Create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+import torch
+from diffusers import AutoPipelineForText2Image
+
+# Load the model
+pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16)
+pipe.enable_model_cpu_offload()
+
+# Define the input and output functions
+def text_to_image(prompt):
+    generator = torch.Generator(device="cpu").manual_seed(0)
+    image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
+    return image
+
+# Create a placeholder
+placeholder = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."
+
+# Create the Gradio interface
+title = "Kandinsky 3.0"
+description = "This model generates an image based on a given text prompt."
+how_to_use = "Input a description of the image you want to generate, for example: 'A forest with a river and a bridge under the moonlight.'"
+examples = [["A dark alley with flickering streetlights and a mysterious figure lurking in the shadows"], 
+            ["A futuristic cityscape with neon lights and flying cars"]]
+
+gr.Interface(fn=text_to_image, inputs=gr.Textbox(placeholder=placeholder), outputs=gr.Image()).launch()