CogView
SwinIR
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CogView | SwinIR | |
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16 | 27 | |
1,593 | 4,082 | |
1.8% | - | |
4.2 | 0.0 | |
7 months ago | 25 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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CogView
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CogView2 web app is available at site replicate.com
This web app, which is mentioned in the CogView (1) GitHub repo, is/was using a "slightly different" model than the CogView2 GitHub repo.
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DALL-E 2 alternative: CogView2 checkpoints now available for download: best released text2image model (9b Transformer)
The web app for CogView2 has been available for months, if I am not mistaken. See this GitHub repo for the link.
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Paper+code "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers", Ding et al 2022
Is the CogView2 demo avaliable here the same as the CogView2 paper released?
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The official CogView web app might be using a new model. Evidence and link in a comment. Example: "wedding portrait. no watermark. royalty free." (3 images)
On March 16, 2022, the following was added to the CogView GitHub repo: "News! The demo for a better and faster CogView2 (formal version, March 2022) is available! The lastest model also supports English input, but to translate them into Chinese often could be better."
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6 of an AI's creations for input text description "fox at night. no watermark." The last image is a mind-bender.
Yes, CogView 2 is free to use, and is available as a web app here. CogView 2's image description text needs to be in simplified Chinese; an English-to-simplified Chinese icon appears after typing 9 characters. The styles are a quick way to add text snippets to the image description text; I forgot to mention that I used "HD Photography" style for this post.
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New Colab notebook "Multi Perceptor VQGAN + CLIP [Public]" from rdurant722. This notebook allows the optional use of a 2nd CLIP model for greater accuracy at the cost of slower processing speed. Link in comment. Example: "Enchanted Forest by James Gurney" at various iterations.
Github https://github.com/THUDM/CogView
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cat in a hat by an artificial intelligence system that composes an image to match a given text description
I used the web app for the new version 2 of CogView, then upscaled with a web app version of SwinIR, and then cropped with a paint app. Both of these are free.
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This image was the result of a text-to-image artificial intelligence system that generated an image for my text description request of "Abstract and non-abstract art combining a cat and tendrils of neon light"
This is from the new version of CogView that was released a few weeks ago. There is a link to the new web version about 1/4 of the way down its GitHub page. It is free to use, with no paid option available. Tip: The input needs to be in simplified Chinese. An English-to-simplified Chinese translator icon appears after typing 9 characters.
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Tip for new CogView version: adding sentence "No watermark." at the end of the text prompt seems to greatly reduce the occurrence of watermarks. See comment for related tip from a developer. Example: "illustration of a happy SpongeBob SquarePants. No watermark."
A method to prevent generating watermark.
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[P] New version of CogView (text-to-image) is available in online demo
GitHub repo for older version of CogView.
SwinIR
- Certain directories (e.g. SwinIR) are empty (version: Empire Media Science A1111 Web UI Installer)
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I used Real-ESRGAN to upscale my image, but if you zoom in you can see that “water particles” looks like some random lines and image overall looks cartoonish. Is there a way to fix it?
003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth
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Any luck changing the upscaler? They seem to be hard coded
I was trying to get a new upscaler working, as someone pointed me to one that did a good job of preserving and creating new details: https://github.com/JingyunLiang/SwinIR
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Spatial-temporal denoising
SwinIR: https://github.com/JingyunLiang/SwinIR
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A Monster Hunter: World Virtual Photography Tutorial - YouTube
Upscalers that I use SwinIR https://github.com/JingyunLiang/SwinIR https://github.com/AUTOMATIC1111/stable-diffusion-webui (Use 'extras' tab for the upscaler function) Topaz Gigapixel AI https://www.topazlabs.com/gigapixel-ai
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what are the alternatives to letsenhance.io?
You could try out chaiNNer, it is a free local/offline application. There are a lot of (upscaling) models which you can download an use with it. You can for example try out SwinIR-L (link will start a model download) or any other model you like depending on your input images.
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[R] Swin transformer while using a rectangular attention window
the relative attention bias can be made non-square in the original implementation, there is a parameter window_size, at 7, that is forced to (7,7) directly, but you can change it easily. https://github.com/JingyunLiang/SwinIR/blob/main/models/network_swinir.py
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Robot dance animation with Robo-Diffusion (1024x576)
Use SwinIR medium model to upscale by 2 times. This will result in a video of 2048x1152.
- Help Need to get my VQGAN images to 10000 x 10000
- Made with Outpainting + Inpainting(original picture and promt in comments)
What are some alternatives?
CogView2 - official code repo for paper "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers"
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
storyteller - Multimodal AI Story Teller, built with Stable Diffusion, GPT, and neural text-to-speech
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
DialogRPT - EMNLP 2020: "Dialogue Response Ranking Training with Large-Scale Human Feedback Data"
Real-ESRGAN-ncnn-vulkan - NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
MPRNet - [CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.
LaTeX-OCR - pix2tex: Using a ViT to convert images of equations into LaTeX code.
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]