Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from gradio_client import Client
+
+def get_instantID(portrait_in, prompt):
+    client = Client("https://instantx-instantid.hf.space/")
+    negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green"
+    result = client.predict(
+		portrait_in,	# filepath  in 'Upload a photo of your face' Image component
+		None,	# filepath  in 'Upload a reference pose image (optional)' Image component
+	    prompt,	# str  in 'Prompt' Textbox component
+		negative_prompt,	# str  in 'Negative Prompt' Textbox component
+		"(No style)",	# Literal['(No style)', 'Watercolor', 'Film Noir', 'Neon', 'Jungle', 'Mars', 'Vibrant Color', 'Snow', 'Line art']  in 'Style template' Dropdown component
+		True,	# bool  in 'Enhance non-face region' Checkbox component
+		20,	# float (numeric value between 20 and 100) in 'Number of sample steps' Slider component
+		0.8,	# float (numeric value between 0 and 1.5) in 'IdentityNet strength (for fedility)' Slider component
+		0.8,	# float (numeric value between 0 and 1.5) in 'Image adapter strength (for detail)' Slider component
+		5,	# float (numeric value between 0.1 and 10.0) in 'Guidance scale' Slider component
+		0,	# float (numeric value between 0 and 2147483647) in 'Seed' Slider component
+		api_name="/generate_image"
+    )
+    print(result)
+    return result[0]
+
+def get_video(image_in, prompt):
+    client = Client("https://modelscope-i2vgen-xl.hf.space/")
+    result = client.predict(
+        image_in,
+        prompt
+    )
+    print(result)
+    return result
+
+def infer(image_in, prompt):
+    iid_img = get_instantID(image_in, prompt)
+    video_res = get_video(iid_img, prompt)
+    print(video_res)
+    return video_res
+
+gr.Interface(
+    fn = infer,
+    inputs = [
+        gr.Image(type="filepath"),
+        gr.Textbox()
+    ],
+    outputs = [
+        gr.Video()
+    ]
+).launch()