FTASD - A Fine Tuning Approach for Stable Diffusion Models
Bibliographic record
Abstract
Image generation is one of the critical tasks performed in today’s world for the expansion of research domains like computer vision and Generative Artificial Intelligence (Generative AI). Therefore, there is an ultimate need for 2-D image synthesis models, which are already introduced by the researchers in the form of stable diffusion models. Recently, various companies like OpenAI and Google AI introduced such models in the computer vision industry. The fundamental approach is to generate target images through a diffusion process. The various applications of stable diffusion include text-to-image generation, image restoration, image-to-image generation, video generation, and facial restoration. In recent years, multiple development plans have been incorporated for improving the image generation models. The improvements have been done in the form of improving the loss function, architecture designs and optimization method. In this paper, we propose a fine-tuning method for improving the performance of various image generation models using Stable Diffusion (SD). Our major focus is on various stable diffusion models which involve text (prompt) to image generation methodology for generating various synthetic images. In our fine tuning process, we leveraged the KerasCV and trained the pre-trained Stable Diffusion Model on a diversified POKEMON (BLIP caption generated) dataset fetched from the Hugging Face database. Our model outperformed the existing KerasCV stable diffusion model which is responsible for text to image generation. We also fine-tuned the model using our self collected Keji National Forest dataset and it produced outstanding application specific results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".