sd-webui-regional-prompter
kohya_ss
sd-webui-regional-prompter | kohya_ss | |
---|---|---|
60 | 132 | |
1,394 | 8,414 | |
- | - | |
8.5 | 9.9 | |
about 1 month ago | 8 days ago | |
Python | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sd-webui-regional-prompter
-
Regional Prompting doesn't seem to be working a lot of the time
So I'm using the Regional Prompter extension https://github.com/hako-mikan/sd-webui-regional-prompter
- Dalle-3 Examples
- Stable Diffussion 1.5 Newbie Question about creating an image with 2 characters
-
"In summary, Stable Diffusion doesn’t really care about commas. But you can use them to organize your prompts for your own orderliness." (Link to quote below.) So... Is there a way to make SD care? To make it "understand" which words we put together to create meaning?
But using Automatic1111, this extension can define a region of the image where the prompt should apply: https://github.com/hako-mikan/sd-webui-regional-prompter
- Train SD for CAPTION WRITING? I'm tired of uploading hairstyle pics and got "male public hair"
- How to fix issue related to generate two guys when aspect ration isn't square?
-
A little bit of party after fighting each other in Smash bros (Text2img, controlnet, regional prompter, adetailer)
Second, install regional prompter and adetailer to automatic1111 webui.Next, go to setting>adetailer and change the "sort bounding boxes" from "none" to "left and right". This means that adetailer will inpaint our subjects starting from the very left to the right, allowing for greater control of what we want.
- What are some must-have/fun extensions or modules?
-
How to control a scene?
You can use ControlNets to control composition in various ways. You can use extensions like multidiffusion upscaler and regional prompter to control the layout of a scene. You can also inpaint details into a scene with the arrangement you want.
- Is there a way to guarantee one model in the image?
kohya_ss
-
Some semi-advanced LoRA & kohya_ss questions
Many of the options are explained here https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters
-
Lora training with Kohya issue
training in BF16 might solve this issue from what I saw in this ticket. I know other people ran into the issue too https://github.com/bmaltais/kohya_ss/issues/1382
-
What is the best way to merge multiple loras in to one model?
for lycoris loras you can use the command-line script from the kohya-ss repo https://github.com/bmaltais/kohya_ss/blob/master/networks/merge_lora.py i have an older version checked out from late july, it had a separate merge_lycoris.py for for this purpose, it's probably unified now in a single file
- Evidence that LoRA extraction in Kohya is broken?
-
Merging Lora with Checkpoint Model?
I usually do that with kohya_ss, a tool made for making LoRAs and finetunes. It might be a bit of a pain to set up just to do this one task, but if nobody gives you an easier method, look into it. https://github.com/bmaltais/kohya_ss
-
How I got Kohya_SS working on Arch Linux, including an up-to-date pip requirements file
After that, make your staging directory, and do the git clone https://github.com/bmaltais/kohya_ss.git, and navigate inside of it. Now, here's where things can become a pain. I used pyenv to set my system level python to 3.10.6 with pyenv global 3.10.6, though you can probably just use "local" and do it for the current shell. You NEED it to be active however before you set up your venv. If you do python --version and get 3.10.6, you're ready for this next part. Make your venv with python -m venv venv. This is the simplest way, it'll create a virtual environment in your current folder named venv. You'll do a source venv/bin/activate and then do which python to make sure it's using the python from the venv. Now for the fun part. The included setup scripts have been flaky for me, so I just went through the requirements and installed everything by hand. I'm going to do this guide right now for nvidia, because I just got a 4090 for this stuff. If this ends up working well for others and there's demand, I'll try to reproduce this for AMD (But I'll be honest, I got an nvidia card because bitsandbytes doesn't have full rocm support, nor do most libraries, so it's not very reliable). After installing everything and testing it works at least at a basic level for dreambooth training, my finished requirements.txt for pip is as below:
-
The best open source LoRA model training tools
Earlier I created a post where I asked for recommendations for LoRA model training tutorials. The first one I looked at used the kohya_ss GUI. That GitHub repo already has two tutorials, which are quite good, so I ended up using those:
-
Script does...nothing
I have tried my best to research this issue and have not come up with much. It is obvious that its a backend issue right? The guides that I used https://github.com/bmaltais/kohya_ss and https://github.com/pyenv-win/pyenv-win/
- Using LoRa on SDXL 1.0 (not using the Kohra GUIs)
-
How do I reduce the size of my Lora models?
I am training on a 12GB 3060 using kohya_ss. Is there a setting or something I'm missing?
What are some alternatives?
sd-webui-latent-couple - Latent Couple extension (two shot diffusion port)
sd_dreambooth_extension
stable-diffusion-webui-composable-lora - This extension replaces the built-in LoRA forward procedure.
EveryDream-trainer - General fine tuning for Stable Diffusion
stable-diffusion-webui-two-shot - Latent Couple extension (two shot diffusion port)
sd-scripts
sd-dynamic-prompts - A custom script for AUTOMATIC1111/stable-diffusion-webui to implement a tiny template language for random prompt generation
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
stable-diffusion-webui-two-shot - Latent Couple extension (two shot diffusion port)
kohya_ss_colab - a (successful) attepmt to port kohya_ss to colab
mixture-of-diffusers - Mixture of Diffusers for scene composition and high resolution image generation
LoRA_Easy_Training_Scripts - A UI made in Pyside6 to make training LoRA/LoCon and other LoRA type models in sd-scripts easy