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EveryDream-trainer Alternatives
Similar projects and alternatives to EveryDream-trainer
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Dreambooth-Stable-Diffusion
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
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kohya-trainer
Discontinued Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning
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Safe-and-Stable-Ckpt2Safetensors-Conversion-Tool-GUI
Convert your Stable Diffusion checkpoints quickly and easily.
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The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better EveryDream-trainer alternative or higher similarity.
EveryDream-trainer reviews and mentions
Posts with mentions or reviews of EveryDream-trainer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-10.
- How should I train Dreambooth to understand a new class?
- SDTools v1.5
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Guide on finetuning a model with mid-sized dataset of family pictures
https://github.com/victorchall/EveryDream-trainer Haven't tried it myself.
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
In terms of training, you've got some really good links and comments to youtube tutorials, but if you're interested in more information about finetuning a model (as opposed to training from scratch), this is a good repo that has a lot of tools for finetuning, including an auto-captioner using BLIP and automatic file renaming. This is the actual finetuning repo.
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Alternative tools to fine tune stable diffusion models?
Every Dream Trainer: Is basically a Dreambooth combine with Fine Tunning, so you can train multiples thing and a lot images: https://github.com/victorchall/EveryDream-trainer
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Training with Dreambooth Models and/or Training with Automatic 1111 Textural Inversion
If you have the GPU for it, I'd recommend training all three things at once with (for example) https://github.com/victorchall/EveryDream-trainer. It recommends using "ground truth" training images - i.e. images from LAION-5B, which Stable Diffusion was originally trained with to have better prior preservation (retaining the flexibility of the original model) while incorporating new concepts, potentially even several different concepts in a single training run.
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Flexible-Diffusion. My first experiment with finetuning. A broad model with better general aesthetics and coherence for different styles! Scroll for 1.5 vs FlexibleDiffusion grids. (BTW, PublicPrompts.art is back!!!)
I used about 300 captioned images (mainly beautiful MJ stuff), and used https://github.com/victorchall/EveryDream-trainer on RunPod for finetuning
- What do you think is the right dataset size to train/refine on dreambooth?
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Practice your christmas cookies before you bake with this SD 1.5 model
SD 1.5 512x512 model for making christmas style cookies of whatever you'd like. trained on 30 512x512 images with manual captions in everydream
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Guide for train/finetune with different image sizes, not dreambooth
This is the good one: https://github.com/victorchall/EveryDream-trainer
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Stats
Basic EveryDream-trainer repo stats
32
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2.4
about 1 year ago
victorchall/EveryDream-trainer is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of EveryDream-trainer is Jupyter Notebook.
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