README.md

metadata

title: CanvasAI
emoji: 📚
colorFrom: red
colorTo: indigo
sdk: gradio
sdk_version: 6.22.0
python_version: '3.12'
app_file: app.py
pinned: false
short_description: AI Image Enhancer, Colourizer and Outpainter
startup_duration_timeout: 1h

CanvasAI

Three AI image tools in one interface — upscale and sharpen any photo, colorize black and white images automatically, or extend a scene beyond its original borders.

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Tools

✨ Enhancement

Real-ESRGAN upscales images 2× or 4×, reconstructing fine detail rather than stretching pixels. Processes large images in tiles to stay within VRAM limits.

🎨 Colorization

DDColor adds natural color to black and white images without changing structure or composition. Post-processed with LAB color space smoothing to reduce color patchiness, and a saturation boost to compensate for DDColor's tendency to under-saturate. Fully automatic — no prompt needed.

🔲 Outpainting

Extends your image in any direction using Stable Diffusion 2 Inpainting. BLIP reads the image and generates an appropriate prompt automatically. Uses a multi-pass 64px incremental approach with feathered mask blending for coherent results.

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How the Outpainting Works

The pipeline extends 64 pixels at a time rather than all at once. Each small pass gives SD 87%+ original image context — SD extrapolates naturally from what it can see. One large extension gives SD only 60-70% context, and the generated content becomes incoherent.

The mask uses GaussianBlur feathering so the boundary between original and generated content is a soft gradient rather than a hard cut. The mask is resized with LANCZOS (not NEAREST) to preserve this gradient when scaling to SD's 768×768 input size.

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Honest Limitations

Enhancement does not deblur. It upscales and sharpens existing detail. Blurry input produces larger blurry output.

Colorization works well on portraits, street scenes, and landscapes. Complex fabric patterns and heavily degraded photographs may produce inconsistent colors. Results will not match commercial colorization tools.

Outpainting quality varies by input. Works best on simple consistent backgrounds at 25% extension or below. Struggles with complex foreground subjects near edges and high extension percentages. The multi-pass approach significantly improves over single-pass but does not eliminate the underlying model's limitations.

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Tips for Best Results

• Enhancement: works on any image, best on photographs with existing sharp detail
• Colorization: portraits and outdoor scenes colorize most consistently
• Outpainting: use Horizontal or Vertical (not Both), keep extension at 25%, use the custom prompt field if BLIP misreads your image

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Models Used

Model	Purpose
RealESRGAN_x4plus	Super resolution
stable-diffusion-2-inpainting	Scene extension
blip-image-captioning-base	Auto prompt generation
cv_ddcolor_image-colorization	B&W colorization

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Privacy

Images are processed in memory and not stored permanently. Uploaded images are deleted when your session ends. Do not upload sensitive or private images.

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GitHub

Full code, architecture notes, and documentation: github.com/Mohit485/CanvasAI