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requirements.txt

@@ -1,5 +1,5 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@38c2ad425a905431b1eb17b9498669f9e49f0dd5#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/38c2ad425a905431b1eb17b9498669f9e49f0dd5/gradio-4.38.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@76c175935019833baef709a5cf401d2263ca72ee#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/76c175935019833baef709a5cf401d2263ca72ee/gradio-4.38.1-py3-none-any.whl
 torch
 transformers
 diffusers

run.ipynb

@@ -1 +1 @@
-{"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}
+{"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  # type: ignore\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  # type: ignore\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

@@ -1,5 +1,5 @@
 import torch
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline  # type: ignore
 import gradio as gr
 
 generator = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
@@ -8,7 +8,7 @@ if torch.cuda.is_available():
     generator = generator.to("cuda")
 
 def generate(prompts):
-  images = generator(list(prompts)).images
+  images = generator(list(prompts)).images  # type: ignore
   return [images]
 
 demo = gr.Interface(generate,