Video summary

I made an AI YouTube channel to see if it's "ez money"

Main summary

Key takeaways

Technology

Technological concepts & workflow (main focus)

  • Goal for the channel: create self-improvement YouTube videos where every visual is AI-generated, but in a style intended to look hand-made enough that at least one viewer believes it’s not AI.
  • Visual style decision: avoid the “hyperrealistic/fake” look; use a static, Microsoft Paint–style aesthetic (simple illustrations, redrawn every ~3 seconds).
  • Core planned “pipeline” (as advised by ChatGPT):
    1. Generate scene ideas
    2. Generate simple illustrations
    3. Convert images into Microsoft Paint-like outputs

Image generation tooling (and problems encountered)

Stable Diffusion direction (installation confusion + GPU issues)

  • ChatGPT recommendation: use Stable Diffusion.
  • Main blocker: installing and configuring the system quickly became confusing (engine vs model).
  • Local approach: used Stable Diffusion Web UI (Automatic1111) via a looked-up GitHub setup, targeting an AMD GPU path.
  • Repeated issues:
    • Torch not able to use GPU
    • Wrong DirectML build for AMD
    • Torch installation failures tied to Python version mismatch

Free/hosted fallback: Amuse AI

  • Moved to Amuse AI when Stable Diffusion installation and costs were unclear.
  • Learned about:
    • Models
    • LoRAs

LoRA/style training concept

  • LoRAs were understood as style/subject adaptations.
  • Training guidance: typically needs ~20–100 images for a custom LoRA.
  • But: platform limitations prevented direct training inside Amuse.

Switching to pre-made style LoRAs

  • Used “Microsoft Paint Portraits” (safe tensors model) and other LoRAs.
  • Major issue: outputs didn’t match Microsoft Paint styling closely; prompts produced inconsistent style.

Final breakthrough: Google Whisk

  • Discovered Whisk can copy a style nearly 1:1 from an uploaded style reference/design.
  • Workflow used:
    • Created a Microsoft Paint–style reference in MS Paint
    • Uploaded both content + style to Google Whisk
    • Result: much closer pixel-style results
  • Drawback: anti-aliasing/blurry edges.
    • Tried Photoshop fixes, but concluded most viewers won’t notice.
    • Kept edge artifacts.

Automation additions for speed

“Auto Whisk” style automation

  • Used a Chrome extension / Auto Whisk approach:
    • Generated many images automatically from prompts (using a TXT list)
    • Downloaded results in bulk
  • Impact: reduced repeated prompting, but editing still required lots of manual correction.

Script and scene description generation

  • ChatGPT-generated scripts were “not good,” so the creator heavily edited them.
  • The edited script was then used to generate sentence-by-sentence scene descriptions, enabling image generation per segment.

Voiceover generation

  • Used ElevenLabs with a cloned voice option (Scottish accent).
  • Avoided full custom voice cloning later due to complexity.
  • Attempted free alternatives (e.g., RVC) but quality was poor → returned to ElevenLabs.

Audio post-processing automation

  • Wrote/used a Python script (via ChatGPT) to remove silence:
    • Trimmed pauses
    • Reduced editing time

Editing pipeline and remaining bottlenecks

Video assembly in Premiere

  • Images were placed based on voiceover timing.
  • Even with automation, images sometimes failed, requiring:
    • Regeneration
    • Masking
  • Failure examples included:
    • dismorphed faces
    • floating objects
    • other generation errors

Face replacement attempt (major time sink)

  • Attempted automation with Python to replace AI-generated faces with a preferred expression.
  • Result: inconsistent placement, causing issues such as:
    • faces on wrong regions
    • double mouths
    • faces in black areas
  • After days and credit limits, the full automation was abandoned.

Scaling attempt with AI “agentic workflows” (Claude Code)

  • Found a tutorial about building “Claude skills” and agentic workflows for more consistent AI output.
  • Implemented a “grand slam” automation concept:

Input topic →

- generate script  
- review/approval  
- generate scene descriptions  
- create multiple image options per scene  
- choose best  
- generate voiceover  
- remove silences  
- assemble video
  • Practical result:
    • Heavy learning curve
    • High usage/credit consumption
  • Also tried:
    • Gemini image generation via API
    • But: Gemini couldn’t generate images on the free tier, and API setup used most usage.

Image library/index idea

  • Built a reusable library of previously generated images with manual descriptions.
  • Added an AI-generated index to support future workflow reuse.

Results / reviews / analysis provided (outcomes)

  • Channel start: “Paint Wizard” began near zero views and with no thumbnail upload (possibly related to an unverified channel limit).
  • Performance improvements:
    • Second video: “The Psychology of Disappearing to Come Back Unrecognizable”
      • ~56 subscribers
      • ~570 views
      • Meaningful comments praising audio/visuals and reducing AI suspicion
    • Third video: “How to Win If You Have High IQ”
      • Initially dominated: ~400 views in 3 days
      • ~59 subscribers
      • Crossed 1,000 views later
  • Key success metric stated: achieved the goal of getting at least one viewer to believe it wasn’t AI, supported by comments.

Product/platform sponsor (mentioned)

Akiflow (calendar/time blocking + AI assist)

  • Features described:
    • Time-blocking
    • Integrations (e.g., Slack/email)
    • Command bar (universal search)
    • Optional AI that learns from project tasks and assigns new tasks
  • Claimed privacy/safety:
    • Data used only to train a private model

Main speakers / sources (as referenced)

  • Primary narrator: the YouTube creator (narrates their own experiments; references “Ryan” in an example, but narration is by the channel creator)
  • Tools/AI systems consulted:
    • ChatGPT
    • Google Gemini
    • Google Whisk
    • Stable Diffusion (plus Automatic1111)
    • Amuse AI
    • Claude (Claude Code)
    • ElevenLabs
    • Akiflow (sponsor)
  • External tutorial/source mentioned:
    • FE Engineer (AMD fix tutorial for Automatic1111)

Original video