multi-subject-render
stable-diffusion
multi-subject-render | stable-diffusion | |
---|---|---|
18 | 383 | |
359 | 65,624 | |
- | 1.3% | |
2.5 | 0.0 | |
about 1 year ago | about 1 month ago | |
Python | Jupyter Notebook | |
- | GNU General Public License v3.0 or later |
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multi-subject-render
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Creating pictures of multiple people with distinct faces
You can use the multi subject renderer https://github.com/Extraltodeus/multi-subject-render.git
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Can I use SD to generate group pictures (of say, me and my cousin, or me and multiple cousins)?
Get this Extension, and as always, please read the docs to avoid problems.
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Find it hard to tune my prompt for more than 2 characters
There's also a script/extension https://github.com/Extraltodeus/multi-subject-render but it's fiddily to get work right, and i think the other workflow is faster.
- Textual Inversion: TI TLDR for the Lazy. How to Make Fake People: Simple TI Traning Using 6 Images and very low Settings. Bonus 1: How to Make Fake People that Look Like Anything you Want. Bonus 2: Why 1980s Nightcrawler dont care about your prompts. With Unedited Image Samples.
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How to do Multiple chars in 1 image
There are some ideas to create multiple different subjects, such as this extension for automatic (https://github.com/Extraltodeus/multi-subject-render), or Area Composition if you are using ComfyUI (https://comfyanonymous.github.io/ComfyUI_examples/area_composition/).
- How to detail 2 objects, each with its own qualities in prompt?
- Ladies in sexy pajamas
- Uhhhhh
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Tips for creating picture with multiple characters?
you can do it with https://github.com/Extraltodeus/multi-subject-render but i don't really know how to use it
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What are you struggling to do?
There is an extension called multi-subject-render that allows you to provide one prompt for the background and a second prompt for the foreground.
stable-diffusion
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Top 7 Text-to-Image Generative AI Models
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.
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Go is bigger than crab!
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.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
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...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
What are some alternatives?
stable-diffusion-webui-depthmap-script - High Resolution Depth Maps for Stable Diffusion WebUI
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
stable-diffusion-webui-distributed - Chains stable-diffusion-webui instances together to facilitate faster image generation.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
depthmap2mask - Create masks out of depthmaps in img2img
diffusers-uncensored - Uncensored fork of diffusers
sdweb-merge-board - Multi-step automation merge tool. Extension/Script for Stable Diffusion UI by AUTOMATIC1111 https://github.com/AUTOMATIC1111/stable-diffusion-webui
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
sd-webui-reactor-force - Fast and Simple Face Swap Extension for StableDiffusion WebUI (A1111, SD.Next, Cagliostro) with NVIDIA GPU Support
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
onnx - Open standard for machine learning interoperability