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"source": [ - "from dataclasses import dataclass\n", - "\n", - "\n", - "@dataclass\n", - "class TrainingConfig:\n", - " image_size = 128 # the generated image resolution\n", - " 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", - 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