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train_batch_size = 16\n", " eval_batch_size = 16 # how many images to sample during evaluation\n", " num_epochs = 50\n", " gradient_accumulation_steps = 1\n", " learning_rate = 1e-4\n", " lr_warmup_steps = 500\n", " save_image_epochs = 10\n", " save_model_epochs = 10\n", " mixed_precision = \"fp16\" # `no` for float32, `fp16` for automatic mixed precision\n", " output_dir = \"ddpm-butterflies-128\" # the model name locally and on the HF Hub\n", "\n", " push_to_hub = True # whether to upload the saved model to the HF Hub\n", " hub_private_repo = False\n", " overwrite_output_dir = True # overwrite the old model when re-running the notebook\n", " seed = 0\n", "\n", "\n", "config = TrainingConfig()" ], "metadata": { "id": "faBx8T9NV1Xv" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d5jOnnaPSKZx" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "\n", "config.dataset_name = \"Drozdik/tattoo_v0\"\n", "dataset = load_dataset(config.dataset_name, split=\"train\")" ] }, { "cell_type": "code", "source": [ "def transform(examples):\n", " images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n", " return {\"images\": images}\n", "\n" ], "metadata": { "id": "CvUPjQmqXsG1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from diffusers import DDPMPipeline\n", "import math\n", "import os\n", "\n", "def make_grid(images, rows, cols):\n", " w, h = images[0].size\n", " grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n", " for i, image in enumerate(images):\n", " grid.paste(image, box=(i % cols * w, i // cols * h))\n", " return grid\n", "\n", "\n", "def evaluate(config, epoch, pipeline):\n", " images = pipeline(\n", " batch_size=config.eval_batch_size,\n", " generator=torch.manual_seed(config.seed),\n", " ).images\n", "\n", " image_grid = make_grid(images, rows=4, cols=4)\n", "\n", " test_dir = os.path.join(config.output_dir, \"samples\")\n", " os.makedirs(test_dir, exist_ok=True)\n", " image_grid.save(f\"{test_dir}/{epoch:04d}.png\")\n", "\n" ], "metadata": { "id": "p6tO2qgGx-m3" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from accelerate import Accelerator\n", "from tqdm.auto import tqdm\n", "from pathlib import Path\n", "import os\n", "\n", "def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):\n", " accelerator = Accelerator(\n", " mixed_precision=config.mixed_precision,\n", " gradient_accumulation_steps=config.gradient_accumulation_steps,\n", " log_with=\"tensorboard\",\n", " project_dir=os.path.join(config.output_dir, \"logs\"),\n", " )\n", "\n", " if accelerator.is_main_process:\n", " os.makedirs(config.output_dir,exist_ok=True)\n", " accelerator.init_trackers(\"train_example\")\n", "\n", " model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(model, optimizer, train_dataloader, lr_scheduler)\n", "\n", " global_step = 0\n", "\n", " for epoch in range(config.num_epochs):\n", " progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)\n", " progress_bar.set_description(f\"Epoch {epoch}\")\n", "\n", " for step, batch in enumerate(train_dataloader):\n", " clean_images = batch[\"images\"]\n", "\n", " noise = torch.randn(clean_images.shape).to(clean_images.device)\n", " bs = clean_images.shape[0]\n", "\n", " timesteps = torch.randint(\n", " 0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device\n", " ).long()\n", "\n", " noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n", "\n", " with accelerator.accumulate(model):\n", " noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n", " loss = F.mse_loss(noise_pred,noise)\n", " accelerator.backward(loss)\n", "\n", " accelerator.clip_grad_norm_(model.parameters(),1.0)\n", " optimizer.step()\n", " lr_scheduler.step()\n", " optimizer.zero_grad()\n", "\n", " progress_bar.update(1)\n", " logs = {\"loss\": loss.detach().item(), \"lr\": lr_scheduler.get_last_lr()[0], \"step\": global_step}\n", " progress_bar.set_postfix(**logs)\n", " accelerator.log(logs, step=global_step)\n", " global_step += 1\n", "\n", " if accelerator.is_main_process:\n", " pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)\n", "\n", " if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:\n", " evaluate(config, epoch, pipeline)\n", " if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:\n", " pipeline.save_pretrained(config.output_dir)\n", "\n", "\n", "\n", " upload_folder(\n", " repo_id=repo_id,\n", " folder_path=args.output_dir,\n", " commit_message=\"End of training\",\n", " ignore_patterns=[\"step_*\", \"epoch_*\"],\n", " )\n", "\n" ], "metadata": { "id": "Ae7g7TaCsnh7" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from accelerate import notebook_launcher\n", "import torch.nn.functional as F\n", "from diffusers.optimization import get_cosine_schedule_with_warmup\n", "import torch\n", "from PIL import Image\n", "from diffusers import DDPMScheduler\n", "from diffusers import UNet2DModel\n", "import torch\n", "from torchvision import transforms\n", "\n", "\n", "\n", "\n", "preprocess = transforms.Compose(\n", " [\n", " transforms.Resize((config.image_size, config.image_size)),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize([.5],[.5]),\n", " ]\n", ")\n", "\n", "dataset.set_transform(transform)\n", "\n", "train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)\n", "\n", "model = UNet2DModel(sample_size=config.image_size,in_channels=3, out_channels=3, layers_per_block=2, block_out_channels=(128,128,256,256,512,512), down_block_types=(\"DownBlock2D\",\"DownBlock2D\",\"DownBlock2D\",\"DownBlock2D\",\"AttnDownBlock2D\",\"DownBlock2D\"), up_block_types=(\"UpBlock2D\",\"AttnUpBlock2D\",\"UpBlock2D\",\"UpBlock2D\",\"UpBlock2D\",\"UpBlock2D\"), )\n", "\n", "sample_image = dataset[0][\"images\"].unsqueeze(0)\n", "print(\"Input shape:\", sample_image.shape)\n", "\n", "print(\"Output shape:\", model(sample_image, timestep=0).sample.shape)\n", "\n", "noise_scheduler = DDPMScheduler(num_train_timesteps=1000)\n", "noise = torch.randn(sample_image.shape)\n", "time_steps = torch.LongTensor([50])\n", "noisy_image = noise_scheduler.add_noise(sample_image, noise, time_steps)\n", "Image.fromarray(((noisy_image.permute(0, 2, 3, 1) + 1.0) * 127.5).type(torch.uint8).numpy()[0])\n", "\n", "\n", "\n", "\n", "optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n", "lr_scheduler = get_cosine_schedule_with_warmup(\n", " optimizer=optimizer,\n", " num_warmup_steps=config.lr_warmup_steps,\n", " num_training_steps=(len(train_dataloader)*config.num_epochs),\n", ")\n", "\n", "\n", "\n", "args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)\n", "\n", "notebook_launcher(train_loop, args, num_processes=1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0cbde1d835d34ce3a7175aa7c287e5d4", "267b24d0ffb343e3a22eaf8b949334b5", "5a5d69d092fb413ca984dd6c327ef111", "cae852c1944a46a2a5a30a744c73819e", "76258362c434485789f07a6fd4a20e8f", "3bafcf30078f41678673ad1ae78a0d12", "d7372c5522ad47a8a0704df86606e906", "e9d4333d5ced472f97834c3bb88a71df", "ab6d5b8062d448dc8673c9225a26b2e7", "e707d48fa9544bfe89c8248cb4f36da1", "f048a9dd5d4e4df3ad5192ba5922f92a", "4e9db15241e344869623be52e9067f06", "ed456a0abd3048fc901d7c06a06a7833", "d7585329f66e45f490ef03c7fc3036f0", "9edfa69150dc47ab98efddae6a7702ca", "2d316c9652b148f5b4c15c08e22807f4", "c98cd2c2ae6d4b0e8a9cf6076ff43817", "de24f2614e6140079858d0d4c51a6fd6", 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training on one GPU.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/274 [00:00" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) 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