Add pipeline tag, library name and license (#1)

- Add pipeline tag, library name and license (870fee5056121be118c8ee68684242343ceb3249)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,3 +1,9 @@
+---
+pipeline_tag: image-to-video
+library_name: diffusers
+license: mit
+---
+
 # VIRES model card
 
 **Model Page**: [VIRES](https://hjzheng.net/projects/VIRES/)
@@ -8,21 +14,42 @@ Summary description and brief definition of inputs and outputs.
 
 ### Description
 
-VIRES is a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. Existing approaches struggle with temporal consistency and accurate alignment with the provided sketch sequence. VIRES leverages the generative priors of text-to-video models to maintain temporal consistency and produce visually pleasing results. We propose the Sequential ControlNet with the standardized self-scaling, which effectively extracts structure layouts and adaptively captures high-contrast sketch details. We further augment the diffusion transformer backbone with the sketch attention to interpret and inject fine-grained sketch semantics. A sketch-aware encoder ensures that repainted results are aligned with the provided sketch sequence.
+VIRES is a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. It leverages the generative priors of text-to-video models to maintain temporal consistency and produce visually pleasing results. Key features include a Sequential ControlNet for structure layout extraction and detail capture, sketch attention for injecting fine-grained semantics, and a sketch-aware encoder for alignment.
+
 
 ### Inputs and outputs
 
 -   **Input:**
-    -  Text string
-    -  Mask Sequence, normalized to 51 x 512 x 512 resolution
-    -  Sketch Sequence, normalized to 51 x 512 x 512 resolution
+    -  Text string describing the desired changes.
+    -  Mask Sequence (51 x 512 x 512 resolution).
+    -  Sketch Sequence (51 x 512 x 512 resolution).
 
 -   **Output:**
-    -   A repainting result video
+    -   A repainted video.
 
 ### Usage
 
-Ref to our GitHub page: https://github.com/suimuc/VIRES/
+A basic example using the `diffusers` library (requires appropriate model weights and dependencies):
+
+```python
+from diffusers import DiffusionPipeline #Import necessary libraries
+# Load the model (replace with your actual paths)
+pipe = DiffusionPipeline.from_pretrained("suimu/VIRES", torch_dtype=torch.float16).to("cuda")
+
+# Prepare inputs: text prompt, mask, and sketch
+prompt = "A cat replaces the dog in this video"
+mask = ... #Load your mask sequence
+sketch = ... #Load your sketch sequence
+
+# Generate the video
+video = pipe(prompt, mask, sketch).videos[0]
+
+# Save or display the video
+...
+```
+
+For complete usage instructions and advanced options, refer to our GitHub page: https://github.com/suimuc/VIRES/
+
 
 ## Citation
 
@@ -33,5 +60,4 @@ Ref to our GitHub page: https://github.com/suimuc/VIRES/
       journal={arXiv preprint arXiv:2411.16199},
       year={2024}
 }
-```
-
+```