Dreambooth-Stable-Diffusion-cpu VS diffusers

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

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?

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.

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

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.

Just found this one: https://github.com/andreae293/Dreambooth-Stable-Diffusion-cpu

I'm missing something about the random factor, from the sample code from https://github.com/huggingface/diffusers/blob/main/README.md

Hey people, I noticed that combining these two methods in a single forward pass increases the controllability of the generation quite a bit. I was kind of puzzled that sometimes ControlNet yielded better results than T2IAdapter for some cases, and sometimes it was the other way around, so I decided to test both at the same time, and results were quite nice. Some visuals and more motivation here: https://github.com/huggingface/diffusers/issues/5847 And it was already merged here: https://github.com/huggingface/diffusers/pull/5869

Open Parti Prompts: The better way to evaluate diffusion models (repo)

You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .

For developers, TensorRT support also exists for the diffusers library via community pipelines. [1] It's limited, but if you're only supporting a subset of features, it can help.

In general, these insane speed boosts comes at the cost of bleeding edge features.

[1] https://github.com/huggingface/diffusers/blob/28e8d1f6ec82a6...

I was training sdxl UNET base model, with the diffusers library, which was going great until around step 210k when the weights suddenly turned back to their original values and stayed that way. I also tried with the ema version, which didn't change at all. I also looked at the tensor's weight values directly which confirmed my suspicions.

Merging LoRAs is essentially taking a weighted average of the LoRA adapter weights. It's more common in other UIs.

diffusers is working on a PR for it: https://github.com/huggingface/diffusers/pull/4473