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README.md

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+---
+language: en
+license: apache-2.0
+library_name: diffusers
+tags: []
+datasets: huggan/smithsonian_butterflies_subset
+metrics: []
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ddpm-butterflies-128
+
+## Model description
+
+This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
+on the `huggan/smithsonian_butterflies_subset` dataset.
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training data
+
+[TODO: describe the data used to train the model]
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 16
+- eval_batch_size: 16
+- gradient_accumulation_steps: 1
+- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
+- lr_scheduler: None
+- lr_warmup_steps: 500
+- ema_inv_gamma: None
+- ema_inv_gamma: None
+- ema_inv_gamma: None
+- mixed_precision: fp16
+
+### Training results
+
+📈 [TensorBoard logs](https://huggingface.co/HuggingFace7/ddpm-butterflies-128/tensorboard?#scalars)
+

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model_index.json

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+{
+  "_class_name": "DDPMPipeline",
+  "_diffusers_version": "0.5.1",
+  "scheduler": [
+    "diffusers",
+    "DDPMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DModel"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.5.1",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": true,
+  "num_train_timesteps": 1000,
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}

unet/config.json

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+{
+  "_class_name": "UNet2DModel",
+  "_diffusers_version": "0.5.1",
+  "act_fn": "silu",
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    128,
+    128,
+    256,
+    256,
+    512,
+    512
+  ],
+  "center_input_sample": false,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "AttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 3,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 128,
+  "time_embedding_type": "positional",
+  "up_block_types": [
+    "UpBlock2D",
+    "AttnUpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ]
+}

unet/diffusion_pytorch_model.bin

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