stable-diffusion
diffusers
stable-diffusion | diffusers | |
---|---|---|
15 | 266 | |
3,618 | 22,763 | |
2.8% | 3.3% | |
0.0 | 9.9 | |
12 months ago | 5 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
stable-diffusion
- diffusion low level question
- Does anyone know a white-label Automatic1111 equivalent?
- How to run a .safetensors model with runwayml/stable-diffusion?
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Stability-AI vs RunwayML Stable Diffusion
What are the differences between the stable diffusion projects by Stability-AI and RunwayML? In addition, a similar project Diffusers by HuggingFace also exists, is that different as well?
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Getty Images is suing the creators of AI art tool Stable Diffusion for scraping its content.
Ok. I refreshed my memory (here). RunwayML did the training. Stability AI donated the compute. At least. 1 member of the german research team has gone to work for RML and basically continued his work there. I'm not clear at all on the relationship between SAI and RML.
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Thank you to all the alpha testers! RunDiffusion.com is ready! Launch a stable diffusion server in minutes on industry leading hardware for $0.50 /hr. Automatic1111 with 20+ Models ready on boot. File browser to persist files. Smart timer so you don't accidentally leave it running. 15 min free!
Pretty sure. There's an in painting model that is available! I heard it's pretty good! https://github.com/runwayml/stable-diffusion
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Captain Picard facepalm outpainting
so use it with the fork it was designed to work on: https://github.com/runwayml/stable-diffusion
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Stability AI's Take on Stable Diffusion 1.5 and the Future of Open Source AI
source: https://github.com/runwayml/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md
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Stable Diffusion v1.5 Weights Released
If this gets taken down because of the cucks at Stability, you can also download the model weights at https://github.com/runwayml/stable-diffusion
- RunwayML fine tuned Stable Diffusion 1.5 model
diffusers
- 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
What are some alternatives?
auto-sd-paint-ext - Extension for AUTOMATIC1111 to add custom backend API for Krita Plugin & more
stable-diffusion-webui - Stable Diffusion web UI
glid-3-xl-stable - stable diffusion training
stable-diffusion - A latent text-to-image diffusion model
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
imaginAIry - Pythonic AI generation of images and videos
invisible-watermark - python library for invisible image watermark (blind image watermark)
auto-sd-krita - AUTOMATIC1111 webUI + Krita Plugin with superb Inpainting
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
yasd-discord-bot - Yet Another Stable Diffusion Discord Bot
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.