Improve model card: Add pipeline tag, library name, links, and usage details

This PR improves the model card for **Universal Few-Shot Spatial Control for Diffusion Models (UFC)** by:

- Adding the `pipeline_tag: text-to-image`, ensuring the model is discoverable under the relevant pipeline at https://huggingface.co/models?pipeline_tag=text-to-image.
- Adding `library_name: diffusers` as the project's code is based on the `diffusers` library, enabling the automated "how to use" snippet.
- Including a direct link to the paper: https://huggingface.co/papers/2509.07530.
- Providing a link to the official GitHub repository for easier access to the code and further details: https://github.com/kietngt00/UFC.
- Incorporating key information about the model, its method, available checkpoints, and usage examples for image generation from the original GitHub README.

Please review these additions.

README.md

@@ -1,3 +1,74 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: text-to-image
+library_name: diffusers
+---
+
+# Universal Few-Shot Spatial Control for Diffusion Models
+
+This repository contains the official implementation of **Universal Few-Shot Spatial Control for Diffusion Models (UFC)**, as presented in the paper [Universal Few-Shot Spatial Control for Diffusion Models](https://huggingface.co/papers/2509.07530).
+
+For the official code and usage instructions, please refer to the [GitHub repository](https://github.com/kietngt00/UFC).
+
+![Results Visualization](https://github.com/kietngt00/UFC/raw/main/assets/results.png)
+
+*Figure 1: Results of our method (**UNet**) learned with **30 examples** on **unseen** spatial conditions. The proposed control adapter guides the pre-trained T2I models in a versatile and data-efficient manner.*
+
+## 🚀 Introduction
+This repository contains the official implementation of **Universal Few-Shot Spatial Control for Diffusion Models (UFC)**.
+
+**UFC** is a versatile few-shot control adapter capable of generalizing to novel spatial conditions, thereby enabling fine-grained control over the structure of generated images. Our method is applicable to both UNet and DiT diffusion backbones.
+
+## Abstract
+> Spatial conditioning in pretrained text-to-image diffusion models has significantly improved fine-grained control over the structure of generated images. However, existing control adapters exhibit limited adaptability and incur high training costs when encountering novel spatial control conditions that differ substantially from the training tasks. To address this limitation, we propose Universal Few-Shot Control (UFC), a versatile few-shot control adapter capable of generalizing to novel spatial conditions. Given a few image-condition pairs of an unseen task and a query condition, UFC leverages the analogy between query and support conditions to construct task-specific control features, instantiated by a matching mechanism and an update on a small set of task-specific parameters. Experiments on six novel spatial control tasks show that UFC, fine-tuned with only 30 annotated examples of novel tasks, achieves fine-grained control consistent with the spatial conditions. Notably, when fine-tuned with 0.1% of the full training data, UFC achieves competitive performance with the fully supervised baselines in various control tasks. We also show that UFC is applicable agnostically to various diffusion backbones and demonstrate its effectiveness on both UNet and DiT architectures.
+
+## 💡 Method
+![System Architecture](https://github.com/kietngt00/UFC/raw/main/assets/architecture.png)
+
+## 📍 Model Checkpoints
+
+#### Checkpoints using **UNet** backbone
+
+| Few-shot Task | Few-shot (30-shot) Fine-tuned Model | Base Meta-trained Model | Description |
+|:-------------:|:-----------------------------------:|:-----------------------:|:-----------:|
+| `Canny`   | [UNet_canny](https://huggingface.co/ntk1507/UFC/tree/main/unet_tuning_logs/UNet_taskgr23_canny_30) | [UNet_taskgr23](https://huggingface.co/ntk1507/UFC/tree/main/unet_logs/UNet_taskgr23) | The base model is trained with 4 tasks: `[Depth, Normal, Pose, Densepose]`|
+| `Hed`     | [UNet_hed](https://huggingface.co/ntk1507/UFC/tree/main/unet_tuning_logs/UNet_taskgr23_hed_30) | [UNet_taskgr23](https://huggingface.co/ntk1507/UFC/tree/main/unet_logs/UNet_taskgr23) | The base model is trained with 4 tasks: `[Depth, Normal, Pose, Densepose]`|
+| `Depth`   | [UNet_depth](https://huggingface.co/ntk1507/UFC/tree/main/unet_tuning_logs/UNet_taskgr13_depth_30) | [UNet_taskgr13](https://huggingface.co/ntk1507/UFC/tree/main/unet_logs/UNet_taskgr13) | The base model is trained with 4 tasks: `[Canny, HED, Pose, Densepose]`|
+| `Normal`  | [UNet_normal](https://huggingface.co/ntk1507/UFC/tree/main/unet_tuning_logs/UNet_taskgr13_normal_30) | [UNet_taskgr13](https://huggingface.co/ntk1507/UFC/tree/main/unet_logs/UNet_taskgr13) | The base model is trained with 4 tasks: `[Canny, HED, Pose, Densepose]`|
+| `Pose`    | [UNet_pose](https://huggingface.co/ntk1507/UFC/tree/main/unet_tuning_logs/UNet_taskgr12_pose_30) | [UNet_taskgr12](https://huggingface.co/ntk1507/UFC/tree/main/unet_logs/UNet_taskgr12) | The base model is trained with 4 tasks: `[Canny, HED, Depth, Normal]`|
+|`Densepose`| [UNet_densepose](https://huggingface.co/ntk1507/UFC/tree/main/unet_tuning_logs/UNet_taskgr12_densepose_30) | [UNet_taskgr12](https://huggingface.co/ntk1507/UFC/tree/main/unet_logs/UNet_taskgr12) | The base model is trained with 4 tasks: `[Canny, HED, Depth, Normal]`|
+
+#### Checkpoints using **DiT** backbone
+
+| Few-shot Task | Few-shot (30-shot) Fine-tuned Model | Base Meta-trained Model | Description |
+|:-------------:|:-----------------------------------:|:-----------------------:|:-----------:|
+| `Canny`   | [DiT_canny](https://huggingface.co/ntk1507/UFC/tree/main/DiT_tuning_logs/DiT_taskgr23_canny) | [DiT_taskgr23](https://huggingface.co/ntk1507/UFC/tree/main/DiT_logs/DiT_taskgr23) | The base model is trained with 4 tasks: `[Depth, Normal, Pose, Densepose]`|
+| `Hed`     | [DiT_hed](https://huggingface.co/ntk1507/UFC/tree/main/DiT_tuning_logs/DiT_taskgr23_hed) | [DiT_taskgr23](https://huggingface.co/ntk1507/UFC/tree/main/DiT_logs/DiT_taskgr23) | The base model is trained with 4 tasks: `[Depth, Normal, Pose, Densepose]`|
+| `Depth`   | [DiT_depth](https://huggingface.co/ntk1507/UFC/tree/main/DiT_tuning_logs/DiT_taskgr13_depth) | [DiT_taskgr13](https://huggingface.co/ntk1507/UFC/tree/main/DiT_logs/DiT_taskgr13) | The base model is trained with 4 tasks: `[Canny, HED, Pose, Densepose]`|
+| `Normal`  | [DiT_normal](https://huggingface.co/ntk1507/UFC/tree/main/DiT_tuning_logs/DiT_taskgr13_normal) | [DiT_taskgr13](https://huggingface.co/ntk1507/UFC/tree/main/DiT_logs/DiT_taskgr13) | The base model is trained with 4 tasks: `[Canny, HED, Pose, Densepose]`|
+| `Pose`    | [DiT_pose](https://huggingface.co/ntk1507/UFC/tree/main/DiT_tuning_logs/DiT_taskgr12_pose) | [DiT_taskgr12](https://huggingface.co/ntk1507/UFC/tree/main/DiT_logs/DiT_taskgr12) | The base model is trained with 4 tasks: `[Canny, HED, Depth, Normal]`|
+|`Densepose`| [DiT_densepose](https://huggingface.co/ntk1507/UFC/tree/main/DiT_tuning_logs/DiT_taskgr12_densepose) | [DiT_taskgr12](https://huggingface.co/ntk1507/UFC/tree/main/DiT_logs/DiT_taskgr12) | The base model is trained with 4 tasks: `[Canny, HED, Depth, Normal]`|
+
+## 🖼️ Image Generation
+
+Script for UFC with **UNet** backbone:
+```bash
+PYTHONPATH=. python eval/UNet_generation.py \
+    --config </path/to/config> \           
+    --ckpt_path </path/to/meta_train_checkpoint> \
+    --task_ckpt_path </path/to/finetune_checkpoint> \
+    --task <task> --shots 5 --batch_size 8 \
+```
+
+Script for UFC with **DiT** backbone is similar, but replacing `UNet_generation.py` with `DiT_generation.py`.
+
+## 🙏 Acknowledgements
+We develop our method based on the [diffusers](https://github.com/huggingface/diffusers) library, the official code of [OminiControl](https://github.com/Yuanshi9815/OminiControl/tree/main), [VTM](https://github.com/GitGyun/visual_token_matching) and [ControlNet](https://github.com/lllyasviel/ControlNet). We gratefully acknowledge the authors for making their code publicly available.
+
+## 📖 Citation
+If you find our work useful, please consider citing our paper:
+
+```bibtex
+@article{
+}
+```