Add model card

This PR adds a model card for the paper [Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2503.18352).

It also adds the relevant pipeline tag and the library name, as well as a link to the Github repository.

README.md

@@ -0,0 +1,41 @@
+---
+pipeline_tag: text-to-image
+library_name: diffusers
+license: mit
+---
+
+# Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models (CVPR 2025)
+
+This repository contains the model introduced in [Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models](https://huggingface.co/papers/2503.18352).
+
+For the official code implementation, please visit https://github.com/zhang0jhon/Diffusion-4K.
+
+## Usage Example (Flux-12B)
+
+The following code snippet demonstrates image generation using the Flux-12B model:
+
+```python
+from diffusers import FluxPipeline
+import torch
+
+pipe = FluxPipeline.from_pretrained("zhang0jhon/flux_wavelet", torch_dtype=torch.float16) # Replace with your model ID
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt, guidance_scale=5.0, height=4096, width=4096, num_inference_steps=50).images[0]
+
+# Save or display the image
+image.save("astronaut_horse.png")
+```
+
+Remember to install the necessary libraries (`pip install diffusers transformers`) and adjust parameters like `guidance_scale`, `height`, `width`, and `num_inference_steps` for optimal results. Refer to the [Github repository](https://github.com/zhang0jhon/Diffusion-4K) for more details and examples.
+
+## Citation
+
+```
+@inproceedings{zhang2025diffusion4k,
+    title={Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models},
+    author={Jinjin Zhang, Qiuyu Huang, Junjie Liu, Xiefan Guo and Di Huang},
+    year={2025},
+    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+}
+```