stable-diffusion-webui-two-shot
sd-extension-system-info
stable-diffusion-webui-two-shot | sd-extension-system-info | |
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29 | 51 | |
412 | 258 | |
- | - | |
3.7 | 9.3 | |
6 months ago | 3 months ago | |
Python | Python | |
MIT License | MIT License |
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stable-diffusion-webui-two-shot
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How do I create widescreen images (21:9) and tell SD to paint the person in the middle?
I tried Regional Prompter and Latent Couple (https://github.com/ashen-sensored/stable-diffusion-webui-two-shot) extension but they don't seem to work properly (latter has awful documentation/examples).
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Consistent environment setup for multiple scenes
Option 5 is to buy a smallish GPU farm and simply rely on good specific and regional prompting pushed through brute forced generations to extract similar looking places out of the thousands of hallucinations. Some loras, checkpoints, regional prompting with the Latent Couple extension in A1111, and an abundant abuse of ControlNet could also help.
- “Elon Musk and Mark Zuckerberg in a cage fight.” (SDXL 0.9)
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Multi-diffusion with LORAs?
Use Latent Couple with Composable LoRA instead
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Why can't it generate people separately? It always seems to combine them. How do I fix this? In this case it is Dwayne Johnson and Kevin Hart.
The solution to this is to use the 'Latent Couple' plugin.
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[Frostveil Series] Up the mountain trail...
All three used the same prompt, which requires the Latent Couple extension.
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MultiDiffusion Region Control plugin for A1111 not installing
git clone -b feature/mask_selection https://github.com/ashen-sensored/stable-diffusion-webui-two-shot.git
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A Simple Comparison of 4 Latest Image Upscaling Strategy in Stable Diffusion WebUI
There are some extensions that break things even when they're disabled. If you're using Latent Couple (two shot), uninstall / delete the folder and and use this fork: https://github.com/ashen-sensored/stable-diffusion-webui-two-shot
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Frostveil, the Nordic realm
I used the Latent Couple extension with the following mask:
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How do I describe an object without those properties being applied to a different part of my image.
I was thinking about this extension.
sd-extension-system-info
- RTX 4070 vs rx 7800 xt
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AMD for AI
I've been using both SD and various LLM on linux without any issue and have done so for months. Windows support is also starting to roll out slowly, with koboldcpp-rocm recently giving me 20-25+t/s for a13B even on windows. you can see what SD performance is like on sites like these. those numbers roughly match what i get on my RX6800 as well (8t/s).
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Stable Diffusion in pure C/C++
That seems a lot worse than a 2060 SUPER with PyTorch in A1111.
https://vladmandic.github.io/sd-extension-system-info/pages/... (search for 2060 SUPER)
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Iterations per second benchmarking question
But usually A1111 users use benchmark on this extension https://github.com/vladmandic/sd-extension-system-info
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Best AMD SD Guide for 2023?
AMD SD = Setup Diaster? it was quite troublesome googling the few linux/amdgpu/rocm/sd vers/configs/params posts online. Also the whole PC may hang during generation which is bad for the harddisk. Your card is way more powerful so may not hang like mine. People are getting 8it/s https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html
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Which one is better? Nvidia Tesla M40 vs Nvidia Tesla P4?
According to system info benchmark, M40 is like 1-2 it/s and P4 is barely better than that.
- Video card price/performance ratio
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--medvram. Should I remove this flag? Running 3090
Anyway to properly "benchmark" the impacts different switches on your image generation speed, it is better to use the benchmarking utility from extension https://github.com/vladmandic/sd-extension-system-info (it also creates a very handy table of results from other users at https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html for you to compare with.
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Searching for install guide for top performance setup on WSL2 (Automatic1111)
I can see that the top performance benchmark results on SD WebUI Benchmark Data (using RTX 4090), are obtained through WSL2 running Automatic1111 on a Linux dist and Python 3.10.11, along with PyTorch 2.1.0.dev+cu121 (like benchmark id: 4126)
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Advice for Optimization on an RTX 8000
You should be able to compare based on the published benchmarks, just replicate the settings based on what's reported https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html
What are some alternatives?
sd-webui-latent-couple - Latent Couple extension (two shot diffusion port)
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
MultiDiffusion - Official Pytorch Implementation for "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation" presenting "MultiDiffusion" (ICML 2023)
tomesd - Speed up Stable Diffusion with this one simple trick!
sd-webui-regional-prompter - set prompt to divided region
voltaML-fast-stable-diffusion - Beautiful and Easy to use Stable Diffusion WebUI
multidiffusion-upscaler-for-automatic1111 - Tiled Diffusion and VAE optimize, licensed under CC BY-NC-SA 4.0
stable-diffusion-webui-directml - Stable Diffusion web UI
stable-diffusion-webui-two-shot - Latent Couple extension (two shot diffusion port)
scribble-diffusion - Turn your rough sketch into a refined image using AI
sd-webui-stablesr - StableSR for Stable Diffusion WebUI - Ultra High-quality Image Upscaler
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability