Caption-upsampling Alternatives

Similar projects and alternatives to caption-upsampling

• Fooocus

  34 35,432 9.8 Python caption-upsampling VS Fooocus

  Focus on prompting and generating

• Stable-Diffusion

  30 1,760 9.8 Jupyter Notebook caption-upsampling VS 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 (by FurkanGozukara)

• InfluxDB

  www.influxdata.com featured

  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.

  

• StableDiffusionTelegram

  1 122 10.0 Python caption-upsampling VS StableDiffusionTelegram

  StableDiffusionTelegram is a telegram bot that allows to generate images using the stable diffusion AI from a telegram bot, in a much more comfortable and simple way.

• sd-webui-inpaint-anything

  10 938 8.7 Python caption-upsampling VS sd-webui-inpaint-anything

  Inpaint Anything extension performs stable diffusion inpainting on a browser UI using masks from Segment Anything.

• CushyStudio

  2 581 10.0 TypeScript caption-upsampling VS CushyStudio

  🛋 The AI and Generative Art platform for everyone

• consistency-models

  6 192 5.6 Python caption-upsampling VS consistency-models

  A Toolkit for OpenAI's Consistency Models.

• diffusionmagic

  2 114 7.8 Python caption-upsampling VS diffusionmagic

  Easy to use Stable diffusion workflows using diffusers

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

• LLM-groundedDiffusion

  1 360 6.6 Python caption-upsampling VS LLM-groundedDiffusion

  LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models (LLM-grounded Diffusion: LMD)