BasicSR
dalle-flow
BasicSR | dalle-flow | |
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5 | 31 | |
6,198 | 2,825 | |
2.3% | 0.1% | |
0.0 | 2.3 | |
6 days ago | 12 months ago | |
Python | Python | |
Apache License 2.0 | - |
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BasicSR
- About Open Source Image and Video Restoration Toolbox
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Super-Resolution Generative Adversarial Networks (SRGAN)
I think you might be interested in https://github.com/XPixelGroup/BasicSR
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Just saw a demo of nvidias super resolution. Is the software already available to the end user and can one upsize ones old „family“ photos to something astonishingly crisp and detailed for prints?
It's not the same but GFPGan works quite well, you might want to check out BasicSR. The Remini mobile app is impressive too.
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Anyone currently able to run Real-ESRGAN notebooks on colab currently?
!pip install git+https://github.com/xinntao/BasicSR.git
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Training ESRGAN: Seemingly impossible
So, I was using a pretty high-end machine with an AMD RX 6900 XT GPU, ready to do some GPU accelerated computing. I had booted into Windows 10. I followed a guide for training on my own dataset. PyTorch was the name of the framework that served as the foundation for [BasicSR[(https://github.com/xinntao/BasicSR), which in turn provided the tools I needed.
dalle-flow
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How to Personalize Stable Diffusion for ALL the Things
Jina AI is really into generative AI. It started out with DALL·E Flow, swiftly followed by DiscoArt. And then…🦗🦗*🦗🦗. At least for a while…
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image generation API similar to Dall-E or Dall-E 2
you can host your own https://github.com/jina-ai/dalle-flow
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[hlky’s/sd-webui] Announcing Sygil.dev & Project Nataili
For example for all the multimodal stuff like clipseg and upscalers, I'm using isolated executors through jina flow: https://github.com/jina-ai/dalle-flow/tree/main/executors
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Who needs prompt2prompt anyway? SD 1.5 inpainting model with clipseg prompt for "hair" and various prompts for different hair colors
clipseg is an image segmentation method used to find a mask for an image from a prompt. I implemented it as an executor for dalle-flow and added it to my bot yasd-discord-bot.
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Sequential token weighting invented by Birch-san@Github allows you to bypass the 77 token limit and use any amount of tokens you want, also allows you to sequentially alter an image
Merged into [dalle-flow](https://github.com/jina-ai/dalle-flow/pull/112) this morning and works on my Discord bot [yasd-discord-bot](https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot).
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I made a discord bot for artsy ML stuff - just finished integrating SD
https://github.com/jina-ai/dalle-flow with ports of some code from https://github.com/lstein/stable-diffusion plus some stuff specific to my uses (mostly more exposed settings and meta data on the outputs).
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AI generated picture "Beatles at Disneyland"
dalle flow - a more advanced version of dall-e mini, running dall-e mega and a diffusion model (free colab), free
- Comparison of DALL-E, Midjourney, Stable Diffusion and more
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Running Dall-e mini on Windows? (Or: Are there any equivalent text-to-image AI's I can run on a windows PC with a 2080 TI?)
Another option is https://github.com/jina-ai/dalle-flow combines DALL-E Mini with some other image processing models, and they have a pre-built Docker image that you could run locally. However, because it loads additional image processing models, you'll need about 21 GB of GPU RAM which is more than a 2080 TI has. You could always try to edit their Dockerfile and re-build it to remove the other models.
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
For the second half of this article, we’ll use meadowdata/meadowrun-dallemini-demo which contains a notebook for running multiple models as sequential batch jobs to generate images using Meadowrun. The combination of models is inspired by jina-ai/dalle-flow.
What are some alternatives?
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
dalle-mini - DALL·E Mini - Generate images from a text prompt
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
jina - ☁️ Build multimodal AI applications with cloud-native stack
ESRGAN - ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
example-app-store - App store search example, using Jina as backend and Streamlit as frontend
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
EGVSR - Efficient & Generic Video Super-Resolution
dalle2-in-python - Use DALL·E 2 in Python
Real-ESRGAN - PyTorch implementation of Real-ESRGAN model
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.