PIXART-α : First Open Source Rival to Midjourney - Better Than Stable Diffusion SDXL - Full Tutorial (Patreon)

Tutorial Link > https://youtu.be/ZiUXf_idIR4

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Introduction to the new PixArt-α (PixArt Alpha) text to image model which is for real better than Stable Diffusion models even from SDXL. PixArt-α is close to the Midjourney level meanwhile being open source and supporting full fine tuning and DreamBooth training. In this tutorial I show how to install and use PixArt-α both locally and on a cloud service RunPod with automatic installers and step by step guidance. 

The link to download resources ⤵️

https://www.patreon.com/posts/pixart-alpha-for-93614549

Stable Diffusion GitHub repository ⤵️

https://github.com/FurkanGozukara/Stable-Diffusion

SECourses Discord To Get Full Support ⤵️

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#PixArt #StableDiffusion #SDXL

0:00 Introduction to PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis and the tutorial content

2:38 What are the requirements to follow this tutorial and install PixArt Alpha

3:05 How to install PixArt Alpha on your machine and start using it

3:59 Where Hugging Face models are downloaded by default and how to change this default cache folder

5:44 How to return back to using default Hugging Face cache folder

6:08 How to fix corrupted files error during installation

6:29 How to start PixArt Web APP after installation has been completed 

7:24 How to use PixArt Web APP and its features

7:59 Comparing a dragon prompt with SDXL base version

8:14 How to use provided styles csv file

8:40 How to start Automatic1111 SD Web UI on your second GPU

8:50 Where the PixArt Web APP generated images are saved

9:30 How to set parameters in your Automatic1111 SD Web UI to generate high quality images

9:49 PixArt generated image vs SDXL generated image for same simple prompt

10:15 Anime style same prompt comparison

10:55 One another strong aspect of the PixArt Alpha model

11:29 Fantasy art style comparison of SDXL vs PixArt-α

11:52 3D style comparison of SDXL vs PixArt-α

12:16 Manga style image generation comparison between SDXL vs PixArt-α

12:44 Comparing PixArt vs SDXL vs Midjourney with same prompt

13:41 How to use LLaVA for captioning and obtaining prompt ideas and generating more amazing images

16:12 Comparison of PixArt vs SDXL prompt following in details

17:29 Getting prompt idea from ChatGPT and comparing SDXL and PixArt prompt following

19:46 PixArt owns hard the SDXL with this new detailed prompt

22:00 How to install PixArt on a RunPod pod / machine

23:54 How to set default Hugging Face cache folder on RunPod / Linux machines

25:05 How to understand RunPod machine / pod is not working correctly and fix it

26:00 How to properly delete files / folders on RunPod machines / pods

26:51 How to connect and use PixArt web UI on a RunPod machine after it was started

28:20 How to download all of the generated images on RunPod with runpodctl very fast

The paper introduces PIXART-α, a Transformer-based text-to-image (T2I) diffusion model designed to significantly lower training costs while maintaining high image generation quality, competitive with leading models like Imagen and Midjourney. It achieves high-resolution synthesis up to 1024x1024 pixels at reduced training costs.

Key Innovations:

Training Strategy Decomposition: The process is divided into three steps focusing on pixel dependency, text-image alignment, and image aesthetic quality. This approach reduces learning costs by starting with a low-cost class-condition model and then pretraining and fine-tuning on data rich in information density and aesthetic quality.

Efficient T2I Transformer: Built on the Diffusion Transformer (DiT) framework, it includes cross-attention modules for text conditions and streamlines computation. A reparameterization technique enables loading parameters from class-condition models, leveraging prior knowledge from ImageNet, thus accelerating training.

High-informative Data: To overcome deficiencies in existing text-image datasets, the paper introduces an auto-labeling pipeline using a vision-language model (LLaVA) to generate captions on the SAM dataset. This dataset is selected for its diverse collection of objects, aiding in creating high-information-density text-image pairs for efficient alignment learning.

Image Quality: The model excels in image quality, artistry, and semantic control, surpassing existing models in user studies and benchmarks.

Broader Implications: The paper suggests that PIXART-α's approach allows individual researchers and startups to develop high-quality T2I models at lower costs, potentially democratizing access to advanced AI-generated content.

The paper concludes with the hope that PIXART-α will inspire the AIGC community and enable more entities to build their own generative models efficiently and affordably.