{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "651c16af-ab8b-4f42-a170-08e72e0f3533", "metadata": {}, "outputs": [], "source": [ "from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel\n", "from rembg import remove\n", "from PIL import Image\n", "import torch\n", "from ip_adapter import IPAdapterXL\n", "from ip_adapter.utils import register_cross_attention_hook, get_net_attn_map, attnmaps2images\n", "from PIL import Image, ImageChops\n", "import numpy as np\n", "\n", "def image_grid(imgs, rows, cols):\n", " assert len(imgs) == rows*cols\n", "\n", " w, h = imgs[0].size\n", " grid = Image.new('RGB', size=(cols*w, rows*h))\n", " grid_w, grid_h = grid.size\n", " \n", " for i, img in enumerate(imgs):\n", " grid.paste(img, box=(i%cols*w, i//cols*h))\n", " return grid" ] }, { "cell_type": "code", "execution_count": 2, "id": "b4810ab9-f6f3-4a27-aa01-7076ac3eefff", "metadata": {}, "outputs": [], "source": [ "base_model_path = \"stabilityai/stable-diffusion-xl-base-1.0\"\n", "image_encoder_path = \"models/image_encoder\"\n", "ip_ckpt = \"sdxl_models/ip-adapter_sdxl_vit-h.bin\"\n", "controlnet_path = \"diffusers/controlnet-depth-sdxl-1.0\"\n", "device = \"cuda\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "3fe3d8e3-a786-434d-8a45-14c8ebee0979", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6883e59476834310b2526be244e07bb3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading pipeline components...: 0%| | 0/7 [00:00" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "np_image = np.array(Image.open('demo_assets/depths/' + obj + '.png'))\n", "np_image = (np_image / 256).astype('uint8')\n", "\n", "depth_map = Image.fromarray(np_image).resize((1024,1024))\n", "\n", "init_img = init_img.resize((1024,1024))\n", "mask = target_mask.resize((1024, 1024))\n", "grid = image_grid([target_mask.resize((256, 256)), ip_image.resize((256, 256)), init_img.resize((256, 256)), depth_map.resize((256, 256))], 1, 4)\n", "\n", "# Visualize each input individually\n", "grid" ] }, { "cell_type": "code", "execution_count": 8, "id": "ccfdc71f-3913-4772-a68f-2266c1f50af4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best quality, high quality\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "955f34c3630a41a7a709cfbf73b1868c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/29 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_samples = 1\n", "images = ip_model.generate(pil_image=ip_image, image=init_img, control_image=depth_map, mask_image=mask, controlnet_conditioning_scale=0.9, num_samples=num_samples, num_inference_steps=30, seed=42)\n", "images[0].show()" ] }, { "cell_type": "code", "execution_count": null, "id": "651fc615-b21d-4a95-985d-9f1eeb53ef49", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "diffuser", "language": "python", "name": "diffuser" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }