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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.16.2
+sdk_version: 3.17.0
 app_file: run.py
 pinned: false
 ---

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_diffusion\n", "### This demo uses a fake model to showcase iterative output. The Image output will update every time a generator is returned until the final image.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "# define core fn, which returns a generator {steps} times before returning the image\n", "def fake_diffusion(steps):\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.random((600, 600, 3))\n", "        yield image\n", "\n", "    image = \"https://i.picsum.photos/id/867/600/600.jpg?hmac=qE7QFJwLmlE_WKI7zMH6SgH5iY5fx8ec6ZJQBwKRT44\" \n", "    yield image\n", "\n", "demo = gr.Interface(fake_diffusion, \n", "                    inputs=gr.Slider(1, 10, 3), \n", "                    outputs=\"image\")\n", "\n", "# define queue - required for generators\n", "demo.queue()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_diffusion\n", "### This demo uses a fake model to showcase iterative output. The Image output will update every time a generator is returned until the final image.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "# define core fn, which returns a generator {steps} times before returning the image\n", "def fake_diffusion(steps):\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.random((600, 600, 3))\n", "        yield image\n", "\n", "    image = \"https://i.picsum.photos/id/867/600/600.jpg?hmac=qE7QFJwLmlE_WKI7zMH6SgH5iY5fx8ec6ZJQBwKRT44\"\n", "    yield image\n", "\n", "\n", "demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs=\"image\")\n", "\n", "# define queue - required for generators\n", "demo.queue()\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -9,14 +9,13 @@ def fake_diffusion(steps):
         image = np.random.random((600, 600, 3))
         yield image
 
-    image = "https://i.picsum.photos/id/867/600/600.jpg?hmac=qE7QFJwLmlE_WKI7zMH6SgH5iY5fx8ec6ZJQBwKRT44" 
+    image = "https://i.picsum.photos/id/867/600/600.jpg?hmac=qE7QFJwLmlE_WKI7zMH6SgH5iY5fx8ec6ZJQBwKRT44"
     yield image
 
-demo = gr.Interface(fake_diffusion, 
-                    inputs=gr.Slider(1, 10, 3), 
-                    outputs="image")
+
+demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs="image")
 
 # define queue - required for generators
 demo.queue()
 
-demo.launch()
+demo.launch()