| --- |
| 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 |
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| A class-conditioned DDPM trained from scratch to generate 128×128 RGB images of Indian Sign Language hand gestures using a linear noise schedule. |
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| The model uses class conditioning on one of 35 ISL classes and supports classifier-free guidance during sampling. |
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| The complete implementation and experiments are available in the [GitHub repository](https://github.com/MahadevBalla/isl-diffusion). |
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| ## Model |
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| - DDPM with UNet2D architecture |
| - 128×128 resolution |
| - RGB images |
| - Class-conditional generation |
| - 35 ISL classes |
| - Classifier-free guidance (CFG) |
| - EMA weights |
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| ## Training |
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| - 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 |
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| ## Sampling |
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| - Default sampler: DDIM |
| - Training diffusion timesteps: 1,000 |
| - Routine inference steps: 100 |
| - Evaluation inference steps: 50 |
| - Default guidance scale: 1.0 |
| - Random seed: 42 |
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| ## Results |
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| At the FID-optimal guidance scale of 1.0: |
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| - FID: 58.25 |
| - Semantic accuracy: 98.0% |
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| The model provides strong class control under the reported evaluation setting. |
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