stable-diffusion
stable-diffusion
stable-diffusion | stable-diffusion | |
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186 | 26 | |
3,145 | 203 | |
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0.0 | 0.0 | |
8 months ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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stable-diffusion
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Possible to load Civitai models in basujindal optimizedSD fork?
I am using this repo: https://github.com/basujindal/stable-diffusion and it works fine with e.g. this model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
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40min to render 2x 256x256 pictures ..
That includes this optimized version : https://github.com/basujindal/stable-diffusion
- [Stable Diffusion] Coincé chez Unet: courir en mode EPS-Prédiction
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How to use safetensors locally (optimized-sd)?
Ah, I wasn't aware of that. I use this version, which was very easy to set up and use by CLI.
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[Stable Diffusion] stabile Diffusion 1.4 - CUDA-Speicherfehler
Used repo recommended in https://github.com/CompVis/stable-diffusion/issues/39 to use https://github.com/basujindal/stable-diffusion - same result.
- [Stable Diffusion] Aide avec Cuda hors de mémoire
- [Stable Diffusion] Comment créer notre propre modèle?
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Help installing optimisedSD please. Thank you so much!
As per the best solution I found, I have download this (https://github.com/basujindal/stable-diffusion) version and pasted the optimizedSD folder in the main (user>stable-diffusion-webui) folder as per site instruction.
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Stable Diffusion Web UI: Using Optimized SD Post-Installation
The git says you can simply grab the OptimizedSD folder and paste it into the installation path, which I did. However, I'm not sure how to call upon its functionality. Again, the reddit post says >Remember to call the optimized python script python optimizedSD/optimized_txt2img.py instead of standard scripts/txt2img. Though I'm not even sure where that script call is performed. Any ideas? Thanks in advance!
- [Stablediffusion] diffusion stable 1.4 - Erreur CUDA de mémoire insuffisante
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! 🤠
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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.
What are some alternatives?
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.
stable-diffusion
diffusers-uncensored - Uncensored fork of diffusers
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
stable-diffusion-webui - Stable Diffusion web UI
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
dream-textures - Stable Diffusion built-in to Blender