taesiri

Feedback and Proposed Adjustments for Image Editing Categories

1. Re-coloring

• Current Status: Definition is acceptable.
• Feedback:
  • Add a note clarifying that users are assumed to use the “accurate definition” of colorize (i.e., converting black & white photos to color).

2. Relight

• Current Definition:
  "Improve or change the lighting conditions of the scene such as the temperature, color, direction, or position of the light source."

taesiri

I'll gather the five most recent research papers from top conferences (CVPR, NeurIPS, ACL, ICCV, ECCV, WACV) related to image editing using natural language instructions. Additionally, I'll determine the best model for this task and identify datasets commonly used in this field. I'll update you once I have the information.

1. Recent Research Papers (2019–2024) on Language-Guided Image Editing

• InstructPix2Pix: Learning to Follow Image Editing Instructions (CVPR 2023) – Tim Brooks, Aleksander Holynski, Alexei A. Efros. This work introduced a diffusion-based model for image editing that follows natural language instructions. To overcome data scarcity, the authors generated a large synthetic training set by pairing a language model (GPT-3) with a text-to-image model (Stable Diffusion) to create many “before and after” image examples with instructions. The resulting model (InstructPix2Pix) is a conditional diffusion network that generalizes to real images and user-written instructions, performing edit

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Bad-GenAI.txt

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diff

44d43
<     6 -> 5;
50d48
<     2 -> 7;
57d54
&lt; 4 -&gt; 5;

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import random

def generate_grid_svg(size=10, cell_size=50, stroke_width=2):
    """
    Generate an SVG with a grid of random vertical or horizontal lines.
    
    Args:
        size (int): Number of cells in both dimensions (creates size x size grid)
        cell_size (int): Size of each cell in pixels
        stroke_width (int): Width of the lines in pixels