Dreambooth-Stable-Diffusion-cpu
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion (by andreae293)
xformers_wheels
By metrolobo
| Dreambooth-Stable-Diffusion-cpu | xformers_wheels | |
|---|---|---|
| 6 | 2 | |
| 14 | 16 | |
| - | - | |
| 10.0 | 0.0 | |
| over 1 year ago | about 1 year ago | |
| Jupyter Notebook | ||
| MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.
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.
Dreambooth-Stable-Diffusion-cpu
Posts with mentions or reviews of Dreambooth-Stable-Diffusion-cpu.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-24.
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Should I use the CPU only dreambooth?
I got a GTX 1650 with 4GB VRAM, which isn't really that good for training. My i5-4670 isn't the most efficient either, but it would still be possible to use. Is the CPU only option in the regular dreambooth out do I have to get this version: https://github.com/andreae293/Dreambooth-Stable-Diffusion-cpu
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Dreambooth on <12GB locally?
I haven't seen any optimizations on the JoePenna or gammagec forks. They are still at 24GB. NMKD mentioned possibly optimizing it more, now that it's included in that GUI. There's also a CPU-only version. I don't really understand the differences between these (which all come from XavierXiao) and the diffusers versions - is it just more optimization or are they fundamentally different?
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Is it possible to fine tune with a 6GB GPU?
There is a CPU-only fork: https://github.com/andreae293/Dreambooth-Stable-Diffusion-cpu. Needs a lot (35-40GB) of RAM. It works like the JoePenna version, not the diffusers version. I don't fully understand the differences between the two, besides that the diffusers version has been more heavily optimized for low VRAM.
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Has anyone had luck on 10 gb vram following this local dreambooth video guide?
if you're willing to wait for it to process you could do what i'm doing (i have a 2070 with 8gb vram) and try this one - https://github.com/andreae293/Dreambooth-Stable-Diffusion-cpu
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Dreambooth in 11GB of VRAM
This https://github.com/andreae293/Dreambooth-Stable-Diffusion-cpu I believe should produce as ckpt file. You're probably testing the CPU on the hugging face version.
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Dreambooth Stable Diffusion training in just 12.5 GB VRAM, using the 8bit adam optimizer from bitsandbytes along with xformers while being 2 times faster.
Just found this one: https://github.com/andreae293/Dreambooth-Stable-Diffusion-cpu
xformers_wheels
Posts with mentions or reviews of xformers_wheels.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-27.
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Dreambooth Stable Diffusion training in just 12.5 GB VRAM, using the 8bit adam optimizer from bitsandbytes along with xformers while being 2 times faster.
I also made a wheels version now that I think should work on P100 too here: https://github.com/metrolobo/xformers_wheels/releases/download/1d31a3ac_various_6/xformers-0.0.14.dev0-cp37-cp37m-linux_x86_64.whl but not tested on any P100 as I just have the free one.
What are some alternatives?
When comparing Dreambooth-Stable-Diffusion-cpu and xformers_wheels you can also consider the following projects:
efficient-dreambooth - [Moved to: https://github.com/smy20011/dreambooth-docker]
huggingface_hub - The official Python client for the Huggingface Hub.
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
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.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
stable-dreambooth-optimized - Dreambooth implementation based on Stable Diffusion with minimal code.
Dreambooth-Stable-Diffusion-cpu vs efficient-dreambooth
xformers_wheels vs huggingface_hub
Dreambooth-Stable-Diffusion-cpu vs bitsandbytes
xformers_wheels vs xformers
Dreambooth-Stable-Diffusion-cpu vs diffusers
xformers_wheels vs Dreambooth-Stable-Diffusion
Dreambooth-Stable-Diffusion-cpu vs xformers
xformers_wheels vs diffusers
Dreambooth-Stable-Diffusion-cpu vs diffusers
xformers_wheels vs diffusers
Dreambooth-Stable-Diffusion-cpu vs stable-dreambooth-optimized
Dreambooth-Stable-Diffusion-cpu vs Dreambooth-Stable-Diffusion