Duplicate from gradio/fake_diffusion

Co-authored-by: Ali Abdalla <aliabd@users.noreply.huggingface.co>

.gitattributes

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DESCRIPTION.md

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+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.

README.md

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+---
+title: fake_diffusion
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.23.0
+app_file: run.py
+pinned: false
+duplicated_from: gradio/fake_diffusion
+---

app.py

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+import gradio as gr
+import numpy as np
+import time
+
+# define core fn, which returns a generator {steps} times before returning the image
+def fake_diffusion(steps):
+    for _ in range(steps):
+        time.sleep(1)
+        image = np.random.random((600, 600, 3))
+        yield image
+
+    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")
+
+# define queue - required for generators
+demo.queue()
+
+demo.launch()

requirements.txt

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+numpy

run.ipynb

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+{"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", "    image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\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

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+import gradio as gr
+import numpy as np
+import time
+
+# define core fn, which returns a generator {steps} times before returning the image
+def fake_diffusion(steps):
+    for _ in range(steps):
+        time.sleep(1)
+        image = np.random.random((600, 600, 3))
+        yield image
+    image = "https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg"
+    yield image
+
+
+demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs="image")
+
+# define queue - required for generators
+demo.queue()
+
+demo.launch()