stylegan2-projecting-images VS diffusers

A Colab notebook.

To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab.

Please head over to the Google Colab

But regardless of what you want to do, you'll probably use Python. In this context, a good way to work with Python is using Jupyter Notebooks. So you should start with installing Python and Jupyter and go from there. If you want to get started without installing anything, Google Colab gives you a remote Jupyter Notebook which runs in the browser for free.

访问 Google Colab 。

Remember school days when you'd share notes with classmates? Jupyter takes that spirit and amplifies it. Once you've crafted your Notebook, you can share it with peers, collaborators, and the world. Platforms like GitHub and Google's Colab natively render Jupyter Notebooks. It's like penning an open letter to the world but in a delightful mix of code, text, and visuals.

Open your web browser and navigate to Google Colab.

Using Google Colab you can develop Python codes, similar to Jupyter Notebooks. You will have an environment prepared with various Python libraries. In addition you have tips on small codes for development, some tutorials, gihub connection, cloud -saved notebooks and more.

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