deforum-stable-diffusion VS diffusers

This looks a fine-tune of the classic zero123 (https://github.com/cvlab-columbia/zero123) I’m excited to check out the quality improvements.

Though 3d model synthesis is one use case, I found the less advertised base reprojection model to be more useful for gamedev at the moment. You can generate a multiview spritesheet from an image, and it’s fast enough for synthesis during a gameplay session. I couldn’t get a good quality/time balance to do the same with the 3d models, and the lack of mesh rigging or animation combined with imperfections in a fully 3d model tends to break the suspension of disbelief compared to what players are used to. I’m this will change as the tech develops and we layer more AI on top (automatic animation synthesis is an active research area).

If you’re interested in this you might also want to check out deforum (https://github.com/deforum-art/deforum-stable-diffusion) which provides even more powerful camera controls on top of stable diffusion designed for full scenes rather than single objects.

2/Deforum – Open source and good at developing trippy videos – I have not used this too much (https://github.com/deforum-art/deforum-stable-diffusion)

Why is StabilityAI releasing what seems to be a clone of Deforum ?

might be deforum (project) (webui extension) or img2img loopback wave (webui extension). it's definitely something with img2img though. that's clear from how it's warping.

Sorry, I missed it, thanks to Deforum: https://github.com/deforum-art/deforum-stable-diffusion

Have you seen Deforum? Kinda sounds like you're about to reinvent it. :)

I'm talking about it https://github.com/deforum-art/deforum-stable-diffusion It is not possible to put in a separate environment.

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