stable-diffusion
stable-diffusion-webui
stable-diffusion | stable-diffusion-webui | |
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26 | 2,808 | |
203 | 129,975 | |
- | - | |
0.0 | 9.9 | |
over 1 year ago | 7 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | MIT |
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stable-diffusion
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Trying to merge model checkpoints and getting an error
Looks like Doggettx is a fork of CompVis/stable-diffusion, as a proof of concept:
- Stable Diffusion links from around September 11, 2022 that I collected for further processing
- Stable Diffusion for AMD GPUs on Windows using DirectML (Txt2Img, Img2Img & Inpainting) easy to setup (Python + Git)
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Has anyone made a commandline client to use Automatic1111's version of Stable Diffusion over the network?
Don't use a UI if you want terminal access. Use a project meant for terminal. https://github.com/Doggettx/stable-diffusion/tree/autocast-improvements
- Looking at cheap high VRAM old tesla cards to run stable diffusion at high res!
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Looking for a script I saw mentioned but can't find. Prompt Editing over Steps
The feature is just called prompt editing or prompt2prompt. It is also implemented in the Automatic1111 webui.
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Any way to fix this?
Depends on what fork you are using but its just means you are running out of vram since it states you only have 4gb of it. You may need to use the optimizedsd scripts and use the Doggettx's attention.py, you can find this in ldm/modules/attention.py (I personally have 2 of those in my own folder since I need to switch them but typically you require 6gb min for sd.
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Jabba The Hutt as a newborn
I installed SD from the CompVis GitHub repo and then swapped in modifications (namely attention.py and main.py) done by u/Doggettx that can be found here to overcome CUDA Out Of Memory issues. Going to try larger image sizes next. I wish you all good luck with concentrating on real work with this imaginatron around! ðŸ¤
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Stable Diffusion Gui Benchmark Results: Loading... Generated 1 image in 5.58s (20/20)
using optimized attention.py and model.py from this github issue.
- This community continues to blow me away. 8 days ago I was amazed by my 1408 x 960 resolution image. With all the new features I'm now doing 6 megapixel native output (3072x2048). That's 24 times more pixels than 512x512. Full workflow in comments.
stable-diffusion-webui
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Show HN: I made an app to use local AI as daily driver
* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.
- https://diffusionbee.com/
- https://github.com/comfyanonymous/ComfyUI
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I would love to be able to have a native stable diffusion experience, my rx 580 takes 30s to generate a single image. But it does work after following https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...
I got this up and running on my windows machine in short order and I don't even know what stable diffusion is.
But again, it would be nice to have first class support to locally participate in the fun.
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Ask HN: What is the state of the art in AI photo enhancement?
In Auto1111, that just uses Image.blend. :)
https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...
- How To Increase Performance Time on MacOS
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Can anyone suggest an AI model that can help me enhance a poorly drawn logo?
I used SDXL in automatic1111 webui for both images. Now that I think about it, the procedure I described was how I made this one, but the one that looks like an illustration was done in two steps. I used the canny ControlNet as I said for the outer part of the logo to preserve the shape of the fonts, but I had to turn it off for the boot to give SDXL leeway to add detail and make it look more like a boot.
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Seeking out an experienced and empathetic coding buddy.
That said, please do learn coding and don't get discouraged when somebody says to learn PyTorch or recommends using a Jupiter notebook with no further information on how to translate the skill into images. I would highly recommend some short term goals. Get your feet wet by taking apart the UIs. The comfy API documentation is here and the A1111 API documentation is here. There is a difference in completeness, welcome to programming. Writing nodes or plugins is also a good way to jump into this world. Custom wildcard logic might be very attractive to you if you aren't the type that want to deal with a nested file structure to simulate logic.
- can't get it working with an AMD gpu
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SD extension that allows for setting override
Possibly Unprompted? https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8094
- Need to write an application to use Stable Diffusion on my desktop PC - which resource should I learn to use?
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4090 Speed Decrease on each Generation/Iteration
version: v1.6.1  •  python: 3.10.13  •  torch: 2.0.1+cu118  •  xformers: 0.0.20  •  gradio: 3.41.2  •  checkpoint: 6e8d4871f8
What are some alternatives?
stable-diffusion
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]
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
SHARK - SHARK - High Performance Machine Learning Distribution
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
dream-textures - Stable Diffusion built-in to Blender
stable-diffusion - A latent text-to-image diffusion model
safetensors - Simple, safe way to store and distribute tensors