| [ | |
| { | |
| "prompt": "Here is some code that is missing a line denoted with \"# <MISSING LINE>\". What should go in the missing line?\n\n\nimport os\nimport shutil\nfrom pathlib import Path\nfrom typing import Optional, Union\n\nimport numpy as np\n\nfrom huggingface_hub import hf_hub_download\n\nfrom ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging\n\n\nif is_onnx_available():\n import onnxruntime as ort\n\n\nlogger = logging.get_logger(__name__)\n\nORT_TO_NP_TYPE = {\n \"tensor(bool)\": np.bool_,\n \"tensor(int8)\": np.int8,\n \"tensor(uint8)\": np.uint8,\n \"tensor(int16)\": np.int16,\n \"tensor(uint16)\": np.uint16,\n \"tensor(int32)\": np.int32,\n \"tensor(uint32)\": np.uint32,\n \"tensor(int64)\": np.int64,\n \"tensor(uint64)\": np.uint64,\n \"tensor(float16)\": np.float16,\n \"tensor(float)\": np.float32,\n \"tensor(double)\": np.float64,\n}\n\n\nclass OnnxRuntimeModel:\n def __init__(self, model=None, **kwargs):\n logger.info(\"`diffusers.OnnxRuntimeModel` is experimental and might change in the future.\")\n self.model = model\n # <MISSING LINE>\n self.latest_model_name = kwargs.get(\"latest_model_name\", ONNX_WEIGHTS_NAME)\n\n def __call__(self, **kwargs):\n inputs = {k: np.array(v) for k, v in kwargs.items()}\n return self.model.run(None, inputs)\n\n @staticmethod\n def load_model(path: Union[str, Path], provider=None, sess_options=None):\n \"\"\"\n Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider`\n\n Arguments:\n path (`str` or `Path`):\n Directory from which to load\n provider(`str`, *optional*):\n Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`\n \"\"\"\n if provider is None:\n logger.info(\"No onnxruntime provider specified, using CPUExecutionProvider\")\n provider = \"CPUExecutionProvider\"\n\n return ort.InferenceSession(path, providers=[provider], sess_options=sess_options)\n", | |
| "response": " self.model_save_dir = kwargs.get(\"model_save_dir\", None)", | |
| "example_id": 1, | |
| "chunks": [ | |
| { | |
| "start_lineno": 133, | |
| "end_lineno": 191, | |
| "document_path": "huggingface_diffusers/src/diffusers/pipelines/onnx_utils.py" | |
| }, | |
| { | |
| "start_lineno": 453, | |
| "end_lineno": 486, | |
| "document_path": "src/diffusers/models/modeling_utils.py" | |
| }, | |
| { | |
| "start_lineno": 486, | |
| "end_lineno": 526, | |
| "document_path": "src/diffusers/models/modeling_flax_utils.py" | |
| }, | |
| { | |
| "start_lineno": 109, | |
| "end_lineno": 151, | |
| "document_path": "scripts/convert_stable_diffusion_checkpoint_to_onnx.py" | |
| }, | |
| { | |
| "start_lineno": 1, | |
| "end_lineno": 26, | |
| "document_path": "scripts/convert_stable_diffusion_checkpoint_to_onnx.py" | |
| }, | |
| { | |
| "start_lineno": 169, | |
| "document_path": "src/diffusers/pipelines/onnx_utils.py" | |
| }, | |
| { | |
| "start_lineno": 93, | |
| "document_path": "src/diffusers/pipelines/onnx_utils.py" | |
| }, | |
| { | |
| "document_path": "scripts/convert_stable_diffusion_checkpoint_to_onnx.py" | |
| }, | |
| { | |
| "end_lineno": 225, | |
| "document_path": "scripts/convert_stable_diffusion_checkpoint_to_onnx.py" | |
| }, | |
| { | |
| "end_lineno": 212, | |
| "document_path": "src/diffusers/pipelines/onnx_utils.py" | |
| } | |
| ] | |
| }, | |
| { | |
| "prompt": null, | |
| "response": " num_hidden_layers=4,", | |
| "example_id": 2, | |
| "chunks": [ | |
| { | |
| "end_lineno": 279, | |
| "document_path": "tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py" | |
| } | |
| ] | |
| }, | |
| { | |
| "prompt": "Here is some code that is missing a line denoted with \"# <MISSING LINE>\". What should go in the missing line? \n\n\n if self.state_in_first_order:\n # 2. Convert to an ODE derivative for 1st order\n derivative = (sample - pred_original_sample) / sigma_hat\n # 3. delta timestep\n dt = sigma_next - sigma_hat\n\n # store for 2nd order step\n self.prev_derivative = derivative\n self.dt = dt\n self.sample = sample\n else:\n # 2. 2nd order / Heun's method\n derivative = (sample - pred_original_sample) / sigma_next\n derivative = (self.prev_derivative + derivative) / 2\n\n # 3. take prev timestep & sample\n dt = self.dt\n sample = self.sample\n\n # free dt and derivative\n # Note, this puts the scheduler in \"first order mode\"\n self.prev_derivative = None\n self.dt = None\n self.sample = None\n\n prev_sample = sample + derivative * dt\n\n if not return_dict:\n return (prev_sample,)\n\n return SchedulerOutput(prev_sample=prev_sample)\n\n def add_noise(\n self,\n original_samples: torch.FloatTensor,\n noise: torch.FloatTensor,\n timesteps: torch.FloatTensor,\n ) -> torch.FloatTensor:\n # Make sure sigmas and timesteps have the same device and dtype as original_samples\n self.sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)\n if original_samples.device.type == \"mps\" and torch.is_floating_point(timesteps):\n # mps does not support float64\n # <MISSING LINE>\n timesteps = timesteps.to(original_samples.device, dtype=torch.float32)\n else:\n self.timesteps = self.timesteps.to(original_samples.device)\n timesteps = timesteps.to(original_samples.device)\n\n step_indices = [self.index_for_timestep(t) for t in timesteps]\n\n sigma = self.sigmas[step_indices].flatten()\n while len(sigma.shape) < len(original_samples.shape):\n sigma = sigma.unsqueeze(-1)\n\n noisy_samples = original_samples + noise * sigma\n return noisy_samples\n\n def __len__(self):\n return self.config.num_train_timesteps\n", | |
| "response": " self.timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)", | |
| "example_id": 3, | |
| "chunks": [ | |
| { | |
| "document_path": "src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py", | |
| "start_lineno": 286, | |
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| { | |
| "document_path": "src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py", | |
| "start_lineno": 242, | |
| "end_lineno": 271 | |
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| "document_path": "src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py", | |
| "start_lineno": 268, | |
| "end_lineno": 297 | |
| }, | |
| { | |
| "document_path": "src/diffusers/schedulers/scheduling_lms_discrete.py", | |
| "start_lineno": 248, | |
| "end_lineno": 277 | |
| }, | |
| { | |
| "document_path": "tests/test_scheduler.py", | |
| "start_lineno": 2693, | |
| "end_lineno": 2727 | |
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| { | |
| "document_path": "src/diffusers/schedulers/scheduling_heun_discrete.py", | |
| "start_lineno": 134, | |
| "end_lineno": 172 | |
| }, | |
| { | |
| "document_path": "src/diffusers/schedulers/scheduling_euler_discrete.py", | |
| "start_lineno": 239, | |
| "end_lineno": 279 | |
| }, | |
| { | |
| "document_path": "src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py", | |
| "start_lineno": 137, | |
| "end_lineno": 172 | |
| } | |
| ] | |
| }, | |
| { | |
| "prompt": "Here is some code that is missing a line denoted with \"# <MISSING LINE>\". What should go in the missing line?\n\n\n\nimport PIL\nfrom diffusers import DiffusionPipeline\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker\nfrom diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler\nfrom diffusers.utils import deprecate, logging\nfrom transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\ndef prepare_mask_and_masked_image(image, mask):\n image = np.array(image.convert(\"RGB\"))\n image = image[None].transpose(0, 3, 1, 2)\n # <MISSING LINE>\n\n mask = np.array(mask.convert(\"L\"))\n mask = mask.astype(np.float32) / 255.0\n mask = mask[None, None]\n mask[mask < 0.5] = 0\n mask[mask >= 0.5] = 1\n mask = torch.from_numpy(mask)\n\n masked_image = image * (mask < 0.5)\n\n return mask, masked_image\n\n\ndef check_size(image, height, width):\n if isinstance(image, PIL.Image.Image):\n w, h = image.size\n elif isinstance(image, torch.Tensor):\n *_, h, w = image.shape\n\n if h != height or w != width:\n raise ValueError(f\"Image size should be {height}x{width}, but got {h}x{w}\")\n\n", | |
| "response": " image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0", | |
| "example_id": 4, | |
| "chunks": [ | |
| [ | |
| { | |
| "document_path": "examples/community/img2img_inpainting.py", | |
| "start_lineno": 14, | |
| "end_lineno": 35 | |
| }, | |
| { | |
| "document_path": "examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py", | |
| "start_lineno": 34, | |
| "end_lineno": 55 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py", | |
| "start_lineno": 106, | |
| "end_lineno": 127 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py", | |
| "start_lineno": 110, | |
| "end_lineno": 131 | |
| }, | |
| { | |
| "document_path": "tests/pipelines/paint_by_example/test_paint_by_example.py", | |
| "start_lineno": 84, | |
| "end_lineno": 105 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py", | |
| "start_lineno": 64, | |
| "end_lineno": 108 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py", | |
| "start_lineno": 1, | |
| "end_lineno": 57 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/repaint/pipeline_repaint.py", | |
| "start_lineno": 51, | |
| "end_lineno": 70 | |
| }, | |
| { | |
| "document_path": "examples/community/lpw_stable_diffusion_onnx.py", | |
| "start_lineno": 410, | |
| "end_lineno": 421 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py", | |
| "start_lineno": 514, | |
| "end_lineno": 524 | |
| } | |
| ] | |
| ] | |
| }, | |
| { | |
| "prompt": "Here is some code that is missing a line denoted with \"# <MISSING LINE>\". What should go in the missing line?\n\n\n # Create parallel version of the train step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0, 1))\n\n # Replicate the train state on each device\n unet_state = jax_utils.replicate(unet_state)\n text_encoder_state = jax_utils.replicate(text_encoder_state)\n vae_params = jax_utils.replicate(vae_params)\n\n # Train!\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n def checkpoint(step=None):\n # Create the pipeline using the trained modules and save it.\n scheduler, _ = FlaxPNDMScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", from_pt=True\n )\n pipeline = FlaxStableDiffusionPipeline(\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n # <MISSING LINE>\n )\n\n outdir = os.path.join(args.output_dir, str(step)) if step else args.output_dir\n pipeline.save_pretrained(\n outdir,\n params={\n \"text_encoder\": get_params_to_save(text_encoder_state.params),\n \"vae\": get_params_to_save(vae_params),\n \"unet\": get_params_to_save(unet_state.params),\n \"safety_checker\": safety_checker.params,\n },\n )\n\n if args.push_to_hub:\n message = f\"checkpoint-{step}\" if step is not None else \"End of training\"\n repo.push_to_hub(commit_message=message, blocking=False, auto_lfs_prune=True)\n\n global_step = 0\n\n epochs = tqdm(range(args.num_train_epochs), desc=\"Epoch ... \", position=0)\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n\n steps_per_epoch = len(train_dataset) // total_train_batch_size\n train_step_progress_bar = tqdm(total=steps_per_epoch, desc=\"Training...\", position=1, leave=False)\n # train\n for batch in train_dataloader:\n batch = shard(batch)\n unet_state, text_encoder_state, train_metric, train_rngs = p_train_step(\n unet_state, text_encoder_state, vae_params, batch, train_rngs\n )\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(jax.local_device_count())\n\n global_step += 1\n if jax.process_index() == 0 and args.save_steps and global_step % args.save_steps == 0:\n checkpoint(global_step)\n if global_step >= args.max_train_steps:\n break\n\n train_metric = jax_utils.unreplicate(train_metric)\n\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n if jax.process_index() == 0:\n checkpoint()\n\n\nif __name__ == \"__main__\":\n main()\n", | |
| "response": " feature_extractor=CLIPFeatureExtractor.from_pretrained(\"openai/clip-vit-base-patch32\")", | |
| "example_id": 5, | |
| "chunks": [ | |
| { | |
| "document_path": "examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py", | |
| "start_lineno": 615, | |
| "end_lineno": 655 | |
| }, | |
| { | |
| "document_path": "examples/text_to_image/train_text_to_image_flax.py", | |
| "start_lineno": 530, | |
| "end_lineno": 590 | |
| }, | |
| { | |
| "document_path": "examples/textual_inversion/textual_inversion_flax.py", | |
| "start_lineno": 620, | |
| "end_lineno": 690 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py", | |
| "start_lineno": 1, | |
| "end_lineno": 50 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py", | |
| "start_lineno": 45, | |
| "end_lineno": 80 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py", | |
| "start_lineno": 68, | |
| "end_lineno": 114 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py", | |
| "start_lineno": 175, | |
| "end_lineno": 225 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py", | |
| "start_lineno": 107, | |
| "end_lineno": 135 | |
| }, | |
| { | |
| "document_path": "src/diffusers/pipelines/stable_diffusion/__init__.py", | |
| "start_lineno": 74, | |
| "end_lineno": 105 | |
| } | |
| ] | |
| }, | |
| { | |
| "prompt": "Here is some code that is missing a line denoted with \"# <MISSING LINE>\". What should go in the missing line?\n\n\n\n if args.with_prior_preservation:\n # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.\n model_pred, model_pred_prior = jnp.split(model_pred, 2, axis=0)\n target, target_prior = jnp.split(target, 2, axis=0)\n\n # Compute instance loss\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n # Compute prior loss\n prior_loss = (target_prior - model_pred_prior) ** 2\n prior_loss = prior_loss.mean()\n\n # Add the prior loss to the instance loss.\n loss = loss + args.prior_loss_weight * prior_loss\n else:\n loss = (target - model_pred) ** 2\n loss = loss.mean()\n\n return loss\n\n grad_fn = jax.value_and_grad(compute_loss)\n loss, grad = grad_fn(params)\n grad = jax.lax.pmean(grad, \"batch\")\n\n new_unet_state = unet_state.apply_gradients(grads=grad[\"unet\"])\n if args.train_text_encoder:\n new_text_encoder_state = text_encoder_state.apply_gradients(grads=grad[\"text_encoder\"])\n else:\n new_text_encoder_state = text_encoder_state\n\n metrics = {\"loss\": loss}\n metrics = jax.lax.pmean(metrics, axis_name=\"batch\")\n\n return new_unet_state, new_text_encoder_state, metrics, new_train_rng\n\n # Create parallel version of the train step\n p_train_step = jax.pmap(train_step, \"batch\", donate_argnums=(0, 1))\n\n # Replicate the train state on each device\n unet_state = jax_utils.replicate(unet_state)\n text_encoder_state = jax_utils.replicate(text_encoder_state)\n vae_params = jax_utils.replicate(vae_params)\n\n # Train!\n num_update_steps_per_epoch = math.ceil(len(train_dataloader))\n\n # Scheduler and math around the number of training steps.\n if args.max_train_steps is None:\n # <MISSING LINE>\n\n args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)\n\n logger.info(\"***** Running training *****\")\n logger.info(f\" Num examples = {len(train_dataset)}\")\n logger.info(f\" Num Epochs = {args.num_train_epochs}\")\n logger.info(f\" Instantaneous batch size per device = {args.train_batch_size}\")\n logger.info(f\" Total train batch size (w. parallel & distributed) = {total_train_batch_size}\")\n logger.info(f\" Total optimization steps = {args.max_train_steps}\")\n\n def checkpoint(step=None):\n # Create the pipeline using the trained modules and save it.\n scheduler, _ = FlaxPNDMScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")\n safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(\n \"CompVis/stable-diffusion-safety-checker\", from_pt=True\n )\n pipeline = FlaxStableDiffusionPipeline(\n text_encoder=text_encoder,\n vae=vae,\n unet=unet,\n tokenizer=tokenizer,\n scheduler=scheduler,\n safety_checker=safety_checker,\n # <MISSING LINE>\n )\n\n outdir = os.path.join(args.output_dir, str(step)) if step else args.output_dir\n pipeline.save_pretrained(\n outdir,\n params={\n \"text_encoder\": get_params_to_save(text_encoder_state.params),\n \"vae\": get_params_to_save(vae_params),\n \"unet\": get_params_to_save(unet_state.params),\n \"safety_checker\": safety_checker.params,\n },\n )\n\n if args.push_to_hub:\n message = f\"checkpoint-{step}\" if step is not None else \"End of training\"\n repo.push_to_hub(commit_message=message, blocking=False, auto_lfs_prune=True)\n\n global_step = 0\n\n epochs = tqdm(range(args.num_train_epochs), desc=\"Epoch ... \", position=0)\n for epoch in epochs:\n # ======================== Training ================================\n\n train_metrics = []\n\n steps_per_epoch = len(train_dataset) // total_train_batch_size\n train_step_progress_bar = tqdm(total=steps_per_epoch, desc=\"Training...\", position=1, leave=False)\n # train\n for batch in train_dataloader:\n batch = shard(batch)\n unet_state, text_encoder_state, train_metric, train_rngs = p_train_step(\n unet_state, text_encoder_state, vae_params, batch, train_rngs\n )\n train_metrics.append(train_metric)\n\n train_step_progress_bar.update(jax.local_device_count())\n\n global_step += 1\n if jax.process_index() == 0 and args.save_steps and global_step % args.save_steps == 0:\n checkpoint(global_step)\n if global_step >= args.max_train_steps:\n break\n\n train_metric = jax_utils.unreplicate(train_metric)\n\n train_step_progress_bar.close()\n epochs.write(f\"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})\")\n\n if jax.process_index() == 0:\n checkpoint()\n\n\nif __name__ == \"__main__\":\n main()\n", | |
| "example_id": 6, | |
| "response": " args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch", | |
| "chunks": [ | |
| { | |
| "document_path": "examples/dreambooth/train_dreambooth_flax.py", | |
| "start_lineno": 605, | |
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| }, | |
| { | |
| "document_path": "examples/dreambooth/train_dreambooth_lora.py", | |
| "start_lineno": 751, | |
| "end_lineno": 791 | |
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| { | |
| "document_path": "examples/dreambooth/train_dreambooth.py", | |
| "start_lineno": 681, | |
| "end_lineno": 721 | |
| }, | |
| { | |
| "document_path": "examples/research_projects/colossalai/train_dreambooth_colossalai.py", | |
| "start_lineno": 529, | |
| "end_lineno": 569 | |
| }, | |
| { | |
| "document_path": "examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py", | |
| "start_lineno": 581, | |
| "end_lineno": 621 | |
| }, | |
| { | |
| "document_path": "examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py", | |
| "start_lineno": 476, | |
| "end_lineno": 516 | |
| }, | |
| { | |
| "document_path": "examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py", | |
| "start_lineno": 666, | |
| "end_lineno": 706 | |
| }, | |
| { | |
| "document_path": "examples/text_to_image/train_text_to_image_flax.py", | |
| "start_lineno": 487, | |
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