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{
"cells": [
{
"cell_type": "markdown",
"id": "1d22b3c2-c883-4947-aff6-0f23aa7b14e7",
"metadata": {},
"source": [
"# Stable Diffusion\n",
"\n",
"Using a pre-trained text-to-image model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de9ec933-22dd-4018-ad17-67de41b5e3e5",
"metadata": {},
"outputs": [],
"source": [
"# pip3 install diffusers transformers accelerate scipy safetensors xformers"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9173c5b8-e9f1-44da-a9e0-2fe36214e602",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "645dcf47-fe61-4291-92a5-9abe7c762221",
"metadata": {},
"outputs": [],
"source": [
"model_id = \"stabilityai/stable-diffusion-2-1\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "78581804-40dc-4453-9052-5f4d27bf84d4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading pipeline components...: 100%|█████████████████████████████████████████████████████| 6/6 [00:00<00:00, 16.38it/s]\n"
]
}
],
"source": [
"# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead\n",
"# Using float32 instead of float16+cuda to compute with CPU rather than GPU\n",
"pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)\n",
"pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n",
"# pipe = pipe.to(\"cuda\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9cadef32-3304-49f0-87c3-fac7caafab12",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████| 50/50 [11:10<00:00, 13.41s/it]\n",
"/opt/homebrew/lib/python3.11/site-packages/diffusers/image_processor.py:88: RuntimeWarning: invalid value encountered in cast\n",
" images = (images * 255).round().astype(\"uint8\")\n"
]
}
],
"source": [
"prompt = \"a photo of a ninja crouched on a torii on a cliff above the sea\"\n",
"image = pipe(prompt).images[0]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "02e99341-5467-46e8-869b-431fb4946864",
"metadata": {},
"outputs": [],
"source": [
"image.save(\"generated_image.png\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
