Initial PLONK deployment for Hugging Face Spaces

.gitignore

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+# Hugging Face Spaces .gitignore
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+
+# Development files
+.ipynb_checkpoints/
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Temporary files
+*.tmp
+*.log
+*.pid
+
+# Original demo files (using streamlit)
+demo/demo.py
+
+# Environment files
+.env
+.env.local
+
+# Model checkpoints (will be downloaded automatically)
+checkpoints/
+*.safetensors
+*.bin
+
+# Large data files
+data/
+datasets/
+*.csv
+*.json
+
+# Training artifacts
+wandb/
+logs/
+outputs/
+
+# Test files
+test_*.py
+*_test.py
+
+# Documentation that's not needed for the Space
+*.md
+!README.md
+
+# Git files
+.git/
+.gitmodules 

README.md

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+---
+title: PLONK Geolocation
+emoji: 🗺️
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🗺️ PLONK: Around the World in 80 Timesteps
+
+A generative approach to global visual geolocation. Upload an image and PLONK will predict where it was taken!
+
+## About
+
+PLONK is a diffusion-based model that predicts the geographic location where a photo was taken based solely on its visual content. This Space uses the PLONK_YFCC model trained on the YFCC100M dataset.
+
+## Features
+
+- **Simple Prediction**: Get a single high-confidence location prediction
+- **Advanced Analysis**: Explore prediction uncertainty with multiple samples and guidance control
+- **Fast CPU Inference**: ~300-500ms per image on CPU-Basic tier
+- **GPU Ready**: Upgrade to T4-small for ~45ms inference time
+
+## Usage
+
+1. Upload an image using the interface
+2. Click "Submit" to get location predictions
+3. For advanced analysis, try different guidance scales:
+   - CFG = 0.0: More diverse predictions (good for uncertainty estimation)
+   - CFG = 2.0: Single confident prediction (best guess)
+
+## API Usage
+
+This Space exposes a REST API compatible with Gradio's prediction format:
+
+```python
+import requests
+
+url = "https://your-space-name.hf.space/api/predict"
+files = {"data": open("image.jpg", "rb")}
+response = requests.post(url, files=files)
+print(response.json())
+```
+
+## Model Performance
+
+- **Latency**: 300-500ms on CPU-Basic, ~45ms on T4 GPU
+- **Memory**: <1GB RAM usage
+- **Throughput**: ~10 req/s on T4 before saturation
+
+## Scaling Options
+
+- **Free CPU-Basic**: Perfect for testing and low-volume usage
+- **T4-small ($0.40/hr)**: 10x faster inference for production
+- **Inference Endpoints**: Auto-scaling with pay-per-use pricing
+
+## Citation
+
+If you use PLONK in your research, please cite:
+
+```bibtex
+@article{dufour2024plonk,
+  title={Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation},
+  author={Dufour, Nicolas and others},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+
+## Links
+
+- 📄 [Project Page](https://nicolas-dufour.github.io/plonk)
+- 💻 [Code Repository](https://github.com/nicolas-dufour/plonk)
+- 🤗 [Model on Hugging Face](https://huggingface.co/nicolas-dufour/PLONK_YFCC) 

app.py

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+import gradio as gr
+import torch
+from plonk.pipe import PlonkPipeline
+import numpy as np
+from PIL import Image
+
+# Initialize the pipeline
+print("Loading PLONK_YFCC model...")
+pipe = PlonkPipeline(model_path="nicolas-dufour/PLONK_YFCC")
+print("Model loaded successfully!")
+
+def predict_geolocation(image):
+    """
+    Predict geolocation from an uploaded image
+    Args:
+        image: PIL Image
+    Returns:
+        str: Formatted latitude and longitude
+    """
+    if image is None:
+        return "Please upload an image"
+    
+    try:
+        # Get prediction using the pipeline
+        # Using single sample with high confidence (cfg=2.0) for best guess
+        predicted_gps = pipe(image, batch_size=1, cfg=2.0, num_steps=32)
+        
+        # Extract latitude and longitude
+        lat, lon = float(predicted_gps[0, 0]), float(predicted_gps[0, 1])
+        
+        # Format the result
+        result = f"Predicted Location:\nLatitude: {lat:.6f}\nLongitude: {lon:.6f}"
+        
+        return result
+        
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+def predict_geolocation_with_samples(image, num_samples=64, cfg=0.0):
+    """
+    Predict geolocation with multiple samples for uncertainty visualization
+    Args:
+        image: PIL Image
+        num_samples: Number of samples to generate
+        cfg: Classifier-free guidance scale
+    Returns:
+        str: Formatted results with statistics
+    """
+    if image is None:
+        return "Please upload an image"
+    
+    try:
+        # Get multiple predictions for uncertainty estimation
+        predicted_gps = pipe(image, batch_size=num_samples, cfg=cfg, num_steps=32)
+        
+        # Calculate statistics
+        lats = predicted_gps[:, 0].astype(float)
+        lons = predicted_gps[:, 1].astype(float)
+        
+        mean_lat, mean_lon = np.mean(lats), np.mean(lons)
+        std_lat, std_lon = np.std(lats), np.std(lons)
+        
+        # Get high confidence prediction
+        high_conf_gps = pipe(image, batch_size=1, cfg=2.0, num_steps=32)
+        conf_lat, conf_lon = float(high_conf_gps[0, 0]), float(high_conf_gps[0, 1])
+        
+        result = f"""Geolocation Prediction Results:
+
+High Confidence Prediction (CFG=2.0):
+Latitude: {conf_lat:.6f}
+Longitude: {conf_lon:.6f}
+
+Sample Statistics ({num_samples} samples, CFG={cfg}):
+Mean Latitude: {mean_lat:.6f} ± {std_lat:.6f}
+Mean Longitude: {mean_lon:.6f} ± {std_lon:.6f}
+        """
+        
+        return result
+        
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+# Create the Gradio interface for simple prediction
+simple_interface = gr.Interface(
+    fn=predict_geolocation,
+    inputs=gr.Image(type="pil", label="Upload an image"),
+    outputs=gr.Textbox(label="Predicted Location", lines=4),
+    title="🗺️ PLONK: Global Visual Geolocation",
+    description="""
+    Upload an image and PLONK will predict where it was taken!
+    
+    This uses the PLONK_YFCC model trained on the YFCC100M dataset.
+    The model predicts latitude and longitude coordinates based on visual content.
+    
+    **Note**: This is running on CPU, so processing may take 300-500ms per image.
+    """,
+    examples=[
+        ["demo/examples/condor.jpg"],
+        ["demo/examples/Kilimanjaro.jpg"], 
+        ["demo/examples/pigeon.png"]
+    ] if any(Path("demo/examples").glob("*")) else None
+)
+
+# Create advanced interface with sampling options
+advanced_interface = gr.Interface(
+    fn=predict_geolocation_with_samples,
+    inputs=[
+        gr.Image(type="pil", label="Upload an image"),
+        gr.Slider(1, 256, value=64, step=1, label="Number of samples"),
+        gr.Slider(0.0, 5.0, value=0.0, step=0.1, label="Guidance scale (CFG)")
+    ],
+    outputs=gr.Textbox(label="Detailed Results", lines=10),
+    title="🗺️ PLONK: Advanced Geolocation with Uncertainty",
+    description="""
+    Advanced interface showing prediction uncertainty through multiple samples.
+    
+    - **Number of samples**: More samples = better uncertainty estimation (but slower)
+    - **Guidance scale**: Higher values = more confident predictions (try 2.0 for best single guess)
+    """,
+)
+
+# Create tabbed interface
+demo = gr.TabbedInterface(
+    [simple_interface, advanced_interface],
+    ["Simple Prediction", "Advanced Analysis"],
+    title="PLONK: Around the World in 80 Timesteps"
+)
+
+if __name__ == "__main__":
+    # Add necessary import for pathlib
+    from pathlib import Path
+    demo.launch() 

plonk/__init__.py

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+from .pipe import PlonkPipeline

plonk/callbacks/__init__.py

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+from .ema import EMACallback
+from .fix_nans import FixNANinGrad
+from .data import IncreaseDataEpoch

plonk/callbacks/data.py

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+from pytorch_lightning.callbacks import Callback
+
+
+class IncreaseDataEpoch(Callback):
+    def __init__(self):
+        super().__init__()
+
+    def on_train_epoch_start(self, trainer, pl_module):
+        epoch = pl_module.current_epoch
+        if hasattr(trainer.datamodule.train_dataset, "shared_epoch"):
+            trainer.datamodule.train_dataset.shared_epoch.set_value(epoch)

plonk/callbacks/ema.py

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+from pytorch_lightning import Callback
+import copy
+import itertools
+import torch
+import contextlib
+from torch.distributed.fsdp import FullyShardedDataParallel
+
+
+class EMACallback(Callback):
+    def __init__(
+        self,
+        module_attr_name,
+        ema_module_attr_name,
+        decay=0.999,
+        start_ema_step=0,
+        init_ema_random=True,
+    ):
+        super().__init__()
+        self.decay = decay
+        self.module_attr_name = module_attr_name
+        self.ema_module_attr_name = ema_module_attr_name
+        self.start_ema_step = start_ema_step
+        self.init_ema_random = init_ema_random
+
+    def on_train_start(self, trainer, pl_module):
+        if pl_module.global_step == 0:
+            if not hasattr(pl_module, self.module_attr_name):
+                raise ValueError(
+                    f"Module {pl_module} does not have attribute {self.module_attr_name}"
+                )
+            if not hasattr(pl_module, self.ema_module_attr_name):
+                pl_module.add_module(
+                    self.ema_module_attr_name,
+                    copy.deepcopy(getattr(pl_module, self.module_attr_name))
+                    .eval()
+                    .requires_grad_(False),
+                )
+            self.reset_ema(pl_module)
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        if pl_module.global_step == self.start_ema_step:
+            self.reset_ema(pl_module)
+        elif (
+            pl_module.global_step < self.start_ema_step
+            and pl_module.global_step % 100 == 0
+        ):
+            ## slow ema updates for visualisation
+            self.update_ema(pl_module, decay=0.9)
+        elif pl_module.global_step > self.start_ema_step:
+            self.update_ema(pl_module, decay=self.decay)
+
+    def update_ema(self, pl_module, decay=0.999):
+        ema_module = getattr(pl_module, self.ema_module_attr_name)
+        module = getattr(pl_module, self.module_attr_name)
+        context_manager = self.get_model_context_manager(module)
+        with context_manager:
+            with torch.no_grad():
+                ema_params = ema_module.state_dict()
+                for name, param in itertools.chain(
+                    module.named_parameters(), module.named_buffers()
+                ):
+                    if name in ema_params:
+                        if param.requires_grad:
+                            ema_params[name].copy_(
+                                ema_params[name].detach().lerp(param.detach(), decay)
+                            )
+
+    def get_model_context_manager(self, module):
+        fsdp_enabled = is_model_fsdp(module)
+        model_context_manager = contextlib.nullcontext()
+        if fsdp_enabled:
+            model_context_manager = module.summon_full_params(module)
+        return model_context_manager
+
+    def reset_ema(self, pl_module):
+        ema_module = getattr(pl_module, self.ema_module_attr_name)
+        if self.init_ema_random:
+            ema_module.init_weights()
+        else:
+            module = getattr(pl_module, self.module_attr_name)
+            context_manager = self.get_model_context_manager(module)
+            with context_manager:
+                ema_params = ema_module.state_dict()
+                for name, param in itertools.chain(
+                    module.named_parameters(), module.named_buffers()
+                ):
+                    if name in ema_params:
+                        ema_params[name].copy_(param.detach())
+
+
+def is_model_fsdp(model: torch.nn.Module) -> bool:
+    try:
+        if isinstance(model, FullyShardedDataParallel):
+            return True
+
+        # Check if model is wrapped with FSDP
+        for _, obj in model.named_children():
+            if isinstance(obj, FullyShardedDataParallel):
+                return True
+        return False
+    except ImportError:
+        return False

plonk/callbacks/fix_nans.py

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+import logging
+from pytorch_lightning.callbacks import Callback
+import torch
+
+log = logging.getLogger(__name__)
+
+
+class FixNANinGrad(Callback):
+    def __init__(self, monitor):
+        super().__init__()
+        self.monitor = monitor
+        self.continuous_nan_batchs = 0
+
+    def on_before_optimizer_step(self, trainer, pl_module, optimizer) -> None:
+        has_nan = []
+        is_inf = []
+        for name, param in pl_module.named_parameters():
+            if param.grad is not None:
+                if torch.isnan(param.grad).any():
+                    has_nan.append(name)
+                if torch.isinf(param.grad).any():
+                    is_inf.append(name)
+                torch.nan_to_num(param.grad, nan=0, posinf=0, neginf=0, out=param.grad)
+        if len(has_nan) > 0:
+            print(f"Found NaN in {has_nan}")
+        if len(is_inf) > 0:
+            print(f"Found Inf in {is_inf}")
+
+    def on_train_batch_end(
+        self,
+        trainer,
+        pl_module,
+        outputs,
+        batch,
+        batch_idx,
+    ) -> None:
+        logs = trainer.callback_metrics
+        i = 0
+        found_metric = False
+        while i < len(self.monitor) and not found_metric:
+            if self.monitor[i] in logs.keys():
+                current = logs[self.monitor[i]].squeeze()
+                found_metric = True
+            else:
+                i += 1
+        if not found_metric:
+            raise ValueError("Asked metric not in logs")
+
+        if not torch.isfinite(current):
+            self.continuous_nan_batchs += 1
+            if self.continuous_nan_batchs >= 5:
+                trainer.should_stop = True
+                log.info("Training interrupted because of NaN in {self.monitor}")
+        else:
+            self.continuous_nan_batchs = 0

plonk/configs/computer/a100.yaml

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+devices: 1
+progress_bar_refresh_rate: 2
+num_workers: 8
+sync_batchnorm: False
+accelerator: gpu
+precision: 32
+strategy: auto
+num_nodes: 1

plonk/configs/computer/cluster-node-a100.yaml

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+devices: 8
+num_workers: 8
+progress_bar_refresh_rate: 2
+sync_batchnorm: True
+accelerator: gpu
+precision: 32
+strategy: ddp
+num_nodes: 1

plonk/configs/computer/cluster-node-v100.yaml

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+devices: 4
+num_workers: 10
+progress_bar_refresh_rate: 2
+sync_batchnorm: True
+accelerator: gpu
+precision: 32
+strategy: ddp
+num_nodes: 1

plonk/configs/computer/cpu.yaml

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+devices: null
+num_workers: 0
+progress_bar_refresh_rate: 2
+sync_batchnorm: False
+accelerator: cpu
+precision: 32
+strategy: auto
+num_nodes: null

plonk/configs/computer/h100.yaml

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+devices: 1
+progress_bar_refresh_rate: 2
+num_workers: 24
+sync_batchnorm: False
+accelerator: gpu
+precision: 32
+strategy: auto
+num_nodes: 1

plonk/configs/computer/v100.yaml

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+devices: 1
+num_workers: 10
+progress_bar_refresh_rate: 2
+sync_batchnorm: False
+accelerator: gpu
+precision: 32
+strategy: auto
+num_nodes: 1

plonk/configs/config.yaml

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+defaults:
+  - model: default
+  - computer: v100
+  - dataset: osv5m_emb
+  - stage: null
+  - _self_
+  - exp: ???
+
+model:
+  val_metrics:
+    _target_: metrics.distance_based.HaversineMetrics
+    acc_radiuses:
+      - 1
+      - 25
+      - 200
+      - 750
+      - 2500
+    acc_area: []
+  test_metrics:
+    _target_: metrics.distance_based.HaversineMetrics
+    acc_radiuses:
+      - 1
+      - 25
+      - 200
+      - 750
+      - 2500
+    acc_area: ${areas}
+
+datamodule:
+  _target_: plonk.data.datamodule.ImageDataModule
+  train_dataset: ${dataset.train_dataset}
+  val_dataset: ${dataset.val_dataset}
+  test_dataset: ${dataset.test_dataset}
+  full_batch_size: ${dataset.full_batch_size}
+  eval_batch_size: ${dataset.eval_batch_size}
+  num_workers: ${computer.num_workers}
+  num_nodes: ${computer.num_nodes}
+  num_devices: ${computer.devices}
+  val_proportion: 0.02
+
+trainer:
+  _target_: pytorch_lightning.Trainer
+  devices: ${computer.devices}
+  accelerator: ${computer.accelerator}
+  strategy: ${computer.strategy}
+  num_nodes: ${computer.num_nodes}
+  precision: ${computer.precision}
+  max_steps: 1000000
+  val_check_interval: 25000
+  check_val_every_n_epoch: null
+
+logger:
+  _target_: pytorch_lightning.loggers.WandbLogger
+  save_dir: ${root_dir}/plonk
+  name: ${experiment_name}${logger_suffix}
+  project: diff_plonk
+  log_model: False
+  offline: False
+
+checkpoints:
+  _target_: pytorch_lightning.callbacks.ModelCheckpoint
+  dirpath: ${root_dir}/plonk/checkpoints/${experiment_name}
+  filename: 'epoch_{epoch}'
+  monitor: val/loss
+  save_last: True
+  save_top_k: 0
+  every_n_epochs: 1
+  enable_version_counter: False
+
+progress_bar:
+  _target_: pytorch_lightning.callbacks.TQDMProgressBar
+  refresh_rate: ${computer.progress_bar_refresh_rate}
+
+data_dir: ${root_dir}/plonk/datasets
+root_dir:  ${hydra:runtime.cwd}
+experiment_name: ${dataset.name}_${model.name}_${experiment_name_suffix}
+experiment_name_suffix: base
+logger_suffix:  ""
+mode: train # change that to eval to do the testing
+areas: ['country', 'region', 'sub-region', 'city']
+class_name: null
+streetclip: False
+blur: False
+text_tuning: False
+
+hydra:
+  run:
+    dir: outputs/${hydra.job.name}/${now:%Y-%m-%d_%H-%M-%S}/${experiment_name}
+  job:
+    chdir: true

plonk/configs/dataset/combined_emb.yaml

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+defaults:
+  - train_transform: empty
+  - test_transform: empty
+  - _self_
+
+name: iNaturalist_OSV5M_YFCC100M_${dataset.embedding_name}
+full_batch_size: 2048
+cond_dim: 1024
+eval_batch_size: 4096
+output_type: emb
+embedding_name: dinov2_vitl14_registers
+
+train_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/YFCC100M/train/ ${data_dir}/osv5m/train/ ${data_dir}/inaturalist/train/ ${data_dir}/osv5m/train/ ${data_dir}/inaturalist/train/
+  train: true
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+val_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/YFCC100M/yfcc4k/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+test_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/YFCC100M/yfcc4k/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []

plonk/configs/dataset/inaturalist_emb.yaml

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+defaults:
+  - train_transform: empty
+  - test_transform: empty
+  - _self_
+
+name: iNaturalist_${dataset.embedding_name}
+full_batch_size: 512
+cond_dim: 1024
+eval_batch_size: 4096
+output_type: emb
+embedding_name: dinov2_vitl14_registers
+
+train_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/inaturalist/train/
+  train: true
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+val_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/inaturalist/val/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+test_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/inaturalist/test/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []

plonk/configs/dataset/osv5m.yaml

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+defaults:
+  - train_transform: fast_clip
+  - test_transform: fast_clip
+  - _self_
+
+name: osv5m
+full_batch_size: 2048
+eval_batch_size: 4096
+train_dataset:
+  _partial_: true
+  _target_: plonk.data.data.OSV5M
+  path: ${data_dir}/osv5m/
+  split: train
+  class_name: ${class_name}
+  transforms: ${dataset.train_transform}
+  is_baseline: ${is_baseline}
+  areas: ${areas}
+  streetclip: ${streetclip}
+  blur: ${blur}
+
+val_dataset:
+  _partial_: true
+  _target_: plonk.data.data.OSV5M
+  path: ${data_dir}/osv5m/
+  split: val
+  class_name: ${class_name}
+  transforms: ${dataset.test_transform}
+  is_baseline: ${is_baseline}
+  areas: ${areas}
+  streetclip: ${streetclip}
+  blur: ${blur}
+
+test_dataset:
+  _partial_: true
+  _target_: plonk.data.data.OSV5M
+  path: ${data_dir}/osv5m/
+  split: test
+  class_name: ${class_name}
+  transforms: ${dataset.test_transform}
+  is_baseline: ${is_baseline}
+  areas: ${areas}
+  streetclip: ${streetclip}
+  blur: ${blur}

plonk/configs/dataset/osv5m_emb.yaml

@@ -0,0 +1,38 @@
+defaults:
+  - train_transform: empty
+  - test_transform: empty
+  - _self_
+
+name: osv5m_${dataset.embedding_name}
+full_batch_size: 1024
+eval_batch_size: 4096
+cond_dim: 1024
+output_type: emb
+embedding_name: street_clip
+
+train_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/osv5m/train/
+  train: true
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+val_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/osv5m/val/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: ["unique_country", "unique_region", "unique_sub-region", "unique_city"]
+
+test_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/osv5m/test/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: ["unique_country", "unique_region", "unique_sub-region", "unique_city"]

plonk/configs/dataset/test_transform/center_crop.yaml

@@ -0,0 +1,12 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: plonk.utils.image_processing.CenterCrop
+    ratio: "1:1"
+  - _target_: torchvision.transforms.Resize
+    size: ${dataset.img_resolution}
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.Normalize
+    mean: 0.5
+    std: 0.5

plonk/configs/dataset/test_transform/clip.yaml

@@ -0,0 +1,2 @@
+_target_: plonk.data.transforms.ClipTransform
+split: val

plonk/configs/dataset/test_transform/empty.yaml

@@ -0,0 +1,2 @@
+_target_: plonk.data.data.null_transform
+_partial_: true

plonk/configs/dataset/test_transform/fast_clip.yaml

@@ -0,0 +1,12 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.Resize
+    size: 224
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.CenterCrop
+    size: 224
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.48145466, 0.4578275, 0.40821073]
+    std: [0.26862954, 0.26130258, 0.27577711]

plonk/configs/dataset/test_transform/fast_resnet.yaml

@@ -0,0 +1,12 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.Resize
+    size: 224
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.CenterCrop
+    size: 224
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.485 ,0.456 ,0.406]
+    std: [0.229, 0.224, 0.225]

plonk/configs/dataset/test_transform/none.yaml

@@ -0,0 +1,6 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: torchvision.transforms.Normalize
+    mean: 0.5
+    std: 0.5

plonk/configs/dataset/train_transform/augmentation.yaml

@@ -0,0 +1,85 @@
+_target_: plonk.data.augmentation.ImageAugmentation
+names: "standard_augmentation,geometric_augmentation,clip_transform"
+
+# always apply clip_transform at the end
+clip_transform:
+  _target_: torchvision.transforms.Compose
+  transforms:
+    - _target_: torchvision.transforms.Resize
+      size: 224
+      interpolation: 3
+      antialias: true
+    - _target_: torchvision.transforms.CenterCrop
+      size: 224
+    - _target_: torchvision.transforms.ToTensor
+    - _target_: torchvision.transforms.Normalize
+      mean: [0.48145466, 0.4578275, 0.40821073]
+      std: [0.26862954, 0.26130258, 0.27577711]
+
+standard_augmentation:
+  _target_: plonk.data.augmentation.StandardAugmentation
+  # by default, we all augmentation methods
+  names: "brightness,contrast,sharpness,color,blur,gaussian_noise"
+
+  # random PIL brigtness
+  brightness:
+    _target_: plonk.data.augmentation.PillowBrightness
+    p: 0.2
+    factor_interval: [0.5, 1.5]
+
+  # random PIL contrast
+  contrast:
+    _target_: plonk.data.augmentation.PillowContrast
+    p: 0.2
+    factor_interval: [0.3, 3]
+
+  # random PIL sharpness
+  sharpness:
+    _target_: plonk.data.augmentation.PillowSharpness
+    p: 0.2
+    factor_interval: [0.5, 30.0]
+
+  # random PIL color
+  color:
+    _target_: plonk.data.augmentation.PillowColor
+    p: 0.2
+    factor_interval: [0.0, 2.0]
+
+  # random PIL blur
+  blur:
+    _target_: plonk.data.augmentation.PillowBlur
+    p: 0.2
+    factor_interval: [1, 2]
+
+  # random numpy gaussian noise
+  gaussian_noise:
+    _target_: plonk.data.augmentation.NumpyGaussianNoise
+    p: 0.2
+    factor_interval: [0.1, 0.04]
+
+geometric_augmentation:
+  _target_: plonk.data.augmentation.GeometricAugmentation
+  # by default, we all augmentation methods
+  names: "random_rotation,random_resized_crop,random_horizontal_flip"
+
+  # random rotation
+  random_rotation:
+    _target_: torchvision.transforms.RandomRotation
+    degrees: [-15, 15]
+
+  # random crop
+  random_resized_crop:
+    _target_: torchvision.transforms.RandomResizedCrop
+    scale: [0.5, 1.0]
+    ratio: [0.9, 1.1]
+    size: 224
+
+  # random horizontal flip
+  random_horizontal_flip:
+    _target_: torchvision.transforms.RandomHorizontalFlip
+    p: 0.5
+
+  # random vertical flip
+  random_vertical_flip:
+    _target_: torchvision.transforms.RandomVerticalFlip
+    p: 0.5

plonk/configs/dataset/train_transform/center_crop.yaml

@@ -0,0 +1,14 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: plonk.utils.image_processing.CenterCrop
+    ratio: "1:1"
+  - _target_: torchvision.transforms.Resize
+    size: ${dataset.img_resolution}
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+    p: 0.5
+  - _target_: torchvision.transforms.Normalize
+    mean: 0.5
+    std: 0.5

plonk/configs/dataset/train_transform/clip.yaml

@@ -0,0 +1,2 @@
+_target_: plonk.data.transforms.ClipTransform
+split: val

plonk/configs/dataset/train_transform/empty.yaml

@@ -0,0 +1,2 @@
+_target_: plonk.data.data.null_transform
+_partial_: true

plonk/configs/dataset/train_transform/fast_clip.yaml

@@ -0,0 +1,12 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.Resize
+    size: 224
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.CenterCrop
+    size: 224
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.48145466, 0.4578275, 0.40821073]
+    std: [0.26862954, 0.26130258, 0.27577711]

plonk/configs/dataset/train_transform/fast_resnet.yaml

@@ -0,0 +1,12 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.Resize
+    size: 224
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.CenterCrop
+    size: 224
+  - _target_: torchvision.transforms.ToTensor
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.485 ,0.456 ,0.406]
+    std: [0.229, 0.224, 0.225]

plonk/configs/dataset/train_transform/none.yaml

@@ -0,0 +1,7 @@
+_target_: torchvision.transforms.Compose
+transforms:
+  - _target_: torchvision.transforms.Resize
+    size: 224
+    interpolation: 3
+    antialias: true
+  - _target_: torchvision.transforms.ToTensor

plonk/configs/dataset/yfcc_emb.yaml

@@ -0,0 +1,38 @@
+defaults:
+  - train_transform: empty
+  - test_transform: empty
+  - _self_
+
+name: iNaturalist_${dataset.embedding_name}
+full_batch_size: 2048
+cond_dim: 1024
+eval_batch_size: 4096
+output_type: emb
+embedding_name: dinov2_vitl14_registers
+
+train_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/YFCC100M/train/
+  train: true
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+val_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/YFCC100M/yfcc4k/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []
+
+test_dataset:
+  _partial_: true
+  _target_: plonk.data.webdataset.GPSWebdataset
+  root: ${data_dir}/YFCC100M/yfcc4k/
+  train: false
+  embedding_name: ${dataset.embedding_name}
+  return_image: false
+  metadata_attributes: []

plonk/configs/exp/YFCC100M_geoadalnmlp_r2_small_sigmoid_diffusion.yaml

@@ -0,0 +1,35 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: emb_cond
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: ddpm
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: diffusion
+dataset:
+  full_batch_size: 1024
+
+experiment_name_suffix: small_sigmoid
+areas: []

plonk/configs/exp/YFCC100M_geoadalnmlp_r3_small_linear_flow_rieman.yaml

@@ -0,0 +1,32 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: linear
+  - override /model/inference_noise_scheduler: linear
+  - override /model/loss: riemannian_flow_matching
+  - override /model/manifold: sphere
+  - override /model/val_sampler: riemannian_flow_matching
+  - override /model/test_sampler: riemannian_flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 1024
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_diffusion.yaml

@@ -0,0 +1,36 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: ddpm
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: diffusion
+
+dataset:
+  full_batch_size: 1024
+
+experiment_name_suffix: small_sigmoid
+areas: []

plonk/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow.yaml

@@ -0,0 +1,38 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: flow_matching
+  - override /model/val_sampler: flow_matching
+  - override /model/test_sampler: flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 1024
+
+experiment_name_suffix: small_sigmoid
+areas: []

plonk/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml

@@ -0,0 +1,40 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: riemannian_flow_matching
+  - override /model/manifold: sphere
+  - override /model/val_sampler: riemannian_flow_matching
+  - override /model/test_sampler: riemannian_flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 1024
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/YFCC100M_geoadalnmlp_von_fisher.yaml

@@ -0,0 +1,26 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: von_fisher
+  - override /model/network: geo_adaln_mlp_von_fisher
+  - override /model/loss: von_fisher
+  - override /model/val_sampler: von_fisher
+  - override /model/test_sampler: von_fisher
+  - _self_
+
+model:
+  network:
+    depth: 11 # To compensate the increase in params
+    dim: 512
+  optimizer:
+    optim:
+      lr: 1e-4
+      weight_decay: 0.05
+dataset:
+  full_batch_size: 1024
+trainer:
+  gradient_clip_val: 0.05
+  gradient_clip_algorithm: norm
+areas: []
+experiment_name_suffix: von_fisher

plonk/configs/exp/YFCC100M_geoadalnmlp_von_fisher_mixture.yaml

@@ -0,0 +1,26 @@
+# @package _global_
+
+defaults:
+  - override /dataset: yfcc_emb
+  - override /model: von_fisher_mixture
+  - override /model/network: geo_adaln_mlp_von_fisher_mixture
+  - override /model/loss: von_fisher_mixture
+  - override /model/val_sampler: von_fisher_mixture
+  - override /model/test_sampler: von_fisher_mixture
+  - _self_
+
+model:
+  network:
+    depth: 11 # To compensate the increase in params
+    dim: 512
+  optimizer:
+    optim:
+      lr: 1e-5
+      weight_decay: 0.05
+dataset:
+  full_batch_size: 1024
+trainer:
+  gradient_clip_val: 0.01
+  gradient_clip_algorithm: norm
+experiment_name_suffix: von_fisher_mixture
+areas: []

plonk/configs/exp/combined_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml

@@ -0,0 +1,40 @@
+# @package _global_
+
+defaults:
+  - override /dataset: combined_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: riemannian_flow_matching
+  - override /model/manifold: sphere
+  - override /model/val_sampler: riemannian_flow_matching
+  - override /model/test_sampler: riemannian_flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 1024
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/iNaturalist_geoadalnmlp_r2_small_sigmoid_diffusion.yaml

@@ -0,0 +1,36 @@
+# @package _global_
+
+defaults:
+  - override /dataset: inaturalist_emb
+  - override /model: emb_cond
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: ddpm
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 256
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.1
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: diffusion
+dataset:
+  full_batch_size: 512
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_diffusion.yaml

@@ -0,0 +1,37 @@
+# @package _global_
+
+defaults:
+  - override /dataset: inaturalist_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: ddpm
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 256
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.1
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: diffusion
+
+dataset:
+  full_batch_size: 512
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow.yaml

@@ -0,0 +1,39 @@
+# @package _global_
+
+defaults:
+  - override /dataset: inaturalist_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: flow_matching
+  - override /model/val_sampler: flow_matching
+  - override /model/test_sampler: flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 256
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.1
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 512
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml

@@ -0,0 +1,40 @@
+# @package _global_
+
+defaults:
+  - override /dataset: inaturalist_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: riemannian_flow_matching
+  - override /model/manifold: sphere
+  - override /model/val_sampler: riemannian_flow_matching
+  - override /model/test_sampler: riemannian_flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 256
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.1
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 512
+
+areas: []
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/iNaturalist_geoadalnmlp_von_fisher.yaml

@@ -0,0 +1,26 @@
+# @package _global_
+
+defaults:
+  - override /dataset: inaturalist_emb
+  - override /model: von_fisher
+  - override /model/network: geo_adaln_mlp_von_fisher
+  - override /model/loss: von_fisher
+  - override /model/val_sampler: von_fisher
+  - override /model/test_sampler: von_fisher
+  - _self_
+
+model:
+  network:
+    depth: 11 # To compensate the increase in params
+    dim: 256
+  optimizer:
+    optim:
+      lr: 1e-4
+      weight_decay: 0.1
+dataset:
+  full_batch_size: 512
+trainer:
+  gradient_clip_val: 0.01
+  gradient_clip_algorithm: norm
+areas: []
+experiment_name_suffix: von_fisher

plonk/configs/exp/iNaturalist_geoadalnmlp_von_fisher_mixture.yaml

@@ -0,0 +1,26 @@
+# @package _global_
+
+defaults:
+  - override /dataset: inaturalist_emb
+  - override /model: von_fisher_mixture
+  - override /model/network: geo_adaln_mlp_von_fisher_mixture
+  - override /model/loss: von_fisher_mixture
+  - override /model/val_sampler: von_fisher_mixture
+  - override /model/test_sampler: von_fisher_mixture
+  - _self_
+
+model:
+  network:
+    depth: 11 # To compensate the increase in params
+    dim: 256
+  optimizer:
+    optim:
+      lr: 1e-5
+      weight_decay: 0.1
+dataset:
+  full_batch_size: 512
+trainer:
+  gradient_clip_val: 0.01
+  gradient_clip_algorithm: norm
+areas: []
+experiment_name_suffix: von_fisher_mixture

plonk/configs/exp/osv_5m_geoadalnmlp_r2_small_sigmoid_diffusion.yaml

@@ -0,0 +1,34 @@
+# @package _global_
+
+defaults:
+  - override /dataset: osv5m_emb
+  - override /model: emb_cond
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: ddpm
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: diffusion
+dataset:
+  full_batch_size: 1024
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/osv_5m_geoadalnmlp_r3_small_linear_flow_riemann.yaml

@@ -0,0 +1,30 @@
+# @package _global_
+
+defaults:
+  - override /dataset: osv5m_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: linear
+  - override /model/inference_noise_scheduler: linear
+  - override /model/loss: riemannian_flow_matching
+  - override /model/manifold: sphere
+  - override /model/val_sampler: riemannian_flow_matching
+  - override /model/test_sampler: riemannian_flow_matching
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  interpolant: flow_matching
+
+dataset:
+  full_batch_size: 1024
+
+experiment_name_suffix: small_sigmoid

plonk/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_diffusion.yaml

@@ -0,0 +1,35 @@
+# @package _global_
+
+defaults:
+  - override /dataset: osv5m_emb
+  - override /model: emb_cond_cartesian
+  - override /model/network: geo_adaln_mlp
+  - override /model/train_noise_scheduler: sigmoid
+  - override /model/inference_noise_scheduler: sigmoid
+  - override /model/loss: ddpm
+  - _self_
+
+model:
+  network:
+    depth: 12
+    dim: 512
+  optimizer:
+    optim:
+      lr: 8e-4
+      weight_decay: 0.05
+  loss:
+    cond_drop_rate: 0.1
+  train_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  inference_noise_scheduler:
+    start: -7
+    end: 3
+    tau: 1.0
+  interpolant: diffusion
+
+dataset:
+  full_batch_size: 1024
+
+experiment_name_suffix: small_sigmoid