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README.md

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+---
+license: mit
+library_name: diffusers
+pipeline_tag: conditional-image-generation
+tags:
+  - diffusers
+  - ddpm
+  - conditional-image-generation
+  - mnist
+  - diffusion-models-class
+---
+
+# DiffusionNumbers DDPM
+
+DiffusionNumbers is a DDPM trained from scratch on the 60k training images of
+[MNIST](https://huggingface.co/datasets/ylecun/mnist) on Apple Silicon. Training
+took approximately 5 hours. It generates 64×64 grayscale images of a requested
+handwritten digit from 0 to 9.
+
+## Usage
+
+Install PyTorch, Diffusers, Safetensors, and Pillow, then run the model as
+follows. Class labels have to be passed to the U-Net during every denoising
+step:
+
+```python
+import torch
+from diffusers import DDPMPipeline
+from PIL import Image
+
+if torch.backends.mps.is_available():
+    device = "mps"
+elif torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+pipe = DDPMPipeline.from_pretrained("DD-65/diffusionnumbers").to(device)
+
+digit = 7
+labels = torch.tensor([digit], device=device)
+images = torch.randn((1, 1, 64, 64), device=device)
+
+pipe.scheduler.set_timesteps(1000)
+for timestep in pipe.scheduler.timesteps:
+    with torch.no_grad():
+        noise_prediction = pipe.unet(
+            images,
+            timestep,
+            class_labels=labels,
+        ).sample
+
+    images = pipe.scheduler.step(
+        noise_prediction,
+        timestep,
+        images,
+    ).prev_sample
+
+image = (images[0, 0] / 2 + 0.5).clamp(0, 1)
+image = image.mul(255).round().to(torch.uint8).cpu().numpy()
+Image.fromarray(image).save("digit-7.png")
+```
+
+Change `digit` to any integer from 0 through 9.
+See also the [Github repo](https://github.com/DD-65/diffusionnumbers-code) for the complete training code and a more elaborate inference script.
+
+## Model details
+
+- Architecture: `UNet2DModel` with a `DDPMScheduler`
+- Parameters: 3.94 million
+- Resolution: 64×64 grayscale (MNIST images have been subsampled)
+- Conditioning: class labels for digits 0–9
+- Training data: 60,000 MNIST training images
+- Training: 40 epochs, batch size 128, AdamW with a `2e-4` learning rate
+- Training objective: epsilon/noise prediction
+- Scheduler training timesteps: 1,000
+
+The original 28×28 images were resized to 64×64 and randomly scaled between
+90% and 110% during training.
+
+## Limitations
+
+The model is designed for experimental image generation. It only generates
+MNIST-like digits and is not a classifier or OCR model. Outputs can be malformed
+or ambiguous, and their style and diversity are limited by the training data.
+
+## License
+
+The model weights are released under the MIT license.
+
+The model was trained on [MNIST](https://huggingface.co/datasets/ylecun/mnist),
+which is also distributed under the MIT license.

model_index.json

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+{
+  "_class_name": "DDPMPipeline",
+  "_diffusers_version": "0.39.0",
+  "scheduler": [
+    "diffusers",
+    "DDPMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DModel"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.39.0",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": true,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "steps_offset": 0,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}

unet/config.json

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+{
+  "_class_name": "UNet2DModel",
+  "_diffusers_version": "0.39.0",
+  "act_fn": "silu",
+  "add_attention": true,
+  "attention_head_dim": 8,
+  "attn_norm_num_groups": null,
+  "block_out_channels": [
+    32,
+    64,
+    128
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "AttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "downsample_type": "conv",
+  "dropout": 0.0,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 1,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2D",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": 10,
+  "num_train_timesteps": null,
+  "out_channels": 1,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 64,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "up_block_types": [
+    "AttnUpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ],
+  "upsample_type": "conv"
+}

unet/diffusion_pytorch_model.safetensors

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+size 15790724