@@ -210,6 +210,48 @@ In this section, we have outlined the architecture and training process for a Di
By leveraging diffusion models, we aim to generate highly detailed and diverse images that surpass traditional GANs, especially in the context of noisy data and image-to-image tasks. We will continue training the model and assess its performance in the following steps.
## Part 4: What About Those Beautiful Images?
In this experiment, we compared the performance of two models: a large pre-trained model (Stable Diffusion 3.5 with quantization) and a smaller model (OFA-Sys small-stable-diffusion).
### Strong Model: Stable Diffusion 3.5 with Quantization
Using 4-bit quantization, this model produced high-quality and creative images from simple textual prompts, even with reduced memory requirements. We tested the model with prompts like:
- "Underwater wheeled bee"
- "Monster buys lollipop"
- "Imagine a world without eggs"
The results were visually appealing and imaginative, showcasing the model's capability to generate intricate and high-quality images.
**Example Results from Stable Diffusion 3.5:**
*Underwater wheeled bee*
*Monster buys lollipop*
*Imagine a world without eggs*
### Smaller Model: OFA-Sys Small-Stable-Diffusion
While the smaller model generated images, the quality and creativity were noticeably lower. The images lacked the detail and originality seen with the larger model, confirming that smaller models are less capable of handling complex, creative prompts.
**Example Results from OFA-Sys Small-Stable-Diffusion:**
*Example result 1 from smaller model*
*Example result 2 from smaller model*
*Example result 3 from smaller model*
### Conclusion
The experiment demonstrates that larger models, like Stable Diffusion 3.5, produce superior image quality and creativity. However, smaller models can still be useful in scenarios with hardware limitations, though they fall short in terms of detail and imagination when compared to their larger counterparts.
