---
license: mit
pipeline_tag: unconditional-image-generation
library_name: diffusers
tags:
  - image-generation
  - conditional-diffusion
  - indian-sign-language
  - classifier-free-guidance
---

# ISL Conditional Diffusion — Linear Noise Schedule

A class-conditioned DDPM trained from scratch to generate 128×128 RGB images of Indian Sign Language hand gestures using a linear noise schedule.

The model uses class conditioning on one of 35 ISL classes and supports classifier-free guidance during sampling.

The complete implementation and experiments are available in the [GitHub repository](https://github.com/MahadevBalla/isl-diffusion).

## Model

- DDPM with UNet2D architecture
- 128×128 resolution
- RGB images
- Class-conditional generation
- 35 ISL classes
- Classifier-free guidance (CFG)
- EMA weights

## Training

- Dataset: 42,000 images (1,200 images per class × 35 classes)
- Noise schedule: linear
- Batch size: 64
- Learning rate: 1e-4
- Mixed precision: fp16
- Training steps: 65,000
- EMA decay: 0.9999
- CFG label dropout: 0.15
- Data augmentation: enabled

## Sampling

- Default sampler: DDIM
- Training diffusion timesteps: 1,000
- Routine inference steps: 100
- Evaluation inference steps: 50
- Default guidance scale: 1.0
- Random seed: 42

## Results

At the FID-optimal guidance scale of 1.0:

- FID: 58.25
- Semantic accuracy: 98.0%

The model provides strong class control under the reported evaluation setting.