Improve model card and add metadata

README.md

@@ -1,154 +1,98 @@
 ---
+license: mit
 library_name: transformers
-tags: []
+pipeline_tag: image-to-image
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for  [Model ID]
 
+This model performs controllable person image generation, allowing precise manipulation of appearance (virtual try-on) and pose (pose transfer). It addresses limitations of prior methods by focusing on precise attention to corresponding regions in reference images, reducing fine-grained detail distortion while maintaining high image quality.  The model is based on a diffusion-based architecture and uses a novel regularization loss to learn flow fields in attention.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model, [Model ID], is a diffusion-based model for controllable person image generation. It leverages a novel "learning flow fields in attention" (Leffa) approach to guide the model's attention mechanism, resulting in improved control over appearance and pose while minimizing distortion of fine-grained details in reference images.  The method is model-agnostic and can be adapted to other diffusion models.
 
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
+- **Model type:** Diffusion model
+- **License:** MIT
 - **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+### Model Sources [optional]
 
 - **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Paper [optional]:** [Link to Hugging Face paper page for 2502.05209]
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The model can be used directly for controllable person image generation tasks, such as virtual try-on and pose transfer.  Input images require preprocessing (details needed).
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be fine-tuned for specific tasks related to person image manipulation or integrated into larger applications requiring controllable person image generation.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
 [More Information Needed]
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Bias, Risks, and Limitations
 
 [More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+[More Information Needed]
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from diffusers import StableDiffusionPipeline
 
-[More Information Needed]
+pipe = StableDiffusionPipeline.from_pretrained("[model_id]", torch_dtype=torch.float16) #Replace [model_id] with the actual model ID.
+pipe = pipe.to("cuda") #Move to GPU if available
+
+# Example usage (requires appropriate preprocessed inputs):
+# image = load_image("path/to/image.png")
+# prompt = "A person wearing a red shirt"
+# image = pipe(prompt=prompt, image=image).images[0]
+# image.save("output.png")
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
 [More Information Needed]
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
 [More Information Needed]
 
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
 [More Information Needed]
 
 ### Results
 
 [More Information Needed]
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
+[More Information Needed]
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
 
 ## Technical Specifications [optional]
 
@@ -160,40 +104,13 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
 **BibTeX:**
 
 [More Information Needed]
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
 [More Information Needed]