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{
"cells": [
{
"cell_type": "markdown",
"id": "f86ede39-8d9f-4da9-bc12-955f2fddd484",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Copyright 2023 Google LLC"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f3cbf47-a52b-48b1-9bd3-3435f92f2174",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# Copyright 2023 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"id": "22de629b-581f-4335-9e7b-f73221d8dbcb",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# ControlNet depth with StyleAligned over SDXL"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "486b7ebb-c483-4bf0-ace8-f8092c2d1f23",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL\n",
"from diffusers.utils import load_image\n",
"from transformers import DPTImageProcessor, DPTForDepthEstimation\n",
"import torch\n",
"import mediapy\n",
"import sa_handler\n",
"import pipeline_calls"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a7e85e7-b5cf-45b2-946a-5ba1e4923586",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# init models\n",
"\n",
"depth_estimator = DPTForDepthEstimation.from_pretrained(\"Intel/dpt-hybrid-midas\").to(\"cuda\")\n",
"feature_processor = DPTImageProcessor.from_pretrained(\"Intel/dpt-hybrid-midas\")\n",
"\n",
"controlnet = ControlNetModel.from_pretrained(\n",
" \"diffusers/controlnet-depth-sdxl-1.0\",\n",
" variant=\"fp16\",\n",
" use_safetensors=True,\n",
" torch_dtype=torch.float16,\n",
").to(\"cuda\")\n",
"vae = AutoencoderKL.from_pretrained(\"madebyollin/sdxl-vae-fp16-fix\", torch_dtype=torch.float16).to(\"cuda\")\n",
"pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(\n",
" \"stabilityai/stable-diffusion-xl-base-1.0\",\n",
" controlnet=controlnet,\n",
" vae=vae,\n",
" variant=\"fp16\",\n",
" use_safetensors=True,\n",
" torch_dtype=torch.float16,\n",
").to(\"cuda\")\n",
"pipeline.enable_model_cpu_offload()\n",
"\n",
"sa_args = sa_handler.StyleAlignedArgs(share_group_norm=False,\n",
" share_layer_norm=False,\n",
" share_attention=True,\n",
" adain_queries=True,\n",
" adain_keys=True,\n",
" adain_values=False,\n",
" )\n",
"handler = sa_handler.Handler(pipeline)\n",
"handler.register(sa_args, )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "94ca26b4-9061-4012-9400-8d97ef212d87",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# get depth maps\n",
"\n",
"image = load_image(\"./example_image/train.png\")\n",
"depth_image1 = pipeline_calls.get_depth_map(image, feature_processor, depth_estimator)\n",
"depth_image2 = load_image(\"./example_image/sun.png\").resize((1024, 1024))\n",
"mediapy.show_images([depth_image1, depth_image2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8f56fe4-559f-49ff-a2d8-460dcfeb56a0",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# run ControlNet depth with StyleAligned\n",
"\n",
"reference_prompt = \"a poster in flat design style\"\n",
"target_prompts = [\"a train in flat design style\", \"the sun in flat design style\"]\n",
"controlnet_conditioning_scale = 0.8\n",
"num_images_per_prompt = 3 # adjust according to VRAM size\n",
"latents = torch.randn(1 + num_images_per_prompt, 4, 128, 128).to(pipeline.unet.dtype)\n",
"for deph_map, target_prompt in zip((depth_image1, depth_image2), target_prompts):\n",
" latents[1:] = torch.randn(num_images_per_prompt, 4, 128, 128).to(pipeline.unet.dtype)\n",
" images = pipeline_calls.controlnet_call(pipeline, [reference_prompt, target_prompt],\n",
" image=deph_map,\n",
" num_inference_steps=50,\n",
" controlnet_conditioning_scale=controlnet_conditioning_scale,\n",
" num_images_per_prompt=num_images_per_prompt,\n",
" latents=latents)\n",
" \n",
" mediapy.show_images([images[0], deph_map] + images[1:], titles=[\"reference\", \"depth\"] + [f'result {i}' for i in range(1, len(images))])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "437ba4bd-6243-486b-8ba5-3b7cd661d53a",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": []
}
],
"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",
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