Upload folder using huggingface_hub

README.md

@@ -9,11 +9,14 @@ app_file: app.py
 pinned: false
 license: mit
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
 @InProceedings{VideoFusion,
         author    = {Luo, Zhengxiong and Chen, Dayou and Zhang, Yingya and Huang, Yan and Wang, Liang and Shen, Yujun and Zhao, Deli and Zhou, Jingren and Tan, Tieniu},
         title     = {VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation},
         booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
         month     = {June},
         year      = {2023}
-    }
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+    }

app.py

@@ -3,12 +3,12 @@ import gradio as gr
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 from diffusers.utils import export_to_video
 
-pipeline = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
+
 
 def generate_video(prompt):
     # load pipeline
-    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
-    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+    pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16").to("cuda")
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config).to("cuda")
 
     # optimize for GPU memory
     pipe.enable_model_cpu_offload()
@@ -21,5 +21,5 @@ def generate_video(prompt):
     video_path = export_to_video(video_frames)
     return video_path
 
-iface = gr.Interface(fn=generate_video, inputs="text", outputs="file")
-iface.launch()
+demo = gr.Interface(fn=generate_video, inputs="text", outputs="file")
+demo.launch(share=True)