Spaces:
Runtime error
Runtime error
File size: 6,356 Bytes
5fc5efa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MasaCtrl: Tuning-free Mutual Self-Attention Control for Consistent Image Synthesis and Editing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"import numpy as np\n",
"\n",
"from tqdm import tqdm\n",
"from einops import rearrange, repeat\n",
"from omegaconf import OmegaConf\n",
"\n",
"from diffusers import DDIMScheduler\n",
"\n",
"from masactrl.diffuser_utils import MasaCtrlPipeline\n",
"from masactrl.masactrl_utils import AttentionBase\n",
"from masactrl.masactrl_utils import regiter_attention_editor_diffusers\n",
"\n",
"from torchvision.utils import save_image\n",
"from torchvision.io import read_image\n",
"from pytorch_lightning import seed_everything\n",
"\n",
"torch.cuda.set_device(6) # set the GPU device"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model Construction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Note that you may add your Hugging Face token to get access to the models\n",
"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"# model_path = \"andite/anything-v4.0\"\n",
"model_path = \"CompVis/stable-diffusion-v1-4\"\n",
"# model_path = \"runwayml/stable-diffusion-v1-5\"\n",
"scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\", clip_sample=False, set_alpha_to_one=False)\n",
"model = MasaCtrlPipeline.from_pretrained(model_path, scheduler=scheduler).to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Real editing with MasaCtrl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from masactrl.masactrl import MutualSelfAttentionControl\n",
"from torchvision.io import read_image\n",
"\n",
"\n",
"def load_image(image_path, device):\n",
" image = read_image(image_path)\n",
" image = image[:3].unsqueeze_(0).float() / 127.5 - 1. # [-1, 1]\n",
" image = F.interpolate(image, (512, 512))\n",
" image = image.to(device)\n",
" return image\n",
"\n",
"\n",
"seed = 42\n",
"seed_everything(seed)\n",
"\n",
"out_dir = \"./workdir/masactrl_real_exp/\"\n",
"os.makedirs(out_dir, exist_ok=True)\n",
"sample_count = len(os.listdir(out_dir))\n",
"out_dir = os.path.join(out_dir, f\"sample_{sample_count}\")\n",
"os.makedirs(out_dir, exist_ok=True)\n",
"\n",
"# source image\n",
"SOURCE_IMAGE_PATH = \"./gradio_app/images/corgi.jpg\"\n",
"source_image = load_image(SOURCE_IMAGE_PATH, device)\n",
"\n",
"source_prompt = \"\"\n",
"target_prompt = \"a photo of a running corgi\"\n",
"prompts = [source_prompt, target_prompt]\n",
"\n",
"# invert the source image\n",
"start_code, latents_list = model.invert(source_image,\n",
" source_prompt,\n",
" guidance_scale=7.5,\n",
" num_inference_steps=50,\n",
" return_intermediates=True)\n",
"start_code = start_code.expand(len(prompts), -1, -1, -1)\n",
"\n",
"# results of direct synthesis\n",
"editor = AttentionBase()\n",
"regiter_attention_editor_diffusers(model, editor)\n",
"image_fixed = model([target_prompt],\n",
" latents=start_code[-1:],\n",
" num_inference_steps=50,\n",
" guidance_scale=7.5)\n",
"\n",
"# inference the synthesized image with MasaCtrl\n",
"STEP = 4\n",
"LAYPER = 10\n",
"\n",
"# hijack the attention module\n",
"editor = MutualSelfAttentionControl(STEP, LAYPER)\n",
"regiter_attention_editor_diffusers(model, editor)\n",
"\n",
"# inference the synthesized image\n",
"image_masactrl = model(prompts,\n",
" latents=start_code,\n",
" guidance_scale=7.5)\n",
"# Note: querying the inversion intermediate features latents_list\n",
"# may obtain better reconstruction and editing results\n",
"# image_masactrl = model(prompts,\n",
"# latents=start_code,\n",
"# guidance_scale=7.5,\n",
"# ref_intermediate_latents=latents_list)\n",
"\n",
"# save the synthesized image\n",
"out_image = torch.cat([source_image * 0.5 + 0.5,\n",
" image_masactrl[0:1],\n",
" image_fixed,\n",
" image_masactrl[-1:]], dim=0)\n",
"save_image(out_image, os.path.join(out_dir, f\"all_step{STEP}_layer{LAYPER}.png\"))\n",
"save_image(out_image[0], os.path.join(out_dir, f\"source_step{STEP}_layer{LAYPER}.png\"))\n",
"save_image(out_image[1], os.path.join(out_dir, f\"reconstructed_source_step{STEP}_layer{LAYPER}.png\"))\n",
"save_image(out_image[2], os.path.join(out_dir, f\"without_step{STEP}_layer{LAYPER}.png\"))\n",
"save_image(out_image[3], os.path.join(out_dir, f\"masactrl_step{STEP}_layer{LAYPER}.png\"))\n",
"\n",
"print(\"Syntheiszed images are saved in\", out_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.5 ('ldm')",
"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": "3.8.5"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "587aa04bacead72c1ffd459abbe4c8140b72ba2b534b24165b36a2ede3d95042"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|