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--- |
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license: apache-2.0 |
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datasets: |
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- aoxo/photorealism-style-adapter-gta-v |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: image-to-image |
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tags: |
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- art |
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--- |
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# Introducing RealFormer - A new approach to Photorealism over Supersampling |
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Introducing RealFormer, a novel image-to-image transformer model designed for enhancing photorealism in images, particularly focused on transforming synthetic images to more realistic ones. |
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## Model Details |
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### Model Description |
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RealFormer is an innovative Vision Transformer (ViT) based architecture that combines elements of Linear Attention (approximation attention) with Swin Transformers and adaptive instance normalization (AdaIN) for style transfer. It's designed to transform images,specifically targeted at the video game and animation industry, potentially enhancing their photorealism or applying style transfer. |
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- **Developed by:** Alosh Denny |
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- **Funded by [optional]:** EmelinLabs |
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- **Shared by [optional]:** EmelinLabs |
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- **Model type:** Image-to-Image Transformer |
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- **Language(s) (NLP):** None (Pre-trained Generative Image Model) |
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- **License:** Apache-2.0 |
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- **Finetuned from model [optional]:** Novel; Pre-trained (not finetuned) |
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### Model Sources [optional] |
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- **Dataset:** [Calibration Dataset for Grand Theft Auto V](https://huggingface.co./datasets/aoxo/photorealism-style-adapter-gta-v), [Pre-Training](https://huggingface.co./datasets/aoxo/latent_diffusion_super_sampling) |
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- **Repository:** [Swin Transformer](https://github.com/microsoft/Swin-Transformer) |
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- **Paper:** [Ze Liu et al. (2021)](https://arxiv.org/abs/2103.14030) |
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## Uses |
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### Direct Use |
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RealFormer is designed for image-to-image translation tasks. It can be used directly for: |
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- Enhancing photorealism in synthetic images (e.g., transforming video game graphics to more realistic images) |
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- Style transfer between rendered frames and post-processed frames |
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- To be incorporated in pipeline with [DLSS](https://developer.nvidia.com/rtx/dlss) |
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### Downstream Use |
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Potential downstream uses could include: |
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- Integration into game engines for real-time graphics enhancement - AdaIN layers are finetunable for video-game-specific usecases. In this implementation, the models have been pretrained on a variety of video for **super-sampling**, **photorealistic style transfer** and **reverse photorealism**. |
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- Pre-processing step in computer vision pipelines to improve input image quality - Decoder layers can be frozen for task-specific usecases. |
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- Photo editing software for synthesized image enhancement |
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### Out-of-Scope Use |
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This model is not recommended for: |
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- Generating or manipulating images in ways that could be deceptive or harmful |
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- Tasks requiring perfect preservation of specific image details, as the transformation process may alter some artifacts of the image |
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- Medical or forensic image analysis where any alteration could lead to misinterpretation. Remember, this is a model, not a classification or detection model. |
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## Bias, Risks, and Limitations |
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- The model may introduce biases present in the training data, potentially altering images in ways that reflect these biases. |
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- There's a risk of over-smoothing or losing fine details in the image transformation process. |
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- The model's performance may vary significantly depending on the input image characteristics and how similar they are to the training data. |
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- As with any image manipulation tool, there's a potential for misuse in creating deceptive or altered images. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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# Instantiate the model |
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model = ViTImage2Image(img_size=512, patch_size=16, emb_dim=768, num_heads=16, num_layers=8, hidden_dim=3072) |
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# Move model to GPU if available |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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# Load an image |
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input_image = load_image('path_to_your_image.png') |
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input_image = input_image.to(device) |
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# Perform inference |
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with torch.no_grad(): |
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output = model(input_image, input_image) # Using input as both content and style for this example |
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# Visualize or save the output |
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visualize_tensor(output, "Output Image") |
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``` |
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## Training Details |
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### Training Data |
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The model was trained on two |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
