stable-diffusion-webui-daam
sd-dynamic-thresholding
stable-diffusion-webui-daam | sd-dynamic-thresholding | |
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4 | 26 | |
50 | 1,025 | |
- | 5.4% | |
10.0 | 7.2 | |
about 1 year ago | 8 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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stable-diffusion-webui-daam
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Looking for a prompt weight extension
You are probably looking for stable-diffusion-webui-daam extension https://github.com/toriato/stable-diffusion-webui-daam
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What's your favorite small tweaks to make? I'll go first
I can also offer an interesting plugin for analyzing the resulting image - Attention Heatmap
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Become A Stable Diffusion Prompt Master By Using DAAM - Attention Heatmap For Each Used Token - Word - “How does an input word influence parts of a generated image?
nope video recorded yesterday night. which extension you installed? the one coming from general link not working. the working one is : https://github.com/toriato/stable-diffusion-webui-daam
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Analyzing Impact of Prompt
Like this one: https://github.com/toriato/stable-diffusion-webui-daam
sd-dynamic-thresholding
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ZeroDiffusion -- a clean zero terminal SNR training 1.5 base model + experimental inpainting model
For outputs to look right, you will need some form of CFG rescale or dynamic thresholding in order to correct for overexposure (A1111 extensions are linked -- I am told that ComfyUI has nodes available for these functions). A good starting point for CFG rescale is 0.7, as recommended in the paper. I strongly suspect that CFG rescale is not an ideal solution and leaves a substantial training-inference gap, and when using zero terminal SNR models I find that Dynamic Thresholding can give better outputs that are closer to what I expect from the data without the brownout often caused by CFG rescale. A potential starting point for Dynamic Thresholding would be: Restart sampler, 15 CFG scale, Mimic CFG scale 15 7.5, Sawtooth on both scale schedulers, 6 for both minimum values, scheduler value 4, do not separate feature channels, ZERO, STD. You will likely have to experiment a lot with Dynamic Thresholding. (edit: small correction to DT settings)
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Dynamic Thresholding for comfyui?
Recently switched from A1111 and i love it so far, flexibility to orchestrate complex workflows automatically instead of manual operations is a life changer. Anyhow, one extension i like on A1111 was this one: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding
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How do I implement Dynamic Thresholding (CFG scale fix) in ComfyUI?
In the Automatic1111 webui, there is a Dynamic Thresholding (CFG scale fix) extension that:
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How to diffuse better faces?
Ive found using ADetailer (https://github.com/Bing-su/adetailer, using their reccomended advanced settings and face_yolov8n.pt) and Dynamic Thresholding (CFG set to 12 and Mimic to 7) has vastly improved my face renders. (https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) GL!
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Kohya UI settings as asked (style+character training)
The output LoRA works best with CFG at 4, because at 7 it gets that gasoline colors and contrast of overbaking, but I guess this is a tradeoff of that many steps in total (5200) since the earlier snapshots were not that good in style and with character details. You can use a workaround like the Dynamic Trescholding extention: https://github.com/mcmonkeyprojects/sd-dynamic-thresholding.git - helps a lot in many cases when you want a high CFG but the model/lora overbakes them (it mimics a lower CFG while keeping the high CFG details and prompt alignment).
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Does anyone know how to create this type of hyper realistic pic?
Use sd-dynamic-thresholding extension (set CFG scale to 12 or more and mimic CFG scale to 7): https://github.com/mcmonkeyprojects/sd-dynamic-thresholding
- ControlNet Reference-Only problems
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What's your favorite small tweaks to make? I'll go first
Tweak this up or down for small changes. Too far and you’ll get a different image. Extensions like Dynamic Thresholding can let you go much higher without the overexposed look.
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Blurred/Low quality/Low details images
Turn CFG scale down or maybe use this extension, I've never used Dynamic Thresholding before but I think its what you want
- Dynamic threshold & Offset noise - The answer to oversaturated images?
What are some alternatives?
stable-diffusion-webui-daam - DAAM for Stable Diffusion Web UI
stable-diffusion-webui-anti-burn - Extension for AUTOMATIC1111/stable-diffusion-webui for smoothing generated images by skipping a few very last steps and averaging together some images before them.
stable-diffusion-webui-sonar - Wrapped k-diffuison samplers with tricks to improve the generated image quality (maybe?), extension script for AUTOMATIC1111/stable-diffusion-webui
Stable-Diffusion - Stable Diffusion, SDXL, LoRA Training, DreamBooth Training, Automatic1111 Web UI, DeepFake, Deep Fakes, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya LoRA, Kandinsky 2, DeepFloyd IF, Midjourney
stable-diffusion-webui-state - Stable Diffusion extension that preserves ui state
adetailer - Auto detecting, masking and inpainting with detection model.
Lora-for-Diffusers - The most easy-to-understand tutorial for using LoRA (Low-Rank Adaptation) within diffusers framework for AI Generation Researchers🔥
multidiffusion-upscaler-for-automatic1111 - Tiled Diffusion and VAE optimize, licensed under CC BY-NC-SA 4.0
sd-webui-neutral-prompt - Collision-free AND keywords for a1111 webui!
sd_webui_SAG
sd-webui-prompt-all-in-one - This is an extension based on sd-webui, aimed at improving the user experience of the prompt/negative prompt input box. It has a more intuitive and powerful input interface function, and provides automatic translation, history record, and bookmarking functions. 这是一个基于 sd-webui 的扩展,旨在提高提示词/反向提示词输入框的使用体验。它拥有更直观、强大的输入界面功能,它提供了自动翻译、历史记录和收藏等功能。
sd-dynamic-prompts - A custom script for AUTOMATIC1111/stable-diffusion-webui to implement a tiny template language for random prompt generation