stablediffusion
diffusers
stablediffusion | diffusers | |
---|---|---|
108 | 266 | |
36,333 | 22,646 | |
1.8% | 2.8% | |
0.0 | 9.9 | |
28 days ago | 3 days ago | |
Python | Python | |
MIT License | 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.
stablediffusion
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Generating AI Images from your own PC
With this tutorial's help, you can generate images with AI on your own computer with Stable Diffusion.
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Midjourney
If your PC has a GPU(Nvidia RTX 30series+ recommended) of VRAM more than 4GB then try training your own Stable Diffusion model.
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RuntimeError: Couldn't clone Stable Diffusion.
Command: "git" clone "https://github.com/Stability-AI/stablediffusion.git" "C:\Users\Naveed\Documents\A1111 Web UI Autoinstaller\stable-diffusion-webui\repositories\stable-diffusion-stability-ai"
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What is the currently most efficient distribution of Stable Diffusion?
Automatic11112 and sygil-webui aren't "distributions" of Stable Diffusion. This is a repository with some distributions of Stable Diffusion.
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Reimagine XL: this is just Controlnet with a credit system right?
New stable diffusion finetune (Stable unCLIP 2.1, Hugging Face) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. Comes in two variants: Stable unCLIP-L and Stable unCLIP-H, which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available here.
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Stability AI has released Reimagine XL to make copies of images in one click
This model will soon be open-sourced in StabilityAI’s GitHub.
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What am I doing wrong please?
Another question, if that's ok? Stable Diffusion 2.0 - https://github.com/Stability-AI/stablediffusion - if I wanted to use that, do I follow along their instructions and it will work on the M1 still, or you advise against it?
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Tools For AI Animation and Filmmaking , Community Rules, ect. (**FAQ**)
Stable Diffusion (2D Image Generation and Animation) https://github.com/CompVis/stable-diffusion (Stable Diffusion V1) https://huggingface.co/CompVis/stable-diffusion (Stable Diffusion Checkpoints 1.1-1.4) https://huggingface.co/runwayml/stable-diffusion-v1-5 (Stable Diffusion Checkpoint 1.5) https://github.com/Stability-AI/stablediffusion (Stable Difusion V2) https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main (Stable Diffusion Checkpoint 2.1) Stable Diffusion Automatic 1111 Webui and Extensions https://github.com/AUTOMATIC1111/stable-diffusion-webui (WebUI - Easier to use) PLEASE NOTE, MANY EXTENSIONS CAN BE INSTALLED FROM THE WEBUI BY CLICK "AVAILABLE" OR "INSTALL FROM URL" BUT YOU MAY STILL NEED TO DOWNLOAD THE MODEL CHECKPOINTS! https://github.com/Mikubill/sd-webui-controlnet (Control Net Extension - Use various models to control your image generation, useful for animation and temporal consistency) https://huggingface.co/lllyasviel/ControlNet/tree/main/models (Control Net Checkpoints -Canny, Normal, OpenPose, Depth, ect.) https://github.com/thygate/stable-diffusion-webui-depthmap-script (Depth Map Extension - Generate high-resolution depthmaps and animated videos or export to 3d modeling programs) https://github.com/graemeniedermayer/stable-diffusion-webui-normalmap-script (Normal Map Extension - Generate high-resolution normal maps for use in 3d programs) https://github.com/d8ahazard/sd_dreambooth_extension (Dream Booth Extension - Train your own objects, people, or styles into Stable Diffusion) https://github.com/deforum-art/sd-webui-deforum (Deforum - Generate Weird 2D animations) https://github.com/deforum-art/sd-webui-text2video (Deforum Text2Video - Generate videos from texts prompts using ModelScope or VideoCrafter)
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Is AI technology really the issue?
Stable Diffusion's code : https://github.com/Stability-AI/stablediffusion
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I've never seen a YAML file alongside a .ckpt or .safetensors
But if you want to run a 2.x-based model, you'll need to download the corresponding YAML file (either the standard one – v2-inference-v.yaml – from Github or the one that is distributed with the model, if it requires a special one), rename it to have the same name as the model, and place it in the models folder alongside the 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?
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion-webui - Stable Diffusion web UI
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
stable-diffusion - A latent text-to-image diffusion model
MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
civitai - A repository of models, textual inversions, and more
invisible-watermark - python library for invisible image watermark (blind image watermark)
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
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) with Stable Diffusion
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.