KoboldAI VS sd-webui-lobe-theme

It’s not that big a task as all that. There are a lot of unaligned models available, and user interfaces that aren’t that hard to use.

https://github.com/henk717/KoboldAI

https://github.com/henk717/KoboldAI

- Hosts pick a quantized community LLM to run, which is (IMO) the real magic of this system. Cloud services tend to run generic Llama chat/instruct models, OpenAI API models, or maybe a single proprietary finetune, but the Llama/Mistral finetuning community is red hot. New finetines and crazy merges/hybrids that outperform llama-chat in specific tasks (mostly Chat/Story/RP) come out every day, and each one has a different "flavor" and format:

https://huggingface.co/models?sort=modified&search=mistral+g...

Sourceforge is our official version but that one is to old to run newer models like Holomax, the releases for United can be found here : https://github.com/henk717/KoboldAI/releases

Colab Check: False, TPU: False INIT | OK | KAI Horde Models INFO | __main__::648 - We loaded the following model backends: KoboldAI API KoboldAI Old Colab Method Huggingface GooseAI Horde OpenAI Read Only INFO | __main__:general_startup:1363 - Running on Repo: https://github.com/henk717/koboldai Branch: INIT | Starting | Flask INIT | OK | Flask INIT | Starting | Webserver INIT | OK | Webserver MESSAGE | Webserver started! You may now connect with a browser at http://127.0.0.1:8501 INIT | Searching | GPU support INIT | Found | GPU support INIT | Starting | LUA bridge INIT | OK | LUA bridge INIT | Starting | LUA Scripts INIT | OK | LUA Scripts Setting Seed Traceback (most recent call last): File "B:\python\lib\site-packages\eventlet\hubs\selects.py", line 59, in wait listeners.get(fileno, hub.noop).cb(fileno) File "B:\python\lib\site-packages\eventlet\greenthread.py", line 221, in main result = function(*args, **kwargs) File "B:\python\lib\site-packages\eventlet\wsgi.py", line 837, in process_request proto.__init__(conn_state, self) File "B:\python\lib\site-packages\eventlet\wsgi.py", line 352, in __init__ self.finish() File "B:\python\lib\site-packages\eventlet\wsgi.py", line 751, in finish BaseHTTPServer.BaseHTTPRequestHandler.finish(self) File "B:\python\lib\socketserver.py", line 811, in finish self.wfile.close() File "B:\python\lib\socket.py", line 687, in write return self._sock.send(b) File "B:\python\lib\site-packages\eventlet\greenio\base.py", line 401, in send return self._send_loop(self.fd.send, data, flags) File "B:\python\lib\site-packages\eventlet\greenio\base.py", line 388, in _send_loop return send_method(data, *args) ConnectionAbortedError: [WinError 10053] An established connection was aborted by the software in your host machine Removing descriptor: 1488 Connection Attempt: 127.0.0.1 INFO | __main__:do_connect:2574 - Client connected! UI_1 TODO: Allow config INFO | modeling.inference_models.hf:set_input_parameters:189 - {'0_Layers': 18, 'CPU_Layers': 10, 'Disk_Layers': 0, 'class': 'model', 'label': 'PygmalionAI_pygmalion-6b', 'id': 'PygmalionAI_pygmalion-6b', 'name': 'PygmalionAI_pygmalion-6b', 'size': '', 'menu': 'Custom', 'path': 'C:\\KoboldAI\\models\\PygmalionAI_pygmalion-6b', 'ismenu': 'false', 'plugin': 'Huggingface'} INIT | Searching | GPU support INIT | Found | GPU support Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 2/2 [00:19<00:00, 9.60s/it] Loading model tensors: 100%|##########| 56/56 [00:05<00:00, 9.52it/s]INIT | Starting | LUA bridge0, 8.93s/it] INIT | OK | LUA bridge INIT | Starting | LUA Scripts INIT | OK | LUA Scripts Setting Seed Connection Attempt: 127.0.0.1 INFO | __main__:do_connect:2574 - Client connected! UI_1

That is our developer version, its selectable in the Colab version dropdown and also available on https://github.com/henk717/koboldai

Download Kobold from THIS LINK:https://github.com/henk717/KoboldAI. I downloaded Kobold from a different Github link and it wouldnt work, you need to get this specific one. Click on "code", then download zip

https://github.com/henk717/KoboldAI https://github.com/LostRuins/koboldcpp/ https://github.com/ggerganov/llama.cpp https://github.com/AUTOMATIC1111/stable-diffusion-webui https://github.com/oobabooga/text-generation-webui

For the third, I don't think Oobabooga supports the horde but KoboldAI does. I won't go into how to install KoboldAI since Oobabooga should give you enough freedom with 7B, 13B and maybe 30B models (depending on available RAM), but KoboldAI lets you download some models directly from the web interface, supports using online service providers to run the models for you, and supports the horde with a list of available models to choose from.

"Your Pytorch installation did not update correctly, you can solve this by running install_requirements.bat in the mode where it deletes the existing runtime. Alternative you can download a fresh copy of the offline installer for KoboldAI United from : https://github.com/henk717/KoboldAI/releases"

upscayl is very approachable, but lacked many features i needed. i ended up using https://github.com/AUTOMATIC1111/stable-diffusion-webui after upscaling became part of my regular workflow, but for someone who just needs a few images enhanced, it's an ideal tool.

For the Git alternative, simply right-click on the location you want to put the Stable Diffusion and select “Git Bash Here”, then paste this on the CLI: git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui

ComfyUI is similar to Houdini in complexity, but immensely powerful. It's a joy to use.

There are also a large amount of resources available for it on YouTube, GitHub (https://github.com/comfyanonymous/ComfyUI_examples), reddit (https://old.reddit.com/r/comfyui), CivitAI, Comfy Workflows (https://comfyworkflows.com/), and OpenArt Flow (https://openart.ai/workflows/).

I still use AUTO1111 (https://github.com/AUTOMATIC1111/stable-diffusion-webui) and the recently released and heavily modified fork of AUTO1111 called Forge (https://github.com/lllyasviel/stable-diffusion-webui-forge).

Seems like an interesting project. Regarding the name, is there permission to use something so similar to AUTOMATIC1111 [1]?

> Diffusers will Cuda out of memory/perform very slowly for huge generations, like 2048x2048 images, while Auto 1111 SDK won't.

Do we have some numbers on this? I have seen AUTOMATIC1111 fall-over whilst using only half the available of GPU VRAM - there seems to be some weirdness where it tries to allocate before de-allocating the last batch or something.

> You can use any of the 6 compatible RealEsrgran models/weights with our RealEsrgran pipeline for upscaling images. Here are the model ids:

I've previously had trouble trying to use AUTOMATIC1111 upscalers, it seems like it needs more GPU VRAM than just generating the image already upscaled.

[1] https://github.com/AUTOMATIC1111/stable-diffusion-webui

You might be thinking of Fooocus: https://github.com/lllyasviel/Fooocus

The Stable Diffusion web interface that got a lot of people's attention originally was Automatic1111: https://github.com/AUTOMATIC1111/stable-diffusion-webui

Fooocus is definitely more beginner friendly. It does a lot of the prompt engineering for you. Automatic1111 has a ton of plugins, most notably ControlNet which gives you fine grained control over the images, but there is a learning curve.

Stable Diffusion https://github.com/AUTOMATIC1111/stable-diffusion-webui

Make it into a stable diffusion server!

There's no link version, you have to run it locally. You install it from here

Repository: AUTOMATIC1111/stable-diffusion-webui · Tag: v1.7.0-RC · Commit: 48fae7c · Released by: AUTOMATIC1111