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Zero
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-09T09:44:30.641366Z",
"start_time": "2024-12-09T09:44:11.789050Z"
}
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import gradio as gr\n",
"from diffusers import DiffusionPipeline\n",
"import matplotlib.pyplot as plt\n",
"import torch\n",
"from PIL import Image\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ddf33e0d3abacc2c",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"#append current path\n",
"sys.path.extend(\"/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/release/hf_demo\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "643e49fd601daf8f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-09T09:44:35.790962Z",
"start_time": "2024-12-09T09:44:35.779496Z"
}
},
"outputs": [],
"source": [
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e03aae2a4e5676dd",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-09T09:44:44.157412Z",
"start_time": "2024-12-09T09:44:37.138452Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/miniforge3/envs/diffusion/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9df8347307674ba8afb0250e23109aa1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pipe = DiffusionPipeline.from_pretrained(\"rhfeiyang/art-free-diffusion-v1\",).to(\"cuda\")\n",
"device = \"cuda\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "83916bc68ff5d914",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-09T09:44:52.694399Z",
"start_time": "2024-12-09T09:44:44.210695Z"
}
},
"outputs": [],
"source": [
"from inference import get_lora_network, inference, get_validation_dataloader\n",
"lora_map = {\n",
" \"None\": \"None\",\n",
" \"Andre Derain\": \"andre-derain_subset1\",\n",
" \"Vincent van Gogh\": \"van_gogh_subset1\",\n",
" \"Andy Warhol\": \"andy_subset1\",\n",
" \"Walter Battiss\": \"walter-battiss_subset2\",\n",
" \"Camille Corot\": \"camille-corot_subset1\",\n",
" \"Claude Monet\": \"monet_subset2\",\n",
" \"Pablo Picasso\": \"picasso_subset1\",\n",
" \"Jackson Pollock\": \"jackson-pollock_subset1\",\n",
" \"Gerhard Richter\": \"gerhard-richter_subset1\",\n",
" \"M.C. Escher\": \"m.c.-escher_subset1\",\n",
" \"Albert Gleizes\": \"albert-gleizes_subset1\",\n",
" \"Hokusai\": \"katsushika-hokusai_subset1\",\n",
" \"Wassily Kandinsky\": \"kandinsky_subset1\",\n",
" \"Gustav Klimt\": \"klimt_subset3\",\n",
" \"Roy Lichtenstein\": \"roy-lichtenstein_subset1\",\n",
" \"Henri Matisse\": \"henri-matisse_subset1\",\n",
" \"Joan Miro\": \"joan-miro_subset2\",\n",
"}\n",
"\n",
"def demo_inference_gen(adapter_choice:str, prompt:str, samples:int=1,seed:int=0, steps=50, guidance_scale=7.5):\n",
" adapter_path = lora_map[adapter_choice]\n",
" if adapter_path not in [None, \"None\"]:\n",
" adapter_path = f\"data/Art_adapters/{adapter_path}/adapter_alpha1.0_rank1_all_up_1000steps.pt\"\n",
"\n",
" prompts = [prompt]*samples\n",
" infer_loader = get_validation_dataloader(prompts)\n",
" network = get_lora_network(pipe.unet, adapter_path)[\"network\"]\n",
" pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,\n",
" height=512, width=512, scales=[1.0],\n",
" save_dir=None, seed=seed,steps=steps, guidance_scale=guidance_scale,\n",
" start_noise=-1, show=False, style_prompt=\"sks art\", no_load=True,\n",
" from_scratch=True)[0][1.0]\n",
" return pred_images\n",
"\n",
"def demo_inference_stylization(adapter_path:str, prompts:list, image:list, start_noise=800,seed:int=0):\n",
" infer_loader = get_validation_dataloader(prompts, image)\n",
" network = get_lora_network(pipe.unet, adapter_path,\"all_up\")[\"network\"]\n",
" pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,\n",
" height=512, width=512, scales=[0.,1.],\n",
" save_dir=None, seed=seed,steps=20, guidance_scale=7.5,\n",
" start_noise=start_noise, show=True, style_prompt=\"sks art\", no_load=True,\n",
" from_scratch=False)\n",
" return pred_images\n",
"\n",
"# def infer(prompt, samples, steps, scale, seed):\n",
"# generator = torch.Generator(device=device).manual_seed(seed)\n",
"# images_list = pipe( # type: ignore\n",
"# [prompt] * samples,\n",
"# num_inference_steps=steps,\n",
"# guidance_scale=scale,\n",
"# generator=generator,\n",
"# )\n",
"# images = []\n",
"# safe_image = Image.open(r\"data/unsafe.png\")\n",
"# print(images_list)\n",
"# for i, image in enumerate(images_list[\"images\"]): # type: ignore\n",
"# if images_list[\"nsfw_content_detected\"][i]: # type: ignore\n",
"# images.append(safe_image)\n",
"# else:\n",
"# images.append(image)\n",
"# return images\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aa33e9d104023847",
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-09T12:09:39.339583Z",
"start_time": "2024-12-09T12:09:38.953936Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<gradio.components.slider.Slider object at 0x7fa12d3a5280>\n",
"Running on local URL: http://127.0.0.1:7876\n",
"Running on public URL: https://be7cce8fec75395c82.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://be7cce8fec75395c82.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train method: None\n",
"Rank: 1, Alpha: 1\n",
"create LoRA for U-Net: 0 modules.\n",
"save dir: None\n",
"['Park with cherry blossom trees, picnicker’s and a clear blue pond in the style of sks art'], seed=949192390\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/data/vision/torralba/selfmanaged/torralba/scratch/jomat/sam_dataset/miniforge3/envs/diffusion/lib/python3.9/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at /opt/conda/conda-bld/pytorch_1712608883701/work/aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
" return F.conv2d(input, weight, bias, self.stride,\n",
"\n",
"00%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:03<00:00, 6.90it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time taken for one batch, Art Adapter scale=1.0: 3.2747044563293457\n"
]
}
],
"source": [
"block = gr.Blocks()\n",
"# Direct infer\n",
"with block:\n",
" with gr.Group():\n",
" with gr.Row():\n",
" text = gr.Textbox(\n",
" label=\"Enter your prompt\",\n",
" max_lines=2,\n",
" placeholder=\"Enter your prompt\",\n",
" container=False,\n",
" value=\"Park with cherry blossom trees, picnicker’s and a clear blue pond.\",\n",
" )\n",
" \n",
"\n",
" \n",
" btn = gr.Button(\"Run\", scale=0)\n",
" gallery = gr.Gallery(\n",
" label=\"Generated images\",\n",
" show_label=False,\n",
" elem_id=\"gallery\",\n",
" columns=[2],\n",
" )\n",
"\n",
" advanced_button = gr.Button(\"Advanced options\", elem_id=\"advanced-btn\")\n",
"\n",
" with gr.Row(elem_id=\"advanced-options\"):\n",
" adapter_choice = gr.Dropdown(\n",
" label=\"Choose adapter\",\n",
" choices=[\"None\", \"Andre Derain\",\"Vincent van Gogh\",\"Andy Warhol\", \"Walter Battiss\",\n",
" \"Camille Corot\", \"Claude Monet\", \"Pablo Picasso\",\n",
" \"Jackson Pollock\", \"Gerhard Richter\", \"M.C. Escher\",\n",
" \"Albert Gleizes\", \"Hokusai\", \"Wassily Kandinsky\", \"Gustav Klimt\", \"Roy Lichtenstein\",\n",
" \"Henri Matisse\", \"Joan Miro\"\n",
" ],\n",
" value=\"None\"\n",
" )\n",
" # print(adapter_choice[0])\n",
" # lora_path = lora_map[adapter_choice.value]\n",
" # if lora_path is not None:\n",
" # lora_path = f\"data/Art_adapters/{lora_path}/adapter_alpha1.0_rank1_all_up_1000steps.pt\"\n",
"\n",
" samples = gr.Slider(label=\"Images\", minimum=1, maximum=4, value=1, step=1)\n",
" steps = gr.Slider(label=\"Steps\", minimum=1, maximum=50, value=20, step=1)\n",
" scale = gr.Slider(\n",
" label=\"Guidance Scale\", minimum=0, maximum=50, value=7.5, step=0.1\n",
" )\n",
" print(scale)\n",
" seed = gr.Slider(\n",
" label=\"Seed\",\n",
" minimum=0,\n",
" maximum=2147483647,\n",
" step=1,\n",
" randomize=True,\n",
" )\n",
"\n",
" gr.on([text.submit, btn.click], demo_inference_gen, inputs=[adapter_choice, text, samples, seed, steps, scale], outputs=gallery)\n",
" advanced_button.click(\n",
" None,\n",
" [],\n",
" text,\n",
" )\n",
"\n",
"\n",
"block.launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3239c12167a5f2cd",
"metadata": {},
"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",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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