Boosted_Dalle
dalle-flow
Boosted_Dalle | dalle-flow | |
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1 | 31 | |
1 | 2,825 | |
- | 0.1% | |
0.0 | 2.3 | |
almost 2 years ago | 12 months ago | |
JavaScript | Python | |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Boosted_Dalle
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Newbie Help: Telegraf telegram bot with async/fetch, media send and async management
What caused me most trouble and brought me to simplify all the code into a single http request, is that the original code for the http request (which I took from here) and put it just the web request code in a single method, scrapping the rest.
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?
dalle2-in-python - Use DALL·E 2 in Python
dalle-mini - DALL·E Mini - Generate images from a text prompt
bottleneck - Job scheduler and rate limiter, supports Clustering
jina - ☁️ Build multimodal AI applications with cloud-native stack
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
BasicSR - Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
chaos-tarot - AI Generated Tarot Reading & Card
example-app-store - App store search example, using Jina as backend and Streamlit as frontend
telegraf - Modern Telegram Bot Framework for Node.js
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.