FTASD - A Fine Tuning Approach for Stable Diffusion Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it