memory_efficient_dreambooth
fast-stable-diffusion
memory_efficient_dreambooth | fast-stable-diffusion | |
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2 | 239 | |
55 | 7,310 | |
- | - | |
10.0 | 8.6 | |
over 1 year ago | 16 days ago | |
Python | Python | |
- | MIT License |
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memory_efficient_dreambooth
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Dreambooth: Are more images better?
Yeah, all were the same subject. I used this script for running dreambooth https://github.com/matteoserva/memory_efficient_dreambooth and to my understanding this does not use the class images at all. I had another training earlier with 1400 faces only @ 20k steps which had much better results. Maybe I should've used 40k steps? I don't know :)
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Dreambooth in 11GB of VRAM
This is my repository with the updated source and a sample launcher: https://github.com/matteoserva/memory_efficient_dreambooth
fast-stable-diffusion
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Working Colab notebooks for training Dreambooth?
I tried using TheLastBen's fast dreambooth trainer. I managed to train a ckpt file but I can't run it.
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Running AUTOMATIC1111 on Google Colab
You have a colab from ThelastBen It uses to be thes best at the time when auto1111 was working in google colab free. https://github.com/TheLastBen/fast-stable-diffusion
- Stability AI releases its latest image-generating model, Stable Diffusion XL 1.0
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Google Colab disconnects after 5 mins of hosting A1111
Using https://github.com/TheLastBen/fast-stable-diffusion
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I'm kinda new to all of this and just wanted to ask... How can I fix something like this? Tried inpaint but didn't work even after changing parameters and img2img make it lose quality...
This repo offers a template how to start with SD on runpod https://github.com/TheLastBen/fast-stable-diffusion. But I know how to code, si I made my own solution.
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Unable to use ControlNet on AUTO1111 GUI - Google Colab Notebook
I can confirm I'm using the latest version of the colab notebook of this repo (https://github.com/TheLastBen/fast-stable-diffusion). Anyone can point to any solutions to this problem? Thanks in advance!
- Automatic 1111 not working
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Useful Links
TheLastBen's Fast DB SD Colabs, +25-50% speed increase, AUTOMATIC1111 + DreamBooth
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Can you use other base model to train your own model with TheLastBen or ShivamShrirao collab?
CalledProcessError Traceback (most recent call last) in () 182 wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/det.py') 183 print('Detecting model version...') --> 184 Custom_Model_Version=check_output('python det.py '+sftnsr+' --MODEL_PATH '+MODEL_PATH, shell=True).decode('utf-8').replace('\n', '') 185 clear_output() 186 print(''+Custom_Model_Version+' Detected')
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How to Install and Run Stable Diffusion in Automatic1111 with Deforum in Google Collab?
have you tried https://github.com/TheLastBen/fast-stable-diffusion ?
What are some alternatives?
stable-dreambooth-optimized - Dreambooth implementation based on Stable Diffusion with minimal code.
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.
Dreambooth-Stable-Diffusion-cpu - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
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.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
stable-diffusion-tensorflow - Stable Diffusion in TensorFlow / Keras
efficient-dreambooth - [Moved to: https://github.com/smy20011/dreambooth-docker]
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
stable-diffusion - A latent text-to-image diffusion model