stable-diffusion-videos
diffusers
stable-diffusion-videos | diffusers | |
---|---|---|
17 | 266 | |
4,234 | 22,763 | |
- | 3.3% | |
2.0 | 9.9 | |
about 1 year ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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stable-diffusion-videos
- How to create it?
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Stable Diffusion Text-to-Video WebUI
Main Code: https://github.com/nateraw/stable-diffusion-videos/
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Messing with the denoising loop can allow you to reach new places in latent space. Over 8+ different research papers/Auto1111 extension ideas in a single pipe. Load once and do lots of different things (SD 2.1 or 1.5)
So I've continued to experiment with how many papers I can fit into a single pipe and have them play nicely together. The images below were created by combining the panorama code from omerbt/MultiDiffusion with the ideas from albarji/mixture-of-diffusers. Also turns out nateraw/stable-diffusion-videos can be seen as a special case of a panorama (in latent space rather than prompt space).
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Comparison of new UniPC sampler method added to Automatic1111
https://huggingface.co/spaces/tomg-group-umd/pez-dispenser https://huggingface.co/spaces/AIML-TUDA/safe-stable-diffusion https://huggingface.co/spaces/AIML-TUDA/semantic-diffusion https://github.com/nateraw/stable-diffusion-videos
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Start Frame -> Stable Diffusion + Linear Interpolation -> End Frame
The goal is to make a (short) video out of a given first and last frame. It is similar to what this guy does (https://github.com/nateraw/stable-diffusion-videos (7sec example video half way down page)). But instead of starting and ending with a prompt, I want to start and end with 2 different frames.
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Stable Diffusion Videos Easy-to-Use Playground & Competition This Week
Hey Y'all! We've been working on a tool that extends Nate Raw's Stable Diffusion Videos repo and makes it as easy as possible to use for artists and are having a competition this week to stress test the beta and see who can use it to make the most compelling short video (40 seconds max)
- Create videos with Stablediffusion. Saw this project and thought someone here might like it.
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Tried to pull off an ultra smooth video where you don't realize the scenes are changing until after-the-fact so I could make an 8hr background video that won't give seizures
Of course! There might be a better process but mainly used: 1.) Nate Raw's repo for morphing between prompts https://github.com/nateraw/stable-diffusion-videos 2.) Google FILM interpolation to smooth out transitions https://github.com/google-research/frame-interpolation
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[video] Packed underground rave in North Korea with dj ill kim headlining
There are directions in the readme and an example script.
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Short interpolation animation between several frames?
This does exactly that - https://github.com/nateraw/stable-diffusion-videos
diffusers
- StableDiffusionSafetyChecker
- ๐งจ diffusers 0.24.0 is out with Kandinsky 3.0, IP Adapters, and others
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What am I missing here? wheres the RND coming from?
I'm missing something about the random factor, from the sample code from https://github.com/huggingface/diffusers/blob/main/README.md
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T2IAdapter+ControlNet at the same time
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
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Won't you benchmark me?
Open Parti Prompts: The better way to evaluate diffusion models (repo)
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kohya_ss error. How do I solve this?
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 .
- Making a ControlNet inpaint for sdxl
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Stable Diffusion Gets a Major Boost with RTX Acceleration
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...
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Mysterious weights when training UNET
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.
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I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images
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
What are some alternatives?
sd-dynamic-prompts - A custom script for AUTOMATIC1111/stable-diffusion-webui to implement a tiny template language for random prompt generation
stable-diffusion-webui - Stable Diffusion web UI
frame-interpolation - FILM: Frame Interpolation for Large Motion, In ECCV 2022.
stable-diffusion - A latent text-to-image diffusion model
dain-ncnn-vulkan - DAIN, Depth-Aware Video Frame Interpolation implemented with ncnn library
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
invisible-watermark - python library for invisible image watermark (blind image watermark)
stable-karlo - Upscaling Karlo text-to-image generation using Stable Diffusion v2.
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
stable-diffusion-tensorflow-IntelMetal - Stable Diffusion in TensorFlow / Keras, Designed for Apple Metal on Intel. Forked from @divamgupta's work [Moved to: https://github.com/soten355/MetalDiffusion]
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.