dalle-flow
argilla
| dalle-flow | argilla | |
|---|---|---|
| 31 | 15 | |
| 2,825 | 3,122 | |
| 0.1% | 1.9% | |
| 2.3 | 9.8 | |
| 12 months ago | about 7 hours ago | |
| Python | Python | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
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.
argilla
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Open-Source Data Collection Platform for LLM Fine-Tuning and RLHF
I'm Dani, CEO and co-founder of Argilla.
Happy to answer any questions you might have and excited to hear your thoughts!
More about Argilla
GitHub: https://github.com/argilla-io/argilla
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Meet Argilla: An Open-Source Data Curation Platform for Large Language Models (LLMs) and MLOps for Natural Language Processing
Github link: https://github.com/argilla-io/argilla
- Show HN: Argilla and AutoTrain – Train custom NLP models without code
- Rubrix release 0.17.0 with support for the spaCy training format
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No training data, no problem! Few-shot NER with a practical example
Rubrix, the open-source tool for data-centric NLP: https://github.com/recognai/rubrix
- [D] Expert Advice is needed on designing a feedback Loop for a (Textual Classification + NER) task in Production.
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[D] How should a former Web Developer, pursue career in Machine Learning?
E.g. https://github.com/recognai/rubrix
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[P] Small-Text: Active Learning for Text Classification in Python
I have already thought about providing an example of how to integrate small-text with one of the existing labeling tools, such as rubrix rubrix, but that hasn't been started yet.
- Finding and correcting text classification label errors with cleanlab and Rubrix | https://rubrix.readthedocs.io/en/master/tutorials/find_label_errors.html
- Rubrix: Open-source tool for building NLP training sets (now with weak supervision)
What are some alternatives?
dalle-mini - DALL·E Mini - Generate images from a text prompt
snorkel - A system for quickly generating training data with weak supervision
jina - ☁️ Build multimodal AI applications with cloud-native stack
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
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.
doccano - Open source annotation tool for machine learning practitioners.
example-app-store - App store search example, using Jina as backend and Streamlit as frontend
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
data-centric-ai - Resources for Data Centric AI
dalle2-in-python - Use DALL·E 2 in Python
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing