stable-diffusion VS k-diffusion

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

stable-diffusion

This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI] (by lstein)

k-diffusion

Karras et al. (2022) diffusion models for PyTorch (by crowsonkb)
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stable-diffusion k-diffusion
142 20
2,438 2,006
- -
9.8 8.9
over 1 year ago about 2 months ago
Jupyter Notebook Python
GNU General Public License v3.0 or later MIT License
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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 2023-04-20.

k-diffusion

Posts with mentions or reviews of k-diffusion. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-11.
  • Fooocus: OSS of prompts and generations based on A111 and ComfyUI
    2 projects | news.ycombinator.com | 11 Aug 2023
    Here's my attempt at an explanation without jargon, you can just read the last paragraph, the first 4 are just context.

    These image models are trained on 1000 steps of noise, where at 0 no noise is added to the training image and at 1000 the image is pure noise. The model's goal it to denoise the image, and it does this knowing how much noise the image has, this makes the model learn how much it should change the image, for example at high noise it changes a lot of pixels and starts building the overall "structure" of the image, and a low noise it changes less pixels and focuses on adding details.

    To use the model you start with pure noise, then the model iteratively denoises that noise until a clean image shows up. A naive approach would take 1000 steps, this means you run the model 1000 times, each time feeding the previous result and telling the model that the noise decreased by 1 until it reaches 0 noise. This takes a long time, up to 15 minutes to generate an image on a mid-range consumer GPU.

    Turns out when you give the model pure noise and tell it there's 1000 steps of noise, the result is not an image that has 999 steps of noise, but an image that looks like it has much less, this means that you can probably skip 50-100 steps of denoising per iteration and still get a very good picture, the issue is: what steps to pick? You could again take a naive approach and just skip every 50 steps for a total of 20 steps, but turns out there's better ways.

    This is where samplers come in, essentially a sampler takes the number of steps you want to take to denoise an image (usually ~20 steps) and it will--among other things--pick which steps to choose each iteration. The most popular samplers are the samplers in the k-diffusion repo[1] or k-samplers for short. Do note that samplers do much more than just pick the steps, they are actually responsible for doing the denoising process itself, some of them even add a small noise after a denoising step among other things.

    The newest open source model, SDXL, is actually 2 models. A base model that can generate images as normal, and a refiner model that is specialized on adding details to images. A typical workflow is to ask the base model for 25 steps of denoise, but only run the first 20, then use the refiner model to do the rest. According to the OP, this was being done without keeping the state of the sampler, that is they were running 2 samplers separately, one for the base model and then start one over for the refiner model. Since the samplers use historical data for optimization, the end result was not ideal.

    [1] https://github.com/crowsonkb/k-diffusion

  • Why does UniPC sampler use DDIM for Hires Fix?
    3 projects | /r/StableDiffusion | 26 Mar 2023
  • Image editing with just text prompt. New Instruct2Pix2Pix paper. Demo link in comments
    4 projects | /r/StableDiffusion | 21 Jan 2023
    git clone https://github.com/crowsonkb/k-diffusion.git
  • Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
    2 projects | /r/StableDiffusion | 9 Dec 2022
    Almost all other samplers come from work done by @RiversHaveWings or Katherine Crowson, which is mostly contained in her work at this repository. She is listed as the principal researcher at Stability AI. Her notes for those samplers are as follows:
  • AUTOMATIC1111 added more samplers, so here's a creepy clown comparison
    4 projects | /r/StableDiffusion | 5 Nov 2022
  • Sampler Comparison (incl. K-Diffusion)
    4 projects | /r/StableDiffusion | 24 Aug 2022
    there's an implementation of the other samplers at the k-diffusion repo. For one integrated with stable diffusion I'd check out this fork of stable that has the files txt2img_k and img2img_k. to use the different samplers just change "K.sampling.sample_lms" on line 276 of img2img_k, or line 285 of txt2img_k to a different sampler, e.g. K.sampling.sample_dpm_2_ancestral.
  • Randomizing Seeds when running locally
    3 projects | /r/StableDiffusion | 21 Aug 2022
    There's a collab version with it but not a github optimizedSD one. I found this but have no idea how to get them working together?
  • Dreambot clone available for running stable-diffusion on local GPU
    5 projects | /r/StableDiffusion | 18 Aug 2022
    Are you planning on adding the k_lms sampling (https://github.com/crowsonkb/k-diffusion) that is default sampler for the discord bot? I noticed on the current official Stable Diffusion repo it doesn't have it and defaults to plms.

What are some alternatives?

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

waifu-diffusion - stable diffusion finetuned on weeb stuff

taming-transformers - Taming Transformers for High-Resolution Image Synthesis

stable-diffusion-webui - Stable Diffusion web UI

diffusers-uncensored - Uncensored fork of diffusers

txt2imghd - A port of GOBIG for Stable Diffusion

dream-textures - Stable Diffusion built-in to Blender

diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.

stable-diffusion - A latent text-to-image diffusion model

tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators

stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM

stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]

stable-diffusion - k_diffusion wrapper included for k_lms sampling. fixed for notebook.