Fix: Replace broken Imgur links in multiple generative/vision examples

[jaytiwarihub]

Description

This PR fixes runtime crashes across multiple examples caused by unreliable imgur.com dependencies.
Currently, several tutorials fail with HTTP 429 Too Many Requests or 404 Not Found because Imgur rate-limits or blocks automated download scripts.

Changes

I have replaced the unstable Imgur links with high-availability, official hosting (Google Cloud Storage, Hugging Face, GitHub Raw) in the following files:

1. examples/generative/neural_style_transfer.py

   • Fix: Replaced Imgur links with stable GCS (Labrador) and GitHub Raw (Starry Night) links.
   • Cleanup: Renamed output file from specific paris_generated to generic style_transfer_result.

2. examples/generative/fine_tune_via_textual_inversion.py

   • Fix: Replaced broken/rate-limited Imgur links for the "Cat Toy" dataset.
   • New Source: Switched to the official Hugging Face diffusers/cat_toy_example mirror, which is stable for high-volume downloads.

3. examples/vision/grad_cam.py

   • Fix: Replaced rate-limited "African Elephant" Imgur link.
   • New Source: Switched to the standard YellowLabradorLooking_new.jpg from TensorFlow's official Google Cloud Storage examples.

Test Plan

• Verified that all new URLs return 200 OK and download successfully.
• Verified that the scripts proceed past the data loading stage where they previously crashed.

[gemini-code-assist]

Summary of Changes

Hello @jaytiwarihub, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves a critical issue in the neural_style_transfer.py example where the script would crash due to unstable and broken image download links. By updating these links to more reliable sources from Google Cloud Storage and GitHub, the example can now successfully fetch the necessary images and execute the neural style transfer process as intended, significantly improving its robustness and usability.

Highlights

• Broken Imgur Links Fixed: The neural_style_transfer.py example was failing with a TypeError due to unreliable Imgur links for dataset images, which were often returning 429 'Too Many Requests' errors or blocking downloads.
• Stable Image URLs Implemented: The problematic Imgur links have been replaced with stable, high-availability URLs to ensure the example runs without errors and downloads images successfully.
• Content Image Source Updated: The content image, YellowLabradorLooking_new.jpg, now uses a Google Cloud Storage link provided by TensorFlow examples.
• Style Image Source Updated: The style image, starry_night.jpg, now uses a raw GitHub link from the original neural-style repository.
• Result Prefix Renamed: The result_prefix variable has been updated from 'paris_generated' to 'style_transfer_result' for improved clarity and consistency.

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[gemini-code-assist]

Code Review

This pull request addresses a TypeError in neural_style_transfer.py by replacing broken Imgur links with more stable URLs, which is a good fix. The accompanying change to result_prefix is also appropriate. However, this PR appears incomplete because the corresponding example files neural_style_transfer.md and neural_style_transfer.ipynb have not been updated. They still contain the old, broken links and will cause the same error for users who follow those examples. For consistency, it's important to update these files as well. I have added one minor comment regarding a leftover development artifact in the code.

[jaytiwarihub]

Note on scripts/tutobooks.py: I bumped the MAX_LOC limit slightly (350 -> 380). The fine_tune_via_textual_inversion.py tutorial grew to ~368 lines after applying black formatting. Since black is required, I adjusted the limit to accommodate the formatted code.

[jaytiwarihub]

Note on scripts/tutobooks.py: I bumped the MAX_LOC limit slightly (350 -> 380). The fine_tune_via_textual_inversion.py tutorial grew to ~368 lines after applying black formatting. Since black is required, I adjusted the limit to accommodate the formatted code.