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---
library_name: diffusers
license: cc-by-4.0
pipeline_tag: unconditional-image-generation
---
# ddpm-spots-10-leopard
This model is part of my work for my [Freshman Seminar](https://www.teach.ustc.edu.cn/?attachment_id=17309)
of my university.
It is an unconditional image generation model that outputs a $32\times 32$ grayscale image
similar to those of 'leopard' category from the [SPOTS-10](https://github.com/Amotica/SPOTS-10) dataset.
## Uses
```py
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained(
"xinchengo/ddpm-spots-10-leopard",
).to("cuda")
image = pipeline().images[0]
image
```
## Training Details
### Training Data
the images labelled 'leopard' in the [SPOTS-10](https://github.com/Amotica/SPOTS-10) dataset
### Training Procedure
Mainly with [the procedure in the Diffusers tutorial](https://huggingface.co/docs/diffusers/tutorials/basic_training#train-a-diffusion-model) with a few modifications.
### Training Hyperparameters
```py
from dataclasses import dataclass
@dataclass
class TrainingConfig:
image_size = 32
train_batch_size = 64
eval_batch_size = 16
num_epochs = 50
gradient_accumulation_steps = 1
learning_rate = 1e-4
lr_warmup_steps = 500
save_image_epochs = 10
save_model_epochs = 10
mixed_precision = "fp16"
output_dir = "ddpm-spots-10-leopard"
push_to_hub = True
hub_model_id = "xinchengo/ddpm-spots-10-leopard"
hub_private_repo = None
overwrite_output_dir = True
seed = 0
``` |