rich-text-to-image
custom-diffusion
rich-text-to-image | custom-diffusion | |
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11 | 11 | |
732 | 1,787 | |
- | 2.0% | |
6.5 | 5.6 | |
7 months ago | 5 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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rich-text-to-image
- FLaNK Stack Weekly 09 Oct 2023
- Expressive text-to-image generation with rich text
- Expressive Text-to-Image Generation with Rich Text
- When will rich-text txt2img be integrated in A1111 or other web based UI?
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Choose Your Weapon: Survival Strategies for Depressed AI Academics
https://rich-text-to-image.github.io/ and https://prompt-to-prompt.github.io/ are pretty good at changing specific details of specific objects without spillover, though I don't know about their token efficiency.
- Bing DALL-E beats this new AI on this very long prompt. Prompt and other AI in comments.
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Creating Images from Text with Rich Text Features
Project Page: https://rich-text-to-image.github.io/
custom-diffusion
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ELITE: new fine-tuning technique that can be trained in less than a second
I think https://github.com/adobe-research/custom-diffusion
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What's the best technology for training faces these days?
I attempted Custom Diffusion which, too, did not yield anywhere near as photorealistic face outputs as Dreambooth.
- [Discussion] Stable Diffusion Models with Subject/Keyword References
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Suggestions for creating prompts with two people that you've added via fine-tuning?
Curious if anyone has better experiences? I've also been trying Adobe's Custom Diffusion code, which probably works the best out of Textual Inversion and Dreambooth, but the code they provide is super buggy and very hard to use with Automatic1111.
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Version 0.1.0 of LoRA released! (alternative to Dreambooth, 3mb sharable files)
Well, actually it is https://github.com/adobe-research/custom-diffusion. It's Adobe, so this is probably only once in the lifetime :P
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How would I go about creating an app like "Lensa" with Stable Diffusion?
A Few Comments - The "caveat" I mentioned above is that there are a few apps that are running Stable Diffusion locally on the user's device. Apple recently released a tool to convert Stable Diffusion models to CoreML. CoreML is their proprietary format for machine learning models, and runs insanely well on Apple devices with a Neural Engine (like newer Macs, iPhones, and iPads). However, this technology is in its infancy, almost certainly isn't capable of "training" a Stable Diffusion model, and isn't anywhere near as fast as running Dreambooth or Stable Diffusion on powerful servers. In the long-run, it might be possible to do all of this processing on a user's device, but it's likely that we're a long ways away from that. - Dreambooth itself isn't that hard to run and play around with yourself, nor is it that hard to integrate into an automated server pipeline, though what it does under the hood is pretty amazing. Dreambooth isn't the only way to "train" a Stable Diffusion model with custom photos, and other people/companies (like Adobe), have found other ways to create amazing AI-generated images with user-provided photos (see their Custom Diffusion GitHub). - Given how long Lensa has been around, and that they're pretty decently funded (they raised $6m in 2019, I believe), it's very likely they they've developed their own in-house way of training Stable Diffusion models, just like the Adobe reference above. But if any of us were to build an app that works like Lensa, a starting point would probably be to use Dreambooth since it's well-built out and easy to integrate, and get similar results. - A very popular way to run Dreambooth is to use a Google Colab notebook, like this one from TheLastBen's GitHub. Since the vast majority of us don't have super powerful GPU cards in our computer, and are just playing around with Dreambooth/Stable Diffusion, the Google Colab notebook lets you go step-by-step to "setup" an environment for doing Dreambooth, but runs it on Google's super-powerful servers. The cool thing about Google Colab, besides not having to have a super-powerful computer yourself to still do Dreambooth with great performance, is that you can look through the code of how the Google Colab notebook works, and that could be a foundation for an engineer to learn how to implement Dreambooth in your own scripts that run on the "Backend Server" for your app.
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How does tiktok’s AI portrait filter work?
If it is Stable Diffusion-based, I'd guess a few things. Given the small size that would be needed to handle this for every possible user, I wonder if it's using something that isn't Dreambooth based, like Adobe's [Custom Diffusion(https://github.com/adobe-research/custom-diffusion), or some in-house variant of that, that's able to basically generate a small file that could be processed for each user.
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How to get the smallest models or portions of Dreambooth-trained models for a specific subject
Adobe Research also has "Custom Diffusion" out: https://github.com/adobe-research/custom-diffusion. It's got a similar goal of ~megabytes sized outputs. Warning that it's got a proprietary license.
- Custom Diffusion - Adobe Research
- What's the difference between custom-diffusion and Dreambooth?
What are some alternatives?
obs-studio-portable - OCI container image of OBS Studio that bundles a curated collection of 3rd party plugins
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
MeshDiffusion - Official implementation of "MeshDiffusion: Score-based Generative 3D Mesh Modeling" (ICLR 2023 Spotlight)
sd-webui-additional-networks
mmgeneration - MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
compel - A prompting enhancement library for transformers-type text embedding systems
configDB - A database like , dynamic config file generator to enable more customization in your python project , from games to ML notebooks and everything between.
sd-webui-cutoff - Cutoff - Cutting Off Prompt Effect
WaveDiff - Official Pytorch Implementation of the paper: Wavelet Diffusion Models are fast and scalable Image Generators (CVPR'23)
autogen - A programming framework for agentic AI. Discord: https://aka.ms/autogen-dc. Roadmap: https://aka.ms/autogen-roadmap
LLM-Adapters - Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"