diffusers
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX. (by huggingface)
sd-webui-modelscope-text2video
Auto1111 extension consisting of implementation of text2video diffusion models (like ModelScope or VideoCrafter) using only Auto1111 webui dependencies [Moved to: https://github.com/deforum-art/sd-webui-text2video] (by deforum-art)
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| diffusers | sd-webui-modelscope-text2video | |
|---|---|---|
| 266 | 17 | |
| 22,543 | 479 | |
| 6.3% | - | |
| 9.9 | 10.0 | |
| 3 days ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
diffusers
Posts with mentions or reviews of diffusers.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-27.
- StableDiffusionSafetyChecker
- 🧨 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
sd-webui-modelscope-text2video
Posts with mentions or reviews of sd-webui-modelscope-text2video.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-05.
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New 1.2B parameter text to video model is out, higher quality than modelscope
Working on it https://github.com/deforum-art/sd-webui-modelscope-text2video/pull/96 (also, will rename the repo to just sd-webui-text2video after that)
- This is fine
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Can someone help me understand what happens with VRAM?
They're linked from the main project's REDME.md under the "Where to get the weights" heading. (https://github.com/deforum-art/sd-webui-modelscope-text2video)
- Trump VS Godzilla - ModelScope + Img2Img
- I'm the creator of LoRA. How can I make it better?
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"Melting World" - Text To Video
Workflow: Text to video AUTO1111 extension https://github.com/deforum-art/sd-webui-modelscope-text2video
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Wake up, samurai! ModelScope text2video fine-tuning repo just dropped! Based on Diffusers, requirements start from GTX 3090 at the moment
Please, give it a try and leave your feedback. Soon fine-tuned models are planned to be usable in the Auto1111 plugin https://github.com/deforum-art/sd-webui-modelscope-text2video/issues/48 as well
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ModelScope text2video is reported to be running at 4 GBs of VRAM with enough effort — still, help needed to bring more optimizations and streamline the process
Meanwhile, if you have good training vids, it'd be nice to collect them somewhere for the future training, like inside the extension repo's Discussions https://github.com/deforum-art/sd-webui-modelscope-text2video/discussions
- The kind of result I'm getting with the new A1111 MS text2video model on a RTX 3060 (12 GB)
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"The Rise Of AI" - Text To Video Short Film
Workflow: Text to video AUTO1111 extension https://github.com/deforum-art/sd-webui-modelscope-text2video
What are some alternatives?
When comparing diffusers and sd-webui-modelscope-text2video you can also consider the following projects:
stable-diffusion-webui - Stable Diffusion web UI
sd-webui-additional-networks
stable-diffusion - A latent text-to-image diffusion model
VideoCrafter - VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
Text-To-Video-Finetuning - Finetune ModelScope's Text To Video model using Diffusers 🧨
invisible-watermark - python library for invisible image watermark (blind image watermark)
sd-webui-text2video - Auto1111 extension implementing text2video diffusion models (like ModelScope or VideoCrafter) using only Auto1111 webui dependencies
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
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.
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
diffusers vs stable-diffusion-webui
sd-webui-modelscope-text2video vs sd-webui-additional-networks
diffusers vs stable-diffusion
sd-webui-modelscope-text2video vs VideoCrafter
diffusers vs lora
sd-webui-modelscope-text2video vs Text-To-Video-Finetuning
diffusers vs invisible-watermark
sd-webui-modelscope-text2video vs sd-webui-text2video
diffusers vs automatic
sd-webui-modelscope-text2video vs lora
diffusers vs Dreambooth-Stable-Diffusion
sd-webui-modelscope-text2video vs modelscope