Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+
+pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.enable_model_cpu_offload()
+
+def infer(prompt, num_inference_steps):
+    #prompt = "Darth Vader is surfing on waves"
+    video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
+    video_path = export_to_video(video_frames)
+    print(video_path)
+    return video_path
+
+css = """
+#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
+a {text-decoration-line: underline; font-weight: 600;}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
+                <div
+                style="
+                    display: inline-flex;
+                    align-items: center;
+                    gap: 0.8rem;
+                    font-size: 1.75rem;
+                "
+                >
+                <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
+                    Zeroscope Text-to-Video
+                </h1>
+                </div>
+                <p style="margin-bottom: 10px; font-size: 94%">
+                A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
+                This model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.
+                
+                </p>
+            </div>""")
+
+        prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves")
+        inference_steps = gr.Slider(minimum=10, maximum=100, step=1, value=40, interactive=False)
+        submit_btn = gr.Button("Submit")
+        video_result = gr.Video(label="Video Output")
+
+    submit_btn.click(fn=infer,
+                    inputs=[prompt_in, inference_steps],
+                    outputs=[video_result])
+
+demo.queue(max_size=12).launch()
+