Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/LBM.jpg filter=lfs diff=lfs merge=lfs -text
+assets/depth_normal.jpg filter=lfs diff=lfs merge=lfs -text
+assets/object_removal.jpg filter=lfs diff=lfs merge=lfs -text
+assets/relight.gif filter=lfs diff=lfs merge=lfs -text
+assets/relight.jpg filter=lfs diff=lfs merge=lfs -text
+assets/relight_2.gif filter=lfs diff=lfs merge=lfs -text
+assets/shadow_control.gif filter=lfs diff=lfs merge=lfs -text
+assets/upscaler.jpg filter=lfs diff=lfs merge=lfs -text
+frpc_linux_amd64_v0.3 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,10 @@
+*.ipynb
+*.png
+*.pyc
+*.gradio
+*.safetensors
+examples/inference/ckpts/*
+examples/inference/examples/*
+envs/*
+checkpoints/*
+*.sh

LICENCE.txt

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README.md

@@ -1,12 +1,183 @@
 ---
-title: Homeoppoer.cachehuggingfacegradiofrpc
-emoji: 📉
-colorFrom: indigo
-colorTo: yellow
+title: homeoppoer.cachehuggingfacegradiofrpc
+app_file: frpc_linux_amd64_v0.3
 sdk: gradio
-sdk_version: 5.35.0
-app_file: app.py
-pinned: false
+sdk_version: 5.29.0
 ---
+# Latent Bridge Matching (LBM)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This repository is the official implementation of the paper [LBM: Latent Bridge Matching for Fast Image-to-Image Translation](http://arxiv.org/abs/2503.07535).
+
+<p align="center">
+    <a href="https://arxiv.org/abs/2503.07535">
+	    <img src='https://img.shields.io/badge/Paper-2503.07535-green' />
+	</a>
+	<a href="https://gojasper.github.io/latent-bridge-matching/">
+	    <img src='https://img.shields.io/badge/Project-page-blue' />
+	</a>
+    <a href='https://creativecommons.org/licenses/by-nd/4.0/legalcode'>
+	    <img src="https://img.shields.io/badge/Licence-CC.BY.NC-purple" />
+	</a>
+      <a href="https://huggingface.co/spaces/jasperai/LBM_relighting">
+	    <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Demo-Object%20Relighting-orange' />
+	</a>
+    <br>
+    </a>
+      <a href="https://huggingface.co/jasperai/LBM_relighting">
+	    <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Ckpt-Object%20Relighting-yellow' />
+	</a>
+    <a href="https://huggingface.co/jasperai/LBM_normals">
+	    <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Ckpt-Normals-yellow' />
+	</a>
+    <a href="https://huggingface.co/jasperai/LBM_depth">
+	    <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Ckpt-Depth-yellow' />
+	</a>
+<p align="center">
+  <img src="assets/relight.jpg" alt="LBM Teaser" width="800"/>
+</p>
+
+
+<!-- link to the demo with link big button -->
+<p align="center">
+    <a href="https://huggingface.co/spaces/jasperai/LBM_relighting">
+	    <b style="font-size: 20px;">DEMO space</b>
+	</a>
+</p>
+
+
+## Abstract
+In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. We show that the method can reach state-of-the-art results for various image-to-image tasks using only a single inference step. In addition to its efficiency, we also demonstrate the versatility of the method across different image translation tasks such as object removal, normal and depth estimation, and object relighting. We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation.
+
+<p align="center">
+		<img style="width:600px;" src="assets/LBM.jpg">
+</p>
+
+## License
+This code is released under the **Creative Commons BY-NC 4.0 license**.
+
+## Considered Use-cases
+We validate the method on various use-cases such as object relighting, image restoration, object removal, depth and normal maps estimation as well as controllable object relighting and shadow generation.
+<details>
+    <summary><b>Image Relighting 🔦</b></summary>
+<p>
+For object relighting, the method should translate the encoded source images created by pasting the foreground onto the target background image to the desired target relighted image.
+</p>
+<p align="center">
+		<img style="width:600px;" src="assets/relight.jpg">
+</p>
+</details>
+<details>
+    <summary><b>Image Restoration 🧹 </b></summary>
+	<p>
+In the context of image restoration, the method shall transport the distribution of the degraded images to the distribution of the clean images.
+</p>
+    <p align="center">
+            <img style="width:600px;" src="assets/upscaler.jpg">
+    </p>
+</details>
+<details>
+    <summary><b>Object Removal ✂️</b></summary>
+	For object removal, the model is trained to find a transport map from the masked images to the images without the objects
+    <p align="center">
+            <img style="width:600px;" src="assets/object_removal.jpg">
+    </p>
+</details>
+<details>
+    <summary><b>Controllable Image Relighting and Shadow Generation🕹️</b></summary>
+	<p>
+	We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation
+	</p>
+    <p align="center">
+            <img style="width:256px;" src="assets/relight.gif"> <img style="width:256px;" src="assets/shadow_control.gif">
+    </p>
+</details>
+<details>
+    <summary><b>Normals and Depth Maps Estimation 🗺️</b></summary>
+	<p>
+	Finally, we also consider common tasks such as normal and depth estimation where the model should translate an input image into a normal or depth map 
+	</p>
+    <p align="center">
+            <img style="width:600px;" src="assets/depth_normal.jpg">
+    </p>
+</details>
+
+
+
+## Setup
+To be up and running, you need first to create a virtual env with at least python3.10 installed and activate it
+
+### With venv
+```bash
+python3.10 -m venv envs/lbm
+source envs/lbm/bin/activate
+```
+
+### With conda
+```bash
+conda create -n lbm python=3.10
+conda activate lbm
+```
+
+Then install the required dependencies and the repo in editable mode
+
+```bash
+pip install --upgrade pip
+pip install -e .
+```
+
+## Inference
+
+We provide in `examples` a simple script to perform depth and normal estimation using the proposed method. 
+
+```bash
+python examples/inference/inference.py \
+--model_name [depth|normals|relighting] \
+--source_image path_to_your_image.jpg \
+--output_path output_images
+```
+
+See the trained models on the HF Hub 🤗
+- [Surface normals Checkpoint](https://huggingface.co/jasperai/LBM_normals)
+- [Depth Checkpoint](https://huggingface.co/jasperai/LBM_depth)
+- [Relighting Checkpoint](https://huggingface.co/jasperai/LBM_relighting)
+
+## Local Gradio Demo 
+To run the local gradio demo, just run the following command:
+```bash
+python examples/inference/gradio_demo.py
+```
+It will download the pretrained model from the HF Hub as well as example images.
+
+## Training
+We provide in `examples\training` an example of a script to train a LBM for surface normal predictions on [`hypersim`](https://github.com/apple/ml-hypersim) see [this](https://github.com/prs-eth/Marigold/blob/main/script/dataset_preprocess/hypersim/README.md) for data processing. 
+
+In `examples\trainig\configs`, you will find the configuration `yaml` associated to the training script. The only thing you need to do is to amend the `SHARDS_PATH_OR_URLS` section of the `yaml` so the model is trained on your own data. 
+
+Please note that this package uses [`webdataset`](https://github.com/webdataset/webdataset) to handle the datastream and so the urls you use should be fomatted according to the  [`webdataset format`](https://github.com/webdataset/webdataset?tab=readme-ov-file#the-webdataset-format). In particular, for this example, each sample in your `.tar` files needs to be composed of a `jpg` file containing the image, a `normal.png` file containing the target normals as well as a `mask.png` containing a mask indicating the valid pixels 
+
+```
+sample = {
+    "jpg": source_image,
+    "normal.png": normals # target_image
+    "mask.png": mask # mask of valid pixels
+}
+```
+
+To train the model, you can use the following command:
+
+```bash
+python examples/training/train_lbm_surface.py examples/training/config/surface.yaml
+```
+
+*Note*: Make sure to update the relevant section of the `yaml` file to use your own data and log the results on your own [WandB](https://wandb.ai/site).
+
+## Citation
+If you find this work useful or use it in your research, please consider citing us
+```bibtex
+@article{chadebec2025lbm,
+      title={LBM: Latent Bridge Matching for Fast Image-to-Image Translation}, 
+      author={Clément Chadebec and Onur Tasar and Sanjeev Sreetharan and Benjamin Aubin},
+      year={2025},
+      journal = {arXiv preprint arXiv:2503.07535},
+}
+```

examples/inference/gradio_demo.py

@@ -0,0 +1,234 @@
+import glob
+import logging
+import os
+from copy import deepcopy
+
+import gradio as gr
+import numpy as np
+import PIL
+import torch
+from huggingface_hub import snapshot_download
+from PIL import Image
+from torchvision.transforms import ToPILImage, ToTensor
+from transformers import AutoModelForImageSegmentation
+from utils import extract_object, resize_and_center_crop
+
+from lbm.inference import get_model
+
+PATH = os.path.dirname(os.path.abspath(__file__))
+os.environ["GRADIO_TEMP_DIR"] = ".gradio"
+
+
+if not os.path.exists(os.path.join(PATH, "ckpts", "relighting")):
+    logging.info(f"Downloading relighting LBM model from HF hub...")
+    model = get_model(
+        f"jasperai/LBM_relighting",
+        save_dir=os.path.join(PATH, "ckpts", "relighting"),
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+    )
+else:
+    model_dir = os.path.join(PATH, "ckpts", "relighting")
+    logging.info(f"Loading relighting LBM model from local...")
+    model = get_model(
+        os.path.join(PATH, "ckpts", "relighting"),
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+    )
+
+ASPECT_RATIOS = {
+    str(512 / 2048): (512, 2048),
+    str(1024 / 1024): (1024, 1024),
+    str(2048 / 512): (2048, 512),
+    str(896 / 1152): (896, 1152),
+    str(1152 / 896): (1152, 896),
+    str(512 / 1920): (512, 1920),
+    str(640 / 1536): (640, 1536),
+    str(768 / 1280): (768, 1280),
+    str(1280 / 768): (1280, 768),
+    str(1536 / 640): (1536, 640),
+    str(1920 / 512): (1920, 512),
+}
+
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+).cuda()
+image_size = (1024, 1024)
+
+if not os.path.exists(os.path.join(PATH, "examples")):
+    logging.info(f"Downloading backgrounds from HF hub...")
+    _ = snapshot_download(
+        "jasperai/LBM_relighting",
+        repo_type="space",
+        allow_patterns="*.jpg",
+        local_dir=PATH,
+    )
+
+
+def evaluate(
+    fg_image: PIL.Image.Image,
+    bg_image: PIL.Image.Image,
+    num_sampling_steps: int = 1,
+):
+
+    ori_h_bg, ori_w_bg = fg_image.size
+    ar_bg = ori_h_bg / ori_w_bg
+    closest_ar_bg = min(ASPECT_RATIOS, key=lambda x: abs(float(x) - ar_bg))
+    dimensions_bg = ASPECT_RATIOS[closest_ar_bg]
+
+    _, fg_mask = extract_object(birefnet, deepcopy(fg_image))
+
+    fg_image = resize_and_center_crop(fg_image, dimensions_bg[0], dimensions_bg[1])
+    fg_mask = resize_and_center_crop(fg_mask, dimensions_bg[0], dimensions_bg[1])
+    bg_image = resize_and_center_crop(bg_image, dimensions_bg[0], dimensions_bg[1])
+
+    img_pasted = Image.composite(fg_image, bg_image, fg_mask)
+
+    img_pasted_tensor = ToTensor()(img_pasted).unsqueeze(0) * 2 - 1
+    batch = {
+        "source_image": img_pasted_tensor.cuda().to(torch.bfloat16),
+    }
+
+    z_source = model.vae.encode(batch[model.source_key])
+
+    output_image = model.sample(
+        z=z_source,
+        num_steps=num_sampling_steps,
+        conditioner_inputs=batch,
+        max_samples=1,
+    ).clamp(-1, 1)
+
+    output_image = (output_image[0].float().cpu() + 1) / 2
+    output_image = ToPILImage()(output_image)
+
+    # paste the output image on the background image
+    output_image = Image.composite(output_image, bg_image, fg_mask)
+
+    output_image.resize((ori_h_bg, ori_w_bg))
+
+    return (np.array(img_pasted), np.array(output_image))
+
+
+with gr.Blocks(title="LBM Object Relighting") as demo:
+    gr.Markdown(
+        f"""
+        # Object Relighting with Latent Bridge Matching
+        This is an interactive demo of [LBM: Latent Bridge Matching for Fast Image-to-Image Translation](https://arxiv.org/abs/2503.07535) *by Jasper Research*. This demo is based on the [LBM relighting checkpoint](https://huggingface.co/jasperai/LBM_relighting).
+    """
+    )
+    gr.Markdown(
+        """
+        If you enjoy the space, please also promote *open-source* by giving a ⭐ to the <a href='https://github.com/gojasper/LBM' target='_blank'>Github Repo</a>.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                fg_image = gr.Image(
+                    type="pil",
+                    label="Input Image",
+                    image_mode="RGB",
+                    height=360,
+                    # width=360,
+                )
+                bg_image = gr.Image(
+                    type="pil",
+                    label="Target Background",
+                    image_mode="RGB",
+                    height=360,
+                    # width=360,
+                )
+
+            with gr.Row():
+                submit_button = gr.Button("Relight", variant="primary")
+            with gr.Row():
+                num_inference_steps = gr.Slider(
+                    minimum=1,
+                    maximum=4,
+                    value=1,
+                    step=1,
+                    label="Number of Inference Steps",
+                )
+
+            bg_gallery = gr.Gallery(
+                # height=450,
+                object_fit="contain",
+                label="Background List",
+                value=[
+                    path
+                    for path in glob.glob(
+                        os.path.join(PATH, "examples/backgrounds/*.jpg")
+                    )
+                ],
+                columns=5,
+                allow_preview=False,
+            )
+
+        with gr.Column():
+            output_slider = gr.ImageSlider(label="Composite vs LBM", type="numpy")
+            output_slider.upload(
+                fn=evaluate,
+                inputs=[fg_image, bg_image, num_inference_steps],
+                outputs=[output_slider],
+            )
+
+    submit_button.click(
+        evaluate,
+        inputs=[fg_image, bg_image, num_inference_steps],
+        outputs=[output_slider],
+    )
+
+    with gr.Row():
+        gr.Examples(
+            fn=evaluate,
+            examples=[
+                [
+                    os.path.join(PATH, "examples/foregrounds/2.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/14.jpg"),
+                    1,
+                ],
+                [
+                    os.path.join(PATH, "examples/foregrounds/10.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/4.jpg"),
+                    1,
+                ],
+                [
+                    os.path.join(PATH, "examples/foregrounds/11.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/24.jpg"),
+                    1,
+                ],
+                [
+                    os.path.join(PATH, "examples/foregrounds/19.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/3.jpg"),
+                    1,
+                ],
+                [
+                    os.path.join(PATH, "examples/foregrounds/4.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/6.jpg"),
+                    1,
+                ],
+                [
+                    os.path.join(PATH, "examples/foregrounds/14.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/22.jpg"),
+                    1,
+                ],
+                [
+                    os.path.join(PATH, "examples/foregrounds/12.jpg"),
+                    os.path.join(PATH, "examples/backgrounds/1.jpg"),
+                    1,
+                ],
+            ],
+            inputs=[fg_image, bg_image, num_inference_steps],
+            outputs=[output_slider],
+            run_on_click=True,
+        )
+
+    def bg_gallery_selected(gal, evt: gr.SelectData):
+        return gal[evt.index][0]
+
+    bg_gallery.select(bg_gallery_selected, inputs=bg_gallery, outputs=bg_image)
+
+if __name__ == "__main__":
+
+    demo.launch(share=True)

examples/inference/inference.py

@@ -0,0 +1,59 @@
+import argparse
+import logging
+import os
+
+import torch
+from PIL import Image
+
+from lbm.inference import evaluate, get_model
+
+PATH = os.path.dirname(os.path.abspath(__file__))
+
+logging.basicConfig(level=logging.INFO)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--source_image", type=str, required=True)
+parser.add_argument("--output_path", type=str, required=True)
+parser.add_argument("--num_inference_steps", type=int, default=1)
+parser.add_argument(
+    "--model_name",
+    type=str,
+    default="normals",
+    choices=["normals", "depth", "relighting"],
+)
+
+
+args = parser.parse_args()
+
+
+def main():
+    # download the weights from HF hub
+    if not os.path.exists(os.path.join(PATH, "ckpts", f"{args.model_name}")):
+        logging.info(f"Downloading {args.model_name} LBM model from HF hub...")
+        model = get_model(
+            f"jasperai/LBM_{args.model_name}",
+            save_dir=os.path.join(PATH, "ckpts", f"{args.model_name}"),
+            torch_dtype=torch.bfloat16,
+            device="cuda",
+        )
+
+    else:
+        model_dir = os.path.join(PATH, "ckpts", f"{args.model_name}")
+        logging.info(f"Loading {args.model_name} LBM model from local...")
+        model = get_model(model_dir, torch_dtype=torch.bfloat16, device="cuda")
+
+    source_image = Image.open(args.source_image).convert("RGB")
+
+    output_image = evaluate(model, source_image, args.num_inference_steps)
+
+    os.makedirs(args.output_path, exist_ok=True)
+
+    source_image.save(os.path.join(args.output_path, "source_image.jpg"))
+    output_image.save(os.path.join(args.output_path, "output_image.jpg"))
+
+    del model
+    torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/utils.py

@@ -0,0 +1,41 @@
+import torch
+from PIL import Image
+from torchvision import transforms
+
+
+def extract_object(birefnet, img):
+    # Data settings
+    image_size = (1024, 1024)
+    transform_image = transforms.Compose(
+        [
+            transforms.Resize(image_size),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+
+    image = img
+    input_images = transform_image(image).unsqueeze(0).cuda()
+
+    # Prediction
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image.size)
+    image = Image.composite(image, Image.new("RGB", image.size, (127, 127, 127)), mask)
+    return image, mask
+
+
+def resize_and_center_crop(image, target_width, target_height):
+    original_width, original_height = image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = image.resize((resized_width, resized_height), Image.LANCZOS)
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return cropped_image

examples/training/config/surface.yaml

@@ -0,0 +1,31 @@
+# wandb
+wandb_project: lbm-surface-flows
+timestep_sampling: custom_timesteps
+unet_input_channels: 4
+vae_num_channels: 4
+selected_timesteps: [250, 500, 750, 1000]
+prob: [0.25, 0.25, 0.25, 0.25]
+pixel_loss_type: lpips # l1 l2
+pixel_loss_weight: 10.0
+latent_loss_type: l2 # l1 l2
+latent_loss_weight: 1.0
+bridge_noise_sigma: 0.005
+conditioning_images_keys: []
+conditioning_masks_keys: []
+
+# SHARDS_PATH_OR_URLS
+train_shards:
+  - pipe:cat PATH_TO_TRAIN_TARS
+
+validation_shards:
+  - pipe:cat PATH_TO_VAL_TARS
+
+batch_size: 4
+learning_rate: 4e-5
+optimizer: AdamW
+num_steps: [1, 4]
+log_interval: 500
+resume_from_checkpoint: true
+max_epochs: 50
+save_interval: 5000
+save_ckpt_path: ./checkpoints

examples/training/train_lbm_surface.py

@@ -0,0 +1,546 @@
+import datetime
+import logging
+import os
+import random
+import re
+import shutil
+from typing import List, Optional
+
+import braceexpand
+import fire
+import torch
+import yaml
+from diffusers import FlowMatchEulerDiscreteScheduler, StableDiffusionXLPipeline
+from diffusers.models import UNet2DConditionModel
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.resnet import ResnetBlock2D
+from pytorch_lightning import Trainer, loggers
+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
+from pytorch_lightning.strategies import FSDPStrategy
+from torch.distributed.fsdp.wrap import ModuleWrapPolicy
+from torchvision.transforms import InterpolationMode
+
+from lbm.data.datasets import DataModule, DataModuleConfig
+from lbm.data.filters import KeyFilter, KeyFilterConfig
+from lbm.data.mappers import (
+    KeyRenameMapper,
+    KeyRenameMapperConfig,
+    MapperWrapper,
+    RescaleMapper,
+    RescaleMapperConfig,
+    TorchvisionMapper,
+    TorchvisionMapperConfig,
+)
+from lbm.models.embedders import (
+    ConditionerWrapper,
+    LatentsConcatEmbedder,
+    LatentsConcatEmbedderConfig,
+)
+from lbm.models.lbm import LBMConfig, LBMModel
+from lbm.models.unets import DiffusersUNet2DCondWrapper
+from lbm.models.vae import AutoencoderKLDiffusers, AutoencoderKLDiffusersConfig
+from lbm.trainer import TrainingConfig, TrainingPipeline
+from lbm.trainer.loggers import WandbSampleLogger
+from lbm.trainer.utils import StateDictAdapter
+
+
+def get_model(
+    backbone_signature: str = "stabilityai/stable-diffusion-xl-base-1.0",
+    vae_num_channels: int = 4,
+    unet_input_channels: int = 4,
+    timestep_sampling: str = "log_normal",
+    selected_timesteps: Optional[List[float]] = None,
+    prob: Optional[List[float]] = None,
+    conditioning_images_keys: Optional[List[str]] = [],
+    conditioning_masks_keys: Optional[List[str]] = [],
+    source_key: str = "source_image",
+    target_key: str = "source_image_paste",
+    mask_key: str = "mask",
+    bridge_noise_sigma: float = 0.0,
+    logit_mean: float = 0.0,
+    logit_std: float = 1.0,
+    pixel_loss_type: str = "lpips",
+    latent_loss_type: str = "l2",
+    latent_loss_weight: float = 1.0,
+    pixel_loss_weight: float = 0.0,
+):
+
+    conditioners = []
+
+    # Load pretrained model as base
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        backbone_signature,
+        torch_dtype=torch.bfloat16,
+    )
+
+    ### MMMDiT ###
+    # Get Architecture
+    denoiser = DiffusersUNet2DCondWrapper(
+        in_channels=unet_input_channels,  # Add downsampled_image
+        out_channels=vae_num_channels,
+        center_input_sample=False,
+        flip_sin_to_cos=True,
+        freq_shift=0,
+        down_block_types=[
+            "DownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+        ],
+        mid_block_type="UNetMidBlock2DCrossAttn",
+        up_block_types=["CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"],
+        only_cross_attention=False,
+        block_out_channels=[320, 640, 1280],
+        layers_per_block=2,
+        downsample_padding=1,
+        mid_block_scale_factor=1,
+        dropout=0.0,
+        act_fn="silu",
+        norm_num_groups=32,
+        norm_eps=1e-05,
+        cross_attention_dim=[320, 640, 1280],
+        transformer_layers_per_block=[1, 2, 10],
+        reverse_transformer_layers_per_block=None,
+        encoder_hid_dim=None,
+        encoder_hid_dim_type=None,
+        attention_head_dim=[5, 10, 20],
+        num_attention_heads=None,
+        dual_cross_attention=False,
+        use_linear_projection=True,
+        class_embed_type=None,
+        addition_embed_type=None,
+        addition_time_embed_dim=None,
+        num_class_embeds=None,
+        upcast_attention=None,
+        resnet_time_scale_shift="default",
+        resnet_skip_time_act=False,
+        resnet_out_scale_factor=1.0,
+        time_embedding_type="positional",
+        time_embedding_dim=None,
+        time_embedding_act_fn=None,
+        timestep_post_act=None,
+        time_cond_proj_dim=None,
+        conv_in_kernel=3,
+        conv_out_kernel=3,
+        projection_class_embeddings_input_dim=None,
+        attention_type="default",
+        class_embeddings_concat=False,
+        mid_block_only_cross_attention=None,
+        cross_attention_norm=None,
+        addition_embed_type_num_heads=64,
+    ).to(torch.bfloat16)
+
+    state_dict = pipe.unet.state_dict()
+
+    del state_dict["add_embedding.linear_1.weight"]
+    del state_dict["add_embedding.linear_1.bias"]
+    del state_dict["add_embedding.linear_2.weight"]
+    del state_dict["add_embedding.linear_2.bias"]
+
+    # Adapt the shapes
+    state_dict_adapter = StateDictAdapter()
+    state_dict = state_dict_adapter(
+        model_state_dict=denoiser.state_dict(),
+        checkpoint_state_dict=state_dict,
+        regex_keys=[
+            r"class_embedding.linear_\d+.(weight|bias)",
+            r"conv_in.weight",
+            r"(down_blocks|up_blocks)\.\d+\.attentions\.\d+\.transformer_blocks\.\d+\.attn\d+\.(to_k|to_v)\.weight",
+            r"mid_block\.attentions\.\d+\.transformer_blocks\.\d+\.attn\d+\.(to_k|to_v)\.weight",
+        ],
+        strategy="zeros",
+    )
+
+    denoiser.load_state_dict(state_dict, strict=True)
+
+    del pipe
+
+    if conditioning_images_keys != [] or conditioning_masks_keys != []:
+
+        latents_concat_embedder_config = LatentsConcatEmbedderConfig(
+            image_keys=conditioning_images_keys,
+            mask_keys=conditioning_masks_keys,
+        )
+        latent_concat_embedder = LatentsConcatEmbedder(latents_concat_embedder_config)
+        latent_concat_embedder.freeze()
+        conditioners.append(latent_concat_embedder)
+
+    # Wrap conditioners and set to device
+    conditioner = ConditionerWrapper(
+        conditioners=conditioners,
+    )
+
+    ## VAE ##
+    # Get VAE model
+    vae_config = AutoencoderKLDiffusersConfig(
+        version=backbone_signature,
+        subfolder="vae",
+        tiling_size=(128, 128),
+    )
+    vae = AutoencoderKLDiffusers(vae_config)
+    vae.freeze()
+    vae.to(torch.bfloat16)
+
+    # LBM Config
+    config = LBMConfig(
+        ucg_keys=None,
+        source_key=source_key,
+        target_key=target_key,
+        mask_key=mask_key,
+        latent_loss_weight=latent_loss_weight,
+        latent_loss_type=latent_loss_type,
+        pixel_loss_type=pixel_loss_type,
+        pixel_loss_weight=pixel_loss_weight,
+        timestep_sampling=timestep_sampling,
+        logit_mean=logit_mean,
+        logit_std=logit_std,
+        selected_timesteps=selected_timesteps,
+        prob=prob,
+        bridge_noise_sigma=bridge_noise_sigma,
+    )
+
+    training_noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+        backbone_signature,
+        subfolder="scheduler",
+    )
+    sampling_noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+        backbone_signature,
+        subfolder="scheduler",
+    )
+
+    # LBM Model
+    model = LBMModel(
+        config,
+        denoiser=denoiser,
+        training_noise_scheduler=training_noise_scheduler,
+        sampling_noise_scheduler=sampling_noise_scheduler,
+        vae=vae,
+        conditioner=conditioner,
+    ).to(torch.bfloat16)
+
+    return model
+
+
+def get_filter_mappers():
+    filters_mappers = [
+        KeyFilter(KeyFilterConfig(keys=["jpg", "normal_aligned.png", "mask.png"])),
+        MapperWrapper(
+            [
+                KeyRenameMapper(
+                    KeyRenameMapperConfig(
+                        key_map={
+                            "jpg": "image",
+                            "normal_aligned.png": "normal",
+                            "mask.png": "mask",
+                        }
+                    )
+                ),
+                TorchvisionMapper(
+                    TorchvisionMapperConfig(
+                        key="image",
+                        transforms=["ToTensor", "Resize"],
+                        transforms_kwargs=[
+                            {},
+                            {
+                                "size": (480, 640),
+                                "interpolation": InterpolationMode.NEAREST_EXACT,
+                            },
+                        ],
+                    )
+                ),
+                TorchvisionMapper(
+                    TorchvisionMapperConfig(
+                        key="normal",
+                        transforms=["ToTensor", "Resize"],
+                        transforms_kwargs=[
+                            {},
+                            {
+                                "size": (480, 640),
+                                "interpolation": InterpolationMode.NEAREST_EXACT,
+                            },
+                        ],
+                    )
+                ),
+                TorchvisionMapper(
+                    TorchvisionMapperConfig(
+                        key="mask",
+                        transforms=["ToTensor", "Resize", "Normalize"],
+                        transforms_kwargs=[
+                            {},
+                            {
+                                "size": (480, 640),
+                                "interpolation": InterpolationMode.NEAREST_EXACT,
+                            },
+                            {"mean": 0.0, "std": 1.0},
+                        ],
+                    )
+                ),
+                RescaleMapper(RescaleMapperConfig(key="image")),
+                RescaleMapper(RescaleMapperConfig(key="normal")),
+            ],
+        ),
+    ]
+
+    return filters_mappers
+
+
+def get_data_module(
+    train_shards: List[str],
+    validation_shards: List[str],
+    batch_size: int,
+):
+
+    # TRAIN
+    train_filters_mappers = get_filter_mappers()
+
+    # unbrace urls
+    train_shards_path_or_urls_unbraced = []
+    for train_shards_path_or_url in train_shards:
+        train_shards_path_or_urls_unbraced.extend(
+            braceexpand.braceexpand(train_shards_path_or_url)
+        )
+
+    # shuffle shards
+    random.shuffle(train_shards_path_or_urls_unbraced)
+
+    # data config
+    data_config = DataModuleConfig(
+        shards_path_or_urls=train_shards_path_or_urls_unbraced,
+        decoder="pil",
+        shuffle_before_split_by_node_buffer_size=20,
+        shuffle_before_split_by_workers_buffer_size=20,
+        shuffle_before_filter_mappers_buffer_size=20,
+        shuffle_after_filter_mappers_buffer_size=20,
+        per_worker_batch_size=batch_size,
+        num_workers=min(10, len(train_shards_path_or_urls_unbraced)),
+    )
+
+    train_data_config = data_config
+
+    # VALIDATION
+    validation_filters_mappers = get_filter_mappers()
+
+    # unbrace urls
+    validation_shards_path_or_urls_unbraced = []
+    for validation_shards_path_or_url in validation_shards:
+        validation_shards_path_or_urls_unbraced.extend(
+            braceexpand.braceexpand(validation_shards_path_or_url)
+        )
+
+    data_config = DataModuleConfig(
+        shards_path_or_urls=validation_shards_path_or_urls_unbraced,
+        decoder="pil",
+        shuffle_before_split_by_node_buffer_size=10,
+        shuffle_before_split_by_workers_buffer_size=10,
+        shuffle_before_filter_mappers_buffer_size=10,
+        shuffle_after_filter_mappers_buffer_size=10,
+        per_worker_batch_size=batch_size,
+        num_workers=min(10, len(train_shards_path_or_urls_unbraced)),
+    )
+
+    validation_data_config = data_config
+
+    # data module
+    data_module = DataModule(
+        train_config=train_data_config,
+        train_filters_mappers=train_filters_mappers,
+        eval_config=validation_data_config,
+        eval_filters_mappers=validation_filters_mappers,
+    )
+
+    return data_module
+
+
+def main(
+    train_shards: List[str] = ["pipe:cat path/to/train/shards"],
+    validation_shards: List[str] = ["pipe:cat path/to/validation/shards"],
+    backbone_signature: str = "stabilityai/stable-diffusion-xl-base-1.0",
+    vae_num_channels: int = 4,
+    unet_input_channels: int = 4,
+    source_key: str = "image",
+    target_key: str = "normal",
+    mask_key: str = "mask",
+    wandb_project: str = "lbm-surface",
+    batch_size: int = 8,
+    num_steps: List[int] = [1, 2, 4],
+    learning_rate: float = 5e-5,
+    learning_rate_scheduler: str = None,
+    learning_rate_scheduler_kwargs: dict = {},
+    optimizer: str = "AdamW",
+    optimizer_kwargs: dict = {},
+    timestep_sampling: str = "uniform",
+    logit_mean: float = 0.0,
+    logit_std: float = 1.0,
+    pixel_loss_type: str = "lpips",
+    latent_loss_type: str = "l2",
+    latent_loss_weight: float = 1.0,
+    pixel_loss_weight: float = 0.0,
+    selected_timesteps: List[float] = None,
+    prob: List[float] = None,
+    conditioning_images_keys: Optional[List[str]] = [],
+    conditioning_masks_keys: Optional[List[str]] = [],
+    config_yaml: dict = None,
+    save_ckpt_path: str = "./checkpoints",
+    log_interval: int = 100,
+    resume_from_checkpoint: bool = True,
+    max_epochs: int = 100,
+    bridge_noise_sigma: float = 0.005,
+    save_interval: int = 1000,
+    path_config: str = None,
+):
+    model = get_model(
+        backbone_signature=backbone_signature,
+        vae_num_channels=vae_num_channels,
+        unet_input_channels=unet_input_channels,
+        source_key=source_key,
+        target_key=target_key,
+        mask_key=mask_key,
+        timestep_sampling=timestep_sampling,
+        logit_mean=logit_mean,
+        logit_std=logit_std,
+        pixel_loss_type=pixel_loss_type,
+        latent_loss_type=latent_loss_type,
+        latent_loss_weight=latent_loss_weight,
+        pixel_loss_weight=pixel_loss_weight,
+        selected_timesteps=selected_timesteps,
+        prob=prob,
+        conditioning_images_keys=conditioning_images_keys,
+        conditioning_masks_keys=conditioning_masks_keys,
+        bridge_noise_sigma=bridge_noise_sigma,
+    )
+
+    data_module = get_data_module(
+        train_shards=train_shards,
+        validation_shards=validation_shards,
+        batch_size=batch_size,
+    )
+
+    train_parameters = ["denoiser.*"]
+
+    # Training Config
+    training_config = TrainingConfig(
+        learning_rate=learning_rate,
+        lr_scheduler_name=learning_rate_scheduler,
+        lr_scheduler_kwargs=learning_rate_scheduler_kwargs,
+        log_keys=["image", "normal", "mask"],
+        trainable_params=train_parameters,
+        optimizer_name=optimizer,
+        optimizer_kwargs=optimizer_kwargs,
+        log_samples_model_kwargs={
+            "input_shape": None,
+            "num_steps": num_steps,
+        },
+    )
+    if (
+        os.path.exists(save_ckpt_path)
+        and resume_from_checkpoint
+        and "last.ckpt" in os.listdir(save_ckpt_path)
+    ):
+        start_ckpt = f"{save_ckpt_path}/last.ckpt"
+        print(f"Resuming from checkpoint: {start_ckpt}")
+
+    else:
+        start_ckpt = None
+
+    pipeline = TrainingPipeline(model=model, pipeline_config=training_config)
+
+    pipeline.save_hyperparameters(
+        {
+            f"embedder_{i}": embedder.config.to_dict()
+            for i, embedder in enumerate(model.conditioner.conditioners)
+        }
+    )
+
+    pipeline.save_hyperparameters(
+        {
+            "denoiser": model.denoiser.config,
+            "vae": model.vae.config.to_dict(),
+            "config_yaml": config_yaml,
+            "training": training_config.to_dict(),
+            "training_noise_scheduler": model.training_noise_scheduler.config,
+            "sampling_noise_scheduler": model.sampling_noise_scheduler.config,
+        }
+    )
+
+    training_signature = (
+        datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        + "-LBM-Surface"
+        + f"{os.environ['SLURM_JOB_ID']}"
+        + f"_{os.environ.get('SLURM_ARRAY_TASK_ID', 0)}"
+    )
+    dir_path = f"{save_ckpt_path}/logs/{training_signature}"
+    if os.environ["SLURM_PROCID"] == "0":
+        os.makedirs(dir_path, exist_ok=True)
+        if path_config is not None:
+            shutil.copy(path_config, f"{save_ckpt_path}/config.yaml")
+    run_name = training_signature
+
+    # Ignore parameters unused during training
+    ignore_states = []
+    for name, param in pipeline.model.named_parameters():
+        ignore = True
+        for regex in ["denoiser."]:
+            pattern = re.compile(regex)
+            if re.match(pattern, name):
+                ignore = False
+        if ignore:
+            ignore_states.append(param)
+
+    # FSDP Strategy
+    strategy = FSDPStrategy(
+        auto_wrap_policy=ModuleWrapPolicy(
+            [
+                UNet2DConditionModel,
+                BasicTransformerBlock,
+                ResnetBlock2D,
+                torch.nn.Conv2d,
+            ]
+        ),
+        activation_checkpointing_policy=ModuleWrapPolicy(
+            [
+                BasicTransformerBlock,
+                ResnetBlock2D,
+            ]
+        ),
+        sharding_strategy="SHARD_GRAD_OP",
+        ignored_states=ignore_states,
+    )
+
+    trainer = Trainer(
+        accelerator="gpu",
+        devices=int(os.environ["SLURM_NPROCS"]) // int(os.environ["SLURM_NNODES"]),
+        num_nodes=int(os.environ["SLURM_NNODES"]),
+        strategy=strategy,
+        default_root_dir="logs",
+        logger=loggers.WandbLogger(
+            project=wandb_project, offline=False, name=run_name, save_dir=save_ckpt_path
+        ),
+        callbacks=[
+            WandbSampleLogger(log_batch_freq=log_interval),
+            LearningRateMonitor(logging_interval="step"),
+            ModelCheckpoint(
+                dirpath=save_ckpt_path,
+                every_n_train_steps=save_interval,
+                save_last=True,
+            ),
+        ],
+        num_sanity_val_steps=0,
+        precision="bf16-mixed",
+        limit_val_batches=2,
+        val_check_interval=1000,
+        max_epochs=max_epochs,
+    )
+
+    trainer.fit(pipeline, data_module, ckpt_path=start_ckpt)
+
+
+def main_from_config(path_config: str = None):
+    with open(path_config, "r") as file:
+        config = yaml.safe_load(file)
+    logging.info(
+        f"Running main with config: {yaml.dump(config, default_flow_style=False)}"
+    )
+    main(**config, config_yaml=config, path_config=path_config)
+
+
+if __name__ == "__main__":
+    fire.Fire(main_from_config)

frpc_linux_amd64_v0.3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c791d1f047b41ff5885772fc4bf20b797c6059bbd82abb9e31de15e55d6a57c4
+size 11907224

pyproject.toml

@@ -0,0 +1,39 @@
+[build-system]
+requires = ["hatchling", "hatch-requirements-txt"]
+build-backend = "hatchling.build"
+
+[project]
+name = "lbm"
+dynamic = ["dependencies", "optional-dependencies"]
+description = "LBM: Latent Bridge Matching for Fast Image-to-Image Translation"
+readme = "README.md"
+requires-python = ">=3.10"
+authors = [
+    { name = "Clement Chadebec", email = "[email protected]" },
+    { name = "Benjamin Aubin", email = "[email protected]" },
+]
+maintainers = [
+    { name = "Clement Chadebec", email = "[email protected]" },
+]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+]
+version = "0.1"
+
+[project.urls]
+Homepage = "https://github.com/gojasper/LBM"
+Repository = "https://github.com/gojasper/LBM"
+
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.hatch.metadata.hooks.requirements_txt]
+files = ["requirements.txt"]
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/lbm"]

requirements.txt

@@ -0,0 +1,24 @@
+# accelerate==1.4.0
+diffusers==0.32.2
+torch==2.7.0
+torchvision>=0.20.0
+black==24.2.0
+einops==0.7.0
+fire>=0.5.0
+gradio==5.29.0
+isort==5.13.2
+lightning==2.5.0
+lpips==0.1.4
+opencv-python==4.9.0.80
+peft==0.9.0
+pydantic>=2.6.1
+scipy>=1.12.0
+sentencepiece>=0.2.0
+timm==0.9.16
+tokenizers>=0.15.2
+torch-fidelity>=0.3.0
+torchmetrics>=1.3.1
+transformers==4.42.3
+wandb==0.16.2
+webdataset>=0.2.86
+kornia==0.8.0

src/lbm/config.py

@@ -0,0 +1,141 @@
+import json
+import os
+import warnings
+from dataclasses import asdict, field
+from typing import Any, Dict, Union
+
+import yaml
+from pydantic import ValidationError
+from pydantic.dataclasses import dataclass
+from yaml import safe_load
+
+
+@dataclass
+class BaseConfig:
+    """This is the BaseConfig class which defines all the useful loading and saving methods
+    of the configs"""
+
+    name: str = field(init=False)
+
+    def __post_init__(self):
+        self.name = self.__class__.__name__
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any]) -> "BaseConfig":
+        """Creates a BaseConfig instance from a dictionnary
+
+        Args:
+            config_dict (dict): The Python dictionnary containing all the parameters
+
+        Returns:
+            :class:`BaseConfig`: The created instance
+        """
+        try:
+            config = cls(**config_dict)
+        except (ValidationError, TypeError) as e:
+            raise e
+        return config
+
+    @classmethod
+    def _dict_from_json(cls, json_path: Union[str, os.PathLike]) -> Dict[str, Any]:
+        try:
+            with open(json_path) as f:
+                try:
+                    config_dict = json.load(f)
+                    return config_dict
+
+                except (TypeError, json.JSONDecodeError) as e:
+                    raise TypeError(
+                        f"File {json_path} not loadable. Maybe not json ? \n"
+                        f"Catch Exception {type(e)} with message: " + str(e)
+                    ) from e
+
+        except FileNotFoundError:
+            raise FileNotFoundError(
+                f"Config file not found. Please check path '{json_path}'"
+            )
+
+    @classmethod
+    def from_json(cls, json_path: str) -> "BaseConfig":
+        """Creates a BaseConfig instance from a JSON config file
+
+        Args:
+            json_path (str): The path to the json file containing all the parameters
+
+        Returns:
+            :class:`BaseConfig`: The created instance
+        """
+        config_dict = cls._dict_from_json(json_path)
+
+        config_name = config_dict.pop("name")
+
+        if cls.__name__ != config_name:
+            warnings.warn(
+                f"You are trying to load a "
+                f"`{ cls.__name__}` while a "
+                f"`{config_name}` is given."
+            )
+
+        return cls.from_dict(config_dict)
+
+    def to_dict(self) -> dict:
+        """Transforms object into a Python dictionnary
+
+        Returns:
+            (dict): The dictionnary containing all the parameters"""
+        return asdict(self)
+
+    def to_json_string(self):
+        """Transforms object into a JSON string
+
+        Returns:
+            (str): The JSON str containing all the parameters"""
+        return json.dumps(self.to_dict())
+
+    def save_json(self, file_path: str):
+        """Saves a ``.json`` file from the dataclass
+
+        Args:
+            file_path (str): path to the file
+        """
+        with open(os.path.join(file_path), "w", encoding="utf-8") as fp:
+            fp.write(self.to_json_string())
+
+    def save_yaml(self, file_path: str):
+        """Saves a ``.yaml`` file from the dataclass
+
+        Args:
+            file_path (str): path to the file
+        """
+        with open(os.path.join(file_path), "w", encoding="utf-8") as fp:
+            yaml.dump(self.to_dict(), fp)
+
+    @classmethod
+    def from_yaml(cls, yaml_path: str) -> "BaseConfig":
+        """Creates a BaseConfig instance from a YAML config file
+
+        Args:
+            yaml_path (str): The path to the yaml file containing all the parameters
+
+        Returns:
+            :class:`BaseConfig`: The created instance
+        """
+        with open(yaml_path, "r") as f:
+            try:
+                config_dict = safe_load(f)
+            except yaml.YAMLError as e:
+                raise yaml.YAMLError(
+                    f"File {yaml_path} not loadable. Maybe not yaml ? \n"
+                    f"Catch Exception {type(e)} with message: " + str(e)
+                ) from e
+
+        config_name = config_dict.pop("name")
+
+        if cls.__name__ != config_name:
+            warnings.warn(
+                f"You are trying to load a "
+                f"`{ cls.__name__}` while a "
+                f"`{config_name}` is given."
+            )
+
+        return cls.from_dict(config_dict)

src/lbm/data/__init__.py

@@ -0,0 +1,62 @@
+"""
+This module contains a collection of data related classes and functions to train the :mod:`cr.models`.
+In a training loop a batch of data is struvtued as a dictionnary on which the modules :mod:`cr.data.datasets`
+and :mod:`cr.data.filters` allow to perform several operations.
+
+
+Examples
+########
+
+Create a DataModule to train a model
+
+.. code-block::python
+
+    from cr.data import DataModule, DataModuleConfig
+    from cr.data.filters import KeyFilter, KeyFilterConfig
+    from cr.data.mappers import KeyRenameMapper, KeyRenameMapperConfig
+
+    # Create the filters and mappers
+    filters_mappers = [
+        KeyFilter(KeyFilterConfig(keys=["image", "txt"])),
+        KeyRenameMapper(
+            KeyRenameMapperConfig(key_map={"jpg": "image", "txt": "text"})
+        )
+    ]
+
+    # Create the DataModule
+    data_module = DataModule(
+        train_config=DataModuleConfig(
+            shards_path_or_urls="your urls or paths",
+            decoder="pil",
+            shuffle_buffer_size=100,
+            per_worker_batch_size=32,
+            num_workers=4,
+        ),
+        train_filters_mappers=filters_mappers,
+        eval_config=DataModuleConfig(
+            shards_path_or_urls="your urls or paths",
+            decoder="pil",
+            shuffle_buffer_size=100,
+            per_worker_batch_size=32,
+            num_workers=4,
+        ),
+        eval_filters_mappers=filters_mappers,
+    )
+
+    # This can then be passed to a :mod:`pytorch_lightning.Trainer` to train a model
+
+
+
+
+
+The :mod:`cr.data` includes the following submodules:
+
+- :mod:`cr.data.datasets`: a collection of :mod:`pytorch_lightning.LightningDataModule` used to train the models. In particular,
+    they can used to create the dataloaders and setup the data pipelines.
+- :mod:`cr.data.filters`: a collection of filters used apply filters on a training batch of data/
+
+"""
+
+from .datasets import DataModule
+
+__all__ = ["DataModule"]

src/lbm/data/datasets/__init__.py

@@ -0,0 +1,9 @@
+"""
+A collection of :mod:`pytorch_lightning.LightningDataModule` used to train the models. In particular,
+they can be used to create the dataloaders and setup the data pipelines.
+"""
+
+from .dataset import DataModule
+from .datasets_config import DataModuleConfig
+
+__all__ = ["DataModule", "DataModuleConfig"]

src/lbm/data/datasets/collation_fn.py

@@ -0,0 +1,41 @@
+from typing import Dict, List, Union
+
+import numpy as np
+import torch
+
+
+def custom_collation_fn(
+    samples: List[Dict[str, Union[int, float, np.ndarray, torch.Tensor]]],
+    combine_tensors: bool = True,
+    combine_scalars: bool = True,
+) -> dict:
+    """
+    Collate function for PyTorch DataLoader.
+
+    Args:
+        samples(List[Dict[str, Union[int, float, np.ndarray, torch.Tensor]]]): List of samples.
+        combine_tensors (bool): Whether to turn lists of tensors into a single tensor.
+        combine_scalars (bool): Whether to turn lists of scalars into a single ndarray.
+    """
+    keys = set.intersection(*[set(sample.keys()) for sample in samples])
+    batched = {key: [] for key in keys}
+    for s in samples:
+        [batched[key].append(s[key]) for key in batched]
+
+    result = {}
+    for key in batched:
+        if isinstance(batched[key][0], (int, float)):
+            if combine_scalars:
+                result[key] = np.array(list(batched[key]))
+        elif isinstance(batched[key][0], torch.Tensor):
+            if combine_tensors:
+                result[key] = torch.stack(list(batched[key]))
+        elif isinstance(batched[key][0], np.ndarray):
+            if combine_tensors:
+                result[key] = np.array(list(batched[key]))
+        else:
+            result[key] = list(batched[key])
+
+    del samples
+    del batched
+    return result

src/lbm/data/datasets/dataset.py

@@ -0,0 +1,243 @@
+from typing import Callable, List, Union
+
+import pytorch_lightning as pl
+import webdataset as wds
+from webdataset import DataPipeline
+
+from ..filters import BaseFilter, FilterWrapper
+from ..mappers import BaseMapper, MapperWrapper
+from .collation_fn import custom_collation_fn
+from .datasets_config import DataModuleConfig
+
+
+class DataPipeline:
+    """
+    DataPipeline class for creating a dataloader from a single configuration
+
+    Args:
+
+        config (DataModuleConfig):
+            Configuration for the dataset
+
+        filters_mappers (Union[List[Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]]):
+            List of filters and mappers for the dataset. These will be sequentially applied.
+
+        batched_filters_mappers (List[Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]]):
+            List of batched transforms for the dataset. These will be sequentially applied.
+    """
+
+    def __init__(
+        self,
+        config: DataModuleConfig,
+        filters_mappers: List[
+            Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]
+        ],
+        batched_filters_mappers: List[
+            Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]
+        ] = None,
+    ):
+        self.config = config
+        self.shards_path_or_urls = config.shards_path_or_urls
+        self.filters_mappers = filters_mappers
+        self.batched_filters_mappers = batched_filters_mappers or []
+
+        if filters_mappers is None:
+            filters_mappers = []
+
+        # set processing pipeline
+        self.processing_pipeline = [wds.decode(config.decoder, handler=config.handler)]
+        self.processing_pipeline.extend(
+            self._add_filters_mappers(
+                filters_mappers=filters_mappers,
+                handler=config.handler,
+            )
+        )
+
+    def _add_filters_mappers(
+        self,
+        filters_mappers: List[
+            Union[
+                FilterWrapper,
+                MapperWrapper,
+            ]
+        ],
+        handler: Callable = wds.warn_and_continue,
+    ) -> List[Union[FilterWrapper, MapperWrapper]]:
+        tmp_pipeline = []
+        for filter_mapper in filters_mappers:
+            if isinstance(filter_mapper, FilterWrapper) or isinstance(
+                filter_mapper, BaseFilter
+            ):
+                tmp_pipeline.append(wds.select(filter_mapper))
+            elif isinstance(filter_mapper, MapperWrapper) or isinstance(
+                filter_mapper, BaseMapper
+            ):
+                tmp_pipeline.append(wds.map(filter_mapper, handler=handler))
+            elif isinstance(filter_mapper) or isinstance(filter_mapper):
+                tmp_pipeline.append(wds.map(filter_mapper, handler=handler))
+            else:
+                raise ValueError("Unknown type of filter/mapper")
+        return tmp_pipeline
+
+    def setup(self):
+        pipeline = [wds.SimpleShardList(self.shards_path_or_urls)]
+
+        # shuffle before split by node
+        if self.config.shuffle_before_split_by_node_buffer_size is not None:
+            pipeline.append(
+                wds.shuffle(
+                    self.config.shuffle_before_split_by_node_buffer_size,
+                    handler=self.config.handler,
+                )
+            )
+        # split by node
+        pipeline.append(wds.split_by_node)
+
+        # shuffle before split by workers
+        if self.config.shuffle_before_split_by_workers_buffer_size is not None:
+            pipeline.append(
+                wds.shuffle(
+                    self.config.shuffle_before_split_by_workers_buffer_size,
+                    handler=self.config.handler,
+                )
+            )
+        # split by worker
+        pipeline.extend(
+            [
+                wds.split_by_worker,
+                wds.tarfile_to_samples(
+                    handler=self.config.handler,
+                    rename_files=self.config.rename_files_fn,
+                ),
+            ]
+        )
+
+        # shuffle before filter mappers
+        if self.config.shuffle_before_filter_mappers_buffer_size is not None:
+            pipeline.append(
+                wds.shuffle(
+                    self.config.shuffle_before_filter_mappers_buffer_size,
+                    handler=self.config.handler,
+                )
+            )
+
+        # apply filters and mappers
+        pipeline.extend(self.processing_pipeline)
+
+        # shuffle after filter mappers
+        if self.config.shuffle_after_filter_mappers_buffer_size is not None:
+            pipeline.append(
+                wds.shuffle(
+                    self.config.shuffle_after_filter_mappers_buffer_size,
+                    handler=self.config.handler,
+                ),
+            )
+
+        # batching
+        pipeline.append(
+            wds.batched(
+                self.config.per_worker_batch_size,
+                collation_fn=custom_collation_fn,
+            )
+        )
+
+        # apply batched transforms
+        pipeline.extend(
+            self._add_filters_mappers(
+                filters_mappers=self.batched_filters_mappers,
+                handler=self.config.handler,
+            )
+        )
+
+        # create the data pipeline
+        pipeline = wds.DataPipeline(*pipeline, handler=self.config.handler)
+
+        # set the pipeline
+        self.pipeline = pipeline
+
+    def dataloader(self):
+        # return the loader
+        return wds.WebLoader(
+            self.pipeline,
+            batch_size=None,
+            num_workers=self.config.num_workers,
+        )
+
+
+class DataModule(pl.LightningDataModule):
+    """
+    Main DataModule class for creating data loaders and training/evaluating models
+
+    Args:
+
+        train_config (DataModuleConfig):
+            Configuration for the training dataset
+
+        train_filters_mappers (Union[List[Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]]):
+            List of filters and mappers for the training dataset. These will be sequentially applied.
+
+        train_batched_filters_mappers (List[Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]]):
+            List of batched transforms for the training dataset. These will be sequentially applied.
+
+        eval_config (DataModuleConfig):
+            Configuration for the evaluation dataset
+
+        eval_filters_mappers (List[Union[FilterWrapper, MapperWrapper]]):
+            List of filters and mappers for the evaluation dataset.These will be sequentially applied.
+
+        eval_batched_filters_mappers (List[Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]]):
+            List of batched transforms for the evaluation dataset. These will be sequentially applied.
+    """
+
+    def __init__(
+        self,
+        train_config: DataModuleConfig,
+        train_filters_mappers: List[
+            Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]
+        ] = None,
+        train_batched_filters_mappers: List[
+            Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]
+        ] = None,
+        eval_config: DataModuleConfig = None,
+        eval_filters_mappers: List[Union[FilterWrapper, MapperWrapper]] = None,
+        eval_batched_filters_mappers: List[
+            Union[BaseMapper, BaseFilter, FilterWrapper, MapperWrapper]
+        ] = None,
+    ):
+        super().__init__()
+
+        self.train_config = train_config
+        self.train_filters_mappers = train_filters_mappers
+        self.train_batched_filters_mappers = train_batched_filters_mappers
+
+        self.eval_config = eval_config
+        self.eval_filters_mappers = eval_filters_mappers
+        self.eval_batched_filters_mappers = eval_batched_filters_mappers
+
+    def setup(self, stage=None):
+        """
+        Setup the data module and create the webdataset processing pipelines
+        """
+
+        # train pipeline
+        self.train_pipeline = DataPipeline(
+            config=self.train_config,
+            filters_mappers=self.train_filters_mappers,
+            batched_filters_mappers=self.train_batched_filters_mappers,
+        )
+        self.train_pipeline.setup()
+
+        # eval pipeline
+        if self.eval_config is not None:
+            self.eval_pipeline = DataPipeline(
+                config=self.eval_config,
+                filters_mappers=self.eval_filters_mappers,
+                batched_filters_mappers=self.eval_batched_filters_mappers,
+            )
+            self.eval_pipeline.setup()
+
+    def train_dataloader(self):
+        return self.train_pipeline.dataloader()
+
+    def val_dataloader(self):
+        return self.eval_pipeline.dataloader()

src/lbm/data/datasets/datasets_config.py

@@ -0,0 +1,42 @@
+from typing import Callable, List, Optional, Union
+
+import webdataset as wds
+from pydantic.dataclasses import dataclass
+
+from ...config import BaseConfig
+
+
+@dataclass
+class DataModuleConfig(BaseConfig):
+    """
+    Configuration for the DataModule
+
+    Args:
+
+        shards_path_or_urls (Union[str, List[str]]): The path or url to the shards. Defaults to None.
+        per_worker_batch_size (int): The batch size for the dataset. Defaults to 16.
+        num_workers (int): The number of workers to use. Defaults to 1.
+        shuffle_before_split_by_node_buffer_size (Optional[int]): The buffer size for the shuffle before split by node. Defaults to 100.
+        shuffle_before_split_by_workers_buffer_size (Optional[int]): The buffer size for the shuffle before split by workers. Defaults to 100.
+        shuffle_before_filter_mappers_buffer_size (Optional[int]): The buffer size for the shuffle before filter mappers. Defaults to 1000.
+        shuffle_after_filter_mappers_buffer_size (Optional[int]): The buffer size for the shuffle after filter mappers. Defaults to 1000.
+        decoder (str): The decoder to use. Defaults to "pil".
+        handler (Callable): A callable to handle the warnings. Defaults to wds.warn_and_continue.
+        rename_files_fn (Optional[Callable[[str], str]]): A callable to rename the files. Defaults to None.
+    """
+
+    shards_path_or_urls: Union[str, List[str]] = None
+    per_worker_batch_size: int = 16
+    num_workers: int = 1
+    shuffle_before_split_by_node_buffer_size: Optional[int] = 100
+    shuffle_before_split_by_workers_buffer_size: Optional[int] = 100
+    shuffle_before_filter_mappers_buffer_size: Optional[int] = 1000
+    shuffle_after_filter_mappers_buffer_size: Optional[int] = 1000
+    decoder: str = "pil"
+    handler: Callable = wds.warn_and_continue
+    rename_files_fn: Optional[Callable[[str], str]] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.rename_files_fn is not None:
+            assert callable(self.rename_files_fn), "rename_files must be a callable"

src/lbm/data/filters/__init__.py

@@ -0,0 +1,12 @@
+from .base import BaseFilter
+from .filter_wrapper import FilterWrapper
+from .filters import KeyFilter
+from .filters_config import BaseFilterConfig, KeyFilterConfig
+
+__all__ = [
+    "BaseFilter",
+    "FilterWrapper",
+    "KeyFilter",
+    "BaseFilterConfig",
+    "KeyFilterConfig",
+]

src/lbm/data/filters/base.py

@@ -0,0 +1,21 @@
+from typing import Any, Dict
+
+from .filters_config import BaseFilterConfig
+
+
+class BaseFilter:
+    """
+    Base class for filters. This class should be subclassed to create a new filter.
+
+    Args:
+
+        config (BaseFilterConfig):
+            Configuration for the filter
+    """
+
+    def __init__(self, config: BaseFilterConfig):
+        self.verbose = config.verbose
+
+    def __call__(self, sample: Dict[str, Any]) -> bool:
+        """This function should be implemented by the subclass"""
+        raise NotImplementedError

src/lbm/data/filters/filter_wrapper.py

@@ -0,0 +1,36 @@
+from typing import Any, Dict, List, Union
+
+from .base import BaseFilter
+
+
+class FilterWrapper:
+    """
+    Wrapper for multiple filters. This class allows to apply multiple filters to a batch of data.
+    The filters are applied in the order they are passed to the wrapper.
+
+    Args:
+
+        filters (List[BaseFilter]):
+            List of filters to apply to the batch of data
+    """
+
+    def __init__(
+        self,
+        filters: Union[List[BaseFilter], None] = None,
+    ):
+        self.filters = filters
+
+    def __call__(self, batch: Dict[str, Any]) -> None:
+        """
+        Forward pass through all filters
+
+        Args:
+
+            batch: batch of data
+        """
+        filter_output = True
+        for filter in self.filters:
+            filter_output = filter(batch)
+            if not filter_output:
+                return False
+        return True

src/lbm/data/filters/filters.py

@@ -0,0 +1,33 @@
+import logging
+
+from .base import BaseFilter
+from .filters_config import KeyFilterConfig
+
+logging.basicConfig(level=logging.INFO)
+
+
+class KeyFilter(BaseFilter):
+    """
+    This filter checks if ALL the given keys are present in the sample
+
+    Args:
+
+        config (KeyFilterConfig): configuration for the filter
+    """
+
+    def __init__(self, config: KeyFilterConfig):
+        super().__init__(config)
+        keys = config.keys
+        if isinstance(keys, str):
+            keys = [keys]
+
+        self.keys = set(keys)
+
+    def __call__(self, batch: dict) -> bool:
+        try:
+            res = self.keys.issubset(set(batch.keys()))
+            return res
+        except Exception as e:
+            if self.verbose:
+                logging.error(f"Error in KeyFilter: {e}")
+            return False

src/lbm/data/filters/filters_config.py

@@ -0,0 +1,32 @@
+from typing import List, Union
+
+from pydantic.dataclasses import dataclass
+
+from ...config import BaseConfig
+
+
+@dataclass
+class BaseFilterConfig(BaseConfig):
+    """
+    Base configuration for filters
+
+    Args:
+
+        verbose (bool):
+            If True, print debug information. Defaults to False"""
+
+    verbose: bool = False
+
+
+@dataclass
+class KeyFilterConfig(BaseFilterConfig):
+    """
+    This filter checks if the keys are present in a sample.
+
+    Args:
+
+        keys (Union[str, List[str]]):
+            Key or list of keys to check. Defaults to "txt"
+    """
+
+    keys: Union[str, List[str]] = "txt"

src/lbm/data/mappers/__init__.py

@@ -0,0 +1,19 @@
+from .base import BaseMapper
+from .mappers import KeyRenameMapper, RescaleMapper, TorchvisionMapper
+from .mappers_config import (
+    KeyRenameMapperConfig,
+    RescaleMapperConfig,
+    TorchvisionMapperConfig,
+)
+from .mappers_wrapper import MapperWrapper
+
+__all__ = [
+    "BaseMapper",
+    "KeyRenameMapper",
+    "RescaleMapper",
+    "TorchvisionMapper",
+    "KeyRenameMapperConfig",
+    "RescaleMapperConfig",
+    "TorchvisionMapperConfig",
+    "MapperWrapper",
+]

src/lbm/data/mappers/base.py

@@ -0,0 +1,26 @@
+from typing import Any, Dict
+
+from .mappers_config import BaseMapperConfig
+
+
+class BaseMapper:
+    """
+    Base class for the mappers used to modify the samples in the data pipeline.
+
+    Args:
+
+        config (BaseMapperConfig):
+            Configuration for the mapper.
+    """
+
+    def __init__(self, config: BaseMapperConfig):
+        self.config = config
+        self.key = config.key
+
+        if config.output_key is None:
+            self.output_key = config.key
+        else:
+            self.output_key = config.output_key
+
+    def map(self, batch: Dict[str, Any], *args, **kwargs) -> Dict[str, Any]:
+        raise NotImplementedError

src/lbm/data/mappers/mappers.py

@@ -0,0 +1,135 @@
+from typing import Any, Dict
+
+from torchvision import transforms
+
+from .base import BaseMapper
+from .mappers_config import (
+    KeyRenameMapperConfig,
+    RescaleMapperConfig,
+    TorchvisionMapperConfig,
+)
+
+
+class KeyRenameMapper(BaseMapper):
+    """
+    Rename keys in a sample according to a key map
+
+    Args:
+
+        config (KeyRenameMapperConfig): Configuration for the mapper
+
+    Examples
+    ########
+
+    1. Rename keys in a sample according to a key map
+
+    .. code-block:: python
+
+        from cr.data.mappers import KeyRenameMapper, KeyRenameMapperConfig
+
+        config = KeyRenameMapperConfig(
+            key_map={"old_key": "new_key"}
+        )
+
+        mapper = KeyRenameMapper(config)
+
+        sample = {"old_key": 1}
+        new_sample = mapper(sample)
+        print(new_sample)  # {"new_key": 1}
+
+    2. Rename keys in a sample according to a key map and a condition key
+
+    .. code-block:: python
+
+        from cr.data.mappers import KeyRenameMapper, KeyRenameMapperConfig
+
+        config = KeyRenameMapperConfig(
+            key_map={"old_key": "new_key"},
+            condition_key="condition",
+            condition_fn=lambda x: x == 1
+        )
+
+        mapper = KeyRenameMapper(config)
+
+        sample = {"old_key": 1, "condition": 1}
+        new_sample = mapper(sample)
+        print(new_sample)  # {"new_key": 1}
+
+        sample = {"old_key": 1, "condition": 0}
+        new_sample = mapper(sample)
+        print(new_sample)  # {"old_key": 1}
+
+    ```
+    """
+
+    def __init__(self, config: KeyRenameMapperConfig):
+        super().__init__(config)
+        self.key_map = config.key_map
+        self.condition_key = config.condition_key
+        self.condition_fn = config.condition_fn
+        self.else_key_map = config.else_key_map
+
+    def __call__(self, batch: Dict[str, Any], *args, **kwrags):
+        if self.condition_key is not None:
+            condition_key = batch[self.condition_key]
+            if self.condition_fn(condition_key):
+                for old_key, new_key in self.key_map.items():
+                    if old_key in batch:
+                        batch[new_key] = batch.pop(old_key)
+
+            elif self.else_key_map is not None:
+                for old_key, new_key in self.else_key_map.items():
+                    if old_key in batch:
+                        batch[new_key] = batch.pop(old_key)
+
+        else:
+            for old_key, new_key in self.key_map.items():
+                if old_key in batch:
+                    batch[new_key] = batch.pop(old_key)
+        return batch
+
+
+class TorchvisionMapper(BaseMapper):
+    """
+    Apply torchvision transforms to a sample
+
+    Args:
+
+        config (TorchvisionMapperConfig): Configuration for the mapper
+    """
+
+    def __init__(self, config: TorchvisionMapperConfig):
+        super().__init__(config)
+        chained_transforms = []
+        for transform, kwargs in zip(config.transforms, config.transforms_kwargs):
+            transform = getattr(transforms, transform)
+            chained_transforms.append(transform(**kwargs))
+        self.transforms = transforms.Compose(chained_transforms)
+
+    def __call__(self, batch: Dict[str, Any], *args, **kwrags) -> Dict[str, Any]:
+        if self.key in batch:
+            batch[self.output_key] = self.transforms(batch[self.key])
+        return batch
+
+
+class RescaleMapper(BaseMapper):
+    """
+    Rescale a sample from [0, 1] to [-1, 1]
+
+    Args:
+
+        config (RescaleMapperConfig): Configuration for the mapper
+    """
+
+    def __init__(self, config: RescaleMapperConfig):
+        super().__init__(config)
+
+    def __call__(self, batch: Dict[str, Any], *args, **kwrags) -> Dict[str, Any]:
+        if isinstance(batch[self.key], list):
+            tmp = []
+            for i, image in enumerate(batch[self.key]):
+                tmp.append(2 * image - 1)
+            batch[self.output_key] = tmp
+        else:
+            batch[self.output_key] = 2 * batch[self.key] - 1
+        return batch

src/lbm/data/mappers/mappers_config.py

@@ -0,0 +1,109 @@
+from typing import Any, Callable, Dict, List, Optional
+
+from pydantic.dataclasses import dataclass
+
+from ...config import BaseConfig
+
+
+@dataclass
+class BaseMapperConfig(BaseConfig):
+    """
+    Base configuration for mappers.
+
+    Args:
+
+        verbose (bool):
+            If True, print debug information. Defaults to False
+
+        key (Optional[str]):
+            Key to apply the mapper to. Defaults to None
+
+        output_key (Optional[str]):
+            Key to store the output of the mapper. Defaults to None
+    """
+
+    verbose: bool = False
+    key: Optional[str] = None
+    output_key: Optional[str] = None
+
+
+@dataclass
+class KeyRenameMapperConfig(BaseMapperConfig):
+    """
+    Rename keys in a sample according to a key map
+
+    Args:
+
+        key_map (Dict[str, str]): Dictionary with the old keys as keys and the new keys as values
+        condition_key (Optional[str]): Key to use for the condition. Defaults to None
+        condition_fn (Optional[Callable[[Any], bool]]): Function to use for the condition to be met so
+            the key map is applied. Defaults to None.
+        else_key_map (Optional[Dict[str, str]]): Dictionary with the old keys as keys and the new keys as values
+            if the condition is not met. Defaults to None *i.e.* the original key will be used.
+    """
+
+    key_map: Dict[str, str] = None
+    condition_key: Optional[str] = None
+    condition_fn: Optional[Callable[[Any], bool]] = None
+    else_key_map: Optional[Dict[str, str]] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        assert self.key_map is not None, "key_map should be provided"
+        assert all(
+            isinstance(old_key, str) and isinstance(new_key, str)
+            for old_key, new_key in self.key_map.items()
+        ), "key_map should be a dictionary with string keys and values"
+        if self.condition_key is not None:
+            assert self.condition_fn is not None, "condition_fn should be provided"
+            assert callable(self.condition_fn), "condition_fn should be callable"
+        if self.condition_fn is not None:
+            assert self.condition_key is not None, "condition_key should be provided"
+            assert isinstance(
+                self.condition_key, str
+            ), "condition_key should be a string"
+        if self.else_key_map is not None:
+            assert all(
+                isinstance(old_key, str) and isinstance(new_key, str)
+                for old_key, new_key in self.else_key_map.items()
+            ), "else_key_map should be a dictionary with string keys and values"
+
+
+@dataclass
+class TorchvisionMapperConfig(BaseMapperConfig):
+    """
+    Apply torchvision transforms to a sample
+
+    Args:
+
+        key (str): Key to apply the transforms to
+        transforms (torchvision.transforms): List of torchvision transforms to apply
+        transforms_kwargs (Dict[str, Any]): List of kwargs for the transforms
+    """
+
+    key: str = "image"
+    transforms: List[str] = None
+    transforms_kwargs: List[Dict[str, Any]] = None
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.transforms is None:
+            self.transforms = []
+        if self.transforms_kwargs is None:
+            self.transforms_kwargs = []
+        assert len(self.transforms) == len(
+            self.transforms_kwargs
+        ), "Number of transforms and kwargs should be same"
+
+
+@dataclass
+class RescaleMapperConfig(BaseMapperConfig):
+    """
+    Rescale a sample from [0, 1] to [-1, 1]
+
+    Args:
+
+        key (str): Key to rescale
+    """
+
+    key: str = "image"

src/lbm/data/mappers/mappers_wrapper.py

@@ -0,0 +1,31 @@
+from typing import Any, Dict, List, Union
+
+from .base import BaseMapper
+
+
+class MapperWrapper:
+    """
+    Wrapper for the mappers to allow iterating over several mappers in one go.
+
+    Args:
+
+        mappers (Union[List[BaseMapper], None]): List of mappers to apply to the batch
+    """
+
+    def __init__(
+        self,
+        mappers: Union[List[BaseMapper], None] = None,
+    ):
+        self.mappers = mappers
+
+    def __call__(self, batch: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Forward pass through all mappers
+
+        Args:
+
+            batch (Dict[str, Any]): batch of data
+        """
+        for mapper in self.mappers:
+            batch = mapper(batch)
+        return batch

src/lbm/inference/__init__.py

@@ -0,0 +1,4 @@
+from .inference import evaluate
+from .utils import get_model
+
+__all__ = ["evaluate", "get_model"]

src/lbm/inference/inference.py

@@ -0,0 +1,70 @@
+import logging
+
+import PIL
+import torch
+from torchvision.transforms import ToPILImage, ToTensor
+
+from lbm.models.lbm import LBMModel
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+ASPECT_RATIOS = {
+    str(512 / 2048): (512, 2048),
+    str(1024 / 1024): (1024, 1024),
+    str(2048 / 512): (2048, 512),
+    str(896 / 1152): (896, 1152),
+    str(1152 / 896): (1152, 896),
+    str(512 / 1920): (512, 1920),
+    str(640 / 1536): (640, 1536),
+    str(768 / 1280): (768, 1280),
+    str(1280 / 768): (1280, 768),
+    str(1536 / 640): (1536, 640),
+    str(1920 / 512): (1920, 512),
+}
+
+
+@torch.no_grad()
+def evaluate(
+    model: LBMModel,
+    source_image: PIL.Image.Image,
+    num_sampling_steps: int = 1,
+):
+    """
+    Evaluate the model on an image coming from the source distribution and generate a new image from the target distribution.
+
+    Args:
+        model (LBMModel): The model to evaluate.
+        source_image (PIL.Image.Image): The source image to evaluate the model on.
+        num_sampling_steps (int): The number of sampling steps to use for the model.
+
+    Returns:
+        PIL.Image.Image: The generated image.
+    """
+
+    ori_h_bg, ori_w_bg = source_image.size
+    ar_bg = ori_h_bg / ori_w_bg
+    closest_ar_bg = min(ASPECT_RATIOS, key=lambda x: abs(float(x) - ar_bg))
+    source_dimensions = ASPECT_RATIOS[closest_ar_bg]
+
+    source_image = source_image.resize(source_dimensions)
+
+    img_pasted_tensor = ToTensor()(source_image).unsqueeze(0) * 2 - 1
+    batch = {
+        "source_image": img_pasted_tensor.cuda().to(torch.bfloat16),
+    }
+
+    z_source = model.vae.encode(batch[model.source_key])
+
+    output_image = model.sample(
+        z=z_source,
+        num_steps=num_sampling_steps,
+        conditioner_inputs=batch,
+        max_samples=1,
+    ).clamp(-1, 1)
+
+    output_image = (output_image[0].float().cpu() + 1) / 2
+    output_image = ToPILImage()(output_image)
+    output_image.resize((ori_h_bg, ori_w_bg))
+
+    return output_image

src/lbm/inference/utils.py

@@ -0,0 +1,222 @@
+import logging
+import os
+from typing import List, Optional
+
+import torch
+import yaml
+from diffusers import FlowMatchEulerDiscreteScheduler
+from huggingface_hub import snapshot_download
+from safetensors.torch import load_file
+
+from lbm.models.embedders import (
+    ConditionerWrapper,
+    LatentsConcatEmbedder,
+    LatentsConcatEmbedderConfig,
+)
+from lbm.models.lbm import LBMConfig, LBMModel
+from lbm.models.unets import DiffusersUNet2DCondWrapper
+from lbm.models.vae import AutoencoderKLDiffusers, AutoencoderKLDiffusersConfig
+
+
+def get_model(
+    model_dir: str,
+    save_dir: Optional[str] = None,
+    torch_dtype: torch.dtype = torch.bfloat16,
+    device: str = "cuda",
+) -> LBMModel:
+    """Download the model from the model directory using either a local path or a path to HuggingFace Hub
+
+    Args:
+        model_dir (str): The path to the model directory containing the model weights and config, can be a local path or a path to HuggingFace Hub
+        save_dir (Optional[str]): The local path to save the model if downloading from HuggingFace Hub. Defaults to None.
+        torch_dtype (torch.dtype): The torch dtype to use for the model. Defaults to torch.bfloat16.
+        device (str): The device to use for the model. Defaults to "cuda".
+
+    Returns:
+        LBMModel: The loaded model
+    """
+    if not os.path.exists(model_dir):
+        local_dir = snapshot_download(
+            model_dir,
+            local_dir=save_dir,
+        )
+        model_dir = local_dir
+
+    model_files = os.listdir(model_dir)
+
+    # check yaml config file is present
+    yaml_file = [f for f in model_files if f.endswith(".yaml")]
+    if len(yaml_file) == 0:
+        raise ValueError("No yaml file found in the model directory.")
+
+    # check safetensors weights file is present
+    safetensors_files = sorted([f for f in model_files if f.endswith(".safetensors")])
+    ckpt_files = sorted([f for f in model_files if f.endswith(".ckpt")])
+    if len(safetensors_files) == 0 and len(ckpt_files) == 0:
+        raise ValueError("No safetensors or ckpt file found in the model directory")
+
+    if len(model_files) == 0:
+        raise ValueError("No model files found in the model directory")
+
+    with open(os.path.join(model_dir, yaml_file[0]), "r") as f:
+        config = yaml.safe_load(f)
+
+    model = _get_model_from_config(**config, torch_dtype=torch_dtype)
+
+    if len(safetensors_files) > 0:
+        logging.info(f"Loading safetensors file: {safetensors_files[-1]}")
+        sd = load_file(os.path.join(model_dir, safetensors_files[-1]))
+        model.load_state_dict(sd, strict=True)
+    elif len(ckpt_files) > 0:
+        logging.info(f"Loading ckpt file: {ckpt_files[-1]}")
+        sd = torch.load(
+            os.path.join(model_dir, ckpt_files[-1]),
+            map_location="cpu",
+        )["state_dict"]
+        sd = {k[6:]: v for k, v in sd.items() if k.startswith("model.")}
+        model.load_state_dict(
+            sd,
+            strict=True,
+        )
+    model.to(device).to(torch_dtype)
+
+    model.eval()
+
+    return model
+
+
+def _get_model_from_config(
+    backbone_signature: str = "stabilityai/stable-diffusion-xl-base-1.0",
+    vae_num_channels: int = 4,
+    unet_input_channels: int = 4,
+    timestep_sampling: str = "log_normal",
+    selected_timesteps: Optional[List[float]] = None,
+    prob: Optional[List[float]] = None,
+    conditioning_images_keys: Optional[List[str]] = [],
+    conditioning_masks_keys: Optional[List[str]] = [],
+    source_key: str = "source_image",
+    target_key: str = "source_image_paste",
+    bridge_noise_sigma: float = 0.0,
+    logit_mean: float = 0.0,
+    logit_std: float = 1.0,
+    pixel_loss_type: str = "lpips",
+    latent_loss_type: str = "l2",
+    latent_loss_weight: float = 1.0,
+    pixel_loss_weight: float = 0.0,
+    torch_dtype: torch.dtype = torch.bfloat16,
+    **kwargs,
+):
+
+    conditioners = []
+
+    denoiser = DiffusersUNet2DCondWrapper(
+        in_channels=unet_input_channels,  # Add downsampled_image
+        out_channels=vae_num_channels,
+        center_input_sample=False,
+        flip_sin_to_cos=True,
+        freq_shift=0,
+        down_block_types=[
+            "DownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+        ],
+        mid_block_type="UNetMidBlock2DCrossAttn",
+        up_block_types=["CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"],
+        only_cross_attention=False,
+        block_out_channels=[320, 640, 1280],
+        layers_per_block=2,
+        downsample_padding=1,
+        mid_block_scale_factor=1,
+        dropout=0.0,
+        act_fn="silu",
+        norm_num_groups=32,
+        norm_eps=1e-05,
+        cross_attention_dim=[320, 640, 1280],
+        transformer_layers_per_block=[1, 2, 10],
+        reverse_transformer_layers_per_block=None,
+        encoder_hid_dim=None,
+        encoder_hid_dim_type=None,
+        attention_head_dim=[5, 10, 20],
+        num_attention_heads=None,
+        dual_cross_attention=False,
+        use_linear_projection=True,
+        class_embed_type=None,
+        addition_embed_type=None,
+        addition_time_embed_dim=None,
+        num_class_embeds=None,
+        upcast_attention=None,
+        resnet_time_scale_shift="default",
+        resnet_skip_time_act=False,
+        resnet_out_scale_factor=1.0,
+        time_embedding_type="positional",
+        time_embedding_dim=None,
+        time_embedding_act_fn=None,
+        timestep_post_act=None,
+        time_cond_proj_dim=None,
+        conv_in_kernel=3,
+        conv_out_kernel=3,
+        projection_class_embeddings_input_dim=None,
+        attention_type="default",
+        class_embeddings_concat=False,
+        mid_block_only_cross_attention=None,
+        cross_attention_norm=None,
+        addition_embed_type_num_heads=64,
+    ).to(torch_dtype)
+
+    if conditioning_images_keys != [] or conditioning_masks_keys != []:
+
+        latents_concat_embedder_config = LatentsConcatEmbedderConfig(
+            image_keys=conditioning_images_keys,
+            mask_keys=conditioning_masks_keys,
+        )
+        latent_concat_embedder = LatentsConcatEmbedder(latents_concat_embedder_config)
+        latent_concat_embedder.freeze()
+        conditioners.append(latent_concat_embedder)
+
+        # Wrap conditioners and set to device
+    conditioner = ConditionerWrapper(
+        conditioners=conditioners,
+    )
+
+    ## VAE ##
+    # Get VAE model
+    vae_config = AutoencoderKLDiffusersConfig(
+        version=backbone_signature,
+        subfolder="vae",
+        tiling_size=(128, 128),
+    )
+    vae = AutoencoderKLDiffusers(vae_config).to(torch_dtype)
+    vae.freeze()
+    vae.to(torch_dtype)
+
+    ## Diffusion Model ##
+    # Get diffusion model
+    config = LBMConfig(
+        source_key=source_key,
+        target_key=target_key,
+        latent_loss_weight=latent_loss_weight,
+        latent_loss_type=latent_loss_type,
+        pixel_loss_type=pixel_loss_type,
+        pixel_loss_weight=pixel_loss_weight,
+        timestep_sampling=timestep_sampling,
+        logit_mean=logit_mean,
+        logit_std=logit_std,
+        selected_timesteps=selected_timesteps,
+        prob=prob,
+        bridge_noise_sigma=bridge_noise_sigma,
+    )
+
+    sampling_noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+        backbone_signature,
+        subfolder="scheduler",
+    )
+
+    model = LBMModel(
+        config,
+        denoiser=denoiser,
+        sampling_noise_scheduler=sampling_noise_scheduler,
+        vae=vae,
+        conditioner=conditioner,
+    ).to(torch_dtype)
+
+    return model

src/lbm/models/base/__init__.py

@@ -0,0 +1,4 @@
+from .base_model import BaseModel
+from .model_config import ModelConfig
+
+__all__ = ["BaseModel", "ModelConfig"]

src/lbm/models/base/base_model.py

@@ -0,0 +1,66 @@
+from typing import Any, Dict
+
+import torch
+import torch.nn as nn
+
+from .model_config import ModelConfig
+
+
+class BaseModel(nn.Module):
+    def __init__(self, config: ModelConfig):
+        nn.Module.__init__(self)
+        self.config = config
+        self.input_key = config.input_key
+        self.device = torch.device("cpu")
+        self.dtype = torch.float32
+
+    def on_fit_start(self, device: torch.device | None = None, *args, **kwargs):
+        """Called when the training starts
+
+        Args:
+            device (Optional[torch.device], optional): The device to use. Usefull to set
+                relevant parameters on the model and embedder to the right device only
+                once at the start of the training. Defaults to None.
+        """
+        if device is not None:
+            self.device = device
+        self.to(self.device)
+
+    def forward(self, batch: Dict[str, Any], *args, **kwargs):
+        raise NotImplementedError("forward method is not implemented")
+
+    def freeze(self):
+        """Freeze the model"""
+        self.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, _ = torch._C._nn._parse_to(*args, **kwargs)
+        self = super().to(
+            device=device,
+            dtype=dtype,
+            non_blocking=non_blocking,
+        )
+
+        if device is not None:
+            self.device = device
+        if dtype is not None:
+            self.dtype = dtype
+        return self
+
+    def compute_metrics(self, batch: Dict[str, Any], *args, **kwargs):
+        """Compute the metrics"""
+        return {}
+
+    def sample(self, batch: Dict[str, Any], *args, **kwargs):
+        """Sample from the model"""
+        return {}
+
+    def log_samples(self, batch: Dict[str, Any], *args, **kwargs):
+        """Log the samples"""
+        return None
+
+    def on_train_batch_end(self, batch: Dict[str, Any], *args, **kwargs):
+        """Update the model an optimization is perforned on a batch."""
+        pass

src/lbm/models/base/model_config.py

@@ -0,0 +1,8 @@
+from pydantic.dataclasses import dataclass
+
+from ...config import BaseConfig
+
+
+@dataclass
+class ModelConfig(BaseConfig):
+    input_key: str = "image"

src/lbm/models/embedders/__init__.py

@@ -0,0 +1,4 @@
+from .conditioners_wrapper import ConditionerWrapper
+from .latents_concat import LatentsConcatEmbedder, LatentsConcatEmbedderConfig
+
+__all__ = ["LatentsConcatEmbedder", "LatentsConcatEmbedderConfig", "ConditionerWrapper"]

src/lbm/models/embedders/base/__init__.py

@@ -0,0 +1,4 @@
+from .base_conditioner import BaseConditioner
+from .base_conditioner_config import BaseConditionerConfig
+
+__all__ = ["BaseConditioner", "BaseConditionerConfig"]

src/lbm/models/embedders/base/base_conditioner.py

@@ -0,0 +1,60 @@
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+
+from ...base.base_model import BaseModel
+from .base_conditioner_config import BaseConditionerConfig
+
+DIM2CONDITIONING = {
+    2: "vector",
+    3: "crossattn",
+    4: "concat",
+}
+
+
+class BaseConditioner(BaseModel):
+    """This is the base class for all the conditioners. This absctacts the conditioning process
+
+    Args:
+
+        config (BaseConditionerConfig): The configuration of the conditioner
+
+    Examples
+    ########
+
+    To use the conditioner, you can import the class and use it as follows:
+
+    .. code-block:: python
+
+        from cr.models.embedders import BaseConditioner, BaseConditionerConfig
+
+        # Create the conditioner config
+        config = BaseConditionerConfig(
+            input_key="text", # The key for the input
+            unconditional_conditioning_rate=0.3, # Drops the conditioning with 30% probability during training
+        )
+
+        # Create the conditioner
+        conditioner = BaseConditioner(config)
+    """
+
+    def __init__(self, config: BaseConditionerConfig):
+        BaseModel.__init__(self, config)
+        self.config = config
+        self.input_key = config.input_key
+        self.dim2outputkey = DIM2CONDITIONING
+        self.ucg_rate = config.unconditional_conditioning_rate
+
+    def forward(
+        self, batch: Dict[str, Any], force_zero_embedding: bool = False, *args, **kwargs
+    ):
+        """
+         Forward pass of the embedder.
+
+        Args:
+
+            batch (Dict[str, Any]): A dictionary containing the input data.
+            force_zero_embedding (bool): Whether to force zero embedding.
+                This will return an embedding with all entries set to 0. Defaults to False.
+        """
+        raise NotImplementedError("Forward pass must be implemented in child class")

src/lbm/models/embedders/base/base_conditioner_config.py

@@ -0,0 +1,27 @@
+from typing import Literal
+
+from pydantic.dataclasses import dataclass
+
+from ....config import BaseConfig
+
+
+@dataclass
+class BaseConditionerConfig(BaseConfig):
+    """This is the ClipEmbedderConfig class which defines all the useful parameters to instantiate the model
+
+    Args:
+
+        input_key (str): The key for the input. Defaults to "text".
+        unconditional_conditioning_rate (float): Drops the conditioning with this probability during training. Defaults to 0.0.
+    """
+
+    input_key: str = "text"
+    unconditional_conditioning_rate: float = 0.0
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        assert (
+            self.unconditional_conditioning_rate >= 0.0
+            and self.unconditional_conditioning_rate <= 1.0
+        ), "Unconditional conditioning rate should be between 0 and 1"