The Wandering Pen

A 2K image generated using Qwen-Image 2.1 Here are some results from using the Qwen Image 2.1 model via a  Google colab notebook for image generation and editing. Generating a 1K image The following 1K image was generated on the L4 GPU in google colab with the following specs : System RAM : 53.0 GB, VRAM : 22.5 GB Task: Text to Image Generation prompt: A Young man in casual clothes and a busty and shapely young woman in a minidress are sitting on a branch of tree in the forest, smiling at each other  Seed: 42 Qwen-Image Model Used: qwen_image_2.1_bf16.safetensors (Comfy-Org) Output Image Size: 1344x768 Steps: 8 CFG: 1 Sampler: euler Scheduler: simple Speed Lora Used: p_qwen_image_2.1_8step_v0.1.safetensors Cache Enabled: True Cache dtype: int4 Completed in 26.78 sec The following image generations and edits were done on the T4 GPU offered by google Colab with the following specs: System RAM : 51.0 GB, VRAM : 16 GB Task: Text to Image Generation Prompt: A Young man in casual c...

  A 27-minute 15fps video generated with ComfyUI on Google Colab.  LongCat-Video is an AI project that pushes the limits of video generation.  Unlike most AI video models that degrade in color or consistency after a few seconds, LongCat-Video enables generating videos longer than 30 seconds,  all while maintaining sharp image quality and stable colors . The models and code were optimized for ComfyUI by Kijai , one of the major contributors to the ComfyUI ecosystem. This optimization makes it much easier to experiment with LongCat-Video and generate long, coherent video sequences right from your browser. Running LongCat-Video on Google Colab I’ve created a Google Colab notebook that automatically sets up ComfyUI with the LongCat-Video models. So far, I’ve tested it on the L4 GPU , but it should also work on the free  T4 if you reduce the number of frames generated per sequence and set blocks_to_swap to 0 if the RAM crashes with the default settings. ...

  A 1920x1080 image generated with wan2.2 The Notebook This is a guide on how to use a wan2.2 google colab notebook for highly impressive text to image, text to video, image to video, and two images (first & last frames) to video generation that outshine results from commercial platforms. Wan 2.2, which is an improvement on wan2.1, is the top open-source video generation model at the time of writing this guide, and the LoRAs built on wan2.1 can still work with wan2.2. This significantly expands its image and video generation capabilities. You can learn more about wan2.2 from this github repository:   https://github.com/Wan-Video/Wan2.2 This notebook enables you to generate at least 5 seconds of a standard definition video from a single image in about 13 minutes using the T4 GPU offered by google colab. It also enables you to generate a 4 second high-definition video in less than 35 minutes. You can also generate 1920x1080 images in less than 4 minutes. Now let’s pro...