UnstableFusion
diffusion-models-class
UnstableFusion | diffusion-models-class | |
---|---|---|
10 | 22 | |
1,232 | 3,221 | |
- | 2.0% | |
0.0 | 6.3 | |
about 1 year ago | 21 days ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 only | 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.
UnstableFusion
- Sunt AI Research Scientist, AMA
-
Show HN: InvokeAI, an open source Stable Diffusion toolkit and WebUI
Shameless plug: I was frustrated with the poor UI of notebook-based frontends so I wrote a desktop version here: https://github.com/ahrm/UnstableFusion .
Here is a video of some of its features: https://www.youtube.com/watch?v=XLOhizAnSfQ&t=1s
- Instead of a boring text, now you can add external image on top of the generated image and make it natural
-
Use advanced inpainting to control the inpainting results (github link in comments)
Added advanced inpainting functionality to UnstableFusion, you can find the project here: https://github.com/ahrm/UnstableFusion
- Stable diffusion desktop frontend with integrated inpainting, img2img, undo and more
- I have a problem — I keep making new GUIs for Stable Diffusion 😛
- GitHub - ahrm/UnstableFusion: A Stable Diffusion desktop frontend with inpainting, img2img and more!
- UnstableFusion - A stable diffusion frontend with inpainting, img2img, and more
-
UnstableFusion - A stable diffusion frontend with inpainting, img2img, and more. Link to the github page in the comments
Github page: https://github.com/ahrm/UnstableFusion
- Show HN: A Stable Diffusion desktop front end with inpainting, img2img and more
diffusion-models-class
- diffusion low level question
- Here's a learning resource
-
[R] Classifier-Free Guidance can be applied to LLMs too. It generally gives results of a model twice the size you apply it to. New SotA on LAMBADA with LLaMA-7B over PaLM-540B and plenty other experimental results.
When you use stable diffusion, you can adjust the classifier free guidance scale to control how much it follows the input prompt. From what I understand(check https://github.com/huggingface/diffusion-models-class/tree/main/unit3), what cfg does is that it generates an unconditional image and an image conditional on the text prompt, and then scale up the difference.
-
Ai Coding roadmap
https://huggingface.co/learn/nlp-course/ https://huggingface.co/docs/transformers (go through the task guide) https://github.com/huggingface/diffusion-models-class http://d2l.ai/ https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
-
How does stable diffusion work from a technical perspective?
I couldn't understand the original paper(havent done meth in a long time). This blog post and short course help me to understand.
-
Using SD programatically with APIs
The Diffusion Models Course is another good resource to learn more technical details.
-
I made a generative 3D game and took a walk in the streets of Paris. Playback speed 30x
Next, you need to become familiar with the diffusion model. I recommend this huggingface's course(https://github.com/huggingface/diffusion-models-class) because it is very high quality and you will learn while using diffusers. At first glance, it may not seem directly related to this game, but in my case, knowing what is happening in diffusers helped me in many ways: trial and error, inspiration for ideas, etc. I had no knowledge of pytorch (the deep learning library used for diffusers), so I also took this course (https://www.udacity.com/course/deep-learning-pytorch--ud188) which was in the prerequisites for that huggingface's course. It was also very good.
- Sunt AI Research Scientist, AMA
-
Dreambooth Hackaton: How can we use a text-to-image model to explore the cinematographic appeal of Torres del Paine 🇨🇱?
Hugging Face Dreambooth Hackaton details
-
[N] Personalise Stable Diffusion models in DreamBooth Hackathon
Details: https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md
What are some alternatives?
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
deforum-stable-diffusion
stable_diffusion.openvino
tutorials - AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
perceiver-pytorch - Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
approachingalmost - Approaching (Almost) Any Machine Learning Problem
ai-notes - notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.
pml-book - "Probabilistic Machine Learning" - a book series by Kevin Murphy
rocm-build - build scripts for ROCm
deforumed-walk - Take a walk in the generated world.
sd-webui-controlnet - WebUI extension for ControlNet