Do You Remember? — That Summer on Port 7860

An anonymous developer recounts the four-year history of the AUTOMATIC1111 Stable Diffusion WebUI, from its creation in August 2022 to its decline by 2026. The project, which began as a single Gradio script, became the most popular interface for running Stable Diffusion locally before being overtaken by newer tools like ComfyUI and Forge. The developer documents the project's rise, peak, and eventual stagnation, marking the end of an era for open-source AI image generation.

A Memoir of AUTOMATIC1111 Stable Diffusion WebUI — from a single line of Gradio script in August 2022, to the closing curtain of an era 2022.08.22 — 2026.07.10 | An open-source legend of roughly four years Do you remember? It feels like it just happened yesterday. An anonymous GitHub user, a Gradio script found in a post on an anonymous image board, a git clone , a run.bat , and then a page popped up in the browser at 127.0.0.1:7860 — prompt on the left, generated image on the right, the progress bar inching forward step by step, VRAM usage jumping around in Task Manager. That thrill of getting Stable Diffusion running on your own machine for the first time, that sense of accomplishment when you finally produced a "decent-looking" image after tweaking parameters for hours — thinking back on it now, it all still feels vivid. But do the math: from that afternoon in August 2022 when the repository was created, to this midsummer of 2026 — it's been nearly four years. Four years. On the timescale of internet products, four years is enough for a platform to be born, peak, and fade into obscurity; enough for a generation of users to dive in, drift away, and forget. A1111 WebUI traced this complete arc. Four years ago, AI image generation was, for most people, synonymous with cloud services like Midjourney — the experience of typing /imagine in Discord and waiting for results. Running Stable Diffusion on a local machine meant opening a terminal, typing command-line instructions, navigating Python dependency hell, and facing a pile of incomprehensible parameters. A1111 packed all of that into a web interface, letting anyone who could type generate images on their own GPU. It wasn't the first to do this, but it was the most complete, the most user-friendly, and the one with the most thriving ecosystem. And now? Open the GitHub Release page, and the last version sits at v1.10.1, dated February 9, 2025 — and it wasn't even published by AUTOMATIC1111 himself. The Issues section has discussion threads asking "Is this project dead?" ^1 . The community's center of gravity has long since shifted to ComfyUI, the node-based workflow engine iterating at a pace of one release per week ^2 . lllyasviel, the creator of ControlNet, started fresh with Forge, which can run FLUX on just 4GB of VRAM ^3 . The WebUI that once changed everything at 127.0.0.1:7860 now lies there quietly, like a veteran who has completed his mission. This article is a complete memoir of it all. From the first line of code on that August afternoon in 2022, to the legacy it leaves behind today in 2026 — every version, every feature, every turning point, I will record as accurately as I can. Because some things, while you still remember them, should be written down. To tell the story of A1111 properly, we have to go back to its cause — the birth of the Stable Diffusion model itself. On August 22, 2022, Stability AI, in collaboration with the CompVis Lab at LMU Munich and Runway ML, publicly released the Stable Diffusion 1.4 model ^4 . It was a text-to-image generation model based on Latent Diffusion Model architecture, with approximately 890 million parameters, open-sourced under the CreativeML Open RAIL-M license. At the time, it was the most capable open-source image generation model, bar none. Its release sent shockwaves through the AI community akin to an earthquake. But the model itself was just a set of weight files and an inference script. The official tool provided by Stability AI was a Python script that had to be run from the command line. For most people without a deep learning background — artists, designers, photography enthusiasts, curious netizens — "open a terminal, activate a conda environment, run python scripts/txt2img.py --prompt 'a cat' " was itself a barrier. And behind that barrier lay a series of obstacles that could make anyone give up at the first step: wrong CUDA version, PyTorch won't install, not enough VRAM, how to fill in the Hugging Face token... On that very day, August 22, 2022, at 14:05:26 UTC, a GitHub user going by the name AUTOMATIC1111 created a public repository called stable-diffusion-webui ^5 . Ten minutes later, at 14:15:46, the first substantive code commit was pushed, with a commit message so terse it consisted of a single word: first ^6 . A web interface for Stable Diffusion, implemented using Gradio library. — Repository description, August 22, 2022 This was no coincidence. A1111's birth and the release of Stable Diffusion 1.4 happened on the same day . Some online sources claim A1111 "appeared about a month after SD's release," which is inaccurate — the repository creation timestamp returned by the GitHub API is ironclad proof ^5 ^6 . AUTOMATIC1111 responded the very day SD 1.4 was open-sourced, and this speed itself tells you something: he didn't act on impulse. He was prepared, waiting for the moment a model would be open-sourced. And on that day, the democratization of AI image generation officially began. Who is AUTOMATIC1111? This question still has no definitive answer. He has never revealed his real identity. His GitHub profile was created in 2022, and his commit records show the author name as AUTOMATIC with the email 16777216c@gmail.com ^6 . 16777216 is 2 to the 24th power, and the trailing c is widely believed to be a nod to 4chan — a connection made more explicit in the project's README. Open the README file of the A1111 repository, and the Credits section reads: Initial Gradio script — posted on 4chan by an Anonymous user. Thank you Anonymous user. — stable-diffusion-webui README This line reveals A1111's true starting point: AUTOMATIC1111 did not build this project from scratch. After Stable Diffusion 1.4 was released, an anonymous user on 4chan posted a simple Gradio-based script that let Stable Diffusion run in a browser. AUTOMATIC1111 took this script, used it as a starting point, rapidly expanded it into a fully-featured web application, and pushed it to GitHub ^4 . This origin story itself carries a distinctively internet-native character: an anonymous person, on an anonymous forum, posts anonymous code, and then another anonymous person picks it up and turns it into a product that changes the entire industry landscape. No company, no funding, no roadshow, no LinkedIn profile. Just a pseudonym on GitHub and a commit history. There was also no affiliation with Stability AI. Stability AI was the upstream model provider; A1111 was an independent community project ^4 . This independence became critically important later — it meant A1111's fate was entirely tied to a single anonymous maintainer, which was both its greatest advantage immune to commercial pressure and its greatest risk no organizational continuity guarantee . On January 5, 2023, this risk manifested in dramatic fashion: AUTOMATIC1111's GitHub account was briefly banned for alleged Terms of Service violations, and the entire repository went offline, causing panic in the community ^4 . The ecosystem's core tool vanished along with its creator's account — the scenario every single-maintainer open-source project dreads most. The account was subsequently restored, and GitHub provided no detailed explanation, but the incident served as a wake-up call for everyone. Ten days later, on January 15, 2023, AUTOMATIC1111 added the AGPL-3.0 open-source license to the project ^7 . For nearly five months prior, this project with tens of thousands of stars had no formal license file — the README even noted "Now with a license " A project that reshaped the AI image generation landscape had been "running naked" legally for almost half a year. This sort of thing could probably only happen in this anonymous, decentralized community. From August 22, 2022, to January 24, 2023 — nearly five months — A1111 never published a versioned Release. Users updated by running git pull to fetch the latest commits, and the changelog was the commit history ^4 . But it was precisely this "versionless" period that laid the foundation for nearly all of A1111's core features. The pace of development was so fast that "feral growth" is not an exaggeration. On the day the repository was created, in addition to the basic txt2img function, AUTOMATIC1111 had already added GFPGAN face restoration and Prompt Matrix ^6 . The next day, August 23, prompt length validation and the --no-half command-line option were added, and img2img mode also gained Prompt Matrix support. On August 24, Textual Inversion support was merged, from a PR submitted by community contributor dogewanwan ^6 . Over the following two weeks, features grew at nearly one per day: ui-config.json allowed interface parameters to persist. The user experience evolved from "fill everything in again each time" to "remember my settings." webui-user.bat , making it easy for non-English speakers and Windows users to launch. <lora:filename:multiplier prompt syntax — this feature would become the cornerstone of the entire SD fine-tuned model ecosystem.In two weeks, a script with only txt2img had transformed into a fully-featured image generation workbench: text-to-image, image-to-image, inpainting, face restoration, upscaling, textual inversion, hypernetwork training, prompt matrix, tiling, LoRA loading... this feature list would not be out of place today. And all of this was done without any version number, without a formal Release, without a changelog. AUTOMATIC1111 and the early community contributors, at a nearly frenetic pace, turned a 4chan script into a complete piece of software. During this period, A1111 also established several conventions that would become de facto standards across the entire Stable Diffusion ecosystem. The three most far-reaching were: Three Legacy Conventions Negative Prompt: In addition to the positive prompt, a separate negative prompt input field was added, telling the model "what not to generate." This design seems obvious in hindsight, but before A1111, most SD inference scripts did not treat it as an independent, first-class input field. Prompt Weighting Syntax: Syntax like word:1.2 let users assign different weights to specific words in the prompt, precisely controlling generation direction. This syntax was later adopted as standard by virtually all model-sharing platforms, including Civitai and Hugging Face. PNG Metadata Embedding: Generated PNG images automatically embedded complete generation parameters model, prompt, negative prompt, sampler, steps, CFG scale, seed, etc. . This meant any image generated by A1111 could be reproduced by "dragging it back into the interface." This convention was also widely adopted across the ecosystem. None of these three conventions were technically complex. But together they constituted a "user experience standard" — when everyone was using A1111, A1111's way of doing things became the standard. Model description pages on Civitai were written according to A1111's usage, tutorials used A1111's interface screenshots, and prompts followed A1111's syntax format. This ecosystem lock-in effect is one of A1111's most profound legacies ^4 . On January 24, 2023, A1111 finally released its first tagged version: v1.0.0-pre ^4 . The -pre suffix betrayed a modest humility — even with tens of thousands of stars and a complete feature set, AUTOMATIC1111 still considered this merely a "pre-release." The significance of v1.0.0-pre lay not in features — by then, txt2img, img2img, inpainting, upscalers, textual inversion, hypernetworks, LoRA, xformers, --medvram / --lowvram , and other core features had long existed — but in formalizing the extension system . From this version on, A1111 provided a standard extension installation mechanism: users could install third-party extensions by entering a GitHub URL in the Extensions tab, or use built-in extensions from the extensions-builtin/ directory ^8 . Additionally, this version provided a sd.webui.zip binary distribution package for users who couldn't even install Python and Git — just extract and double-click run.bat to run Windows 10 + NVIDIA GPU only ^9 . At a time when AI image generation tutorials were still stuck on "how to configure a conda environment," this one-click launcher dramatically lowered the barrier to entry. From v1.0.0-pre onward, A1111 entered a rhythmic release cycle. Over the next roughly year and a half, from v1.1.0 to v1.10.0, eleven major versions were released, each bringing substantial feature updates and architectural improvements. This period was A1111's golden age. Below is the complete version chronicle of A1111 from v1.1.0 to v1.10.1. All dates are from the official GitHub Release pages ^9 . The first formal version number dropping the -pre . The core change was switching to torch 2.0.0 except for AMD GPUs and introducing a "Random number generator source" setting, making the same seed produce identical images across different GPUs — previously, the same seed could produce different results on different hardware, making parameter sharing and reproduction unreliable ^9 . Also supported Gradio theme API, defaulted to TCMalloc on Linux to fix memory leaks, added "resize by"/"resize to" tabs to img2img, and a progress recovery button after session refresh. No longer waited for model loading at startup, significantly accelerating launch speed. Multiple LoRA improvements: activation by metadata name, fixed some LoRAs not working, provided legacy method compatibility option. Gradio upgraded to 3.29.0, torch to 2.0.1, added --subpath option for reverse proxy deployment ^9 . This version introduced two important optimizations: Token Merging via dbolya/tomesd , reducing computation without significantly degrading image quality to improve generation speed; and TAESD Tiny AutoEncoder for Stable Diffusion , a low-cost real-time preview solution that let users see a low-resolution preview during generation ^9 . Additionally, LoRA began computing hashes and including them in infotext, enabling hash-based matching of local LoRAs when pasting parameters; users could select cross-attention optimization methods from the UI. On the code management front, Ruff Python and ESLint JavaScript code checking tools were introduced — a signal that the project had grown large enough to require standardized management ^9 . Inpainting scaling control, custom k-diffusion scheduler settings, sysinfo system info tab. Parameter pasting could infer styles from prompts. xformers upgraded to 0.0.20, added custom unet support and persistent conds caching optional optimization . Removed taming transformers dependency, beginning dependency slimming ^9 . A milestone version. Two core updates made it one of the most important versions in A1111's history: First, SDXL Stable Diffusion XL support . Stability AI released SDXL 1.0 in July 2023, the largest model upgrade since SD 1.5, with parameters increasing from 890 million to approximately 6.6 billion and native resolution increasing from 512×512 to 1024×1024. A1111 provided native support within a month of SDXL's release requiring the --no-half-vae flag ^9 ^10 . Second, LoRA extension restructuring . Network types previously handled by the LyCORIS extension — LoCon, LoHa, and others — were integrated into the built-in extension system. From v1.5.0 onward, A1111 natively supported multiple network types ^10 . This change seemed technical, but it directly fueled the explosive growth of LoRA models on Civitai — users no longer needed to install the LyCORIS extension separately to load various fine-tuned models. Also added: a user metadata system for custom networks, an expanded LoRA metadata editor, a protection mechanism that automatically switched to 32-bit float VAE when NaN appeared in generated images, and API endpoints server-kill/restart/stop . pip install skipping already-installed packages sped up startup by about 2 seconds ^9 . Another milestone. SDXL Refiner support — SDXL's two-stage inference Base model generates a rough image, Refiner model refines details was built in as a sequential inference process ^10 ^9 . RNG source added an NV option, making CPU/AMD/Mac generation consistent with NVIDIA. Hires fix could use a different checkpoint for the second pass. Option to keep multiple loaded models in memory reducing load wait when switching . On the sampler front, new options were added including Restart, DPM++ 2M SDE Exponential/Heun/Karras, and DPM++ 3M SDE series, with DPM++ 2M Karras becoming the default sampler. DDIM, PLMS, and UniPC were refactored to uniformly use CFG denoiser, enabling support for img2img, AND prompt combinations, and SDXL ^9 . Architecturally, shared.py and webui.py were split into multiple files, StableDiffusionProcessing was converted to a dataclass, and LoRA patches were moved from inside torch to a dedicated module. These refactors indicated the codebase had grown large enough to require modular management. VAE also gained support for selecting independent VAE per checkpoint ^9 . Settings tab refactored with search box and categories. SSD-1B model support Segmind Stable Diffusion 1B, a distilled and compressed SDXL . SD 2.1 Turbo support . HyperTile optimization — accelerating large-resolution generation by processing attention layers in tiles. LyCORIS GLora and OFT network inference support. lora-embedding bundling system. Initial IPEX support for Intel Arc GPUs. Script metadata and DAG sorting mechanism ^9 . torch upgraded to 2.1.2. Multiple important updates: Soft Inpainting smoother inpainting edge transitions ; FP8 precision support reducing VRAM and RAM usage ; SDXL-Inpaint model support ; official LCM Latent Consistency Model sampler support — LCM could generate high-quality images in 4-8 steps, a multiple-times speedup over traditional 20-30 step samplers ^9 . Architecturally, adopted Spandrel as the unified interface for upscaling and face restoration, removing basicsr, gfpgan, realesrgan and their many dependencies from requirements — an important decoupling that made adding upscaling and face restoration models modular ^9 . Zero terminal SNR noise schedule option note: this is a seed-breaking change . DAT upscaler model support. Extra Networks tree view. Huawei Ascend NPU support. Prompt comments support. Automatic backward version compatibility ^9 . Scheduler selection in the main UI. sgm uniform scheduler for SDXL-Lightning models. DoRA Weight-Decomposed Low-Rank Adaptation support — DoRA is an improved version of LoRA, achieving better fine-tuning results with fewer parameters, supporting multiple network types including LoRA/LoHa/LoKr. LyCORIS BOFT network support. Refiner switching changed to be based on model timesteps. Callback reordering UI. diskcache library for caching. Cover image embedded in safetensors metadata ^9 . v1.9.1 AVIF support, schedulers API endpoint , v1.9.3 April 22, bug fixes , v1.9.4 May 28, pin setuptools version to fix startup error followed in succession ^9 . The last version published by AUTOMATIC1111 himself, and the latest generation of models A1111 supported: Stable Diffusion 3 support . SD3's text encoder T5 was disabled by default could be enabled in settings , Euler sampler was recommended, and timestamp samplers like DDIM were not yet supported ^9 . New schedulers included Align Your Steps, KL Optimal, Normal, DDIM, Simple, and Beta. New sampler: DDIM CFG++. This version also contained a series of large-scale performance optimizations Performance 1/6 through 6/6 series PRs : use checkpoint=False , replacing einops.rearrange with native torch operators, precomputing is sdxl inpaint flag, keeping sigmas on CPU, --precision half to avoid type conversion during inference, LDM optimization patches, and more ^9 . These optimizations further squeezed inference performance while maintaining generation quality. Just one fix: a bug with image upscaling on CPU. But this version was published not by AUTOMATIC1111, but by maintainer w-e-w ^9 . This marked a change in A1111's maintenance structure — the original maintainer had effectively withdrawn from day-to-day development. Version Release Timeline Summary: | Version | Release Date | Milestone Features | |---|---|---| | v1.0.0-pre | 2023-01-24 | First tagged release, extension system formalized | | v1.1.0 | 2023-05-01 | torch 2.0.0, RNG source setting | | v1.2.0 | 2023-05-13 | Startup acceleration, LoRA improvements | | v1.3.0 | 2023-05-27 | Token Merging, TAESD preview | | v1.4.0 | 2023-06-27 | Custom UNet, conds caching | | v1.5.0 | 2023-07-25 | SDXL support, LoRA extension restructuring | | v1.6.0 | 2023-08-31 | SDXL Refiner, architecture refactoring | | v1.7.0 | 2023-12-16 | SSD-1B, SD 2.1 Turbo, HyperTile | | v1.8.0 | 2024-03-02 | FP8, LCM samplers, Spandrel | | v1.9.0 | 2024-04-13 | DoRA support, scheduler selection | | v1.10.0 | 2024-07-27 | SD3 support, performance optimization series | | v1.10.1 | 2025-02-09 | Final version published by w-e-w | If A1111's core features were the skeleton, the extension system was the muscle that gave it real flesh. And among all extensions, none had a more profound impact than ControlNet. On February 10, 2023, a researcher going by the name lllyasviel later revealed to be Lvmin Zhang released ControlNet ^11 . It was a technology that made diffusion models "obedient": by inputting a reference image edge detection map, depth map, body pose map, etc. , it guided the model to generate new images structurally consistent with the reference. Before this, Stable Diffusion's generation results were primarily guided by prompts, with limited control — you could tell it "draw a standing person," but you couldn't precisely control that person's pose. ControlNet changed that. After ControlNet's release, community developer Mikubill quickly launched the sd-webui-controlnet extension, making ControlNet usable directly within the A1111 interface ^11 . In April 2023, lllyasviel released ControlNet 1.1, improving model quality and control precision. From then on, the ControlNet extension became a standard fixture for nearly every A1111 user — an A1111 instance without ControlNet installed was like a car without a steering wheel. ControlNet was just the tip of the extension ecosystem iceberg. During A1111's golden age, the Extensions tab contained hundreds of extensions covering virtually every imaginable function: | Extension | Function | Impact | |---|---|---| sd-webui-controlnet | ControlNet integration | De facto standard for structural control | | Deforum | Animation generation | Turned A1111 from a static image tool into an animation tool | | Ultimate SD Upscale | Tiled upscaling | Broke VRAM limits, enabling ultra-high-resolution generation | | ADetailer | Automatic face restoration | Auto-detects and fixes facial defects during batch generation | | ReActor / roop | Face swapping | Replaces faces in generated images with specified faces | | Segment Anything | Auto-segmentation | Auto-selects regions using Meta's SAM model | | Dynamic Prompts | Dynamic prompting | Template-based batch generation with variables and conditional logic | | Canvas Zoom | Canvas zoom | Improved inpainting canvas interaction | The extension system's design philosophy was simple: allow community developers to add features without modifying core code. But this "plugin-based" design infinitely expanded A1111's capability boundaries. An artist wanted specific style control? Someone wrote an extension. A developer wanted batch processing workflows? Someone wrote an extension. A researcher wanted to test a new sampling algorithm? Someone wrote an extension. From v1.5.0, the extensions page began displaying GitHub star counts, helping users gauge extension popularity. From v1.6.0, --disable-extra-extensions and --disable-all-extensions flags were supported, letting users quickly rule out extension interference when troubleshooting ^9 . These details showed that AUTOMATIC1111 recognized the extension system's importance and was actively maintaining its health. July 2023 was one of the most euphoric months for the A1111 community. Stability AI released Stable Diffusion XL SDXL 1.0, the largest upgrade to the SD series since version 1.5. SDXL had approximately 6.6 billion parameters, native resolution of 1024×1024 previous SD 1.x/2.x was 512×512 , and significant improvements in image quality, text rendering, and compositional understanding. A1111 provided native SDXL support in v1.5.0 July 25, 2023 ^9 ^10 . Immediately after, v1.6.0 August 31 added SDXL Refiner support and the --medvram-sdxl flag enabling medvram only for SDXL, a targeted downgrade when VRAM is insufficient ^9 . The community reaction was explosive — Reddit's r/StableDiffusion was flooded with SDXL-generated images, and the number of SDXL fine-tuned models on Civitai surpassed an entire year's worth of SD 1.5 model growth within a month or two. SDXL's release also spawned an important model series: Pony. Pony was essentially an SDXL fine-tuned model that excelled at anime/anthropomorphic character generation. Since A1111 had full SDXL support from v1.5.0/v1.6.0, Pony models could be loaded directly without special adaptation ^1 . v1.8.0 March 2024 further added SDXL-Inpaint model support, and v1.9.0 April added the sgm uniform scheduler for SDXL-Lightning models — SDXL-Lightning was a distilled model released by Stability AI that could generate images in 2-4 steps, extremely fast ^9 . A1111's support for the SDXL ecosystem tracked upstream closely, with no lag. This period was also the peak of A1111's user growth. GitHub star count soared from tens of thousands in early 2023 to approximately 136,000 by mid-2024 ^12 . Model-sharing platforms like Civitai and Hugging Face standardized their model pages around A1111's usage conventions, further cementing A1111's position as the "default interface" ^4 . During the period from 2023 to mid-2024, "Stable Diffusion WebUI" was virtually synonymous with "A1111." From day one, A1111 was fighting a war with VRAM. Stable Diffusion models typically required 6-8GB of VRAM, while most users in 2022 had GPUs with only 4-6GB. How to run large models on small VRAM was the throughline of A1111's performance optimization. The earliest optimization tools were the --medvram and --lowvram command-line flags, which reduced peak VRAM usage by time-scheduling model components between CPU and GPU ^8 . The tradeoff was speed — frequent CPU-GPU data transfers slowed generation down, but at least it could run on lower-end GPUs. --xformers used Facebook's xformers library to optimize attention computation, reducing VRAM usage and accelerating inference without quality degradation ^8 . These three flags were introduced between September and October 2022, A1111's earliest performance optimizations. In subsequent versions, performance optimizations continued to evolve: | Optimization | Introduced Version | Principle | |---|---|---| | TCMalloc Linux default | v1.1.0 | Replaces standard malloc, reduces memory fragmentation and leaks | | Token Merging tomesd | v1.3.0 | Merges redundant tokens to reduce computation, trades minor quality for speed | | TAESD real-time preview | v1.3.0 | Lightweight VAE decoder for real-time preview during generation | | Persistent conds cache | v1.4.0 | Caches conditioning computation results, reduces redundant computation | --medvram-sdxl | v1.6.0 | Enables medvram only for SDXL, targeted downgrade | | HyperTile | v1.7.0 | Processes attention layers in tiles, accelerates large-resolution generation | | FP8 precision | v1.8.0 | 8-bit float inference, halves VRAM usage | | LCM samplers | v1.8.0 | 4-8 step generation, multiple-times speedup | | Performance 1/6–6/6 series | v1.10.0 | Multi-pronged: native operator replacement, sigmas on CPU, type conversion optimization | These optimizations layered on top of each other, enabling A1111 to improve inference efficiency for contemporary models several-fold over four years. A 512×512 image that took 30 seconds to generate in 2022 might only take 5-8 seconds on the same hardware in 2024. The introduction of LCM samplers made "instant generation" possible. For a synchronous Gradio-based web application like A1111, every second of optimization translated directly into improved user experience. In the second half of 2024, A1111's story began moving in a different direction. The catalyst was FLUX. In August 2024, Black Forest Labs a company founded by the original authors of the Stable Diffusion paper released the FLUX.1 model series. FLUX adopted a completely different architecture from Stable Diffusion — based on Rectified Flow Transformer rather than U-Net diffusion model — achieving generational improvements in image quality, text rendering, and prompt adherence. But A1111 couldn't run FLUX. FLUX's architecture was incompatible with A1111's underlying processing mechanisms, requiring extensive modifications ^3 . By this time, AUTOMATIC1111 had essentially ceased development activity — the last commit on the master branch was on July 27, 2024 ^5 , the day v1.10.0 was released. After that, no new commits were pushed to the main branch. The community waited months, but no FLUX support came, nor any public statement from AUTOMATIC1111. In early 2025, anxious posts began appearing in GitHub Issues and Discussions: "Is this a dead project?" — GitHub Discussion title, early 2025"Why did you stop updating?" — GitHub Discussion title, early 2025 The community pointed out: no new releases since July 2024, 44 open Pull Requests left unmerged for extended periods, no commits approved for months ^1 . On February 9, 2025, maintainer w-e-w published v1.10.1 — a minor patch fixing only a CPU image upscaling bug ^9 . This was not AUTOMATIC1111's return, but other maintainers attempting to keep the project minimally functional. After that, even this kind of minor patch never appeared again. FLUX wasn't the only shock. During the same period, new-generation models like SD3 which A1111 supported in v1.10.0, though community reception of SD3 was mixed , Pony SDXL fine-tune, which A1111 could run , and various video generation models emerged. The AI image generation field was undergoing an architectural paradigm shift, and A1111's underlying architecture was designed for U-Net diffusion models — adapting to new architectures required extensive low-level changes. An anonymous, single-maintainer-dependent open-source project, with no organizational continuity mechanism once the maintainer stopped activity. This was the structural risk embedded in A1111 from its inception — the January 2023 GitHub ban incident was a rehearsal, and the second-half 2024 stagnation was its final realization. A1111's stagnation was not the end of the story, but the beginning of a new one. On top of and alongside A1111, a batch of alternatives rapidly rose, each filling the gaps A1111 left behind. On January 17, 2023, a developer going by comfyanonymous created ComfyUI on GitHub ^2 . Unlike A1111's "form-filling" interface, ComfyUI adopted a node-based workflow: users built image generation pipelines by dragging nodes and connecting wires, with each node representing an operation load model, encode text, sample, decode, etc. . This design appeared to have a higher barrier, but actually provided far greater flexibility than A1111. In A1111, if you wanted to try a new inference pipeline say, upscale first, then inpaint, then face restore , you needed to find the corresponding extension and rely on the extension author's provided interface. In ComfyUI, you just connected a few nodes. More importantly, node workflows could be saved as JSON files and shared — a complete generation pipeline for an image could be precisely reproduced, which was hugely valuable for professional users and researchers. By mid-2026, ComfyUI had grown to an astonishing scale: 5,248 commits, 216 branches, version v0.21.1, with a weekly release cycle ^2 . The project migrated from a personal account to the Comfy-Org organization, launched a desktop application Windows/macOS and Comfy Cloud cloud service, and transformed into a commercial product. On the model support front, ComfyUI natively supported virtually all mainstream image models from SD 1.x to FLUX 2, as well as video SVD, Mochi, LTX-Video, Hunyuan Video , audio, and 3D models ^2 . In early 2024, lllyasviel — yes, the ControlNet author — released Stable Diffusion WebUI Forge ^3 . Forge was built on A1111 1.10.1, aiming to be "the Forge of SD WebUI" named after Minecraft Forge , rewriting resource management and optimizing inference performance without changing A1111's interface conventions. Forge's core appeal was twofold: first, vastly optimized VRAM management — 4GB of VRAM could run SDXL, with significantly improved generation speed; second, full support for the FLUX model series ^3 — exactly what official A1111 hadn't delivered. For A1111 veterans who didn't want to learn node-based workflows but wanted to run FLUX, Forge was virtually the only choice. Forge also added DDPM, DPM++ 2M Turbo, and other samplers, earning the moniker "A1111 on steroids" ^3 . Fooocus , also developed by lllyasviel, took the minimalist route. Its interface resembled Midjourney — users just entered a prompt, and all other parameters were automatically configured. It targeted users who "didn't want to fiddle with parameters, just wanted to generate images" ^13 . SD WebUI ReForge , initiated by Panchovix. After Forge's own development slowed, ReForge continued Forge's optimization and experimental feature line ^14 . InvokeAI , an earlier project 2022 , focused on a unified canvas workflow with a more professional and polished interface, suitable for studios and teams, though with a relatively smaller extension ecosystem ^13 . | Project | Interface Style | Maintenance Status | FLUX Support | GitHub Stars approx. | |---|---|---|---|---| | A1111 | Form-based | Stagnant | Not supported | ~164k ^1 | | ComfyUI | Node-based | Extremely active | Native support | ~84k–110k ^2 | | Forge | Form-based A1111 compatible | Low activity | Full support | Thousands to tens of thousands | | Fooocus | Minimalist Midjourney-like | Low activity | Supported | Tens of thousands | | InvokeAI | Canvas-based | Active | Supported | ~20k | It should be noted that some online articles claim "ComfyUI's star count surpassed A1111 by mid-2025," but according to more authoritative recent June 2026 data, A1111 still leads in cumulative star count approximately 164k vs ComfyUI's 84k-110k ^1 . However, ComfyUI's growth rate and activity level far exceed A1111's, and the gap is closing rapidly. In terms of community attention and actual usage, ComfyUI has become the standard tool for advanced users and professionals, while A1111 and its Forge branch remain popular among users who prefer a simpler interface ^13 . Technical competition was only one aspect. A1111's decline was more profoundly reflected in the migration of community sentiment. In Reddit's r/StableDiffusion community, users who switched from A1111 to ComfyUI universally reported: faster generation on the same hardware, more flexible complex workflows, and better support for new models like FLUX ^13 . Many tutorial creators on YouTube and Bilibili had migrated from A1111 to ComfyUI, and new tutorial series almost defaulted to using ComfyUI as the teaching environment. A new consensus formed in the community: 2025-2026 Community Consensus Beginners: Start with A1111 or Forge, understand the basics, then consider switching to ComfyUI. A1111 users hitting a ceiling: Switch to ComfyUI. When you need finer control, more complex workflows, or want to try new models, ComfyUI is the answer. Professional users and researchers: ComfyUI has become the standard tool. A1111 and Forge remain suitable for scenarios preferring a simple interface and quick generation. But to be fair, A1111 has not been completely abandoned. It has the most extensive extension ecosystem and tutorial library — content accumulated over four years can't be replaced overnight. For many beginners, A1111's "form-filling" interface remains the most intuitive entry point into AI image generation. Those conventions formed in 2022-2023 — negative prompts, prompt weighting syntax, PNG metadata embedding — have been deeply embedded in the entire ecosystem. Even as users move to other tools, these conventions continue to function. The numbers on GitHub also tell this story silently. As of June 2026, the A1111 repository had approximately 164,000 stars, 30,400 forks, 586+ contributors, and 7,689 commits ^1 . These numbers make A1111 one of the most-starred open-source AI projects in history. The star count continues to grow slowly — perhaps dozens of people each day discover this repository for the first time, hit the star button, and then might soon discover that its Release page hasn't been updated in a long time. Looking back at A1111's complete trajectory — from the first commit on August 22, 2022, to the last substantive update on July 27, 2024, to a bug-fix patch published by someone else in February 2025 — its active lifespan was less than two years. But in those less than two years, it accomplished something worthy of the internet history books: it moved AI image generation from the command line into the browser, from a researcher's tool into a toy for the masses. Before A1111, running Stable Diffusion locally required Python programming knowledge, command-line proficiency, and the patience to deal with dependency conflicts. After A1111, anyone with an NVIDIA GPU and hands that could type could generate images on their own computer. This "anyone" included artists, designers, novelists, game developers, students, retirees — all those who couldn't write code but possessed creativity. A1111's legacy is not just "ease of use." It left behind a set of technical conventions and cultural habits that still profoundly influence the entire field: Prompt syntax standard . The word:1.2 attention weighting syntax, the negative prompt field, the prompt matrix — these conventions pioneered or popularized by A1111 have become de facto standards across the entire Stable Diffusion ecosystem. Model descriptions on Civitai are written in this syntax, third-party tools parse this syntax, and even ComfyUI is compatible with it in some scenarios ^4 . PNG metadata embedding . Generated images carry complete generation parameters, and dragging them back into the interface reproduces the generation. This seemingly simple function created a culture of "sharing an image means sharing its parameters" in the AI image generation community. How an image was generated no longer required asking in a forum post — you could just read it from the image file ^4 . Extension system paradigm . Allowing community developers to add features without modifying core code — this design philosophy not only spawned A1111's own massive extension ecosystem but also provided a paradigm reference for later tools, including ComfyUI's custom node system. The practice of democratization . Beyond cloud services like Midjourney and DALL-E, A1111 made local, free, privacy-controllable AI image generation accessible to the masses. You didn't need to subscribe to any service, upload any images to the cloud, or worry about who might see your prompts — everything happened on your own machine. This "my GPU, my rules" spirit is one of the core values of the open-source AI community, and A1111 was its most successful practitioner ^4 . As for AUTOMATIC1111 himself, he never revealed his identity, never gave an interview, never posted personal remarks on social media. After his GitHub activity went silent, the community had various speculations — some said burnout, some said he moved on to other projects, some said real-life reasons. But there are no definitive answers, and perhaps there never will be. This actually gives the whole story a romantic quality belonging to the early internet era: an anonymous person, building on anonymous code from an anonymous forum, made a tool, changed the world, and then quietly left. No IPO, no TED talk, no Time magazine cover. Just a repository on GitHub that no longer updates, and 164,000 stars. Do you remember? That page at 127.0.0.1:7860 , that afternoon watching the progress bar inch forward, that image you finally tuned to exactly what you wanted. It feels like it was yesterday. But do the math — it's been nearly four years. Some things become obsolete, some tools get replaced, some repositories stop updating. But what they changed — giving ordinary people the ability to create images — has happened, permanently. That is A1111's legacy. And a legacy does not vanish just because its creator departs.