CogVideo
diffusers
CogVideo | diffusers | |
---|---|---|
39 | 266 | |
3,512 | 22,881 | |
1.6% | 3.8% | |
2.4 | 9.9 | |
11 months ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
CogVideo
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InstructPix2Pix Video: "Turn the wave into trash"
Additionally two open source demo models [CogVideo[(https://github.com/THUDM/CogVideo) by a groups of cs students a model by [Antonia Antonova](https://antonia.space/text-to-video-generation) and have presented their own innovative methods of generating video from text
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Effortpost: The Future Of Media Synthesis and AI Art
The second thing that will happen is the appearance of AI video and audio. Google has shown two programs for video generation, one which is fairly high quality and the other which can make long videos with several scenes. Meta has also demonstrated their own. We've already seen other projects like CogVideo, as well as many others that are currently being worked on. It's likely that these techniques will become so refined that over the next year or two, they'll have a similar boom to image generation programs. And eventually, they'll have a similar application in video editing, once coherence is adequate enough. Select a person's shirt, and it stays that for the remainder of the scene. Change an actor's hairstyle in real time, or add characters that didn't exist into a scene and let the computer figure out the desired level of realism. This'll revolutionize VFX to a degree where making an effects heavy will be less about wrangling complex toolsets and more about making aesthetic choices of style and placement.
- AI Content Generation, Part 1: Machine Learning Basics
- Can we please make a general update on all the "most important" news/repos available?
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Stable Diffusion Public Release โ Stability.ai
Check out https://github.com/THUDM/CogVideo - progress is being made on coherent video generation.
Characters and dialogue are effectively solved, just look at GPT-3.
The entity behind StableDiffusion is also supporting generative music art, so let's see what is coming out of that: https://www.harmonai.org/
We are currently far away from generating a production quality movie with AI, but I don't think it's going to be nearly as long as a lifetime. In my opinion, we'll have high quality AI shorts within the decade.
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How far away are we from have AI like DALL-E 2 be able to create other media like 3d models or video?
CogVideo and a CogView web app.
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Does training transformers on large corpuses of music files have some hidden difficulty which makes it impossible?
A better comparison to AI music generation would be video generation, which has not improved much since i saw first examples some years ago. The last iteration is stuff like CogVideo and this is only able to generate 4 second videos with mid-strong artifacts.
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[R] CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers + Gradio Web Demo
github: https://github.com/THUDM/CogVideo
- CogVideo: Code and 94B Model for Text-to-Video Generation via Transformers
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CogVideo (text-to-video) model, code, and demo are available
GitHub repo.
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?
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
stable-diffusion-webui - Stable Diffusion web UI
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion-webui-feature-showcase - Feature showcase for stable-diffusion-webui
invisible-watermark - python library for invisible image watermark (blind image watermark)
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, 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.