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UniPC
[NeurIPS 2023] UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
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sd-dynamic-prompts
A custom script for AUTOMATIC1111/stable-diffusion-webui to implement a tiny template language for random prompt generation
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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stable-diffusion-videos
Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts
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diffusers
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
https://github.com/wl-zhao/UniPC Accessed 3/11/2023.
I use the Dynamic Prompts extension for Auto1111. I put the prompts in a file called SamplePrompt.txt, each prompt on a separate line, then place the file in the extension's wildcard folder. I reference it in the prompt with:
https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/7705 Accessed 3/11/2023.
Ah thanks, I think I see the issue. I am on https://github.com/lshqqytiger/stable-diffusion-webui-directml which I see hasn't been updated yet.
This community has published many XY plots of CFG versus steps. https://github.com/thekitchenscientist/sd_lite/wiki/recommended The consistent theme is low CFG, lower steps; high CFG, more steps. UniPC can reach convergence in as few as 8 steps, so I increased by 1/3 to account for more complex prompts needing longer
https://huggingface.co/spaces/tomg-group-umd/pez-dispenser https://huggingface.co/spaces/AIML-TUDA/safe-stable-diffusion https://huggingface.co/spaces/AIML-TUDA/semantic-diffusion https://github.com/nateraw/stable-diffusion-videos
And this PR from wl-zhao on the Diffusers repo says:
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