UltraPixel: Revolutionizing ultra-high resolution image generation with rich image details

General Introduction

UltraPixel is an advanced ultra-high resolution image generation technology designed to create extremely high-quality, detailed images. Developed by GitHub user catcathh and presented at NeurIPS 2024, UltraPixel supports the generation of images at any resolution from 1K to 6K, with results comparable to those of the MidJourney and DALL-E 3, significantly improving the quality and detail of image generation.

Online experience: https://huggingface.co/spaces/roubaofeipi/UltraPixel-demo

• High resolution image generation : Supports image generation at any resolution from 1K to 6K.
• Multiple model support : A variety of pre-training models are provided so that users can choose the appropriate model according to their needs.
• Text-guided image generation : Generate high-quality images with detailed text descriptions.
• Gradio Interface : Provides a Gradio interface to facilitate image generation operations.
• Efficient memory management : Support tiled decoding to save memory resources.

Using Help

Installation process

1. Installation of dependencies : Run the following command to install the required dependencies:

pip install -r requirements.txt

1. Download pre-trained model : Download the pre-trained model according to the link in the README file and place it in the models Catalog.
2. Download UltraPixel Specifications : Download the new parameters for UltraPixel from the link provided and place them in the models Catalog.
3. surname Conghere (literary)Download the new parameters for UltraPixel.
4. Model List:https://github.com/catcathh/UltraPixel/blob/main/models/models_checklist.txt

Usage Process

1. Text-guided image generation ::
   • Run the following command to start the Gradio interface:

     CUDA_VISIBLE_DEVICES=0 python app.py
     

   • Or just run the following command to generate an image:

     CUDA_VISIBLE_DEVICES=0 python inference/test_t2i.py
     

   • Tip: In order to produce beautiful images, it is recommended to use detailed descriptions of the subject, background, color, light and atmosphere, and to use high-quality modifiers such as "high-quality", "detail-rich", "8k ", "photo-realistic", "cinematic lighting effects", etc. For example:

     A stunning sunset image with serene mountains in the background and a sky filled with vibrant oranges and purples, high quality, great detail, 8k, photo-realistic, cinematic lighting, perfect.
     

2. memory management ::
   • To save memory resources, it is recommended that you add the --stage_a_tiled Parameters.

Sample Tips

• Generate personalized images of cats ::
  Download the personalization model and run the following command to generate the personalization results:

CUDA_VISIBLE_DEVICES=0 python inference/test_t2i.py --model_path path_to_personalized_model

One-click installation package

Graphics card can not be less than 16G, memory can not be less than 40G

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