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The SD XL Refiner, released at the same time, has the same architecture as SD XL, but it was trained for adding fine details to preexisting images via text-conditional img2img.
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in 2022 which can fine-tune the model to generate precise, personalised outputs that depict a specific subject, following training via a set of images which depict the subject.
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The CCDH, a campaign group, tested four of the largest public-facing AI platforms: Midjourney, OpenAI's ChatGPT Plus, Stability.ai's DreamStudio and Microsoft's Image Creator.
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Accessibility for individual developers can also be a problem. In order to customize the model for new use cases that are not included in the dataset, such as generating
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The denoising step can be flexibly conditioned on a string of text, an image, or another modality. The encoded conditioning data is exposed to denoising U-Nets via a
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652:"guided image synthesis") through its diffusion-denoising mechanism. In addition, the model also allows the use of prompts to partially alter existing images via
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A "hypernetwork" is a small pretrained neural network that is applied to various points within a larger neural network, and refers to the technique created by
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Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli (March 12, 2015). "Deep Unsupervised Learning using Nonequilibrium Thermodynamics".
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Esser, Patrick; Kulal, Sumith; Blattmann, Andreas; Entezari, Rahim; Müller, Jonas; Saini, Harry; Levi, Yam; Lorenz, Dominik; Sauer, Axel (March 5, 2024),
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There are different methods for performing img2img. The main method is SDEdit, which first adds noise to an image, then denoises it as usual in text2img.
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and outpainting, when used with an appropriate user interface that supports such features, of which numerous different open source implementations exist.
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with greater than 80% probability. Final rounds of training additionally dropped 10% of text conditioning to improve Classifier-Free Diffusion Guidance.
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The technical license for the model was released by the CompVis group at Ludwig Maximilian University of Munich. Development was led by Patrick Esser of
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518:. An investigation by Bayerischer Rundfunk showed that LAION's datasets, hosted on Hugging Face, contain large amounts of private and sensitive data.
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million in the text encoder, Stable Diffusion is considered relatively lightweight by 2022 standards, and unlike other diffusion models, it can run on
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1055:(2022). This paper describes CFG, which allows the text encoding vector to steer the diffusion model towards creating the image described by the text.
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to allow users to identify an image as generated by Stable Diffusion, although this watermark loses its efficacy if the image is resized or rotated.
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adaptations of Stable Diffusion created through additional retraining have been used for a variety of different use-cases, from medical imaging to
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A depth-guided model, named "depth2img", was introduced with the release of Stable Diffusion 2.0 on November 24, 2022; this model infers the
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1049:(2021, updated in 2022). This paper describes the latent diffusion model (LDM). This is the backbone of the Stable Diffusion architecture.
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It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as
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Stable Diffusion is recommended to be run with 10 GB or more VRAM, however users with less VRAM may opt to load the weights in
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Stable Diffusion was trained on pairs of images and captions taken from LAION-5B, a publicly available dataset derived from
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inclined to dismiss most of the lawsuit filed by Andersen, McKernan, and Ortiz but allowed them to file a new complaint.
832:, the recent methods used for image compression in Stable Diffusion face limitations in preserving small text and faces.
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604:. There are three methods in which user-accessible fine-tuning can be applied to a Stable Diffusion model checkpoint:
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1641:. International Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA. pp. 10684–10695.
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along with the attention mechanism, resulting in the desired image depicting a representation of the trained concept.
300:(a German nonprofit which assembled the dataset on which Stable Diffusion was trained) as supporters of the project.
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To address the limitations of the model's initial training, end-users may opt to implement additional training to
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All released by CompVis. There is no "version 1.0". 1.1 gave rise to 1.2, and 1.2 gave rise to both 1.3 and 1.4.
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375:, and an optional text encoder. The VAE encoder compresses the image from pixel space to a smaller dimensional
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An image generated with Stable Diffusion based on the text prompt "a photograph of an astronaut riding a horse"
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359:. Introduced in 2015, diffusion models are trained with the objective of removing successive applications of
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3572:"Startup Behind AI Image Generator Stable Diffusion Is In Talks To Raise At A Valuation Up To $ 1 Billion"
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have been brought up, due to such images generated by Stable Diffusion being shared on websites such as
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2578:"I thrashed the RTX 4090 for 8 hours straight training Stable Diffusion to paint like my uncle Hermann"
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1389:"Stable Diffusion came from the Machine Vision & Learning research group (CompVis) @LMU_Muenchen"
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1199:". The user owns the rights to their generated output images, and is free to use them commercially.
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until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on
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Zhang, Lvmin (February 10, 2023). "Adding Conditional Control to Text-to-Image Diffusion Models".
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1946:"Exploring 12 Million of the 2.3 Billion Images Used to Train Stable Diffusion's Image Generator"
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877:. In addition to Stability's interfaces, many third party open source interfaces exist, such as
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3634:"Hackers Target AI Users With Malicious Stable Diffusion Tool on GitHub to Protest 'Art Theft'"
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with a computational donation from Stability and training data from non-profit organizations.
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and an important link was made between this purely physical field and deep learning in 2015.
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Rombach, Robin; Blattmann, Andreas; Lorenz, Dominik; Esser, Patrick; Ommer, Björn (2022).
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The ability of img2img to add noise to the original image makes it potentially useful for
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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1475:. CompVis - Machine Vision and Learning Research Group, LMU Munich. September 17, 2022.
1067:(2022). Describes rectified flow, which is used for the backbone architecture of SD 3.0.
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process used by Stable Diffusion. The model generates images by iteratively denoising
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2754:"stable-diffusion-tools/emphasis at master · JohannesGaessler/stable-diffusion-tools"
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Ho, Jonathan; Salimans, Tim (July 25, 2022). "Classifier-Free Diffusion Guidance".
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generation outputs to match more specific use-cases, a process also referred to as
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2547:"NVIDIA Quietly Launches GeForce RTX 3080 12GB: More VRAM, More Power, More Money"
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
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2187:"A startup wants to democratize the tech behind DALL-E 2, consequences be damned"
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SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
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249:. This marked a departure from previous proprietary text-to-image models such as
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3953:: Investigation on sensitive and private data in Stable Diffusions training data
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3007:"A friendly guide to local AI image gen with Stable Diffusion and Automatic1111"
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2414:"Stability AI releases Stable Diffusion XL, its next-gen image synthesis model"
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3661:"AI art tools Stable Diffusion and Midjourney targeted with copyright lawsuit"
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1328:"Leaked deck raises questions over Stability AI's Series A pitch to investors"
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3913:"Step by Step visual introduction to Diffusion Models. - Blog by Kemal Erdem"
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characters ("waifu diffusion"), new data and further training are required.
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2907:"Stable Diffusion in your pocket? "Draw Things" brings AI images to iPhone"
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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
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3507:"This artist is dominating AI-generated art. And he's not happy about it"
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1976:"This artist is dominating AI-generated art. And he's not happy about it"
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Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
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Scaling Rectified Flow Transformers for High-resolution Image Synthesis
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Learning Transferable Visual Models From Natural Language Supervision
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355:, developed by the CompVis (Computer Vision & Learning) group at
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Diagram of the latent diffusion architecture used by Stable Diffusion
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Initialized with the weights of 1.2, not 1.4. Released by RunwayML.
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Demonstration of the effect of negative prompts on image generation
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1499:"The new killer app: Creating AI art will absolutely crush your PC"
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developer Kurumuz in 2021, originally intended for text-generation
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The 3.0 version completely changes the backbone. Not a UNet, but a
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1358:"Revolutionizing image generation by AI: Turning text into images"
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is a deep learning generation model developed by researchers from
241:, and it can run on most consumer hardware equipped with a modest
215:. Its development involved researchers from the CompVis Group at
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Models in Stable Diffusion series before SD 3 all used a kind of
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3541:"Midjourneyを超えた？ 無料の作画AI｢ #StableDiffusion ｣が｢AIを民主化した｣と断言できる理由"
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3723:"US judge finds flaws in artists' lawsuit against AI companies"
3480:"LICENSE.md · stabilityai/stable-diffusion-xl-base-1.0 at main"
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1883:
Liu, Xingchao; Gong, Chengyue; Liu, Qiang (September 7, 2022),
1573:
1188:
1172:
635:
614:
584:
The creators of Stable Diffusion acknowledge the potential for
574:
507:
278:
250:
3452:"High-Resolution Image Synthesis With Latent Diffusion Models"
1796:"Text-to-Image Generation with Stable Diffusion and OpenVINO™"
4543:
4523:
4513:
4508:
4503:
4498:
4461:
4293:
2933:"AI can be easily used to make fake election photos - report"
2496:"Riffusion - Stable diffusion for real-time music generation"
1803:
1180:
1114:
865:
Stability provides an online image generation service called
558:
491:
372:
363:
on training images, which can be thought of as a sequence of
297:
1635:
High-Resolution Image Synthesis with Latent Diffusion Models
1047:
High-Resolution Image Synthesis with Latent Diffusion Models
994:
The XL 1.0 base model has 3.5 billion parameters, making it
4533:
3449:
1855:
1828:
1631:
829:
825:
570:
498:
taking up 8.5% of the subset, followed by websites such as
542:
for a total of 150,000 GPU-hours, at a cost of $ 600,000.
3299:"stabilityai/stable-diffusion-xl-base-1.0 · Hugging Face"
1602:
1467:
1465:
1463:
1461:
1459:
1457:
1455:
1453:
1451:
1420:
1418:
1416:
1414:
428:
3028:"Fooocus is the easiest way to create AI art on your PC"
2962:"Stability AI open sources its AI-powered design studio"
2784:"Stable Diffusion v2.1 and DreamStudio Updates 7-Dec 22"
2600:
1539:"Anyone can use this AI art generator — that's the risk"
1298:"Diffuse The Rest - a Hugging Face Space by huggingface"
1272:"How to Run Stable Diffusion Locally to Generate Images"
3427:
2988:"Stability AI is open-sourcing its DreamStudio web app"
1010:
Distilled from XL 1.0 to run in fewer diffusion steps.
881:, which is the most popular and offers extra features,
2493:
1632:
Rombach; Blattmann; Lorenz; Esser; Ommer (June 2022).
1448:
1411:
869:. The company also released an open source version of
3783:"From RAIL to Open RAIL: Topologies of RAIL Licenses"
801:: Modified image created with Stable Diffusion XL 1.0
667:
to tradeoff model performance with lower VRAM usage.
3602:"Illegal trade in AI child sex abuse images exposed"
3244:"stabilityai/stable-diffusion-2-base · Hugging Face"
2322:
1320:
465:, which implements the rectified flow method with a
3874:"言葉で指示した画像を凄いAIが描き出す「Stable Diffusion」 ～画像は商用利用も可能"
1043:(2021). This paper describes SDEdit, aka "img2img".
3632:
2008:Brunner, Katharina; Harlan, Elisa (July 7, 2023).
795:: Original image created with Stable Diffusion 1.5
545:SD3 was trained at a cost of around $ 10 million.
394:. For conditioning on text, the fixed, pretrained
269:Stable Diffusion originated from a project called
3714:
3269:"stabilityai/stable-diffusion-2-1 · Hugging Face"
2691:
2689:
2687:
2685:
1269:
4889:
2877:
2056:
745:Each txt2img generation will involve a specific
237:. Its code and model weights have been released
3813:"Ready or not, mass video deepfakes are coming"
3214:"stabilityai/stable-diffusion-2 · Hugging Face"
3181:"runwayml/stable-diffusion-v1-5 · Hugging Face"
1197:legally protected characteristics or categories
3805:
3125:"CompVis/stable-diffusion-v1-4 · Hugging Face"
2807:
2682:
2667:
2644:
2156:"CompVis/stable-diffusion-v1-4 · Hugging Face"
1241:
967:Retrained from scratch on a filtered dataset.
738:Generated images are tagged with an invisible
200:and is considered to be a part of the ongoing
3979:
3871:
3046:"ComfyUI Workflows and what you need to know"
2295:
2293:
2291:
2007:
753:Additional text2img features are provided by
3993:
2260:
2258:
2256:
2254:
2252:
1750:: CS1 maint: multiple names: authors list (
1111:sexualized depictions of underage characters
367:. Stable Diffusion consists of 3 parts: the
3946:Interactive Explanation of Stable Diffusion
3843:"License - a Hugging Face Space by CompVis"
3538:
2647:"愛犬の合成画像を生成できるAI 文章で指示するだけでコスプレ 米Googleが開発"
2241:: CS1 maint: numeric names: authors list (
1156:for using its images in the training data.
996:around 3.5x larger than previous versions.
670:
4923:Works involved in plagiarism controversies
3986:
3972:
3534:
3532:
2985:
2855:"Stable Diffusion Based Image Compression"
2776:
2624:"NovelAI Improvements on Stable Diffusion"
2575:
2288:
2097:
1882:
166:
108:
3745:
3683:
3498:
3463:
3434:
2889:
2853:Bühlmann, Matthias (September 28, 2022).
2837:
2813:
2673:
2607:
2478:
2353:"Generating images with Stable Diffusion"
2249:
2215:
2103:
2035:Schuhmann, Christoph (November 2, 2022),
2034:
1892:
1865:
1835:
1735:
1646:
1081:SD 2.0: 0.2 million hours on A100 (40GB).
273:, developed in Germany by researchers at
3720:
3505:Heikkilä, Melissa (September 16, 2022).
3504:
3099:Latent Auto-recursive Composition Engine
2930:
2852:
2516:
2444:"hakurei/waifu-diffusion · Hugging Face"
2118:
1627:
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1532:
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1085:
315:
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3630:
3612:from the original on September 21, 2023
3582:from the original on September 30, 2023
3529:
3354:"stabilityai/sdxl-turbo · Hugging Face"
3279:from the original on September 21, 2023
3224:from the original on September 21, 2023
3191:from the original on September 21, 2023
2959:
2904:
2794:from the original on December 10, 2022.
2506:from the original on December 16, 2022.
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2016:from the original on September 12, 2023
1939:
1937:
1935:
1933:
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1536:
1473:"Stable Diffusion Repository on GitHub"
1368:from the original on September 17, 2022
591:
419:million parameters in the U-Net and 123
233:, a kind of deep generative artificial
16:Image-generating machine learning model
4890:
3884:from the original on November 14, 2022
3853:from the original on September 4, 2022
3733:from the original on September 6, 2023
3551:from the original on December 10, 2022
2803:
2801:
2634:from the original on October 27, 2022.
2590:from the original on November 9, 2022.
2517:Mercurio, Anthony (October 31, 2022),
2131:from the original on September 6, 2022
1715:David, Foster. "8. Diffusion Models".
1679:
1677:
1675:
1673:
1491:
1436:from the original on September 5, 2022
1426:"Stable Diffusion Launch Announcement"
1308:from the original on September 5, 2022
789:Demonstration of img2img modification
521:
217:Ludwig Maximilian University of Munich
3967:
3517:from the original on January 14, 2023
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3135:from the original on January 11, 2023
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2865:from the original on November 2, 2022
2827:
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2735:from the original on October 18, 2022
2731:, Shield Mountain, November 2, 2022,
2707:from the original on January 20, 2023
2657:from the original on August 31, 2022.
2527:from the original on October 31, 2022
2363:from the original on October 31, 2022
2333:from the original on October 16, 2023
2276:from the original on January 17, 2023
2197:from the original on January 19, 2023
2166:from the original on January 11, 2023
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2003:
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1986:from the original on January 14, 2023
1956:from the original on January 20, 2023
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1696:from the original on November 1, 2022
1658:from the original on January 20, 2023
1620:
1549:from the original on January 21, 2023
1537:Vincent, James (September 15, 2022).
1521:
1479:from the original on January 18, 2023
1278:from the original on October 13, 2023
980:Initialized with the weights of 2.0.
879:AUTOMATIC1111 Stable Diffusion Web UI
761:
457:Diffusion model § Rectified flow
196:technology is the premier product of
4824:Generative adversarial network (GAN)
3958:Negative Prompts in Stable Diffusion
3823:from the original on August 31, 2022
3763:from the original on August 30, 2022
3689:
3379:"Adversarial Diffusion Distillation"
3309:from the original on October 8, 2023
3004:
2764:from the original on October 2, 2022
2645:Yuki Yamashita (September 1, 2022).
2557:from the original on August 27, 2023
2454:from the original on October 8, 2023
2424:from the original on August 21, 2023
2266:"Stable Diffusion with 🧨 Diffusers"
2074:from the original on August 26, 2022
1943:
1928:
1607:Computer Vision & Learning Group
1589:"Stable Diffusion 3: Research Paper"
1509:from the original on August 31, 2022
4903:Deep learning software applications
3690:Korn, Jennifer (January 17, 2023).
3659:Vincent, James (January 16, 2023).
3563:
3025:
2905:Edwards, Benj (November 10, 2022).
2798:
1683:
1670:
1387:Mostaque, Emad (November 2, 2022).
1259:from the original on July 26, 2023.
581:, which has only about 12 GB.
79:SDXL 1.0 (model) / July 26, 2023
13:
3793:from the original on July 27, 2023
3702:from the original on March 1, 2023
3671:from the original on March 9, 2023
3631:Maiberg, Emanuel (June 11, 2024).
3203:
2998:
2822:
2393:from the original on July 26, 2023
2119:Mostaque, Emad (August 28, 2022).
2086:
2038:CLIP+MLP Aesthetic Score Predictor
1998:
1900:
1873:
1844:
1817:
1776:from the original on June 25, 2023
1686:"The Illustrated Stable Diffusion"
1561:
1399:from the original on July 20, 2023
1338:from the original on June 29, 2023
1159:In July 2023, U.S. District Judge
1109:Controversy around photorealistic
1053:Classifier-Free Diffusion Guidance
860:
194:generative artificial intelligence
14:
4939:
3900:
3872:Katsuo Ishida (August 26, 2022).
3753:"Stable Diffusion Public Release"
3721:Brittain, Blake (July 19, 2023).
2986:Weatherbed, Jess (May 17, 2023).
2544:
2185:Wiggers, Kyle (August 12, 2022).
2045:from the original on June 8, 2023
1911:"Rectified Flow — Rectified Flow"
1714:
1290:
1270:Ryan O'Connor (August 23, 2022).
663:precision instead of the default
44:Runway, CompVis, and Stability AI
4862:
4861:
4841:
3951:"We Are All Raw Material for AI"
3102:(M.S. Computer Science thesis).
2931:Wendling, Mike (March 6, 2024).
2010:"We Are All Raw Material for AI"
1132:In January 2023, three artists,
779:
770:
701:
690:
679:
534:The model was trained using 256
480:
27:
3865:
3835:
3775:
3652:
3624:
3594:
3569:
3539:Ryo Shimizu (August 26, 2022).
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3371:
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2846:
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2719:
2661:
2638:
2616:
2594:
2576:Dave James (October 28, 2022).
2569:
2538:
2510:
2487:
2466:
2436:
2412:Edwards, Benj (July 27, 2023).
2405:
2375:
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2315:
2209:
2178:
2112:
2028:
1968:
1788:
1758:
1723:
1708:
1595:
1581:
1122:hack on an extension of ComfyUI
646:
567:algorithmically generated music
336:
257:which were accessible only via
4774:Recurrent neural network (RNN)
4764:Differentiable neural computer
3787:Responsible AI Licenses (RAIL)
3096:Huang, Yenkai (May 10, 2024).
3005:Mann, Tobias (June 29, 2024).
2960:Wiggers, Kyle (May 18, 2023).
2494:Seth Forsgren; Hayk Martiros.
2301:"Stable Diffusion 2.0 Release"
1944:Baio, Andy (August 30, 2022).
1380:
1263:
1144:lawsuit against Stability AI,
1018:February 2024 (early preview)
897:user interface, essentially a
548:
264:
1:
4819:Variational autoencoder (VAE)
4779:Long short-term memory (LSTM)
4046:Computational learning theory
2012:. Bayerischer Rundfunk (BR).
1234:
1175:, Stable Diffusion makes its
1127:
851:
729:: "round stones, round rocks"
435:version of Stable Diffusion.
303:
4799:Convolutional neural network
3928:"U-Net for Stable Diffusion"
2216:emad_9608 (April 19, 2024).
1766:"Stable diffusion pipelines"
353:latent diffusion model (LDM)
275:Ludwig Maximilian University
202:artificial intelligence boom
7:
4898:Artificial intelligence art
4794:Multilayer perceptron (MLP)
1209:Artificial intelligence art
1202:
1140:, and Karla Ortiz, filed a
908:
899:visual programming language
405:takes inspiration from the
10:
4944:
4870:Artificial neural networks
4784:Gated recurrent unit (GRU)
4010:Differentiable programming
3932:U-Net for Stable Diffusion
2064:"LAION-Aesthetics | LAION"
1569:"CompVis/Latent-diffusion"
1166:
613:in 2022 with support from
463:Rectified Flow Transformer
454:
340:
188:released in 2022 based on
4837:
4751:
4695:
4624:
4557:
4429:
4329:
4322:
4276:
4240:
4203:Artificial neural network
4183:
4059:
4026:Automatic differentiation
3999:
2697:"Stable Diffusion web UI"
1073:(2024). Describes SD 3.0.
450:
392:cross-attention mechanism
151:
141:
129:
119:
88:
84:
72:
68:
60:
48:
38:
26:
4908:Text-to-image generation
4031:Neuromorphic engineering
3994:Differentiable computing
3458:. pp. 10684–10695.
1717:Generative Deep Learning
671:Text to image generation
438:
245:with at least 4 GB
4804:Residual neural network
4220:Artificial Intelligence
1719:(2 ed.). O'Reilly.
1061:(2023). Describes SDXL.
369:variational autoencoder
3545:Business Insider Japan
2121:"Cost of construction"
1142:copyright infringement
365:denoising autoencoders
343:Latent diffusion model
333:
313:
226:Stable Diffusion is a
147:Creative ML OpenRAIL-M
4913:Unsupervised learning
4759:Neural Turing machine
4347:Human image synthesis
3907:Stable Diffusion Demo
3511:MIT Technology Review
3329:"Announcing SDXL 1.0"
2703:. November 10, 2022.
2383:"Announcing SDXL 1.0"
1980:MIT Technology Review
1249:"Announcing SDXL 1.0"
1229:Imagen (Google Brain)
1193:exploiting ... minors
1177:source code available
1086:Usage and controversy
319:
311:
283:Heidelberg University
4850:Computer programming
4829:Graph neural network
4404:Text-to-video models
4382:Text-to-image models
4230:Large language model
4215:Scientific computing
4021:Statistical manifold
4016:Information geometry
3404:"Stable Diffusion 3"
2630:. October 11, 2022.
2218:"10m is about right"
1024:A family of models.
717:: no negative prompt
592:End-user fine-tuning
4196:In-context learning
4036:Pattern recognition
3878:Impress Corporation
3819:. August 30, 2022.
3817:The Washington Post
3789:. August 18, 2022.
3155:"CompVis (CompVis)"
2728:invisible-watermark
2359:. August 24, 2022.
1171:Unlike models like
1120:In June of 2024, a
932:1.1, 1.2, 1.3, 1.4
914:
611:Tel Aviv University
540:Amazon Web Services
522:Training procedures
431:-only if using the
186:text-to-image model
136:Text-to-image model
23:
4789:Echo state network
4677:Jürgen Schmidhuber
4372:Facial recognition
4367:Speech recognition
4277:Software libraries
3050:thinkdiffusion.com
1690:jalammar.github.io
913:
818:data anonymization
762:Image modification
626:transformer models
334:
314:
105:/generative-models
40:Original author(s)
21:
4918:Art controversies
4885:
4884:
4647:Stephen Grossberg
4620:
4619:
3608:. June 27, 2023.
3161:. August 23, 2023
3104:Dartmouth College
2551:www.anandtech.com
2222:r/StableDiffusion
1915:www.cs.utexas.edu
1028:
1027:
822:data augmentation
740:digital watermark
640:Boston University
579:GeForce 30 series
516:Wikimedia Commons
175:
174:
4935:
4875:Machine learning
4865:
4864:
4845:
4600:Action selection
4590:Self-driving car
4397:Stable Diffusion
4362:Speech synthesis
4327:
4326:
4191:Machine learning
4067:Gradient descent
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1345:
1343:
1324:
1318:
1317:
1315:
1313:
1294:
1288:
1287:
1285:
1283:
1267:
1261:
1260:
1245:
915:
912:
783:
774:
705:
694:
683:
586:algorithmic bias
422:
418:
271:Latent Diffusion
192:techniques. The
178:Stable Diffusion
171:
170:
163:
160:
158:
112:
107:
104:
102:
100:
31:
24:
22:Stable Diffusion
20:
4943:
4942:
4938:
4937:
4936:
4934:
4933:
4932:
4888:
4887:
4886:
4881:
4833:
4747:
4713:Google DeepMind
4691:
4657:Geoffrey Hinton
4616:
4553:
4479:Project Debater
4425:
4323:Implementations
4318:
4272:
4236:
4179:
4121:Backpropagation
4055:
4041:Tensor calculus
3995:
3992:
3936:
3934:
3926:
3917:
3915:
3911:
3903:
3898:
3897:
3887:
3885:
3880:(in Japanese).
3870:
3866:
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3854:
3841:
3840:
3836:
3826:
3824:
3811:
3810:
3806:
3796:
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3625:
3615:
3613:
3600:
3599:
3595:
3585:
3583:
3568:
3564:
3554:
3552:
3547:(in Japanese).
3537:
3530:
3520:
3518:
3503:
3499:
3489:
3487:
3486:. July 26, 2023
3478:
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3118:
3108:
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3094:
3090:
3080:
3078:
3070:
3069:
3065:
3055:
3053:
3052:. December 2023
3044:
3043:
3039:
3024:
3020:
3003:
2999:
2984:
2980:
2970:
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2958:
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2738:
2736:
2725:
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2710:
2708:
2695:
2694:
2683:
2666:
2662:
2653:(in Japanese).
2643:
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2621:
2617:
2599:
2595:
2574:
2570:
2560:
2558:
2543:
2539:
2530:
2528:
2520:Waifu Diffusion
2515:
2511:
2492:
2488:
2471:
2467:
2457:
2455:
2442:
2441:
2437:
2427:
2425:
2410:
2406:
2396:
2394:
2381:
2380:
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2366:
2364:
2357:Paperspace Blog
2351:
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2321:
2320:
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2279:
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2250:
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2179:
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2117:
2113:
2096:
2087:
2077:
2075:
2062:
2061:
2057:
2048:
2046:
2033:
2029:
2019:
2017:
2006:
1999:
1989:
1987:
1974:
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1969:
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1311:
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1296:
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1281:
1279:
1268:
1264:
1247:
1246:
1242:
1237:
1205:
1169:
1130:
1088:
918:Version number
911:
863:
861:User Interfaces
854:
807:
806:
805:
804:
786:
785:
784:
776:
775:
764:
735:
734:
733:
732:
723:: "green trees"
708:
707:
706:
697:
696:
695:
686:
685:
684:
673:
649:
636:Google Research
602:personalization
594:
551:
524:
483:
459:
453:
441:
427:GPUs, and even
420:
416:
351:(DM), called a
349:diffusion model
345:
339:
306:
267:
231:diffusion model
165:
155:
115:
97:
80:
64:August 22, 2022
61:Initial release
34:
17:
12:
11:
5:
4941:
4931:
4930:
4925:
4920:
4915:
4910:
4905:
4900:
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4882:
4880:
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4857:
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4838:
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4832:
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4826:
4821:
4816:
4811:
4806:
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4796:
4791:
4786:
4781:
4776:
4771:
4766:
4761:
4755:
4753:
4749:
4748:
4746:
4745:
4740:
4735:
4730:
4725:
4720:
4715:
4710:
4705:
4699:
4697:
4693:
4692:
4690:
4689:
4687:Ilya Sutskever
4684:
4679:
4674:
4669:
4664:
4659:
4654:
4652:Demis Hassabis
4649:
4644:
4642:Ian Goodfellow
4639:
4634:
4628:
4626:
4622:
4621:
4618:
4617:
4615:
4614:
4609:
4608:
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4597:
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4567:
4561:
4559:
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4516:
4511:
4506:
4501:
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4394:
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4369:
4364:
4359:
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4349:
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4339:
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4331:
4324:
4320:
4319:
4317:
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4311:
4306:
4301:
4296:
4291:
4286:
4280:
4278:
4274:
4273:
4271:
4270:
4265:
4260:
4255:
4250:
4244:
4242:
4238:
4237:
4235:
4234:
4233:
4232:
4225:Language model
4222:
4217:
4212:
4211:
4210:
4200:
4199:
4198:
4187:
4185:
4181:
4180:
4178:
4177:
4175:Autoregression
4172:
4167:
4166:
4165:
4155:
4153:Regularization
4150:
4149:
4148:
4143:
4138:
4128:
4123:
4118:
4116:Loss functions
4113:
4108:
4103:
4098:
4093:
4092:
4091:
4081:
4076:
4075:
4074:
4063:
4061:
4057:
4056:
4054:
4053:
4051:Inductive bias
4048:
4043:
4038:
4033:
4028:
4023:
4018:
4013:
4005:
4003:
3997:
3996:
3991:
3990:
3983:
3976:
3968:
3962:
3961:
3954:
3948:
3943:
3924:
3909:
3902:
3901:External links
3899:
3896:
3895:
3864:
3847:huggingface.co
3834:
3804:
3774:
3744:
3713:
3682:
3651:
3623:
3593:
3570:Cai, Kenrick.
3562:
3528:
3497:
3484:huggingface.co
3471:
3442:
3420:
3395:
3370:
3358:huggingface.co
3345:
3320:
3303:huggingface.co
3290:
3273:huggingface.co
3260:
3248:huggingface.co
3235:
3218:huggingface.co
3202:
3185:huggingface.co
3172:
3159:huggingface.co
3146:
3129:huggingface.co
3116:
3088:
3063:
3037:
3026:Hachman, Mak.
3018:
2997:
2978:
2952:
2923:
2897:
2876:
2845:
2821:
2797:
2775:
2745:
2718:
2681:
2660:
2637:
2615:
2593:
2568:
2537:
2509:
2486:
2465:
2448:huggingface.co
2435:
2404:
2374:
2344:
2314:
2287:
2270:huggingface.co
2248:
2208:
2177:
2160:huggingface.co
2142:
2111:
2085:
2055:
2027:
1997:
1967:
1927:
1899:
1872:
1843:
1816:
1787:
1770:huggingface.co
1757:
1722:
1707:
1684:Alammar, Jay.
1669:
1619:
1594:
1580:
1560:
1520:
1490:
1447:
1410:
1379:
1349:
1319:
1302:huggingface.co
1289:
1262:
1239:
1238:
1236:
1233:
1232:
1231:
1226:
1221:
1216:
1211:
1204:
1201:
1168:
1165:
1161:William Orrick
1138:Kelly McKernan
1134:Sarah Andersen
1129:
1126:
1087:
1084:
1083:
1082:
1077:Training cost
1075:
1074:
1068:
1062:
1056:
1050:
1044:
1038:
1026:
1025:
1022:
1019:
1016:
1012:
1011:
1008:
1006:
1005:November 2023
1003:
999:
998:
992:
989:
986:
982:
981:
978:
976:
975:December 2022
973:
969:
968:
965:
963:
962:November 2022
960:
956:
955:
952:
949:
946:
942:
941:
938:
936:
933:
929:
928:
925:
922:
919:
910:
907:
905:applications.
893:, which has a
862:
859:
853:
850:
803:
802:
796:
788:
787:
778:
777:
769:
768:
767:
766:
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731:
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724:
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689:
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669:
648:
645:
644:
643:
629:
618:
593:
590:
550:
547:
523:
520:
482:
479:
455:Main article:
452:
449:
440:
437:
361:Gaussian noise
341:Main article:
338:
335:
305:
302:
266:
263:
259:cloud services
235:neural network
173:
172:
153:
149:
148:
145:
139:
138:
133:
127:
126:
121:
117:
116:
114:
113:
94:
92:
86:
85:
82:
81:
78:
76:
74:Stable release
70:
69:
66:
65:
62:
58:
57:
52:
46:
45:
42:
36:
35:
32:
15:
9:
6:
4:
3:
2:
4940:
4929:
4928:2022 software
4926:
4924:
4921:
4919:
4916:
4914:
4911:
4909:
4906:
4904:
4901:
4899:
4896:
4895:
4893:
4876:
4873:
4871:
4868:
4867:
4860:
4856:
4853:
4851:
4848:
4847:
4844:
4840:
4839:
4836:
4830:
4827:
4825:
4822:
4820:
4817:
4815:
4812:
4810:
4807:
4805:
4802:
4800:
4797:
4795:
4792:
4790:
4787:
4785:
4782:
4780:
4777:
4775:
4772:
4770:
4767:
4765:
4762:
4760:
4757:
4756:
4754:
4752:Architectures
4750:
4744:
4741:
4739:
4736:
4734:
4731:
4729:
4726:
4724:
4721:
4719:
4716:
4714:
4711:
4709:
4706:
4704:
4701:
4700:
4698:
4696:Organizations
4694:
4688:
4685:
4683:
4680:
4678:
4675:
4673:
4670:
4668:
4665:
4663:
4660:
4658:
4655:
4653:
4650:
4648:
4645:
4643:
4640:
4638:
4635:
4633:
4632:Yoshua Bengio
4630:
4629:
4627:
4623:
4613:
4612:Robot control
4610:
4606:
4603:
4602:
4601:
4598:
4596:
4593:
4591:
4588:
4586:
4583:
4581:
4578:
4576:
4573:
4571:
4568:
4566:
4563:
4562:
4560:
4556:
4550:
4547:
4545:
4542:
4540:
4537:
4535:
4532:
4530:
4529:Chinchilla AI
4527:
4525:
4522:
4520:
4517:
4515:
4512:
4510:
4507:
4505:
4502:
4500:
4497:
4495:
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4490:
4487:
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4480:
4477:
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4472:
4468:
4465:
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4460:
4458:
4455:
4453:
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4448:
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4440:
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4428:
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4415:
4412:
4410:
4407:
4406:
4405:
4402:
4398:
4395:
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4390:
4388:
4385:
4384:
4383:
4380:
4378:
4375:
4373:
4370:
4368:
4365:
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4360:
4358:
4355:
4353:
4350:
4348:
4345:
4343:
4340:
4338:
4335:
4334:
4332:
4328:
4325:
4321:
4315:
4312:
4310:
4307:
4305:
4302:
4300:
4297:
4295:
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4290:
4287:
4285:
4282:
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4279:
4275:
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4264:
4261:
4259:
4256:
4254:
4251:
4249:
4246:
4245:
4243:
4239:
4231:
4228:
4227:
4226:
4223:
4221:
4218:
4216:
4213:
4209:
4208:Deep learning
4206:
4205:
4204:
4201:
4197:
4194:
4193:
4192:
4189:
4188:
4186:
4182:
4176:
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4168:
4164:
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4160:
4159:
4156:
4154:
4151:
4147:
4144:
4142:
4139:
4137:
4134:
4133:
4132:
4129:
4127:
4124:
4122:
4119:
4117:
4114:
4112:
4109:
4107:
4104:
4102:
4099:
4097:
4096:Hallucination
4094:
4090:
4087:
4086:
4085:
4082:
4080:
4077:
4073:
4070:
4069:
4068:
4065:
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4052:
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4044:
4042:
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4034:
4032:
4029:
4027:
4024:
4022:
4019:
4017:
4014:
4012:
4011:
4007:
4006:
4004:
4002:
3998:
3989:
3984:
3982:
3977:
3975:
3970:
3969:
3966:
3959:
3955:
3952:
3949:
3947:
3944:
3933:
3929:
3925:
3914:
3910:
3908:
3905:
3904:
3883:
3879:
3875:
3868:
3852:
3848:
3844:
3838:
3822:
3818:
3814:
3808:
3792:
3788:
3784:
3778:
3762:
3758:
3754:
3748:
3732:
3728:
3724:
3717:
3701:
3697:
3693:
3686:
3670:
3666:
3662:
3655:
3640:
3635:
3627:
3616:September 26,
3611:
3607:
3603:
3597:
3581:
3577:
3573:
3566:
3550:
3546:
3542:
3535:
3533:
3521:September 26,
3516:
3512:
3508:
3501:
3485:
3481:
3475:
3466:
3461:
3457:
3453:
3446:
3437:
3432:
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3409:
3405:
3399:
3384:
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3374:
3359:
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3349:
3334:
3330:
3324:
3308:
3304:
3300:
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3134:
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3022:
3014:
3013:
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3001:
2993:
2989:
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2793:
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4718:Hugging Face
4682:David Silver
4396:
4330:Audio–visual
4184:Applications
4163:Augmentation
4008:
3935:. Retrieved
3931:
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3877:
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3857:September 5,
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3408:Stability AI
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3333:Stability AI
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2911:Ars Technica
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2651:ITmedia Inc.
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2418:Ars Technica
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2387:Stability AI
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1809:February 10,
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1440:September 6,
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1430:Stability.Ai
1429:
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1331:
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1312:September 5,
1310:. Retrieved
1301:
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1265:
1253:stability.ai
1252:
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1224:Hugging Face
1170:
1158:
1154:Getty Images
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487:Common Crawl
484:
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389:
377:latent space
346:
337:Architecture
326:random noise
287:
270:
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225:
206:
198:Stability AI
177:
176:
55:Stability AI
50:Developer(s)
18:
4866:Categories
4814:Autoencoder
4769:Transformer
4637:Alex Graves
4585:OpenAI Five
4489:IBM Watsonx
4111:Convolution
4089:Overfitting
3706:January 22,
3675:January 16,
3586:October 31,
2869:November 2,
2768:November 2,
2739:November 2,
2561:October 31,
2531:October 31,
2458:October 31,
2367:October 31,
2337:October 31,
2280:October 31,
2201:November 2,
2170:November 2,
2049:November 2,
1990:November 2,
1960:November 2,
1800:openvino.ai
1700:October 31,
1030:Key papers
1021:800M to 8B
903:3D modeling
871:DreamStudio
867:DreamStudio
549:Limitations
536:Nvidia A100
467:Transformer
265:Development
213:text prompt
4892:Categories
4855:Technology
4708:EleutherAI
4667:Fei-Fei Li
4662:Yann LeCun
4575:Q-learning
4558:Decisional
4484:IBM Watson
4392:Midjourney
4284:TensorFlow
4131:Activation
4084:Regression
4079:Clustering
3937:August 31,
3918:August 31,
3888:October 4,
3827:August 31,
3767:August 31,
3555:October 4,
3490:January 1,
3465:2112.10752
3436:2103.00020
3388:January 1,
3363:January 1,
3338:January 1,
3313:August 17,
3283:August 17,
3253:January 1,
3228:August 17,
3195:August 17,
3139:August 17,
3076:github.com
2966:TechCrunch
2891:2302.05543
2839:2210.12100
2815:2108.01073
2675:2108.01073
2609:2208.01618
2480:2210.04133
2428:August 21,
2397:August 21,
2191:TechCrunch
2105:2207.12598
1894:2209.03003
1867:2403.03206
1837:2307.01952
1746:cite arXiv
1737:1503.03585
1648:2112.10752
1513:August 31,
1362:www.lmu.de
1235:References
1214:Midjourney
1185:harassment
1150:DeviantArt
1146:Midjourney
1128:Litigation
988:July 2023
924:Parameter
895:node-based
852:ControlNet
838:layer mask
747:seed value
654:inpainting
632:DreamBooth
563:Fine-tuned
512:DeviantArt
383:backbone,
357:LMU Munich
304:Technology
294:EleutherAI
255:Midjourney
209:inpainting
120:Written in
90:Repository
4738:MIT CSAIL
4703:Anthropic
4672:Andrew Ng
4570:AlphaZero
4414:VideoPoet
4377:AlphaFold
4314:MindSpore
4268:SpiNNaker
4263:Memristor
4170:Diffusion
4146:Rectifier
4126:Batchnorm
4106:Attention
4101:Adversary
3737:August 6,
3665:The Verge
3639:404 Media
3072:"ComfyUI"
2992:The Verge
2500:Riffusion
2227:April 25,
1543:The Verge
1332:sifted.eu
1002:XL Turbo
755:front-end
598:fine-tune
529:watermark
500:WordPress
496:Pinterest
410:diffusion
403:diffusion
401:The name
322:denoising
190:diffusion
157:stability
4846:Portals
4605:Auto-GPT
4437:Word2vec
4241:Hardware
4158:Datasets
4060:Concepts
3882:Archived
3851:Archived
3821:Archived
3791:Archived
3761:Archived
3731:Archived
3700:Archived
3669:Archived
3644:June 14,
3610:Archived
3606:BBC News
3580:Archived
3549:Archived
3515:Archived
3413:March 5,
3307:Archived
3277:Archived
3222:Archived
3189:Archived
3165:March 6,
3133:Archived
3109:July 10,
3081:July 10,
3056:July 10,
2971:July 10,
2942:July 10,
2916:July 10,
2863:Archived
2792:Archived
2762:Archived
2733:archived
2705:Archived
2655:Archived
2632:Archived
2588:Archived
2583:PC Gamer
2555:Archived
2525:archived
2504:Archived
2452:Archived
2422:Archived
2391:Archived
2361:Archived
2331:Archived
2327:laion.ai
2309:Archived
2274:Archived
2237:cite web
2195:Archived
2164:Archived
2129:Archived
2072:Archived
2068:laion.ai
2043:archived
2014:Archived
1984:Archived
1954:Archived
1950:Waxy.org
1920:March 6,
1780:June 22,
1774:Archived
1694:Archived
1653:Archived
1547:Archived
1507:Archived
1477:Archived
1434:Archived
1403:June 22,
1397:Archived
1372:June 21,
1366:Archived
1342:June 20,
1336:Archived
1306:Archived
1276:Archived
1257:Archived
1203:See also
909:Releases
538:GPUs on
504:Blogspot
433:OpenVINO
425:consumer
415:With 860
385:denoises
330:concepts
239:publicly
4728:Meta AI
4565:AlphaGo
4549:PanGu-Σ
4519:ChatGPT
4494:Granite
4442:Seq2seq
4421:Whisper
4342:WaveNet
4337:AlexNet
4309:Flux.jl
4289:PyTorch
4141:Sigmoid
4136:Softmax
4001:General
3727:Reuters
3032:PCWorld
2937:bbc.com
2628:NovelAI
2323:"LAION"
2125:Twitter
1503:PCWorld
1393:Twitter
1219:Craiyon
1167:License
985:XL 1.0
890:ComfyUI
884:Fooocus
873:called
665:float32
661:float16
622:NovelAI
371:(VAE),
152:Website
143:License
4743:Huawei
4723:OpenAI
4625:People
4595:MuZero
4457:Gemini
4452:Claude
4387:DALL-E
4299:Theano
3576:Forbes
2859:Medium
2758:GitHub
2701:GitHub
1603:"Home"
1574:GitHub
1282:May 4,
1189:doxing
1173:DALL-E
1148:, and
927:Notes
727:Bottom
721:Centre
615:Nvidia
575:Nvidia
508:Flickr
451:SD 3.0
421:
417:
381:ResNet
290:Runway
279:Munich
251:DALL-E
228:latent
221:Runway
164:
124:Python
99:github
4809:Mamba
4580:SARSA
4544:LLaMA
4539:BLOOM
4524:GPT-J
4514:GPT-4
4509:GPT-3
4504:GPT-2
4499:GPT-1
4462:LaMDA
4294:Keras
3460:arXiv
3431:arXiv
2886:arXiv
2834:arXiv
2810:arXiv
2670:arXiv
2604:arXiv
2475:arXiv
2100:arXiv
1889:arXiv
1862:arXiv
1832:arXiv
1804:Intel
1732:arXiv
1656:(PDF)
1643:arXiv
1639:(PDF)
1181:libel
1115:Pixiv
991:3.5B
951:983M
845:depth
799:Right
559:anime
492:LAION
439:SD XL
373:U-Net
298:LAION
180:is a
4733:Mila
4534:PaLM
4467:Bard
4447:BERT
4430:Text
4409:Sora
3939:2024
3920:2024
3890:2022
3859:2022
3829:2022
3799:2023
3769:2022
3739:2023
3708:2023
3677:2023
3646:2024
3618:2023
3588:2022
3557:2022
3523:2022
3492:2024
3415:2024
3390:2024
3365:2024
3340:2024
3315:2023
3285:2023
3255:2024
3230:2023
3197:2023
3167:2024
3141:2023
3111:2024
3083:2024
3058:2024
2973:2024
2944:2024
2918:2024
2871:2022
2770:2022
2741:2022
2713:2022
2563:2022
2533:2022
2460:2022
2430:2023
2399:2023
2369:2022
2339:2022
2282:2022
2243:link
2229:2024
2203:2022
2172:2022
2137:2022
2080:2022
2051:2022
2022:2023
1992:2022
1962:2022
1922:2024
1811:2024
1782:2023
1752:link
1702:2022
1664:2022
1614:2024
1555:2022
1515:2022
1485:2022
1442:2022
1405:2023
1374:2023
1344:2023
1314:2022
1284:2023
1015:3.0
972:2.1
959:2.0
945:1.5
830:WebP
828:and
826:JPEG
820:and
793:Left
638:and
571:VRAM
514:and
396:CLIP
320:The
296:and
281:and
253:and
247:VRAM
219:and
131:Type
101:.com
4474:NMT
4357:OCR
4352:HWR
4304:JAX
4258:VPU
4253:TPU
4248:IPU
4072:SGD
3696:CNN
1191:, "
715:Top
577:'s
429:CPU
277:in
243:GPU
159:.ai
4894::
3930:.
3876:.
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3845:.
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3531:^
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