LLM-Enhanced Image Generation with Adaptive Denoising basis Feedback Loop and Quality Slider #8822
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Hey guys, this is my first post (not only to this community but github at large). I am not a coder, neither I have a lot of experience in the field of AI image generation.
I am just curious and have been reading a lot of how image generation works lately. I thought of some ways to potentially improve the process. Because of my non-technical background, I am unable to evaluate if this is actually feasible or not. So posting here in the hope of someone who knows their way around can get some inspiration from it and build something that can potentially improve some areas of image generation.
Full disclosure : I have done my research from multiple AI LLM tools like chatgpt and gemini and their research modules. I tried to collate everything as well I could, so if anyone wants to read, they have a comprehensive document. But at the end of the day, the document is largely created by AI platforms. I will just post the TLDR in this textbox for a quick read, and you can download the full pdf to read more into this, if you want. Thanks to whoever is reading!
The Problem: Current AI image tools are often rigid, using a fixed number of steps for every image. This leads to:
Inconsistent Quality: Images can have "quirky eyes, teeth, and eyebrows" or miss key prompt details because the AI doesn't fully understand the user's intent.
Wasted Time & Compute: Simple images take as long as complex ones, wasting resources, as performance gains often flatten after a few dozen steps.
Limited Control: Users lack easy ways to balance quality and speed, often resorting to manual trial-and-error.
My Solution: A modular system with two main innovations:
LLM-Powered Prompt Analyzer: This "brain" uses a fine-tuned Large Language Model (LLM) to break down complex prompts into structured "semantic tokens" (e.g., object_token: "cyber-samurai", aesthetic_token: "photorealistic"). This gives the AI a clear checklist for generation, improving accuracy.
Adaptive Denoising Loop: This "engine" uses real-time feedback to adjust the image generation process.
Multi-Tiered Quality Evaluator: Acts as the "eyes," using models like YOLO or Grounding DINO for object detection and CLIP-IQA or ImageReward for aesthetic quality. It generates a "collective confidence score". (Still needs to be fine-tuned ALOT)
Adaptive Step Controller: Uses this score to either stop early if quality is met (saving time) or add more steps/guidance if quality is low.↔️ Speed" Slider UI: A simple user interface (UI) with modes like "Fast Draft," "Balanced," and "High Precision" that intuitively control the target quality score, making complex adjustments easy for users.
"Precision
Modularity: Each component can be used independently, allowing wider adoption and fostering innovation in the AI community.
Success Metrics: I aim for ≥ 30% faster generation times, ≥ 90% quality match in "High Precision" mode, and a clear distinction between the slider's modes.
AI Image Generation Platform Proposal.pdf
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