Stable-Lime-v1.1

Stable-Lime-v1.1 is an unconditional diffusion model based on the Denoising Diffusion Probabilistic Models (DDPM) architecture. It has been trained specifically to generate images representing the "essence of Lime."

Model Details

• Model Type: Unconditional Image Generation (Diffusion)
• Architecture: UNet2DModel with DDPMScheduler
• Framework: PyTorch & Hugging Face Diffusers
• Resolution: $128 \times 128$ pixels
• Channels: 3 (RGB)
• License: MIT (Assumed based on open-source usage)

Intended Use

This model is designed for:

• Generating $128 \times 128$ images of limes (or lime-like textures).
• Educational purposes regarding the implementation of DDPM loops.
• Low-resolution, "retro" aesthetic generation.

Out of Scope:

• Text-to-Image generation (this model does not accept text prompts).
• High-resolution photorealism (limited by the 128px architecture).

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Training Data

The model was trained on a proprietary dataset located at dataset_lime/processed.

• Preprocessing: Images were resized to $128 \times 128$ and normalized to the range $[-1, 1]$.
• Augmentation: Random horizontal flips were applied during training to improve generalization.

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Training Procedure

Hyperparameters

The model was trained using the following configuration ("The Lime Settings"):

Parameter	Value	Description
Batch Size	16	Small batch size suitable for consumer GPUs.
Learning Rate	$1 \times 10^{-4}$	Optimizer step size (AdamW).
Epochs	70
Timesteps	1000	Number of diffusion noise steps.
Image Size	128	Output resolution.

Architecture Specification

The U-Net architecture utilizes a deep structure with attention mechanisms in the lower bottleneck layers:

• Block Output Channels: (128, 128, 256, 256, 512, 512)
• Downsampling: 4x DownBlock2D, 1x AttnDownBlock2D, 1x DownBlock2D
• Upsampling: Mirror of downsampling blocks.

Loss Function

The model optimizes the Mean Squared Error (MSE) between the actual noise added and the predicted noise:

$$L=\text{MSE}(ϵ,{ϵ}_{\theta }(x_t,t))L\; =\; \backslash text\{MSE\}(\backslash epsilon,\; \backslash epsilon\_\backslash theta(x\_t,\; t))$$

Where $\epsilon$ is the Gaussian noise and $\epsilon_\theta$ is the model's prediction at timestep $t$.