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README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Diffusers With Batching 3-x
-emoji: 😻
-colorFrom: pink
-colorTo: blue
+title: diffusers_with_batching_3-x 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.3.0
-app_file: app.py
+sdk_version: 3.50.1
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

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+torch
+transformers
+diffusers

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: diffusers_with_batching"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers diffusers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import torch\n", "from diffusers import DiffusionPipeline\n", "import gradio as gr\n", "\n", "generator = DiffusionPipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\")\n", "# move to GPU if available\n", "if torch.cuda.is_available():\n", "    generator = generator.to(\"cuda\")\n", "\n", "def generate(prompts):\n", "  images = generator(list(prompts)).images\n", "  return [images]\n", "\n", "demo = gr.Interface(generate, \n", "             \"textbox\", \n", "             \"image\", \n", "             batch=True, \n", "             max_batch_size=4  # Set the batch size based on your CPU/GPU memory\n", ").queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import torch
+from diffusers import DiffusionPipeline
+import gradio as gr
+
+generator = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
+# move to GPU if available
+if torch.cuda.is_available():
+    generator = generator.to("cuda")
+
+def generate(prompts):
+  images = generator(list(prompts)).images
+  return [images]
+
+demo = gr.Interface(generate, 
+             "textbox", 
+             "image", 
+             batch=True, 
+             max_batch_size=4  # Set the batch size based on your CPU/GPU memory
+).queue()
+
+if __name__ == "__main__":
+    demo.launch()