dalle-mini VS stable-diffusion

Compare dalle-mini vs stable-diffusion and see what are their differences.

dalle-mini

DALL·E Mini - Generate images from a text prompt (by borisdayma)
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dalle-mini stable-diffusion
3,451 387
14,782 69,545
0.2% 0.9%
5.2 0.0
over 1 year ago 8 months ago
Python Jupyter Notebook
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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dalle-mini

Posts with mentions or reviews of dalle-mini. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-01-23.

stable-diffusion

Posts with mentions or reviews of stable-diffusion. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-01-23.
  • Janus Pro 1B running 100% locally in-browser on WebGPU
    1 project | news.ycombinator.com | 28 Jan 2025
    > 860M UNet and 123M text encoder

    Yep! Less than 1B in total. [0]

    [0] https://github.com/CompVis/stable-diffusion/blob/main/README...

  • Top 3 Open-Source AI Image Generation Projects on GitHub
    3 projects | dev.to | 23 Jan 2025
    Stable Diffusion, developed by Stability AI, is a robust text-to-image generation model designed to create high-quality, creative images based on textual prompts. Project URL: Stable Diffusion on GitHub
  • Deploying AI Models with Amazon Web Services: A Practical Guide
    3 projects | dev.to | 11 Dec 2024
    Stable Diffusion (GitHub Repository)
  • The Path to StyleGan2 – Progressive Growing GAN
    3 projects | news.ycombinator.com | 5 Aug 2024
    Latent diffusion models operate in katent space. This space is generated by an encoder and decoded back into pixel space by a decoder. The encoder-decoder form a generator which is trained to have good visual quality through the use of an adversarial loss.

    So the encoder produces a latent space that is more efficient to train a diffusion model on, since diffusion models use Unet-like architecture that must be run many times for a single inference. The latent space is restricted by a KL penalty to a Gaussian shape such that any sample from that shape will map through the decoder to a high-quality image. This makes the generative job of the diffusion model much easier because it can focus on content and semantics rather than pixel-level details

    You can see the two optimisers at work in the AutoencoderKL class in the Stable Diffusion source code here: https://github.com/CompVis/stable-diffusion/blob/main/ldm/mo...

  • Top 7 Text-to-Image Generative AI Models
    1 project | dev.to | 6 May 2024
    Stable Diffusion: It is based on a kind of diffusion model called a latent diffusion model, which is trained to remove noise from images in an iterative process. It is one of the first text-to-image models that can run on consumer hardware and has its code and model weights publicly available.
  • Go is bigger than crab!
    3 projects | dev.to | 8 Oct 2023
    Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
  • Why & How to check Invisible Watermark
    3 projects | /r/StableDiffusion | 10 Sep 2023
    an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
  • How to create an Image generating AI?
    1 project | /r/ArtificialInteligence | 12 Jul 2023
    It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
  • Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
    1 project | /r/StableDiffusion | 11 Jul 2023
    Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
  • Automatic1111 - Multiple GPUs
    3 projects | /r/StableDiffusionInfo | 8 Jul 2023

What are some alternatives?

When comparing dalle-mini and stable-diffusion you can also consider the following projects:

DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch

GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

fast-stable-diffusion - fast-stable-diffusion + DreamBooth

dalle-2-preview

Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models

diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"

VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text

stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]

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Nutrient - The #1 PDF SDK Library
Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
nutrient.io
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