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golden-examples.json

@@ -0,0 +1,300 @@
+[
+  {
+    "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,
+        "end_lineno": 314
+      },
+      {
+        "document_path": "src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py",
+        "start_lineno": 242,
+        "end_lineno": 271
+      },
+      {
+        "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
+      },
+      {
+        "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,
+        "end_lineno": 645
+      },
+
+      {
+        "document_path": "examples/dreambooth/train_dreambooth_lora.py",
+        "start_lineno": 751,
+        "end_lineno": 791
+      },
+
+      {
+        "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,
+        "end_lineno": 527
+      },
+
+      {
+        "document_path": "examples/text_to_image/train_text_to_image_lora.py",
+        "start_lineno": 589,
+        "end_lineno": 629
+      },
+
+      {
+        "document_path": "examples/text_to_image/train_text_to_image.py",
+        "start_lineno": 520,
+        "end_lineno": 560
+      },
+
+      {
+        "document_path": "examples/textual_inversion/textual_inversion_flax.py",
+        "start_lineno": 569,
+        "end_lineno": 609
+      },
+
+      {
+        "document_path": "examples/textual_inversion/textual_inversion.py",
+        "start_lineno": 597,
+        "end_lineno": 637
+      }
+    ]
+  }
+]

huggingface_diffusers/.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,51 @@
+name: "\U0001F41B Bug Report"
+description: Report a bug on diffusers
+labels: [ "bug" ]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks a lot for taking the time to file this issue 🤗.
+        Issues do not only help to improve the library, but also publicly document common problems, questions, workflows for the whole community!
+        Thus, issues are of the same importance as pull requests when contributing to this library ❤️.
+        In order to make your issue as **useful for the community as possible**, let's try to stick to some simple guidelines:
+        - 1. Please try to be as precise and concise as possible.
+             *Give your issue a fitting title. Assume that someone which very limited knowledge of diffusers can understand your issue. Add links to the source code, documentation other issues, pull requests etc...*
+        - 2. If your issue is about something not working, **always** provide a reproducible code snippet. The reader should be able to reproduce your issue by **only copy-pasting your code snippet into a Python shell**.
+             *The community cannot solve your issue if it cannot reproduce it. If your bug is related to training, add your training script and make everything needed to train public. Otherwise, just add a simple Python code snippet.*
+        - 3. Add the **minimum amount of code / context that is needed to understand, reproduce your issue**.
+             *Make the life of maintainers easy. `diffusers` is getting many issues every day. Make sure your issue is about one bug and one bug only. Make sure you add only the context, code needed to understand your issues - nothing more. Generally, every issue is a way of documenting this library, try to make it a good documentation entry.*
+  - type: markdown
+    attributes:
+      value: |
+        For more in-detail information on how to write good issues you can have a look [here](https://huggingface.co/course/chapter8/5?fw=pt)
+  - type: textarea
+    id: bug-description
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is. If you intend to submit a pull request for this issue, tell us in the description. Thanks!
+      placeholder: Bug description
+    validations:
+      required: true
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Reproduction
+      description: Please provide a minimal reproducible code which we can copy/paste and reproduce the issue.
+      placeholder: Reproduction
+    validations:
+      required: true
+  - type: textarea
+    id: logs
+    attributes:
+      label: Logs
+      description: "Please include the Python logs if you can."
+      render: shell
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: Please share your system info with us. You can run the command `diffusers-cli env` and copy-paste its output below.
+      placeholder: diffusers version, platform, python version, ...
+    validations:
+      required: true

huggingface_diffusers/.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,4 @@
+contact_links:
+  - name: Blank issue
+    url: https://github.com/huggingface/diffusers/issues/new
+    about: General usage questions and community discussions

huggingface_diffusers/.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: "\U0001F680 Feature request"
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

huggingface_diffusers/.github/ISSUE_TEMPLATE/feedback.md

@@ -0,0 +1,12 @@
+---
+name: "💬 Feedback about API Design"
+about: Give feedback about the current API design
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**What API design would you like to have changed or added to the library? Why?**
+
+**What use case would this enable or better enable? Can you give us a code example?**

huggingface_diffusers/.github/ISSUE_TEMPLATE/new-model-addition.yml

@@ -0,0 +1,31 @@
+name: "\U0001F31F New model/pipeline/scheduler addition"
+description: Submit a proposal/request to implement a new diffusion model / pipeline / scheduler
+labels: [ "New model/pipeline/scheduler" ]
+
+body:
+  - type: textarea
+    id: description-request
+    validations:
+      required: true
+    attributes:
+      label: Model/Pipeline/Scheduler description
+      description: |
+        Put any and all important information relative to the model/pipeline/scheduler
+
+  - type: checkboxes
+    id: information-tasks
+    attributes:
+      label: Open source status
+      description: |
+          Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `diffusers`.
+      options:
+        - label: "The model implementation is available"
+        - label: "The model weights are available (Only relevant if addition is not a scheduler)."
+
+  - type: textarea
+    id: additional-info
+    attributes:
+      label: Provide useful links for the implementation
+      description: |
+        Please provide information regarding the implementation, the weights, and the authors.
+        Please mention the authors by @gh-username if you're aware of their usernames.

huggingface_diffusers/.github/actions/setup-miniconda/action.yml

@@ -0,0 +1,146 @@
+name: Set up conda environment for testing
+
+description: Sets up miniconda in your ${RUNNER_TEMP} environment and gives you the ${CONDA_RUN} environment variable so you don't have to worry about polluting non-empeheral runners anymore
+
+inputs:
+  python-version:
+    description: If set to any value, dont use sudo to clean the workspace
+    required: false
+    type: string
+    default: "3.9"
+  miniconda-version:
+    description: Miniconda version to install
+    required: false
+    type: string
+    default: "4.12.0"
+  environment-file:
+    description: Environment file to install dependencies from
+    required: false
+    type: string
+    default: ""
+
+runs:
+  using: composite
+  steps:
+      # Use the same trick from https://github.com/marketplace/actions/setup-miniconda
+      # to refresh the cache daily. This is kind of optional though
+      - name: Get date
+        id: get-date
+        shell: bash
+        run: echo "::set-output name=today::$(/bin/date -u '+%Y%m%d')d"
+      - name: Setup miniconda cache
+        id: miniconda-cache
+        uses: actions/cache@v2
+        with:
+          path: ${{ runner.temp }}/miniconda
+          key: miniconda-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}
+      - name: Install miniconda (${{ inputs.miniconda-version }})
+        if: steps.miniconda-cache.outputs.cache-hit != 'true'
+        env:
+          MINICONDA_VERSION: ${{ inputs.miniconda-version }}
+        shell: bash -l {0}
+        run: |
+          MINICONDA_INSTALL_PATH="${RUNNER_TEMP}/miniconda"
+          mkdir -p "${MINICONDA_INSTALL_PATH}"
+          case ${RUNNER_OS}-${RUNNER_ARCH} in
+            Linux-X64)
+              MINICONDA_ARCH="Linux-x86_64"
+              ;;
+            macOS-ARM64)
+              MINICONDA_ARCH="MacOSX-arm64"
+              ;;
+            macOS-X64)
+              MINICONDA_ARCH="MacOSX-x86_64"
+              ;;
+            *)
+            echo "::error::Platform ${RUNNER_OS}-${RUNNER_ARCH} currently unsupported using this action"
+              exit 1
+              ;;
+          esac
+          MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-py39_${MINICONDA_VERSION}-${MINICONDA_ARCH}.sh"
+          curl -fsSL "${MINICONDA_URL}" -o "${MINICONDA_INSTALL_PATH}/miniconda.sh"
+          bash "${MINICONDA_INSTALL_PATH}/miniconda.sh" -b -u -p "${MINICONDA_INSTALL_PATH}"
+          rm -rf "${MINICONDA_INSTALL_PATH}/miniconda.sh"
+      - name: Update GitHub path to include miniconda install
+        shell: bash
+        run: |
+          MINICONDA_INSTALL_PATH="${RUNNER_TEMP}/miniconda"
+          echo "${MINICONDA_INSTALL_PATH}/bin" >> $GITHUB_PATH
+      - name: Setup miniconda env cache (with env file)
+        id: miniconda-env-cache-env-file
+        if: ${{ runner.os }} == 'macOS' && ${{ inputs.environment-file }} != ''
+        uses: actions/cache@v2
+        with:
+          path: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}
+          key: miniconda-env-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}-${{ hashFiles(inputs.environment-file) }}
+      - name: Setup miniconda env cache (without env file)
+        id: miniconda-env-cache
+        if: ${{ runner.os }} == 'macOS' && ${{ inputs.environment-file }} == ''
+        uses: actions/cache@v2
+        with:
+          path: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}
+          key: miniconda-env-${{ runner.os }}-${{ runner.arch }}-${{ inputs.python-version }}-${{ steps.get-date.outputs.today }}
+      - name: Setup conda environment with python (v${{ inputs.python-version }})
+        if: steps.miniconda-env-cache-env-file.outputs.cache-hit != 'true' && steps.miniconda-env-cache.outputs.cache-hit != 'true'
+        shell: bash
+        env:
+          PYTHON_VERSION: ${{ inputs.python-version }}
+          ENV_FILE: ${{ inputs.environment-file }}
+        run: |
+          CONDA_BASE_ENV="${RUNNER_TEMP}/conda-python-${PYTHON_VERSION}"
+          ENV_FILE_FLAG=""
+          if [[ -f "${ENV_FILE}" ]]; then
+            ENV_FILE_FLAG="--file ${ENV_FILE}"
+          elif [[ -n "${ENV_FILE}" ]]; then
+            echo "::warning::Specified env file (${ENV_FILE}) not found, not going to include it"
+          fi
+          conda create \
+            --yes \
+            --prefix "${CONDA_BASE_ENV}" \
+            "python=${PYTHON_VERSION}" \
+            ${ENV_FILE_FLAG} \
+            cmake=3.22 \
+            conda-build=3.21 \
+            ninja=1.10 \
+            pkg-config=0.29 \
+            wheel=0.37
+      - name: Clone the base conda environment and update GitHub env
+        shell: bash
+        env:
+          PYTHON_VERSION: ${{ inputs.python-version }}
+          CONDA_BASE_ENV: ${{ runner.temp }}/conda-python-${{ inputs.python-version }}
+        run: |
+          CONDA_ENV="${RUNNER_TEMP}/conda_environment_${GITHUB_RUN_ID}"
+          conda create \
+            --yes \
+            --prefix "${CONDA_ENV}" \
+            --clone "${CONDA_BASE_ENV}"
+          # TODO: conda-build could not be cloned because it hardcodes the path, so it
+          # could not be cached
+          conda install --yes -p ${CONDA_ENV} conda-build=3.21
+          echo "CONDA_ENV=${CONDA_ENV}" >> "${GITHUB_ENV}"
+          echo "CONDA_RUN=conda run -p ${CONDA_ENV} --no-capture-output" >> "${GITHUB_ENV}"
+          echo "CONDA_BUILD=conda run -p ${CONDA_ENV} conda-build" >> "${GITHUB_ENV}"
+          echo "CONDA_INSTALL=conda install -p ${CONDA_ENV}" >> "${GITHUB_ENV}"
+      - name: Get disk space usage and throw an error for low disk space
+        shell: bash
+        run: |
+          echo "Print the available disk space for manual inspection"
+          df -h
+          # Set the minimum requirement space to 4GB
+          MINIMUM_AVAILABLE_SPACE_IN_GB=4
+          MINIMUM_AVAILABLE_SPACE_IN_KB=$(($MINIMUM_AVAILABLE_SPACE_IN_GB * 1024 * 1024))
+          # Use KB to avoid floating point warning like 3.1GB
+          df -k | tr -s ' ' | cut -d' ' -f 4,9 | while read -r LINE;
+          do
+            AVAIL=$(echo $LINE | cut -f1 -d' ')
+            MOUNT=$(echo $LINE | cut -f2 -d' ')
+            if [ "$MOUNT" = "/" ]; then
+              if [ "$AVAIL" -lt "$MINIMUM_AVAILABLE_SPACE_IN_KB" ]; then
+                echo "There is only ${AVAIL}KB free space left in $MOUNT, which is less than the minimum requirement of ${MINIMUM_AVAILABLE_SPACE_IN_KB}KB. Please help create an issue to PyTorch Release Engineering via https://github.com/pytorch/test-infra/issues and provide the link to the workflow run."
+                exit 1;
+              else
+                echo "There is ${AVAIL}KB free space left in $MOUNT, continue"
+              fi
+            fi
+          done

huggingface_diffusers/.github/workflows/build_docker_images.yml

@@ -0,0 +1,50 @@
+name: Build Docker images (nightly)
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0 * * *" # every day at midnight
+
+concurrency:
+  group: docker-image-builds
+  cancel-in-progress: false
+
+env:
+  REGISTRY: diffusers
+
+jobs:
+  build-docker-images:
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+
+    strategy:
+      fail-fast: false
+      matrix:
+        image-name:
+          - diffusers-pytorch-cpu
+          - diffusers-pytorch-cuda
+          - diffusers-flax-cpu
+          - diffusers-flax-tpu
+          - diffusers-onnxruntime-cpu
+          - diffusers-onnxruntime-cuda
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ env.REGISTRY }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Build and push
+        uses: docker/build-push-action@v3
+        with:
+          no-cache: true
+          context: ./docker/${{ matrix.image-name }}
+          push: true
+          tags: ${{ env.REGISTRY }}/${{ matrix.image-name }}:latest

huggingface_diffusers/.github/workflows/build_documentation.yml

@@ -0,0 +1,18 @@
+name: Build documentation
+
+on:
+  push:
+    branches:
+      - main
+      - doc-builder*
+      - v*-release
+
+jobs:
+   build:
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
+    with:
+      commit_sha: ${{ github.sha }}
+      package: diffusers
+      languages: en ko
+    secrets:
+      token: ${{ secrets.HUGGINGFACE_PUSH }}

huggingface_diffusers/.github/workflows/build_pr_documentation.yml

@@ -0,0 +1,17 @@
+name: Build PR Documentation
+
+on:
+  pull_request:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
+    with:
+      commit_sha: ${{ github.event.pull_request.head.sha }}
+      pr_number: ${{ github.event.number }}
+      package: diffusers
+      languages: en ko

huggingface_diffusers/.github/workflows/delete_doc_comment.yml

@@ -0,0 +1,13 @@
+name: Delete dev documentation
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
+    with:
+      pr_number: ${{ github.event.number }}
+      package: diffusers

huggingface_diffusers/.github/workflows/nightly_tests.yml

@@ -0,0 +1,162 @@
+name: Nightly tests on main
+
+on:
+  schedule:
+    - cron: "0 0 * * *" # every day at midnight
+
+env:
+  DIFFUSERS_IS_CI: yes
+  HF_HOME: /mnt/cache
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  PYTEST_TIMEOUT: 600
+  RUN_SLOW: yes
+  RUN_NIGHTLY: yes
+
+jobs:
+  run_nightly_tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Nightly PyTorch CUDA tests on Ubuntu
+            framework: pytorch
+            runner: docker-gpu
+            image: diffusers/diffusers-pytorch-cuda
+            report: torch_cuda
+          - name: Nightly Flax TPU tests on Ubuntu
+            framework: flax
+            runner: docker-tpu
+            image: diffusers/diffusers-flax-tpu
+            report: flax_tpu
+          - name: Nightly ONNXRuntime CUDA tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-gpu
+            image: diffusers/diffusers-onnxruntime-cuda
+            report: onnx_cuda
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ ${{ matrix.config.runner == 'docker-tpu' && '--privileged' || '--gpus 0'}}
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: NVIDIA-SMI
+        if: ${{ matrix.config.runner == 'docker-gpu' }}
+        run: |
+          nvidia-smi
+
+      - name: Install dependencies
+        run: |
+          python -m pip install -e .[quality,test]
+          python -m pip install -U git+https://github.com/huggingface/transformers
+          python -m pip install git+https://github.com/huggingface/accelerate
+
+      - name: Environment
+        run: |
+          python utils/print_env.py
+
+      - name: Run nightly PyTorch CUDA tests
+        if: ${{ matrix.config.framework == 'pytorch' }}
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "not Flax and not Onnx" \
+            --make-reports=tests_${{ matrix.config.report }} \
+            tests/
+
+      - name: Run nightly Flax TPU tests
+        if: ${{ matrix.config.framework == 'flax' }}
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+        run: |
+          python -m pytest -n 0 \
+            -s -v -k "Flax" \
+            --make-reports=tests_${{ matrix.config.report }} \
+            tests/
+
+      - name: Run nightly ONNXRuntime CUDA tests
+        if: ${{ matrix.config.framework == 'onnxruntime' }}
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+        run: |
+          python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+            -s -v -k "Onnx" \
+            --make-reports=tests_${{ matrix.config.report }} \
+            tests/
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: ${{ matrix.config.report }}_test_reports
+          path: reports
+
+  run_nightly_tests_apple_m1:
+    name: Nightly PyTorch MPS tests on MacOS
+    runs-on: [ self-hosted, apple-m1 ]
+
+    steps:
+      - name: Checkout diffusers
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+
+      - name: Clean checkout
+        shell: arch -arch arm64 bash {0}
+        run: |
+          git clean -fxd
+
+      - name: Setup miniconda
+        uses: ./.github/actions/setup-miniconda
+        with:
+          python-version: 3.9
+
+      - name: Install dependencies
+        shell: arch -arch arm64 bash {0}
+        run: |
+          ${CONDA_RUN} python -m pip install --upgrade pip
+          ${CONDA_RUN} python -m pip install -e .[quality,test]
+          ${CONDA_RUN} python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+          ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
+
+      - name: Environment
+        shell: arch -arch arm64 bash {0}
+        run: |
+          ${CONDA_RUN} python utils/print_env.py
+
+      - name: Run nightly PyTorch tests on M1 (MPS)
+        shell: arch -arch arm64 bash {0}
+        env:
+          HF_HOME: /System/Volumes/Data/mnt/cache
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+        run: |
+          ${CONDA_RUN} python -m pytest -n 1 -s -v --make-reports=tests_torch_mps tests/
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: cat reports/tests_torch_mps_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: torch_mps_test_reports
+          path: reports

huggingface_diffusers/.github/workflows/pr_quality.yml

@@ -0,0 +1,50 @@
+name: Run code quality checks
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  check_code_quality:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: |
+          black  --check --preview examples tests src utils scripts
+          isort --check-only examples tests src utils scripts
+          flake8 examples tests src utils scripts
+          doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+
+  check_repository_consistency:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.7"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[quality]
+      - name: Check quality
+        run: |
+          python utils/check_copies.py
+          python utils/check_dummies.py

huggingface_diffusers/.github/workflows/pr_tests.yml

@@ -0,0 +1,155 @@
+name: Fast tests for PRs
+
+on:
+  pull_request:
+    branches:
+      - main
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  DIFFUSERS_IS_CI: yes
+  OMP_NUM_THREADS: 4
+  MKL_NUM_THREADS: 4
+  PYTEST_TIMEOUT: 60
+
+jobs:
+  run_fast_tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Fast PyTorch CPU tests on Ubuntu
+            framework: pytorch
+            runner: docker-cpu
+            image: diffusers/diffusers-pytorch-cpu
+            report: torch_cpu
+          - name: Fast Flax CPU tests on Ubuntu
+            framework: flax
+            runner: docker-cpu
+            image: diffusers/diffusers-flax-cpu
+            report: flax_cpu
+          - name: Fast ONNXRuntime CPU tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-cpu
+            image: diffusers/diffusers-onnxruntime-cpu
+            report: onnx_cpu
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Install dependencies
+      run: |
+        apt-get update && apt-get install libsndfile1-dev -y
+        python -m pip install -e .[quality,test]
+        python -m pip install -U git+https://github.com/huggingface/transformers
+        python -m pip install git+https://github.com/huggingface/accelerate
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run fast PyTorch CPU tests
+      if: ${{ matrix.config.framework == 'pytorch' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run fast ONNXRuntime CPU tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: pr_${{ matrix.config.report }}_test_reports
+        path: reports
+
+  run_fast_tests_apple_m1:
+    name: Fast PyTorch MPS tests on MacOS
+    runs-on: [ self-hosted, apple-m1 ]
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: Clean checkout
+      shell: arch -arch arm64 bash {0}
+      run: |
+        git clean -fxd
+
+    - name: Setup miniconda
+      uses: ./.github/actions/setup-miniconda
+      with:
+        python-version: 3.9
+
+    - name: Install dependencies
+      shell: arch -arch arm64 bash {0}
+      run: |
+        ${CONDA_RUN} python -m pip install --upgrade pip
+        ${CONDA_RUN} python -m pip install -e .[quality,test]
+        ${CONDA_RUN} python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+        ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
+        ${CONDA_RUN} python -m pip install -U git+https://github.com/huggingface/transformers
+
+    - name: Environment
+      shell: arch -arch arm64 bash {0}
+      run: |
+        ${CONDA_RUN} python utils/print_env.py
+
+    - name: Run fast PyTorch tests on M1 (MPS)
+      shell: arch -arch arm64 bash {0}
+      env:
+        HF_HOME: /System/Volumes/Data/mnt/cache
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        ${CONDA_RUN} python -m pytest -n 0 -s -v --make-reports=tests_torch_mps tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_torch_mps_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: pr_torch_mps_test_reports
+        path: reports

huggingface_diffusers/.github/workflows/push_tests.yml

@@ -0,0 +1,156 @@
+name: Slow tests on main
+
+on:
+  push:
+    branches:
+      - main
+
+env:
+  DIFFUSERS_IS_CI: yes
+  HF_HOME: /mnt/cache
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  PYTEST_TIMEOUT: 600
+  RUN_SLOW: yes
+
+jobs:
+  run_slow_tests:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - name: Slow PyTorch CUDA tests on Ubuntu
+            framework: pytorch
+            runner: docker-gpu
+            image: diffusers/diffusers-pytorch-cuda
+            report: torch_cuda
+          - name: Slow Flax TPU tests on Ubuntu
+            framework: flax
+            runner: docker-tpu
+            image: diffusers/diffusers-flax-tpu
+            report: flax_tpu
+          - name: Slow ONNXRuntime CUDA tests on Ubuntu
+            framework: onnxruntime
+            runner: docker-gpu
+            image: diffusers/diffusers-onnxruntime-cuda
+            report: onnx_cuda
+
+    name: ${{ matrix.config.name }}
+
+    runs-on: ${{ matrix.config.runner }}
+
+    container:
+      image: ${{ matrix.config.image }}
+      options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ ${{ matrix.config.runner == 'docker-tpu' && '--privileged' || '--gpus 0'}}
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      if : ${{ matrix.config.runner == 'docker-gpu' }}
+      run: |
+        nvidia-smi
+
+    - name: Install dependencies
+      run: |
+        python -m pip install -e .[quality,test]
+        python -m pip install -U git+https://github.com/huggingface/transformers
+        python -m pip install git+https://github.com/huggingface/accelerate
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run slow PyTorch CUDA tests
+      if: ${{ matrix.config.framework == 'pytorch' }}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "not Flax and not Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run slow Flax TPU tests
+      if: ${{ matrix.config.framework == 'flax' }}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 0 \
+          -s -v -k "Flax" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Run slow ONNXRuntime CUDA tests
+      if: ${{ matrix.config.framework == 'onnxruntime' }}
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile \
+          -s -v -k "Onnx" \
+          --make-reports=tests_${{ matrix.config.report }} \
+          tests/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: ${{ matrix.config.report }}_test_reports
+        path: reports
+
+  run_examples_tests:
+    name: Examples PyTorch CUDA tests on Ubuntu
+
+    runs-on: docker-gpu
+
+    container:
+      image: diffusers/diffusers-pytorch-cuda
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
+
+    steps:
+    - name: Checkout diffusers
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: NVIDIA-SMI
+      run: |
+        nvidia-smi
+
+    - name: Install dependencies
+      run: |
+        python -m pip install -e .[quality,test,training]
+        python -m pip install git+https://github.com/huggingface/accelerate
+        python -m pip install -U git+https://github.com/huggingface/transformers
+
+    - name: Environment
+      run: |
+        python utils/print_env.py
+
+    - name: Run example tests on GPU
+      env:
+        HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+      run: |
+        python -m pytest -n 1 --max-worker-restart=0 --dist=loadfile -s -v --make-reports=examples_torch_cuda examples/
+
+    - name: Failure short reports
+      if: ${{ failure() }}
+      run: cat reports/examples_torch_cuda_failures_short.txt
+
+    - name: Test suite reports artifacts
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: examples_test_reports
+        path: reports

huggingface_diffusers/.github/workflows/stale.yml

@@ -0,0 +1,27 @@
+name: Stale Bot
+
+on:
+  schedule:
+    - cron: "0 15 * * *"
+
+jobs:
+  close_stale_issues:
+    name: Close Stale Issues
+    if: github.repository == 'huggingface/diffusers'
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Setup Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+
+    - name: Install requirements
+      run: |
+        pip install PyGithub
+    - name: Close stale issues
+      run: |
+        python utils/stale.py

huggingface_diffusers/.github/workflows/typos.yml

@@ -0,0 +1,14 @@
+name: Check typos
+
+on:
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: typos-action
+        uses: crate-ci/typos@v1.12.4

huggingface_diffusers/.gitignore

@@ -0,0 +1,171 @@
+# Initially taken from Github's Python gitignore file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# tests and logs
+tests/fixtures/cached_*_text.txt
+logs/
+lightning_logs/
+lang_code_data/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vs
+.vscode
+
+# Pycharm
+.idea
+
+# TF code
+tensorflow_code
+
+# Models
+proc_data
+
+# examples
+runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
+/examples/rag/sweep
+
+# data
+/data
+serialization_dir
+
+# emacs
+*.*~
+debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
+
+# pre-commit
+.pre-commit*
+
+# .lock
+*.lock
+
+# DS_Store (MacOS)
+.DS_Store
+# RL pipelines may produce mp4 outputs
+*.mp4
+
+# dependencies
+/transformers

huggingface_diffusers/CODE_OF_CONDUCT.md

@@ -0,0 +1,129 @@
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+feedback@huggingface.co.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

huggingface_diffusers/CONTRIBUTING.md

@@ -0,0 +1,294 @@
+<!---
+Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# How to contribute to diffusers?
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code
+is thus not the only way to help the community. Answering questions, helping
+others, reaching out and improving the documentations are immensely valuable to
+the community.
+
+It also helps us if you spread the word: reference the library from blog posts
+on the awesome projects it made possible, shout out on Twitter every time it has
+helped you, or simply star the repo to say "thank you".
+
+Whichever way you choose to contribute, please be mindful to respect our
+[code of conduct](https://github.com/huggingface/diffusers/blob/main/CODE_OF_CONDUCT.md).
+
+## You can contribute in so many ways!
+
+There are 4 ways you can contribute to diffusers:
+* Fixing outstanding issues with the existing code;
+* Implementing [new diffusion pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines#contribution), [new schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) or [new models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)
+* [Contributing to the examples](https://github.com/huggingface/diffusers/tree/main/examples) or to the documentation;
+* Submitting issues related to bugs or desired new features.
+
+In particular there is a special [Good First Issue](https://github.com/huggingface/diffusers/contribute) listing. 
+It will give you a list of open Issues that are open to anybody to work on. Just comment in the issue that you'd like to work on it. 
+In that same listing you will also find some Issues with `Good Second Issue` label. These are
+typically slightly more complicated than the Issues with just `Good First Issue` label. But if you
+feel you know what you're doing, go for it.
+
+*All are equally valuable to the community.*
+
+## Submitting a new issue or feature request
+
+Do your best to follow these guidelines when submitting an issue or a feature
+request. It will make it easier for us to come back to you quickly and with good
+feedback.
+
+### Did you find a bug?
+
+The 🧨 Diffusers library is robust and reliable thanks to the users who notify us of
+the problems they encounter. So thank you for reporting an issue.
+
+First, we would really appreciate it if you could **make sure the bug was not
+already reported** (use the search bar on Github under Issues).
+
+### Do you want to implement a new diffusion pipeline / diffusion model?
+
+Awesome! Please provide the following information:
+
+* Short description of the diffusion pipeline and link to the paper;
+* Link to the implementation if it is open-source;
+* Link to the model weights if they are available.
+
+If you are willing to contribute the model yourself, let us know so we can best
+guide you.
+
+### Do you want a new feature (that is not a model)?
+
+A world-class feature request addresses the following points:
+
+1. Motivation first:
+  * Is it related to a problem/frustration with the library? If so, please explain
+    why. Providing a code snippet that demonstrates the problem is best.
+  * Is it related to something you would need for a project? We'd love to hear
+    about it!
+  * Is it something you worked on and think could benefit the community?
+    Awesome! Tell us what problem it solved for you.
+2. Write a *full paragraph* describing the feature;
+3. Provide a **code snippet** that demonstrates its future use;
+4. In case this is related to a paper, please attach a link;
+5. Attach any additional information (drawings, screenshots, etc.) you think may help.
+
+If your issue is well written we're already 80% of the way there by the time you
+post it.
+
+## Start contributing! (Pull Requests)
+
+Before writing code, we strongly advise you to search through the existing PRs or
+issues to make sure that nobody is already working on the same thing. If you are
+unsure, it is always a good idea to open an issue to get some feedback.
+
+You will need basic `git` proficiency to be able to contribute to
+🧨 Diffusers. `git` is not the easiest tool to use but it has the greatest
+manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
+Git](https://git-scm.com/book/en/v2) is a very good reference.
+
+Follow these steps to start contributing ([supported Python versions](https://github.com/huggingface/diffusers/blob/main/setup.py#L426)):
+
+1. Fork the [repository](https://github.com/huggingface/diffusers) by
+   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
+   under your GitHub user account.
+
+2. Clone your fork to your local disk, and add the base repository as a remote:
+
+   ```bash
+   $ git clone git@github.com:<your Github handle>/diffusers.git
+   $ cd diffusers
+   $ git remote add upstream https://github.com/huggingface/diffusers.git
+   ```
+
+3. Create a new branch to hold your development changes:
+
+   ```bash
+   $ git checkout -b a-descriptive-name-for-my-changes
+   ```
+
+   **Do not** work on the `main` branch.
+
+4. Set up a development environment by running the following command in a virtual environment:
+
+   ```bash
+   $ pip install -e ".[dev]"
+   ```
+
+   (If diffusers was already installed in the virtual environment, remove
+   it with `pip uninstall diffusers` before reinstalling it in editable
+   mode with the `-e` flag.)
+
+   To run the full test suite, you might need the additional dependency on `transformers` and `datasets` which requires a separate source
+   install:
+
+   ```bash
+   $ git clone https://github.com/huggingface/transformers
+   $ cd transformers
+   $ pip install -e .
+   ```
+
+   ```bash
+   $ git clone https://github.com/huggingface/datasets
+   $ cd datasets
+   $ pip install -e .
+   ```
+
+   If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
+   library.
+
+5. Develop the features on your branch.
+
+   As you work on the features, you should make sure that the test suite
+   passes. You should run the tests impacted by your changes like this:
+
+   ```bash
+   $ pytest tests/<TEST_TO_RUN>.py
+   ```
+
+   You can also run the full suite with the following command, but it takes
+   a beefy machine to produce a result in a decent amount of time now that
+   Diffusers has grown a lot. Here is the command for it:
+
+   ```bash
+   $ make test
+   ```
+
+   For more information about tests, check out the
+   [dedicated documentation](https://huggingface.co/docs/diffusers/testing)
+
+   🧨 Diffusers relies on `black` and `isort` to format its source code
+   consistently. After you make changes, apply automatic style corrections and code verifications
+   that can't be automated in one go with:
+
+   ```bash
+   $ make style
+   ```
+
+   🧨 Diffusers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
+   control runs in CI, however you can also run the same checks with:
+
+   ```bash
+   $ make quality
+   ```
+
+   Once you're happy with your changes, add changed files using `git add` and
+   make a commit with `git commit` to record your changes locally:
+
+   ```bash
+   $ git add modified_file.py
+   $ git commit
+   ```
+
+   It is a good idea to sync your copy of the code with the original
+   repository regularly. This way you can quickly account for changes:
+
+   ```bash
+   $ git fetch upstream
+   $ git rebase upstream/main
+   ```
+
+   Push the changes to your account using:
+
+   ```bash
+   $ git push -u origin a-descriptive-name-for-my-changes
+   ```
+
+6. Once you are satisfied (**and the checklist below is happy too**), go to the
+   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
+   to the project maintainers for review.
+
+7. It's ok if maintainers ask you for changes. It happens to core contributors
+   too! So everyone can see the changes in the Pull request, work in your local
+   branch and push the changes to your fork. They will automatically appear in
+   the pull request.
+
+
+### Checklist
+
+1. The title of your pull request should be a summary of its contribution;
+2. If your pull request addresses an issue, please mention the issue number in
+   the pull request description to make sure they are linked (and people
+   consulting the issue know you are working on it);
+3. To indicate a work in progress please prefix the title with `[WIP]`. These
+   are useful to avoid duplicated work, and to differentiate it from PRs ready
+   to be merged;
+4. Make sure existing tests pass;
+5. Add high-coverage tests. No quality testing = no merge.
+   - If you are adding new `@slow` tests, make sure they pass using
+     `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
+   - If you are adding a new tokenizer, write tests, and make sure
+     `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
+   CircleCI does not run the slow tests, but github actions does every night!
+6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_bert.py` for an
+   example.
+7. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
+   the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference 
+   them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
+   If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
+   to this dataset.
+
+### Tests
+
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
+the [tests folder](https://github.com/huggingface/diffusers/tree/main/tests).
+
+We like `pytest` and `pytest-xdist` because it's faster. From the root of the
+repository, here's how to run tests with `pytest` for the library:
+
+```bash
+$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
+```
+
+In fact, that's how `make test` is implemented (sans the `pip install` line)!
+
+You can specify a smaller set of tests in order to test only the feature
+you're working on.
+
+By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to
+`yes` to run them. This will download many gigabytes of models — make sure you
+have enough disk space and a good Internet connection, or a lot of patience!
+
+```bash
+$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
+```
+
+This means `unittest` is fully supported. Here's how to run tests with
+`unittest`:
+
+```bash
+$ python -m unittest discover -s tests -t . -v
+$ python -m unittest discover -s examples -t examples -v
+```
+
+
+### Style guide
+
+For documentation strings, 🧨 Diffusers follows the [google style](https://google.github.io/styleguide/pyguide.html).
+
+**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
+
+### Syncing forked main with upstream (HuggingFace) main
+
+To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
+when syncing the main branch of a forked repository, please, follow these steps:
+1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked main.
+2. If a PR is absolutely necessary, use the following steps after checking out your branch:
+```
+$ git checkout -b your-branch-for-syncing
+$ git pull --squash --no-commit upstream main
+$ git commit -m '<your message without GitHub references>'
+$ git push --set-upstream origin your-branch-for-syncing
+```

huggingface_diffusers/LICENSE

@@ -0,0 +1,201 @@
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huggingface_diffusers/MANIFEST.in

@@ -0,0 +1,2 @@
+include LICENSE
+include src/diffusers/utils/model_card_template.md

huggingface_diffusers/Makefile

@@ -0,0 +1,98 @@
+.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples
+
+# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
+export PYTHONPATH = src
+
+check_dirs := examples scripts src tests utils
+
+modified_only_fixup:
+	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
+	@if test -n "$(modified_py_files)"; then \
+		echo "Checking/fixing $(modified_py_files)"; \
+		black --preview $(modified_py_files); \
+		isort $(modified_py_files); \
+		flake8 $(modified_py_files); \
+	else \
+		echo "No library .py files were modified"; \
+	fi
+
+# Update src/diffusers/dependency_versions_table.py
+
+deps_table_update:
+	@python setup.py deps_table_update
+
+deps_table_check_updated:
+	@md5sum src/diffusers/dependency_versions_table.py > md5sum.saved
+	@python setup.py deps_table_update
+	@md5sum -c --quiet md5sum.saved || (printf "\nError: the version dependency table is outdated.\nPlease run 'make fixup' or 'make style' and commit the changes.\n\n" && exit 1)
+	@rm md5sum.saved
+
+# autogenerating code
+
+autogenerate_code: deps_table_update
+
+# Check that the repo is in a good state
+
+repo-consistency:
+	python utils/check_dummies.py
+	python utils/check_repo.py
+	python utils/check_inits.py
+
+# this target runs checks on all files
+
+quality:
+	black --check --preview $(check_dirs)
+	isort --check-only $(check_dirs)
+	flake8 $(check_dirs)
+	doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
+	python utils/check_doc_toc.py
+
+# Format source code automatically and check is there are any problems left that need manual fixing
+
+extra_style_checks:
+	python utils/custom_init_isort.py
+	doc-builder style src/diffusers docs/source --max_len 119 --path_to_docs docs/source
+	python utils/check_doc_toc.py --fix_and_overwrite
+
+# this target runs checks on all files and potentially modifies some of them
+
+style:
+	black --preview $(check_dirs)
+	isort $(check_dirs)
+	${MAKE} autogenerate_code
+	${MAKE} extra_style_checks
+
+# Super fast fix and check target that only works on relevant modified files since the branch was made
+
+fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
+
+# Make marked copies of snippets of codes conform to the original
+
+fix-copies:
+	python utils/check_copies.py --fix_and_overwrite
+	python utils/check_dummies.py --fix_and_overwrite
+
+# Run tests for the library
+
+test:
+	python -m pytest -n auto --dist=loadfile -s -v ./tests/
+
+# Run tests for examples
+
+test-examples:
+	python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/
+
+
+# Release stuff
+
+pre-release:
+	python utils/release.py
+
+pre-patch:
+	python utils/release.py --patch
+
+post-release:
+	python utils/release.py --post_release
+
+post-patch:
+	python utils/release.py --post_release --patch

huggingface_diffusers/README.md

@@ -0,0 +1,563 @@
+<p align="center">
+    <br>
+    <img src="./docs/source/en/imgs/diffusers_library.jpg" width="400"/>
+    <br>
+<p>
+<p align="center">
+    <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE">
+        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
+    </a>
+    <a href="https://github.com/huggingface/diffusers/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg">
+    </a>
+    <a href="CODE_OF_CONDUCT.md">
+        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
+    </a>
+</p>
+
+🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves
+as a modular toolbox for inference and training of diffusion models.
+
+More precisely, 🤗 Diffusers offers:
+
+- State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)). Check [this overview](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/README.md#pipelines-summary) to see all supported pipelines and their corresponding official papers.
+- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
+- Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
+- Training examples to show how to train the most popular diffusion model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)).
+
+## Installation
+
+### For PyTorch
+
+**With `pip`** (official package)
+    
+```bash
+pip install --upgrade diffusers[torch]
+```
+
+**With `conda`** (maintained by the community)
+
+```sh
+conda install -c conda-forge diffusers
+```
+
+### For Flax
+
+**With `pip`**
+
+```bash
+pip install --upgrade diffusers[flax]
+```
+
+**Apple Silicon (M1/M2) support**
+
+Please, refer to [the documentation](https://huggingface.co/docs/diffusers/optimization/mps).
+
+## Contributing
+
+We ❤️  contributions from the open-source community! 
+If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).
+You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.
+- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute
+- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
+- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
+
+Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or
+just hang out ☕.
+
+## Quickstart
+
+In order to get started, we recommend taking a look at two notebooks:
+
+- The [Getting started with Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines.
+  Take a look at this notebook to learn how to use the pipeline abstraction, which takes care of everything (model, scheduler, noise handling) for you, and also to understand each independent building block in the library.
+- The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffusion models training methods. This notebook takes a step-by-step approach to training your
+  diffusion models on an image dataset, with explanatory graphics. 
+  
+## Stable Diffusion is fully compatible with `diffusers`!  
+
+Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) and [RunwayML](https://runwayml.com/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 4GB VRAM.
+See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
+
+
+### Text-to-Image generation with Stable Diffusion
+
+First let's install
+
+```bash
+pip install --upgrade diffusers transformers accelerate
+```
+
+We recommend using the model in [half-precision (`fp16`)](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) as it gives almost always the same results as full
+precision while being roughly twice as fast and requiring half the amount of GPU RAM.
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+```
+
+#### Running the model locally
+
+You can also simply download the model folder and pass the path to the local folder to the `StableDiffusionPipeline`.
+
+```
+git lfs install
+git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
+```
+
+Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can run stable diffusion
+as follows:
+
+```python
+pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5")
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+```
+
+If you are limited by GPU memory, you might want to consider chunking the attention computation in addition 
+to using `fp16`.
+The following snippet should result in less than 4GB VRAM.
+
+```python
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+pipe.enable_attention_slicing()
+image = pipe(prompt).images[0]  
+```
+
+If you wish to use a different scheduler (e.g.: DDIM, LMS, PNDM/PLMS), you can instantiate
+it before the pipeline and pass it to `from_pretrained`.
+    
+```python
+from diffusers import LMSDiscreteScheduler
+
+pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+    
+image.save("astronaut_rides_horse.png")
+```
+
+If you want to run Stable Diffusion on CPU or you want to have maximum precision on GPU, 
+please run the model in the default *full-precision* setting:
+
+```python
+from diffusers import StableDiffusionPipeline
+
+pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+
+# disable the following line if you run on CPU
+pipe = pipe.to("cuda")
+
+prompt = "a photo of an astronaut riding a horse on mars"
+image = pipe(prompt).images[0]  
+    
+image.save("astronaut_rides_horse.png")
+```
+
+### JAX/Flax
+
+Diffusers offers a JAX / Flax implementation of Stable Diffusion for very fast inference. JAX shines specially on TPU hardware because each TPU server has 8 accelerators working in parallel, but it runs great on GPUs too.
+
+Running the pipeline with the default PNDMScheduler:
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="flax", dtype=jax.numpy.bfloat16
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
+**Note**:
+If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+
+from diffusers import FlaxStableDiffusionPipeline
+
+pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+prompt_ids = pipeline.prepare_inputs(prompt)
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+
+images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
+Diffusers also has a Image-to-Image generation pipeline with Flax/Jax
+```python
+import jax
+import numpy as np
+import jax.numpy as jnp
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+import requests
+from io import BytesIO
+from PIL import Image
+from diffusers import FlaxStableDiffusionImg2ImgPipeline
+
+def create_key(seed=0):
+    return jax.random.PRNGKey(seed)
+rng = create_key(0)
+
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+response = requests.get(url)
+init_img = Image.open(BytesIO(response.content)).convert("RGB")
+init_img = init_img.resize((768, 512))
+
+prompts = "A fantasy landscape, trending on artstation"
+
+pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4", revision="flax",
+    dtype=jnp.bfloat16,
+)
+
+num_samples = jax.device_count()
+rng = jax.random.split(rng, jax.device_count())
+prompt_ids, processed_image = pipeline.prepare_inputs(prompt=[prompts]*num_samples, image = [init_img]*num_samples)
+p_params = replicate(params)
+prompt_ids = shard(prompt_ids)
+processed_image = shard(processed_image)
+
+output = pipeline(
+    prompt_ids=prompt_ids, 
+    image=processed_image, 
+    params=p_params, 
+    prng_seed=rng, 
+    strength=0.75, 
+    num_inference_steps=50, 
+    jit=True, 
+    height=512,
+    width=768).images
+
+output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
+```
+
+Diffusers also has a Text-guided inpainting pipeline with Flax/Jax
+
+```python
+import jax
+import numpy as np
+from flax.jax_utils import replicate
+from flax.training.common_utils import shard
+import PIL
+import requests
+from io import BytesIO
+
+
+from diffusers import FlaxStableDiffusionInpaintPipeline
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+init_image = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained("xvjiarui/stable-diffusion-2-inpainting")
+
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+prng_seed = jax.random.PRNGKey(0)
+num_inference_steps = 50
+
+num_samples = jax.device_count()
+prompt = num_samples * [prompt]
+init_image = num_samples * [init_image]
+mask_image = num_samples * [mask_image]
+prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)
+
+
+# shard inputs and rng
+params = replicate(params)
+prng_seed = jax.random.split(prng_seed, jax.device_count())
+prompt_ids = shard(prompt_ids)
+processed_masked_images = shard(processed_masked_images)
+processed_masks = shard(processed_masks)
+
+images = pipeline(prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True).images
+images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
+```
+
+### Image-to-Image text-guided generation with Stable Diffusion
+
+The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images.
+
+```python
+import requests
+import torch
+from PIL import Image
+from io import BytesIO
+
+from diffusers import StableDiffusionImg2ImgPipeline
+
+# load the pipeline
+device = "cuda"
+model_id_or_path = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
+
+# or download via git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
+# and pass `model_id_or_path="./stable-diffusion-v1-5"`.
+pipe = pipe.to(device)
+
+# let's download an initial image
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((768, 512))
+
+prompt = "A fantasy landscape, trending on artstation"
+
+images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+
+images[0].save("fantasy_landscape.png")
+```
+You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
+
+### In-painting using Stable Diffusion
+
+The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and a text prompt.
+
+```python
+import PIL
+import requests
+import torch
+from io import BytesIO
+
+from diffusers import StableDiffusionInpaintPipeline
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+init_image = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
+image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
+```
+
+### Tweak prompts reusing seeds and latents
+
+You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked.
+Please have a look at [Reusing seeds for deterministic generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/reusing_seeds).
+
+## Fine-Tuning Stable Diffusion
+
+Fine-tuning techniques make it possible to adapt Stable Diffusion to your own dataset, or add new subjects to it. These are some of the techniques supported in `diffusers`:
+
+Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. It does so by learning new 'words' in the embedding space of the pipeline's text encoder. These special words can then be used within text prompts to achieve very fine-grained control of the resulting images. 
+
+- Textual Inversion. Capture novel concepts from a small set of sample images, and associate them with new "words" in the embedding space of the text encoder. Please, refer to [our training examples](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) or [documentation](https://huggingface.co/docs/diffusers/training/text_inversion) to try for yourself.
+
+- Dreambooth. Another technique to capture new concepts in Stable Diffusion. This method fine-tunes the UNet (and, optionally, also the text encoder) of the pipeline to achieve impressive results. Please, refer to [our training example](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) and [training report](https://huggingface.co/blog/dreambooth) for additional details and training recommendations.
+
+- Full Stable Diffusion fine-tuning. If you have a more sizable dataset with a specific look or style, you can fine-tune Stable Diffusion so that it outputs images following those examples. This was the approach taken to create [a Pokémon Stable Diffusion model](https://huggingface.co/justinpinkney/pokemon-stable-diffusion) (by Justing Pinkney / Lambda Labs), [a Japanese specific version of Stable Diffusion](https://huggingface.co/spaces/rinna/japanese-stable-diffusion) (by [Rinna Co.](https://github.com/rinnakk/japanese-stable-diffusion/) and others. You can start at [our text-to-image fine-tuning example](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) and go from there.
+
+
+## Stable Diffusion Community Pipelines
+
+The release of Stable Diffusion as an open source model has fostered a lot of interesting ideas and experimentation. 
+Our [Community Examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community) contains many ideas worth exploring, like interpolating to create animated videos, using CLIP Guidance for additional prompt fidelity, term weighting, and much more! [Take a look](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview) and [contribute your own](https://huggingface.co/docs/diffusers/using-diffusers/contribute_pipeline).
+
+## Other Examples
+
+There are many ways to try running Diffusers! Here we outline code-focused tools (primarily using `DiffusionPipeline`s and Google Colab) and interactive web-tools.
+
+### Running Code
+
+If you want to run the code yourself 💻, you can try out:
+- [Text-to-Image Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256)
+```python
+# !pip install diffusers["torch"] transformers
+from diffusers import DiffusionPipeline
+
+device = "cuda"
+model_id = "CompVis/ldm-text2im-large-256"
+
+# load model and scheduler
+ldm = DiffusionPipeline.from_pretrained(model_id)
+ldm = ldm.to(device)
+
+# run pipeline in inference (sample random noise and denoise)
+prompt = "A painting of a squirrel eating a burger"
+image = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images[0]
+
+# save image
+image.save("squirrel.png")
+```
+- [Unconditional Diffusion with discrete scheduler](https://huggingface.co/google/ddpm-celebahq-256)
+```python
+# !pip install diffusers["torch"]
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+
+model_id = "google/ddpm-celebahq-256"
+device = "cuda"
+
+# load model and scheduler
+ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
+ddpm.to(device)
+
+# run pipeline in inference (sample random noise and denoise)
+image = ddpm().images[0]
+
+# save image
+image.save("ddpm_generated_image.png")
+```
+- [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256)
+- [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
+
+**Other Image Notebooks**:
+* [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+
+**Diffusers for Other Modalities**:
+* [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+* [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
+
+### Web Demos
+If you just want to play around with some web demos, you can try out the following 🚀 Spaces:
+| Model                          	| Hugging Face Spaces                                                                                                                                               	|
+|--------------------------------	|-------------------------------------------------------------------------------------------------------------------------------------------------------------------	|
+| Text-to-Image Latent Diffusion 	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/text2img-latent-diffusion) 	|
+| Faces generator                	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/celeba-latent-diffusion)    	|
+| DDPM with different schedulers 	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/fusing/celeba-diffusion)           	|
+| Conditional generation from sketch  	| [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/huggingface/diffuse-the-rest)           	|
+| Composable diffusion | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Shuang59/Composable-Diffusion)           	|
+
+## Definitions
+
+**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
+*Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/10695622/174349667-04e9e485-793b-429a-affe-096e8199ad5b.png" width="800"/>
+    <br>
+    <em> Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
+<p>
+    
+**Schedulers**: Algorithm class for both **inference** and **training**.
+The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training. Also known as **Samplers**.
+*Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902)
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/10695622/174349706-53d58acc-a4d1-4cda-b3e8-432d9dc7ad38.png" width="800"/>
+    <br>
+    <em> Sampling and training algorithms. Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
+<p>
+    
+
+**Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ...
+*Examples*: Glide, Latent-Diffusion, Imagen, DALL-E 2
+
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/10695622/174348898-481bd7c2-5457-4830-89bc-f0907756f64c.jpeg" width="550"/>
+    <br>
+    <em> Figure from ImageGen (https://imagen.research.google/). </em>
+<p>
+    
+## Philosophy
+
+- Readability and clarity is preferred over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
+- Diffusers is **modality independent** and focuses on providing pretrained models and tools to build systems that generate **continuous outputs**, *e.g.* vision and audio.
+- Diffusion models and schedulers are provided as concise, elementary building blocks. In contrast, diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of another library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).
+
+## In the works
+
+For the first release, 🤗 Diffusers focuses on text-to-image diffusion techniques. However, diffusers can be used for much more than that! Over the upcoming releases, we'll be focusing on:
+
+- Diffusers for audio
+- Diffusers for reinforcement learning (initial work happening in https://github.com/huggingface/diffusers/pull/105).
+- Diffusers for video generation
+- Diffusers for molecule generation (initial work happening in https://github.com/huggingface/diffusers/pull/54)
+
+A few pipeline components are already being worked on, namely:
+
+- BDDMPipeline for spectrogram-to-sound vocoding
+- GLIDEPipeline to support OpenAI's GLIDE model
+- Grad-TTS for text to audio generation / conditional audio generation
+
+We want diffusers to be a toolbox useful for diffusers models in general; if you find yourself limited in any way by the current API, or would like to see additional models, schedulers, or techniques, please open a [GitHub issue](https://github.com/huggingface/diffusers/issues) mentioning what you would like to see.
+
+## Credits
+
+This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:
+
+- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)
+- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion)
+- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim).
+- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)
+
+We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights.
+
+## Citation
+
+```bibtex
+@misc{von-platen-etal-2022-diffusers,
+  author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
+  title = {Diffusers: State-of-the-art diffusion models},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/huggingface/diffusers}}
+}
+```

huggingface_diffusers/_typos.toml

@@ -0,0 +1,13 @@
+# Files for typos
+# Instruction:  https://github.com/marketplace/actions/typos-action#getting-started
+
+[default.extend-identifiers]
+
+[default.extend-words]
+NIN="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
+nd="np" # nd may be np (numpy)
+parms="parms" # parms is used in scripts/convert_original_stable_diffusion_to_diffusers.py
+
+
+[files]
+extend-exclude = ["_typos.toml"]

huggingface_diffusers/docker/diffusers-flax-cpu/Dockerfile

@@ -0,0 +1,44 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --upgrade --no-cache-dir \
+        clu \
+        "jax[cpu]>=0.2.16,!=0.3.2" \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

huggingface_diffusers/docker/diffusers-flax-tpu/Dockerfile

@@ -0,0 +1,46 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# follow the instructions here: https://cloud.google.com/tpu/docs/run-in-container#train_a_jax_model_in_a_docker_container
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        "jax[tpu]>=0.2.16,!=0.3.2" \
+        -f https://storage.googleapis.com/jax-releases/libtpu_releases.html && \
+    python3 -m pip install --upgrade --no-cache-dir \
+        clu \
+        "flax>=0.4.1" \
+        "jaxlib>=0.1.65" && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \        
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

huggingface_diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile

@@ -0,0 +1,44 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        onnxruntime \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

huggingface_diffusers/docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -0,0 +1,44 @@
+FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        "onnxruntime-gpu>=1.13.1" \
+        --extra-index-url https://download.pytorch.org/whl/cu117 && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

huggingface_diffusers/docker/diffusers-pytorch-cpu/Dockerfile

@@ -0,0 +1,43 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

huggingface_diffusers/docker/diffusers-pytorch-cuda/Dockerfile

@@ -0,0 +1,43 @@
+FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt update && \
+    apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.8 \
+                   python3-pip \
+                   python3.8-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip && \
+    python3 -m pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        --extra-index-url https://download.pytorch.org/whl/cu117 && \
+    python3 -m pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]

huggingface_diffusers/examples/README.md

@@ -0,0 +1,70 @@
+<!---
+Copyright 2022 The HuggingFace Team. All rights reserved.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# 🧨 Diffusers Examples
+
+Diffusers examples are a collection of scripts to demonstrate how to effectively use the `diffusers` library
+for a variety of use cases involving training or fine-tuning.
+
+**Note**: If you are looking for **official** examples on how to use `diffusers` for inference, 
+please have a look at [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)
+
+Our examples aspire to be **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.
+More specifically, this means:
+
+- **Self-contained**: An example script shall only depend on "pip-install-able" Python packages that can be found in a `requirements.txt` file. Example scripts shall **not** depend on any local files. This means that one can simply download an example script, *e.g.* [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py), install the required dependencies, *e.g.* [requirements.txt](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/requirements.txt) and execute the example script.
+- **Easy-to-tweak**: While we strive to present as many use cases as possible, the example scripts are just that - examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. To help you with that, most of the examples fully expose the preprocessing of the data and the training loop to allow you to tweak and edit them as required.
+- **Beginner-friendly**: We do not aim for providing state-of-the-art training scripts for the newest models, but rather examples that can be used as a way to better understand diffusion models and how to use them with the `diffusers` library. We often purposefully leave out certain state-of-the-art methods if we consider them too complex for beginners.
+- **One-purpose-only**: Examples should show one task and one task only. Even if a task is from a modeling 
+point of view very similar, *e.g.* image super-resolution and image modification tend to use the same model and training method, we want examples to showcase only one task to keep them as readable and easy-to-understand as possible.
+
+We provide **official** examples that cover the most popular tasks of diffusion models.
+*Official* examples are **actively** maintained by the `diffusers` maintainers and we try to rigorously follow our example philosophy as defined above. 
+If you feel like another important example should exist, we are more than happy to welcome a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) or directly a [Pull Request](https://github.com/huggingface/diffusers/compare) from you!
+
+Training examples show how to pretrain or fine-tune diffusion models for a variety of tasks. Currently we support:
+
+| Task | 🤗 Accelerate | 🤗 Datasets | Colab
+|---|---|:---:|:---:|
+| [**Unconditional Image Generation**](./unconditional_image_generation) | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [**Text-to-Image fine-tuning**](./text_to_image) | ✅ | ✅ | 
+| [**Textual Inversion**](./textual_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
+| [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)
+| [**Reinforcement Learning for Control**](https://github.com/huggingface/diffusers/blob/main/examples/rl/run_diffusers_locomotion.py)                    | - | - | coming soon.
+
+## Community
+
+In addition, we provide **community** examples, which are examples added and maintained by our community.
+Community examples can consist of both *training* examples or *inference* pipelines.
+For such examples, we are more lenient regarding the philosophy defined above and also cannot guarantee to provide maintenance for every issue.
+Examples that are useful for the community, but are either not yet deemed popular or not yet following our above philosophy should go into the [community examples](https://github.com/huggingface/diffusers/tree/main/examples/community) folder. The community folder therefore includes training examples and inference pipelines.
+**Note**: Community examples can be a [great first contribution](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) to show to the community how you like to use `diffusers` 🪄.
+
+## Research Projects
+
+We also provide **research_projects** examples that are maintained by the community as defined in the respective research project folders. These examples are useful and offer the extended capabilities which are complementary to the official examples. You may refer to [research_projects](https://github.com/huggingface/diffusers/tree/main/examples/research_projects) for details.
+
+## Important note
+
+To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install .
+```
+Then cd in the example folder of your choice and run
+```bash
+pip install -r requirements.txt
+```

huggingface_diffusers/examples/community/README.md

@@ -0,0 +1,953 @@
+# Community Examples
+
+> **For more information about community pipelines, please have a look at [this issue](https://github.com/huggingface/diffusers/issues/841).**
+
+**Community** examples consist of both inference and training examples that have been added by the community.
+Please have a look at the following table to get an overview of all community examples. Click on the **Code Example** to get a copy-and-paste ready code example that you can try out.
+If a community doesn't work as expected, please open an issue and ping the author on it.
+
+| Example                                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                      | Colab                                                                                                                                                                                                              |                                                     Author |
+|:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:|
+| CLIP Guided Stable Diffusion           | Doing CLIP guidance for text to image generation with Stable Diffusion                                                                                                                                                                                                                                                                                                                                                                                                                                   | [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion)     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) |             [Suraj Patil](https://github.com/patil-suraj/) | 
+| One Step U-Net (Dummy)                 | Example showcasing of how to use Community Pipelines (see https://github.com/huggingface/diffusers/issues/841)                                                                                                                                                                                                                                                                                                                                                                                           | [One Step U-Net](#one-step-unet)                                  | -                                                                                                                                                                                                                  | [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Stable Diffusion Interpolation         | Interpolate the latent space of Stable Diffusion between different prompts/seeds                                                                                                                                                                                                                                                                                                                                                                                                                         | [Stable Diffusion Interpolation](#stable-diffusion-interpolation) | -                                                                                                                                                                                                                  |                    [Nate Raw](https://github.com/nateraw/) |
+| Stable Diffusion Mega                  | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega)                   | -                                                                                                                                                                                                                  | [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Long Prompt Weighting Stable Diffusion | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt.                                                                                                                                                                                                                                                                                                                                                                                                  | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion)                                                                 | -                                                                                                                                                                                                                  |                        [SkyTNT](https://github.com/SkyTNT) |
+| Speech to Image                        | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images                                                                                                                                                                                                                                                                                                                                                                                                            | [Speech to Image](#speech-to-image)                               | -                                                                                                                                                                                                                  | [Mikail Duzenli](https://github.com/MikailINTech)
+| Wild Card Stable Diffusion | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values                                                                                                                                                                                                                                                                                                     | [Wildcard Stable Diffusion](#wildcard-stable-diffusion)                                                                 | -                                                                                                                                                                                                                  |                        [Shyam Sudhakaran](https://github.com/shyamsn97) |
+| [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts.                                                                                                                                                                                                                                                                                                     | [Composable Stable Diffusion](#composable-stable-diffusion)                                                                 | -                                                                                                                                                                                                                  |                        [Mark Rich](https://github.com/MarkRich) |
+| Seed Resizing Stable Diffusion| Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation.                                                                                                                                                                                                                                                                                                     | [Seed Resizing](#seed-resizing)                                                                 | -                                                                                                                                                                                                                  |                        [Mark Rich](https://github.com/MarkRich) |
+| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image| [Imagic Stable Diffusion](#imagic-stable-diffusion)                                                                 | -                                                                                                                                                                                                                  |                        [Mark Rich](https://github.com/MarkRich) |
+| Multilingual Stable Diffusion| Stable Diffusion Pipeline that supports prompts in 50 different languages.                                                                                                                                                                                                                                                                                                     | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline)                                                                 | -                                                                                                                                                                                                                  |                        [Juan Carlos Piñeros](https://github.com/juancopi81) |
+| Image to Image Inpainting Stable Diffusion | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting| [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion)                                                                 | -                                                                                                                                                                                                                  |                        [Alex McKinney](https://github.com/vvvm23) |
+| Text Based Inpainting Stable Diffusion | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting| [Text Based Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion)                                                                 | -                                                                                                                                                                                                                  |                        [Dhruv Karan](https://github.com/unography) |
+| Bit Diffusion | Diffusion on discrete data | [Bit Diffusion](#bit-diffusion) | -  |[Stuti R.](https://github.com/kingstut) |
+| K-Diffusion Stable Diffusion | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py) | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion) | -  | [Patrick von Platen](https://github.com/patrickvonplaten/) |
+| Checkpoint Merger Pipeline | Diffusion Pipeline that enables merging of saved model checkpoints | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline)                   | -                                                                                                                                                                                                                  | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) | 
+Stable Diffusion v1.1-1.4 Comparison | Run all 4 model checkpoints for Stable Diffusion and compare their results together | [Stable Diffusion Comparison](#stable-diffusion-comparisons) | - | [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
+MagicMix | Diffusion Pipeline for semantic mixing of an image and a text prompt | [MagicMix](#magic-mix) | - | [Partho Das](https://github.com/daspartho) |
+| Stable UnCLIP | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ). | [Stable UnCLIP](#stable-unclip) | -  |[Ray Wang](https://wrong.wang) |
+
+
+
+To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
+```py
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
+```
+
+## Example usages
+
+### CLIP Guided Stable Diffusion
+
+CLIP guided stable diffusion can help to generate more realistic images 
+by guiding stable diffusion at every denoising step with an additional CLIP model.
+
+The following code requires roughly 12GB of GPU RAM.
+
+```python
+from diffusers import DiffusionPipeline
+from transformers import CLIPFeatureExtractor, CLIPModel
+import torch
+
+
+feature_extractor = CLIPFeatureExtractor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
+clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16)
+
+
+guided_pipeline = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    custom_pipeline="clip_guided_stable_diffusion",
+    clip_model=clip_model,
+    feature_extractor=feature_extractor,
+    
+    torch_dtype=torch.float16,
+)
+guided_pipeline.enable_attention_slicing()
+guided_pipeline = guided_pipeline.to("cuda")
+
+prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
+
+generator = torch.Generator(device="cuda").manual_seed(0)
+images = []
+for i in range(4):
+    image = guided_pipeline(
+        prompt,
+        num_inference_steps=50,
+        guidance_scale=7.5,
+        clip_guidance_scale=100,
+        num_cutouts=4,
+        use_cutouts=False,
+        generator=generator,
+    ).images[0]
+    images.append(image)
+    
+# save images locally
+for i, img in enumerate(images):
+    img.save(f"./clip_guided_sd/image_{i}.png")
+```
+
+The `images` list contains a list of PIL images that can be saved locally or displayed directly in a google colab.
+Generated images tend to be of higher qualtiy than natively using stable diffusion. E.g. the above script generates the following images:
+
+![clip_guidance](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/clip_guidance/merged_clip_guidance.jpg).
+
+### One Step Unet
+
+The dummy "one-step-unet" can be run as follows:
+
+```python
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
+pipe()
+```
+
+**Note**: This community pipeline is not useful as a feature, but rather just serves as an example of how community pipelines can be added (see https://github.com/huggingface/diffusers/issues/841).
+
+### Stable Diffusion Interpolation
+
+The following code can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    revision='fp16',
+    torch_dtype=torch.float16,
+    safety_checker=None,  # Very important for videos...lots of false positives while interpolating
+    custom_pipeline="interpolate_stable_diffusion",
+).to('cuda')
+pipe.enable_attention_slicing()
+
+frame_filepaths = pipe.walk(
+    prompts=['a dog', 'a cat', 'a horse'],
+    seeds=[42, 1337, 1234],
+    num_interpolation_steps=16,
+    output_dir='./dreams',
+    batch_size=4,
+    height=512,
+    width=512,
+    guidance_scale=8.5,
+    num_inference_steps=50,
+)
+```
+
+The output of the `walk(...)` function returns a list of images saved under the folder as defined in `output_dir`. You can use these images to create videos of stable diffusion. 
+
+> **Please have a look at https://github.com/nateraw/stable-diffusion-videos for more in-detail information on how to create videos using stable diffusion as well as more feature-complete functionality.**
+
+### Stable Diffusion Mega
+
+The Stable Diffusion Mega Pipeline lets you use the main use cases of the stable diffusion pipeline in a single class.
+
+```python
+#!/usr/bin/env python3
+from diffusers import DiffusionPipeline
+import PIL
+import requests
+from io import BytesIO
+import torch
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
+pipe.to("cuda")
+pipe.enable_attention_slicing()
+
+
+### Text-to-Image
+
+images = pipe.text2img("An astronaut riding a horse").images
+
+### Image-to-Image
+
+init_image = download_image("https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")
+
+prompt = "A fantasy landscape, trending on artstation"
+
+images = pipe.img2img(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+
+### Inpainting
+
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+init_image = download_image(img_url).resize((512, 512))
+mask_image = download_image(mask_url).resize((512, 512))
+
+prompt = "a cat sitting on a bench"
+images = pipe.inpaint(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.75).images
+```
+
+As shown above this one pipeline can run all both "text-to-image", "image-to-image", and "inpainting" in one pipeline.
+
+### Long Prompt Weighting Stable Diffusion
+Features of this custom pipeline:
+- Input a prompt without the 77 token length limit.
+- Includes tx2img, img2img. and inpainting pipelines.
+- Emphasize/weigh part of your prompt with parentheses as so: `a baby deer with (big eyes)`
+- De-emphasize part of your prompt as so: `a [baby] deer with big eyes`
+- Precisely weigh part of your prompt as so: `a baby deer with (big eyes:1.3)`
+
+Prompt weighting equivalents:
+- `a baby deer with` == `(a baby deer with:1.0)`
+- `(big eyes)` == `(big eyes:1.1)`
+- `((big eyes))` == `(big eyes:1.21)`
+- `[big eyes]` == `(big eyes:0.91)`
+
+You can run this custom pipeline as so:
+
+#### pytorch
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    'hakurei/waifu-diffusion',
+    custom_pipeline="lpw_stable_diffusion",
+    
+    torch_dtype=torch.float16
+)
+pipe=pipe.to("cuda")
+
+prompt = "best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms"
+neg_prompt = "lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry"
+
+pipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3).images[0]
+
+```
+
+#### onnxruntime
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    'CompVis/stable-diffusion-v1-4',
+    custom_pipeline="lpw_stable_diffusion_onnx",
+    revision="onnx",
+    provider="CUDAExecutionProvider"
+)
+
+prompt = "a photo of an astronaut riding a horse on mars, best quality"
+neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
+
+pipe.text2img(prompt,negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]
+
+```
+
+if you see `Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors`. Do not worry, it is normal.
+
+### Speech to Image
+
+The following code can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.
+
+```Python
+import torch
+
+import matplotlib.pyplot as plt
+from datasets import load_dataset
+from diffusers import DiffusionPipeline
+from transformers import (
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+)
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
+audio_sample = ds[3]
+
+text = audio_sample["text"].lower()
+speech_data = audio_sample["audio"]["array"]
+
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
+processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+
+diffuser_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="speech_to_image_diffusion",
+    speech_model=model,
+    speech_processor=processor,
+    
+    torch_dtype=torch.float16,
+)
+
+diffuser_pipeline.enable_attention_slicing()
+diffuser_pipeline = diffuser_pipeline.to(device)
+
+output = diffuser_pipeline(speech_data)
+plt.imshow(output.images[0])
+```
+This example produces the following image:
+
+![image](https://user-images.githubusercontent.com/45072645/196901736-77d9c6fc-63ee-4072-90b0-dc8b903d63e3.png)
+
+### Wildcard Stable Diffusion
+Following the great examples from https://github.com/jtkelm2/stable-diffusion-webui-1/blob/master/scripts/wildcards.py and https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts#wildcards, here's a minimal implementation that allows for users to add "wildcards", denoted by `__wildcard__` to prompts that are used as placeholders for randomly sampled values given by either a dictionary or a `.txt` file. For example:
+
+Say we have a prompt:
+
+```
+prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
+```
+
+We can then define possible values to be sampled for `animal`, `object`, and `clothing`. These can either be from a `.txt` with the same name as the category.
+
+The possible values can also be defined / combined by using a dictionary like: `{"animal":["dog", "cat", mouse"]}`.
+
+The actual pipeline works just like `StableDiffusionPipeline`, except the `__call__` method takes in:
+
+`wildcard_files`: list of file paths for wild card replacement
+`wildcard_option_dict`: dict with key as `wildcard` and values as a list of possible replacements
+`num_prompt_samples`: number of prompts to sample, uniformly sampling wildcards
+
+A full example:
+
+create `animal.txt`, with contents like:
+
+```
+dog
+cat
+mouse
+```
+
+create `object.txt`, with contents like:
+
+```
+chair
+sofa
+bench
+```
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="wildcard_stable_diffusion",
+    
+    torch_dtype=torch.float16,
+)
+prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
+out = pipe(
+    prompt,
+    wildcard_option_dict={
+        "clothing":["hat", "shirt", "scarf", "beret"]
+    },
+    wildcard_files=["object.txt", "animal.txt"],
+    num_prompt_samples=1
+)
+```
+
+### Composable Stable diffusion 
+
+[Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) proposes conjunction and negation (negative prompts) operators for compositional generation with conditional diffusion models.
+
+```python
+import torch as th
+import numpy as np
+import torchvision.utils as tvu
+
+from diffusers import DiffusionPipeline
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--prompt", type=str, default="mystical trees | A magical pond | dark",
+                    help="use '|' as the delimiter to compose separate sentences.")
+parser.add_argument("--steps", type=int, default=50)
+parser.add_argument("--scale", type=float, default=7.5)
+parser.add_argument("--weights", type=str, default="7.5 | 7.5 | -7.5")
+parser.add_argument("--seed", type=int, default=2)
+parser.add_argument("--model_path", type=str, default="CompVis/stable-diffusion-v1-4")
+parser.add_argument("--num_images", type=int, default=1)
+args = parser.parse_args()
+
+has_cuda = th.cuda.is_available()
+device = th.device('cpu' if not has_cuda else 'cuda')
+
+prompt = args.prompt
+scale = args.scale
+steps = args.steps
+
+pipe = DiffusionPipeline.from_pretrained(
+    args.model_path,
+    custom_pipeline="composable_stable_diffusion",
+).to(device)
+
+pipe.safety_checker = None
+
+images = []
+generator = th.Generator("cuda").manual_seed(args.seed)
+for i in range(args.num_images):
+    image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps,
+                 weights=args.weights, generator=generator).images[0]
+    images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)
+grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)
+tvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')
+
+```
+
+### Imagic Stable Diffusion
+Allows you to edit an image using stable diffusion. 
+
+```python
+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+import os
+from diffusers import DiffusionPipeline, DDIMScheduler
+has_cuda = torch.cuda.is_available()
+device = torch.device('cpu' if not has_cuda else 'cuda')
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+        safety_checker=None,
+    use_auth_token=True,
+    custom_pipeline="imagic_stable_diffusion",
+    scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
+).to(device)
+generator = torch.Generator("cuda").manual_seed(0)
+seed = 0
+prompt = "A photo of Barack Obama smiling with a big grin"
+url = 'https://www.dropbox.com/s/6tlwzr73jd1r9yk/obama.png?dl=1'
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((512, 512))
+res = pipe.train(
+    prompt,
+    image=init_image,
+    generator=generator)
+res = pipe(alpha=1, guidance_scale=7.5, num_inference_steps=50)
+os.makedirs("imagic", exist_ok=True)
+image = res.images[0]
+image.save('./imagic/imagic_image_alpha_1.png')
+res = pipe(alpha=1.5, guidance_scale=7.5, num_inference_steps=50)
+image = res.images[0]
+image.save('./imagic/imagic_image_alpha_1_5.png')
+res = pipe(alpha=2, guidance_scale=7.5, num_inference_steps=50)
+image = res.images[0]
+image.save('./imagic/imagic_image_alpha_2.png')
+```
+
+### Seed Resizing 
+Test seed resizing. Originally generate an image in 512 by 512, then generate image with same seed at 512 by 592 using seed resizing. Finally, generate 512 by 592 using original stable diffusion pipeline.
+
+```python
+import torch as th
+import numpy as np
+from diffusers import DiffusionPipeline
+
+has_cuda = th.cuda.is_available()
+device = th.device('cpu' if not has_cuda else 'cuda')
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=True,
+    custom_pipeline="seed_resize_stable_diffusion"
+).to(device)
+
+def dummy(images, **kwargs):
+    return images, False
+
+pipe.safety_checker = dummy
+
+
+images = []
+th.manual_seed(0)
+generator = th.Generator("cuda").manual_seed(0)
+
+seed = 0
+prompt = "A painting of a futuristic cop"
+
+width = 512
+height = 512
+
+res = pipe(
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    height=height,
+    width=width,
+    generator=generator)
+image = res.images[0]
+image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))
+
+
+th.manual_seed(0)
+generator = th.Generator("cuda").manual_seed(0)
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=True,
+    custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
+).to(device)
+
+width = 512
+height = 592
+
+res = pipe(
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    height=height,
+    width=width,
+    generator=generator)
+image = res.images[0]
+image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))
+
+pipe_compare = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=True,
+    custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
+).to(device)
+
+res = pipe_compare(
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    height=height,
+    width=width,
+    generator=generator
+)
+
+image = res.images[0]
+image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))
+```
+
+### Multilingual Stable Diffusion Pipeline
+
+The following code can generate an images from texts in different languages using the pre-trained [mBART-50 many-to-one multilingual machine translation model](https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt) and Stable Diffusion.
+
+```python
+from PIL import Image
+
+import torch
+
+from diffusers import DiffusionPipeline
+from transformers import (
+    pipeline,
+    MBart50TokenizerFast,
+    MBartForConditionalGeneration,
+)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+device_dict = {"cuda": 0, "cpu": -1}
+
+# helper function taken from: https://huggingface.co/blog/stable_diffusion
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows*cols
+
+    w, h = imgs[0].size
+    grid = Image.new('RGB', size=(cols*w, rows*h))
+    grid_w, grid_h = grid.size
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i%cols*w, i//cols*h))
+    return grid
+
+# Add language detection pipeline
+language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
+language_detection_pipeline = pipeline("text-classification",
+                                       model=language_detection_model_ckpt,
+                                       device=device_dict[device])
+
+# Add model for language translation
+trans_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
+trans_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device)
+
+diffuser_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="multilingual_stable_diffusion",
+    detection_pipeline=language_detection_pipeline,
+    translation_model=trans_model,
+    translation_tokenizer=trans_tokenizer,
+    
+    torch_dtype=torch.float16,
+)
+
+diffuser_pipeline.enable_attention_slicing()
+diffuser_pipeline = diffuser_pipeline.to(device)
+
+prompt = ["a photograph of an astronaut riding a horse", 
+          "Una casa en la playa",
+          "Ein Hund, der Orange isst",
+          "Un restaurant parisien"]
+
+output = diffuser_pipeline(prompt)
+
+images = output.images
+
+grid = image_grid(images, rows=2, cols=2)
+```
+
+This example produces the following images:
+![image](https://user-images.githubusercontent.com/4313860/198328706-295824a4-9856-4ce5-8e66-278ceb42fd29.png)
+
+### Image to Image Inpainting Stable Diffusion
+
+Similar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.
+
+`image`, `inner_image`, and `mask` should have the same dimensions. `inner_image` should have an alpha (transparency) channel.
+
+The aim is to overlay two images, then mask out the boundary between `image` and `inner_image` to allow stable diffusion to make the connection more seamless.
+For example, this could be used to place a logo on a shirt and make it blend seamlessly.
+
+```python
+import PIL
+import torch
+
+from diffusers import DiffusionPipeline
+
+image_path = "./path-to-image.png"
+inner_image_path = "./path-to-inner-image.png"
+mask_path = "./path-to-mask.png"
+
+init_image = PIL.Image.open(image_path).convert("RGB").resize((512, 512))
+inner_image = PIL.Image.open(inner_image_path).convert("RGBA").resize((512, 512))
+mask_image = PIL.Image.open(mask_path).convert("RGB").resize((512, 512))
+
+pipe = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    custom_pipeline="img2img_inpainting",
+    
+    torch_dtype=torch.float16
+)
+pipe = pipe.to("cuda")
+
+prompt = "Your prompt here!"
+image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
+```
+
+![2 by 2 grid demonstrating image to image inpainting.](https://user-images.githubusercontent.com/44398246/203506577-ec303be4-887e-4ebd-a773-c83fcb3dd01a.png)
+
+### Text Based Inpainting Stable Diffusion
+
+Use a text prompt to generate the mask for the area to be inpainted.
+Currently uses the CLIPSeg model for mask generation, then calls the standard Stable Diffusion Inpainting pipeline to perform the inpainting.
+
+```python
+from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+from diffusers import DiffusionPipeline
+
+from PIL import Image
+import requests
+
+processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+
+pipe = DiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    custom_pipeline="text_inpainting",
+    segmentation_model=model,
+    segmentation_processor=processor
+)
+pipe = pipe.to("cuda")
+
+
+url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw).resize((512, 512))
+text = "a glass"  # will mask out this text
+prompt = "a cup"  # the masked out region will be replaced with this
+
+image = pipe(image=image, text=text, prompt=prompt).images[0]
+```
+
+### Bit Diffusion 
+Based https://arxiv.org/abs/2208.04202, this is used for diffusion on discrete data - eg, discreate image data, DNA sequence data. An unconditional discreate image can be generated like this: 
+
+```python
+from diffusers import DiffusionPipeline
+pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="bit_diffusion")
+image = pipe().images[0]
+
+```
+
+### Stable Diffusion with K Diffusion
+
+Make sure you have @crowsonkb's https://github.com/crowsonkb/k-diffusion installed:
+
+```
+pip install k-diffusion
+```
+
+You can use the community pipeline as follows:
+
+```python
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
+pipe = pipe.to("cuda")
+
+prompt = "an astronaut riding a horse on mars"
+pipe.set_scheduler("sample_heun")
+generator = torch.Generator(device="cuda").manual_seed(seed)
+image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]
+
+image.save("./astronaut_heun_k_diffusion.png")
+```
+
+To make sure that K Diffusion and `diffusers` yield the same results:
+
+**Diffusers**:
+```python
+from diffusers import DiffusionPipeline, EulerDiscreteScheduler
+
+seed = 33
+
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+generator = torch.Generator(device="cuda").manual_seed(seed)
+image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
+```
+
+![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler.png)
+
+**K Diffusion**:
+```python
+from diffusers import DiffusionPipeline, EulerDiscreteScheduler
+
+seed = 33
+
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+pipe.set_scheduler("sample_euler")
+generator = torch.Generator(device="cuda").manual_seed(seed)
+image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
+```
+
+![diffusers_euler](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/k_diffusion/astronaut_euler_k_diffusion.png)
+
+### Checkpoint Merger Pipeline
+Based on the AUTOMATIC1111/webui for checkpoint merging. This is a custom pipeline that merges upto 3 pretrained model checkpoints as long as they are in the HuggingFace model_index.json format.
+
+The checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect atleast 13GB RAM Usage on Kaggle GPU kernels and
+on colab you might run out of the 12GB memory even while merging two checkpoints.
+
+Usage:-
+```python
+from diffusers import DiffusionPipeline
+
+#Return a CheckpointMergerPipeline class that allows you to merge checkpoints. 
+#The checkpoint passed here is ignored. But still pass one of the checkpoints you plan to 
+#merge for convenience
+pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger")
+
+#There are multiple possible scenarios:
+#The pipeline with the merged checkpoints is returned in all the scenarios
+
+#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparision.( attrs with _ as prefix )
+merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","CompVis/stable-diffusion-v1-2"], interp = "sigmoid", alpha = 0.4)
+
+#Incompatible checkpoints in model_index.json but merge might be possible. Use force = True to ignore model_index.json compatibility
+merged_pipe_1 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion"], force = True, interp = "sigmoid", alpha = 0.4)
+
+#Three checkpoint merging. Only "add_difference" method actually works on all three checkpoints. Using any other options will ignore the 3rd checkpoint.
+merged_pipe_2 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion","prompthero/openjourney"], force = True, interp = "add_difference", alpha = 0.4)
+
+prompt = "An astronaut riding a horse on Mars"
+
+image = merged_pipe(prompt).images[0]
+
+```
+Some examples along with the merge details:
+
+1. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" ; Sigmoid interpolation; alpha = 0.8 
+
+![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stability_v1_4_waifu_sig_0.8.png)
+
+2. "hakurei/waifu-diffusion" + "prompthero/openjourney" ; Inverse Sigmoid interpolation; alpha = 0.8 
+
+![Stable plus Waifu Sigmoid 0.8](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/waifu_openjourney_inv_sig_0.8.png)
+
+
+3. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" + "prompthero/openjourney"; Add Difference interpolation; alpha = 0.5 
+
+![Stable plus Waifu plus openjourney add_diff 0.5](https://huggingface.co/datasets/NagaSaiAbhinay/CheckpointMergerSamples/resolve/main/stable_waifu_openjourney_add_diff_0.5.png)
+
+
+### Stable Diffusion Comparisons
+
+This Community Pipeline enables the comparison between the 4 checkpoints that exist for Stable Diffusion. They can be found through the following links:
+1. [Stable Diffusion v1.1](https://huggingface.co/CompVis/stable-diffusion-v1-1)
+2. [Stable Diffusion v1.2](https://huggingface.co/CompVis/stable-diffusion-v1-2)
+3. [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v1-3)
+4. [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
+
+```python
+from diffusers import DiffusionPipeline
+import matplotlib.pyplot as plt
+
+pipe = DiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', custom_pipeline='suvadityamuk/StableDiffusionComparison')
+pipe.enable_attention_slicing()
+pipe = pipe.to('cuda')
+prompt = "an astronaut riding a horse on mars"
+output = pipe(prompt)
+
+plt.subplots(2,2,1)
+plt.imshow(output.images[0])
+plt.title('Stable Diffusion v1.1')
+plt.axis('off')
+plt.subplots(2,2,2)
+plt.imshow(output.images[1])
+plt.title('Stable Diffusion v1.2')
+plt.axis('off')
+plt.subplots(2,2,3)
+plt.imshow(output.images[2])
+plt.title('Stable Diffusion v1.3')
+plt.axis('off')
+plt.subplots(2,2,4)
+plt.imshow(output.images[3])
+plt.title('Stable Diffusion v1.4')
+plt.axis('off')
+
+plt.show()
+```
+
+As a result, you can look at a grid of all 4 generated images being shown together, that captures a difference the advancement of the training between the 4 checkpoints.
+
+### Magic Mix
+
+Implementation of the [MagicMix: Semantic Mixing with Diffusion Models](https://arxiv.org/abs/2210.16056) paper. This is a Diffusion Pipeline for semantic mixing of an image and a text prompt to create a new concept while preserving the spatial layout and geometry of the subject in the image. The pipeline takes an image that provides the layout semantics and a prompt that provides the content semantics for the mixing process.
+
+There are 3 parameters for the method-
+- `mix_factor`: It is the interpolation constant used in the layout generation phase. The greater the value of `mix_factor`, the greater the influence of the prompt on the layout generation process.
+- `kmax` and `kmin`: These determine the range for the layout and content generation process. A higher value of kmax results in loss of more information about the layout of the original image and a higher value of kmin results in more steps for content generation process.
+
+Here is an example usage-
+
+```python
+from diffusers import DiffusionPipeline, DDIMScheduler
+from PIL import Image
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="magic_mix",
+    scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
+).to('cuda')
+
+img = Image.open('phone.jpg')
+mix_img = pipe(
+    img, 
+    prompt = 'bed', 
+    kmin = 0.3,
+    kmax = 0.5,
+    mix_factor = 0.5,
+    )
+mix_img.save('phone_bed_mix.jpg')
+```
+The `mix_img` is a PIL image that can be saved locally or displayed directly in a google colab. Generated image is a mix of the layout semantics of the given image and the content semantics of the prompt.
+
+E.g. the above script generates the following image:
+
+`phone.jpg`
+
+![206903102-34e79b9f-9ed2-4fac-bb38-82871343c655](https://user-images.githubusercontent.com/59410571/209578593-141467c7-d831-4792-8b9a-b17dc5e47816.jpg)
+
+`phone_bed_mix.jpg`
+
+![206903104-913a671d-ef53-4ae4-919d-64c3059c8f67](https://user-images.githubusercontent.com/59410571/209578602-70f323fa-05b7-4dd6-b055-e40683e37914.jpg)
+
+For more example generations check out this [demo notebook](https://github.com/daspartho/MagicMix/blob/main/demo.ipynb).
+
+
+### Stable UnCLIP
+
+UnCLIPPipeline("kakaobrain/karlo-v1-alpha") provide a prior model that can generate clip image embedding from text.
+StableDiffusionImageVariationPipeline("lambdalabs/sd-image-variations-diffusers") provide a decoder model than can generate images from clip image embedding.
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "kakaobrain/karlo-v1-alpha",
+    torch_dtype=torch.float16,
+    custom_pipeline="stable_unclip",
+    decoder_pipe_kwargs=dict(
+        image_encoder=None,
+    ),
+)
+pipeline.to(device)
+
+prompt = "a shiba inu wearing a beret and black turtleneck"
+random_generator = torch.Generator(device=device).manual_seed(1000)
+output = pipeline(
+    prompt=prompt,
+    width=512,
+    height=512,
+    generator=random_generator,
+    prior_guidance_scale=4,
+    prior_num_inference_steps=25,
+    decoder_guidance_scale=8,
+    decoder_num_inference_steps=50,
+)
+
+image = output.images[0]
+image.save("./shiba-inu.jpg")
+
+# debug
+
+# `pipeline.decoder_pipe` is a regular StableDiffusionImageVariationPipeline instance.
+# It is used to convert clip image embedding to latents, then fed into VAE decoder.
+print(pipeline.decoder_pipe.__class__)
+# <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline'>
+
+# this pipeline only use prior module in "kakaobrain/karlo-v1-alpha"
+# It is used to convert clip text embedding to clip image embedding.
+print(pipeline)
+# StableUnCLIPPipeline {
+#   "_class_name": "StableUnCLIPPipeline",
+#   "_diffusers_version": "0.12.0.dev0",
+#   "prior": [
+#     "diffusers",
+#     "PriorTransformer"
+#   ],
+#   "prior_scheduler": [
+#     "diffusers",
+#     "UnCLIPScheduler"
+#   ],
+#   "text_encoder": [
+#     "transformers",
+#     "CLIPTextModelWithProjection"
+#   ],
+#   "tokenizer": [
+#     "transformers",
+#     "CLIPTokenizer"
+#   ]
+# }
+
+# pipeline.prior_scheduler is the scheduler used for prior in UnCLIP.
+print(pipeline.prior_scheduler)
+# UnCLIPScheduler {
+#   "_class_name": "UnCLIPScheduler",
+#   "_diffusers_version": "0.12.0.dev0",
+#   "clip_sample": true,
+#   "clip_sample_range": 5.0,
+#   "num_train_timesteps": 1000,
+#   "prediction_type": "sample",
+#   "variance_type": "fixed_small_log"
+# }
+```
+
+
+`shiba-inu.jpg`
+
+
+![shiba-inu](https://user-images.githubusercontent.com/16448529/209185639-6e5ec794-ce9d-4883-aa29-bd6852a2abad.jpg)
+

huggingface_diffusers/examples/community/bit_diffusion.py

@@ -0,0 +1,264 @@
+from typing import Optional, Tuple, Union
+
+import torch
+
+from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel
+from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
+from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
+from einops import rearrange, reduce
+
+
+BITS = 8
+
+
+# convert to bit representations and back taken from https://github.com/lucidrains/bit-diffusion/blob/main/bit_diffusion/bit_diffusion.py
+def decimal_to_bits(x, bits=BITS):
+    """expects image tensor ranging from 0 to 1, outputs bit tensor ranging from -1 to 1"""
+    device = x.device
+
+    x = (x * 255).int().clamp(0, 255)
+
+    mask = 2 ** torch.arange(bits - 1, -1, -1, device=device)
+    mask = rearrange(mask, "d -> d 1 1")
+    x = rearrange(x, "b c h w -> b c 1 h w")
+
+    bits = ((x & mask) != 0).float()
+    bits = rearrange(bits, "b c d h w -> b (c d) h w")
+    bits = bits * 2 - 1
+    return bits
+
+
+def bits_to_decimal(x, bits=BITS):
+    """expects bits from -1 to 1, outputs image tensor from 0 to 1"""
+    device = x.device
+
+    x = (x > 0).int()
+    mask = 2 ** torch.arange(bits - 1, -1, -1, device=device, dtype=torch.int32)
+
+    mask = rearrange(mask, "d -> d 1 1")
+    x = rearrange(x, "b (c d) h w -> b c d h w", d=8)
+    dec = reduce(x * mask, "b c d h w -> b c h w", "sum")
+    return (dec / 255).clamp(0.0, 1.0)
+
+
+# modified scheduler step functions for clamping the predicted x_0 between -bit_scale and +bit_scale
+def ddim_bit_scheduler_step(
+    self,
+    model_output: torch.FloatTensor,
+    timestep: int,
+    sample: torch.FloatTensor,
+    eta: float = 0.0,
+    use_clipped_model_output: bool = True,
+    generator=None,
+    return_dict: bool = True,
+) -> Union[DDIMSchedulerOutput, Tuple]:
+    """
+    Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+    process from the learned model outputs (most often the predicted noise).
+    Args:
+        model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+        timestep (`int`): current discrete timestep in the diffusion chain.
+        sample (`torch.FloatTensor`):
+            current instance of sample being created by diffusion process.
+        eta (`float`): weight of noise for added noise in diffusion step.
+        use_clipped_model_output (`bool`): TODO
+        generator: random number generator.
+        return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
+    Returns:
+        [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
+        [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+        returning a tuple, the first element is the sample tensor.
+    """
+    if self.num_inference_steps is None:
+        raise ValueError(
+            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+        )
+
+    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
+    # Ideally, read DDIM paper in-detail understanding
+
+    # Notation (<variable name> -> <name in paper>
+    # - pred_noise_t -> e_theta(x_t, t)
+    # - pred_original_sample -> f_theta(x_t, t) or x_0
+    # - std_dev_t -> sigma_t
+    # - eta -> η
+    # - pred_sample_direction -> "direction pointing to x_t"
+    # - pred_prev_sample -> "x_t-1"
+
+    # 1. get previous step value (=t-1)
+    prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
+
+    # 2. compute alphas, betas
+    alpha_prod_t = self.alphas_cumprod[timestep]
+    alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
+
+    beta_prod_t = 1 - alpha_prod_t
+
+    # 3. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+
+    # 4. Clip "predicted x_0"
+    scale = self.bit_scale
+    if self.config.clip_sample:
+        pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
+
+    # 5. compute variance: "sigma_t(η)" -> see formula (16)
+    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
+    variance = self._get_variance(timestep, prev_timestep)
+    std_dev_t = eta * variance ** (0.5)
+
+    if use_clipped_model_output:
+        # the model_output is always re-derived from the clipped x_0 in Glide
+        model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
+
+    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
+
+    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+    prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
+
+    if eta > 0:
+        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
+        device = model_output.device if torch.is_tensor(model_output) else "cpu"
+        noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
+        variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * noise
+
+        prev_sample = prev_sample + variance
+
+    if not return_dict:
+        return (prev_sample,)
+
+    return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
+
+
+def ddpm_bit_scheduler_step(
+    self,
+    model_output: torch.FloatTensor,
+    timestep: int,
+    sample: torch.FloatTensor,
+    prediction_type="epsilon",
+    generator=None,
+    return_dict: bool = True,
+) -> Union[DDPMSchedulerOutput, Tuple]:
+    """
+    Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+    process from the learned model outputs (most often the predicted noise).
+    Args:
+        model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+        timestep (`int`): current discrete timestep in the diffusion chain.
+        sample (`torch.FloatTensor`):
+            current instance of sample being created by diffusion process.
+        prediction_type (`str`, default `epsilon`):
+            indicates whether the model predicts the noise (epsilon), or the samples (`sample`).
+        generator: random number generator.
+        return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
+    Returns:
+        [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
+        [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+        returning a tuple, the first element is the sample tensor.
+    """
+    t = timestep
+
+    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
+        model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
+    else:
+        predicted_variance = None
+
+    # 1. compute alphas, betas
+    alpha_prod_t = self.alphas_cumprod[t]
+    alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
+    beta_prod_t = 1 - alpha_prod_t
+    beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+    # 2. compute predicted original sample from predicted noise also called
+    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
+    if prediction_type == "epsilon":
+        pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+    elif prediction_type == "sample":
+        pred_original_sample = model_output
+    else:
+        raise ValueError(f"Unsupported prediction_type {prediction_type}.")
+
+    # 3. Clip "predicted x_0"
+    scale = self.bit_scale
+    if self.config.clip_sample:
+        pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
+
+    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
+    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+    pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
+    current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
+
+    # 5. Compute predicted previous sample µ_t
+    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+    pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
+
+    # 6. Add noise
+    variance = 0
+    if t > 0:
+        noise = torch.randn(
+            model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=generator
+        ).to(model_output.device)
+        variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise
+
+    pred_prev_sample = pred_prev_sample + variance
+
+    if not return_dict:
+        return (pred_prev_sample,)
+
+    return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
+
+
+class BitDiffusion(DiffusionPipeline):
+    def __init__(
+        self,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, DDPMScheduler],
+        bit_scale: Optional[float] = 1.0,
+    ):
+        super().__init__()
+        self.bit_scale = bit_scale
+        self.scheduler.step = (
+            ddim_bit_scheduler_step if isinstance(scheduler, DDIMScheduler) else ddpm_bit_scheduler_step
+        )
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        height: Optional[int] = 256,
+        width: Optional[int] = 256,
+        num_inference_steps: Optional[int] = 50,
+        generator: Optional[torch.Generator] = None,
+        batch_size: Optional[int] = 1,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        **kwargs,
+    ) -> Union[Tuple, ImagePipelineOutput]:
+        latents = torch.randn(
+            (batch_size, self.unet.in_channels, height, width),
+            generator=generator,
+        )
+        latents = decimal_to_bits(latents) * self.bit_scale
+        latents = latents.to(self.device)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        for t in self.progress_bar(self.scheduler.timesteps):
+            # predict the noise residual
+            noise_pred = self.unet(latents, t).sample
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+
+        image = bits_to_decimal(latents)
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

huggingface_diffusers/examples/community/checkpoint_merger.py

@@ -0,0 +1,285 @@
+import glob
+import os
+from typing import Dict, List, Union
+
+import torch
+
+from diffusers.utils import is_safetensors_available
+
+
+if is_safetensors_available():
+    import safetensors.torch
+
+from diffusers import DiffusionPipeline, __version__
+from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
+from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
+from huggingface_hub import snapshot_download
+
+
+class CheckpointMergerPipeline(DiffusionPipeline):
+    """
+    A class that that supports merging diffusion models based on the discussion here:
+    https://github.com/huggingface/diffusers/issues/877
+
+    Example usage:-
+
+    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
+
+    merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
+
+    merged_pipe.to('cuda')
+
+    prompt = "An astronaut riding a unicycle on Mars"
+
+    results = merged_pipe(prompt)
+
+    ## For more details, see the docstring for the merge method.
+
+    """
+
+    def __init__(self):
+        self.register_to_config()
+        super().__init__()
+
+    def _compare_model_configs(self, dict0, dict1):
+        if dict0 == dict1:
+            return True
+        else:
+            config0, meta_keys0 = self._remove_meta_keys(dict0)
+            config1, meta_keys1 = self._remove_meta_keys(dict1)
+            if config0 == config1:
+                print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.")
+                return True
+        return False
+
+    def _remove_meta_keys(self, config_dict: Dict):
+        meta_keys = []
+        temp_dict = config_dict.copy()
+        for key in config_dict.keys():
+            if key.startswith("_"):
+                temp_dict.pop(key)
+                meta_keys.append(key)
+        return (temp_dict, meta_keys)
+
+    @torch.no_grad()
+    def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs):
+        """
+        Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed
+        in the argument 'pretrained_model_name_or_path_list' as a list.
+
+        Parameters:
+        -----------
+            pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.
+
+            **kwargs:
+                Supports all the default DiffusionPipeline.get_config_dict kwargs viz..
+
+                cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map.
+
+                alpha - The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
+                    would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
+
+                interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_difference" and None.
+                    Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported.
+
+                force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
+
+        """
+        # Default kwargs from DiffusionPipeline
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        resume_download = kwargs.pop("resume_download", False)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        device_map = kwargs.pop("device_map", None)
+
+        alpha = kwargs.pop("alpha", 0.5)
+        interp = kwargs.pop("interp", None)
+
+        print("Received list", pretrained_model_name_or_path_list)
+        print(f"Combining with alpha={alpha}, interpolation mode={interp}")
+
+        checkpoint_count = len(pretrained_model_name_or_path_list)
+        # Ignore result from model_index_json comparision of the two checkpoints
+        force = kwargs.pop("force", False)
+
+        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
+        if checkpoint_count > 3 or checkpoint_count < 2:
+            raise ValueError(
+                "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
+                " passed."
+            )
+
+        print("Received the right number of checkpoints")
+        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
+        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
+
+        # Validate that the checkpoints can be merged
+        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
+        config_dicts = []
+        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
+            config_dict = DiffusionPipeline.load_config(
+                pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                resume_download=resume_download,
+                force_download=force_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+            )
+            config_dicts.append(config_dict)
+
+        comparison_result = True
+        for idx in range(1, len(config_dicts)):
+            comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx])
+            if not force and comparison_result is False:
+                raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.")
+                print(config_dicts[0], config_dicts[1])
+        print("Compatible model_index.json files found")
+        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
+        cached_folders = []
+        for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts):
+            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
+            allow_patterns = [os.path.join(k, "*") for k in folder_names]
+            allow_patterns += [
+                WEIGHTS_NAME,
+                SCHEDULER_CONFIG_NAME,
+                CONFIG_NAME,
+                ONNX_WEIGHTS_NAME,
+                DiffusionPipeline.config_name,
+            ]
+            requested_pipeline_class = config_dict.get("_class_name")
+            user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
+
+            cached_folder = (
+                pretrained_model_name_or_path
+                if os.path.isdir(pretrained_model_name_or_path)
+                else snapshot_download(
+                    pretrained_model_name_or_path,
+                    cache_dir=cache_dir,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    use_auth_token=use_auth_token,
+                    revision=revision,
+                    allow_patterns=allow_patterns,
+                    user_agent=user_agent,
+                )
+            )
+            print("Cached Folder", cached_folder)
+            cached_folders.append(cached_folder)
+
+        # Step 3:-
+        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
+        final_pipe = DiffusionPipeline.from_pretrained(
+            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
+        )
+        final_pipe.to(self.device)
+
+        checkpoint_path_2 = None
+        if len(cached_folders) > 2:
+            checkpoint_path_2 = os.path.join(cached_folders[2])
+
+        if interp == "sigmoid":
+            theta_func = CheckpointMergerPipeline.sigmoid
+        elif interp == "inv_sigmoid":
+            theta_func = CheckpointMergerPipeline.inv_sigmoid
+        elif interp == "add_diff":
+            theta_func = CheckpointMergerPipeline.add_difference
+        else:
+            theta_func = CheckpointMergerPipeline.weighted_sum
+
+        # Find each module's state dict.
+        for attr in final_pipe.config.keys():
+            if not attr.startswith("_"):
+                checkpoint_path_1 = os.path.join(cached_folders[1], attr)
+                if os.path.exists(checkpoint_path_1):
+                    files = list(
+                        (
+                            *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
+                            *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
+                        )
+                    )
+                    checkpoint_path_1 = files[0] if len(files) > 0 else None
+                if checkpoint_path_2 is not None and os.path.exists(checkpoint_path_2):
+                    files = list(
+                        (
+                            *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
+                            *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
+                        )
+                    )
+                    checkpoint_path_2 = files[0] if len(files) > 0 else None
+                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
+                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
+                if checkpoint_path_1 is None and checkpoint_path_2 is None:
+                    print(f"Skipping {attr}: not present in 2nd or 3d model")
+                    continue
+                try:
+                    module = getattr(final_pipe, attr)
+                    if isinstance(module, bool):  # ignore requires_safety_checker boolean
+                        continue
+                    theta_0 = getattr(module, "state_dict")
+                    theta_0 = theta_0()
+
+                    update_theta_0 = getattr(module, "load_state_dict")
+                    theta_1 = (
+                        safetensors.torch.load_file(checkpoint_path_1)
+                        if (is_safetensors_available() and checkpoint_path_1.endswith(".safetensors"))
+                        else torch.load(checkpoint_path_1, map_location="cpu")
+                    )
+                    theta_2 = None
+                    if checkpoint_path_2:
+                        theta_2 = (
+                            safetensors.torch.load_file(checkpoint_path_2)
+                            if (is_safetensors_available() and checkpoint_path_2.endswith(".safetensors"))
+                            else torch.load(checkpoint_path_2, map_location="cpu")
+                        )
+
+                    if not theta_0.keys() == theta_1.keys():
+                        print(f"Skipping {attr}: key mismatch")
+                        continue
+                    if theta_2 and not theta_1.keys() == theta_2.keys():
+                        print(f"Skipping {attr}:y mismatch")
+                except Exception as e:
+                    print(f"Skipping {attr} do to an unexpected error: {str(e)}")
+                    continue
+                print(f"MERGING {attr}")
+
+                for key in theta_0.keys():
+                    if theta_2:
+                        theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha)
+                    else:
+                        theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha)
+
+                del theta_1
+                del theta_2
+                update_theta_0(theta_0)
+
+                del theta_0
+        return final_pipe
+
+    @staticmethod
+    def weighted_sum(theta0, theta1, theta2, alpha):
+        return ((1 - alpha) * theta0) + (alpha * theta1)
+
+    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
+    @staticmethod
+    def sigmoid(theta0, theta1, theta2, alpha):
+        alpha = alpha * alpha * (3 - (2 * alpha))
+        return theta0 + ((theta1 - theta0) * alpha)
+
+    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
+    @staticmethod
+    def inv_sigmoid(theta0, theta1, theta2, alpha):
+        import math
+
+        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
+        return theta0 + ((theta1 - theta0) * alpha)
+
+    @staticmethod
+    def add_difference(theta0, theta1, theta2, alpha):
+        return theta0 + (theta1 - theta2) * (1.0 - alpha)

huggingface_diffusers/examples/community/clip_guided_stable_diffusion.py

@@ -0,0 +1,351 @@
+import inspect
+from typing import List, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from torchvision import transforms
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
+
+
+class MakeCutouts(nn.Module):
+    def __init__(self, cut_size, cut_power=1.0):
+        super().__init__()
+
+        self.cut_size = cut_size
+        self.cut_power = cut_power
+
+    def forward(self, pixel_values, num_cutouts):
+        sideY, sideX = pixel_values.shape[2:4]
+        max_size = min(sideX, sideY)
+        min_size = min(sideX, sideY, self.cut_size)
+        cutouts = []
+        for _ in range(num_cutouts):
+            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
+            offsetx = torch.randint(0, sideX - size + 1, ())
+            offsety = torch.randint(0, sideY - size + 1, ())
+            cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
+            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
+        return torch.cat(cutouts)
+
+
+def spherical_dist_loss(x, y):
+    x = F.normalize(x, dim=-1)
+    y = F.normalize(y, dim=-1)
+    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
+
+
+def set_requires_grad(model, value):
+    for param in model.parameters():
+        param.requires_grad = value
+
+
+class CLIPGuidedStableDiffusion(DiffusionPipeline):
+    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
+    - https://github.com/Jack000/glid-3-xl
+    - https://github.dev/crowsonkb/k-diffusion
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        clip_model: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            clip_model=clip_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
+        self.cut_out_size = (
+            feature_extractor.size
+            if isinstance(feature_extractor.size, int)
+            else feature_extractor.size["shortest_edge"]
+        )
+        self.make_cutouts = MakeCutouts(self.cut_out_size)
+
+        set_requires_grad(self.text_encoder, False)
+        set_requires_grad(self.clip_model, False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    def freeze_vae(self):
+        set_requires_grad(self.vae, False)
+
+    def unfreeze_vae(self):
+        set_requires_grad(self.vae, True)
+
+    def freeze_unet(self):
+        set_requires_grad(self.unet, False)
+
+    def unfreeze_unet(self):
+        set_requires_grad(self.unet, True)
+
+    @torch.enable_grad()
+    def cond_fn(
+        self,
+        latents,
+        timestep,
+        index,
+        text_embeddings,
+        noise_pred_original,
+        text_embeddings_clip,
+        clip_guidance_scale,
+        num_cutouts,
+        use_cutouts=True,
+    ):
+        latents = latents.detach().requires_grad_()
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
+            latent_model_input = latents / ((sigma**2 + 1) ** 0.5)
+        else:
+            latent_model_input = latents
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample
+
+        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)):
+            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
+            beta_prod_t = 1 - alpha_prod_t
+            # compute predicted original sample from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
+
+            fac = torch.sqrt(beta_prod_t)
+            sample = pred_original_sample * (fac) + latents * (1 - fac)
+        elif isinstance(self.scheduler, LMSDiscreteScheduler):
+            sigma = self.scheduler.sigmas[index]
+            sample = latents - sigma * noise_pred
+        else:
+            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")
+
+        sample = 1 / self.vae.config.scaling_factor * sample
+        image = self.vae.decode(sample).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        if use_cutouts:
+            image = self.make_cutouts(image, num_cutouts)
+        else:
+            image = transforms.Resize(self.cut_out_size)(image)
+        image = self.normalize(image).to(latents.dtype)
+
+        image_embeddings_clip = self.clip_model.get_image_features(image)
+        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+
+        if use_cutouts:
+            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
+            dists = dists.view([num_cutouts, sample.shape[0], -1])
+            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
+        else:
+            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale
+
+        grads = -torch.autograd.grad(loss, latents)[0]
+
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents.detach() + grads * (sigma**2)
+            noise_pred = noise_pred_original
+        else:
+            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
+        return noise_pred, latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        clip_guidance_scale: Optional[float] = 100,
+        clip_prompt: Optional[Union[str, List[str]]] = None,
+        num_cutouts: Optional[int] = 4,
+        use_cutouts: Optional[bool] = True,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        # get prompt text embeddings
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+        # duplicate text embeddings for each generation per prompt
+        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if clip_guidance_scale > 0:
+            if clip_prompt is not None:
+                clip_text_input = self.tokenizer(
+                    clip_prompt,
+                    padding="max_length",
+                    max_length=self.tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                ).input_ids.to(self.device)
+            else:
+                clip_text_input = text_input.input_ids.to(self.device)
+            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
+            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
+            # duplicate text embeddings clip for each generation per prompt
+            text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=0)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+            # duplicate unconditional embeddings for each generation per prompt
+            uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform classifier free guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # perform clip guidance
+            if clip_guidance_scale > 0:
+                text_embeddings_for_guidance = (
+                    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
+                )
+                noise_pred, latents = self.cond_fn(
+                    latents,
+                    t,
+                    i,
+                    text_embeddings_for_guidance,
+                    noise_pred,
+                    text_embeddings_clip,
+                    clip_guidance_scale,
+                    num_cutouts,
+                    use_cutouts,
+                )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        # scale and decode the image latents with vae
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, None)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

huggingface_diffusers/examples/community/composable_stable_diffusion.py

@@ -0,0 +1,583 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import is_accelerate_available
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+from ...utils import deprecate, logging
+from . import StableDiffusionPipelineOutput
+from .safety_checker import StableDiffusionSafetyChecker
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class ComposableStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding.
+
+        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
+        steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+        if self.safety_checker is not None:
+            # TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
+            # fix by only offloading self.safety_checker for now
+            cpu_offload(self.safety_checker.vision_model, device)
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(self, prompt, height, width, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if latents is None:
+            if device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        weights: Optional[str] = "",
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        if "|" in prompt:
+            prompt = [x.strip() for x in prompt.split("|")]
+            print(f"composing {prompt}...")
+
+            if not weights:
+                # specify weights for prompts (excluding the unconditional score)
+                print("using equal positive weights (conjunction) for all prompts...")
+                weights = torch.tensor([guidance_scale] * len(prompt), device=self.device).reshape(-1, 1, 1, 1)
+            else:
+                # set prompt weight for each
+                num_prompts = len(prompt) if isinstance(prompt, list) else 1
+                weights = [float(w.strip()) for w in weights.split("|")]
+                # guidance scale as the default
+                if len(weights) < num_prompts:
+                    weights.append(guidance_scale)
+                else:
+                    weights = weights[:num_prompts]
+                assert len(weights) == len(prompt), "weights specified are not equal to the number of prompts"
+                weights = torch.tensor(weights, device=self.device).reshape(-1, 1, 1, 1)
+        else:
+            weights = guidance_scale
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # composable diffusion
+        if isinstance(prompt, list) and batch_size == 1:
+            # remove extra unconditional embedding
+            # N = one unconditional embed + conditional embeds
+            text_embeddings = text_embeddings[len(prompt) - 1 :]
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # predict the noise residual
+                noise_pred = []
+                for j in range(text_embeddings.shape[0]):
+                    noise_pred.append(
+                        self.unet(latent_model_input[:1], t, encoder_hidden_states=text_embeddings[j : j + 1]).sample
+                    )
+                noise_pred = torch.cat(noise_pred, dim=0)
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred[:1], noise_pred[1:]
+                    noise_pred = noise_pred_uncond + (weights * (noise_pred_text - noise_pred_uncond)).sum(
+                        dim=0, keepdims=True
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # 8. Post-processing
+        image = self.decode_latents(latents)
+
+        # 9. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+
+        # 10. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

huggingface_diffusers/examples/community/imagic_stable_diffusion.py

@@ -0,0 +1,502 @@
+"""
+modeled after the textual_inversion.py / train_dreambooth.py and the work
+of justinpinkney here: https://github.com/justinpinkney/stable-diffusion/blob/main/notebooks/imagic.ipynb
+"""
+
+import inspect
+import warnings
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+import PIL
+from accelerate import Accelerator
+from diffusers import DiffusionPipeline
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+from packaging import version
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.Resampling.BILINEAR,
+        "bilinear": PIL.Image.Resampling.BILINEAR,
+        "bicubic": PIL.Image.Resampling.BICUBIC,
+        "lanczos": PIL.Image.Resampling.LANCZOS,
+        "nearest": PIL.Image.Resampling.NEAREST,
+    }
+else:
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.LINEAR,
+        "bilinear": PIL.Image.BILINEAR,
+        "bicubic": PIL.Image.BICUBIC,
+        "lanczos": PIL.Image.LANCZOS,
+        "nearest": PIL.Image.NEAREST,
+    }
+# ------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def preprocess(image):
+    w, h = image.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+class ImagicStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for imagic image editing.
+    See paper here: https://arxiv.org/pdf/2210.09276.pdf
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offsensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def train(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        generator: Optional[torch.Generator] = None,
+        embedding_learning_rate: float = 0.001,
+        diffusion_model_learning_rate: float = 2e-6,
+        text_embedding_optimization_steps: int = 500,
+        model_fine_tuning_optimization_steps: int = 1000,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        message = "Please use `image` instead of `init_image`."
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
+        image = init_image or image
+
+        accelerator = Accelerator(
+            gradient_accumulation_steps=1,
+            mixed_precision="fp16",
+        )
+
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        # Freeze vae and unet
+        self.vae.requires_grad_(False)
+        self.unet.requires_grad_(False)
+        self.text_encoder.requires_grad_(False)
+        self.unet.eval()
+        self.vae.eval()
+        self.text_encoder.eval()
+
+        if accelerator.is_main_process:
+            accelerator.init_trackers(
+                "imagic",
+                config={
+                    "embedding_learning_rate": embedding_learning_rate,
+                    "text_embedding_optimization_steps": text_embedding_optimization_steps,
+                },
+            )
+
+        # get text embeddings for prompt
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = torch.nn.Parameter(
+            self.text_encoder(text_input.input_ids.to(self.device))[0], requires_grad=True
+        )
+        text_embeddings = text_embeddings.detach()
+        text_embeddings.requires_grad_()
+        text_embeddings_orig = text_embeddings.clone()
+
+        # Initialize the optimizer
+        optimizer = torch.optim.Adam(
+            [text_embeddings],  # only optimize the embeddings
+            lr=embedding_learning_rate,
+        )
+
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess(image)
+
+        latents_dtype = text_embeddings.dtype
+        image = image.to(device=self.device, dtype=latents_dtype)
+        init_latent_image_dist = self.vae.encode(image).latent_dist
+        image_latents = init_latent_image_dist.sample(generator=generator)
+        image_latents = 0.18215 * image_latents
+
+        progress_bar = tqdm(range(text_embedding_optimization_steps), disable=not accelerator.is_local_main_process)
+        progress_bar.set_description("Steps")
+
+        global_step = 0
+
+        logger.info("First optimizing the text embedding to better reconstruct the init image")
+        for _ in range(text_embedding_optimization_steps):
+            with accelerator.accumulate(text_embeddings):
+                # Sample noise that we'll add to the latents
+                noise = torch.randn(image_latents.shape).to(image_latents.device)
+                timesteps = torch.randint(1000, (1,), device=image_latents.device)
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
+
+                loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                accelerator.backward(loss)
+
+                optimizer.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+            logs = {"loss": loss.detach().item()}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+        accelerator.wait_for_everyone()
+
+        text_embeddings.requires_grad_(False)
+
+        # Now we fine tune the unet to better reconstruct the image
+        self.unet.requires_grad_(True)
+        self.unet.train()
+        optimizer = torch.optim.Adam(
+            self.unet.parameters(),  # only optimize unet
+            lr=diffusion_model_learning_rate,
+        )
+        progress_bar = tqdm(range(model_fine_tuning_optimization_steps), disable=not accelerator.is_local_main_process)
+
+        logger.info("Next fine tuning the entire model to better reconstruct the init image")
+        for _ in range(model_fine_tuning_optimization_steps):
+            with accelerator.accumulate(self.unet.parameters()):
+                # Sample noise that we'll add to the latents
+                noise = torch.randn(image_latents.shape).to(image_latents.device)
+                timesteps = torch.randint(1000, (1,), device=image_latents.device)
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = self.scheduler.add_noise(image_latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = self.unet(noisy_latents, timesteps, text_embeddings).sample
+
+                loss = F.mse_loss(noise_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                accelerator.backward(loss)
+
+                optimizer.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+            logs = {"loss": loss.detach().item()}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+        accelerator.wait_for_everyone()
+        self.text_embeddings_orig = text_embeddings_orig
+        self.text_embeddings = text_embeddings
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        alpha: float = 1.2,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        guidance_scale: float = 7.5,
+        eta: float = 0.0,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+        if self.text_embeddings is None:
+            raise ValueError("Please run the pipe.train() before trying to generate an image.")
+        if self.text_embeddings_orig is None:
+            raise ValueError("Please run the pipe.train() before trying to generate an image.")
+
+        text_embeddings = alpha * self.text_embeddings_orig + (1 - alpha) * self.text_embeddings
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens = [""]
+            max_length = self.tokenizer.model_max_length
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.view(1, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (1, self.unet.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if self.device.type == "mps":
+            # randn does not exist on mps
+            latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                self.device
+            )
+        else:
+            latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

huggingface_diffusers/examples/community/img2img_inpainting.py

@@ -0,0 +1,463 @@
+import inspect
+from typing import Callable, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+import PIL
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def prepare_mask_and_masked_image(image, mask):
+    image = np.array(image.convert("RGB"))
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+    mask = np.array(mask.convert("L"))
+    mask = mask.astype(np.float32) / 255.0
+    mask = mask[None, None]
+    mask[mask < 0.5] = 0
+    mask[mask >= 0.5] = 1
+    mask = torch.from_numpy(mask)
+
+    masked_image = image * (mask < 0.5)
+
+    return mask, masked_image
+
+
+def check_size(image, height, width):
+    if isinstance(image, PIL.Image.Image):
+        w, h = image.size
+    elif isinstance(image, torch.Tensor):
+        *_, h, w = image.shape
+
+    if h != height or w != width:
+        raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}")
+
+
+def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):
+    inner_image = inner_image.convert("RGBA")
+    image = image.convert("RGB")
+
+    image.paste(inner_image, paste_offset, inner_image)
+    image = image.convert("RGB")
+
+    return image
+
+
+class ImageToImageInpaintingPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-guided image-to-image inpainting using Stable Diffusion. *This is an experimental feature*.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        inner_image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            inner_image (`torch.Tensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent
+                regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with
+                the last channel representing the alpha channel, which will be used to blend `inner_image` with
+                `image`. If not provided, it will be forcibly cast to RGBA.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # check if input sizes are correct
+        check_size(image, height, width)
+        check_size(inner_image, height, width)
+        check_size(mask_image, height, width)
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        num_channels_latents = self.vae.config.latent_channels
+        latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # overlay the inner image
+        image = overlay_inner_image(image, inner_image)
+
+        # prepare mask and masked_image
+        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
+        mask = mask.to(device=self.device, dtype=text_embeddings.dtype)
+        masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype)
+
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8))
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
+        masked_image_latents = 0.18215 * masked_image_latents
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
+        masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
+
+        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+        masked_image_latents = (
+            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+        )
+
+        num_channels_mask = mask.shape[1]
+        num_channels_masked_image = masked_image_latents.shape[1]
+
+        if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
+            raise ValueError(
+                f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+                f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+                f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
+                f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                " `pipeline.unet` or your `mask_image` or `image` input."
+            )
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+            # concat latents, mask, masked_image_latents in the channel dimension
+            latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

huggingface_diffusers/examples/community/interpolate_stable_diffusion.py

@@ -0,0 +1,524 @@
+import inspect
+import time
+from pathlib import Path
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
+    """helper function to spherically interpolate two arrays v1 v2"""
+
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+class StableDiffusionWalkPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        text_embeddings: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
+                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
+                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
+                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
+                the supplied `prompt`.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if text_embeddings is None:
+            if isinstance(prompt, str):
+                batch_size = 1
+            elif isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+            # get prompt text embeddings
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+
+            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+                print(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+        else:
+            batch_size = text_embeddings.shape[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = self.tokenizer.model_max_length
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def embed_text(self, text):
+        """takes in text and turns it into text embeddings"""
+        text_input = self.tokenizer(
+            text,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        with torch.no_grad():
+            embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
+        return embed
+
+    def get_noise(self, seed, dtype=torch.float32, height=512, width=512):
+        """Takes in random seed and returns corresponding noise vector"""
+        return torch.randn(
+            (1, self.unet.in_channels, height // 8, width // 8),
+            generator=torch.Generator(device=self.device).manual_seed(seed),
+            device=self.device,
+            dtype=dtype,
+        )
+
+    def walk(
+        self,
+        prompts: List[str],
+        seeds: List[int],
+        num_interpolation_steps: Optional[int] = 6,
+        output_dir: Optional[str] = "./dreams",
+        name: Optional[str] = None,
+        batch_size: Optional[int] = 1,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        guidance_scale: Optional[float] = 7.5,
+        num_inference_steps: Optional[int] = 50,
+        eta: Optional[float] = 0.0,
+    ) -> List[str]:
+        """
+        Walks through a series of prompts and seeds, interpolating between them and saving the results to disk.
+
+        Args:
+            prompts (`List[str]`):
+                List of prompts to generate images for.
+            seeds (`List[int]`):
+                List of seeds corresponding to provided prompts. Must be the same length as prompts.
+            num_interpolation_steps (`int`, *optional*, defaults to 6):
+                Number of interpolation steps to take between prompts.
+            output_dir (`str`, *optional*, defaults to `./dreams`):
+                Directory to save the generated images to.
+            name (`str`, *optional*, defaults to `None`):
+                Subdirectory of `output_dir` to save the generated images to. If `None`, the name will
+                be the current time.
+            batch_size (`int`, *optional*, defaults to 1):
+                Number of images to generate at once.
+            height (`int`, *optional*, defaults to 512):
+                Height of the generated images.
+            width (`int`, *optional*, defaults to 512):
+                Width of the generated images.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+
+        Returns:
+            `List[str]`: List of paths to the generated images.
+        """
+        if not len(prompts) == len(seeds):
+            raise ValueError(
+                f"Number of prompts and seeds must be equalGot {len(prompts)} prompts and {len(seeds)} seeds"
+            )
+
+        name = name or time.strftime("%Y%m%d-%H%M%S")
+        save_path = Path(output_dir) / name
+        save_path.mkdir(exist_ok=True, parents=True)
+
+        frame_idx = 0
+        frame_filepaths = []
+        for prompt_a, prompt_b, seed_a, seed_b in zip(prompts, prompts[1:], seeds, seeds[1:]):
+            # Embed Text
+            embed_a = self.embed_text(prompt_a)
+            embed_b = self.embed_text(prompt_b)
+
+            # Get Noise
+            noise_dtype = embed_a.dtype
+            noise_a = self.get_noise(seed_a, noise_dtype, height, width)
+            noise_b = self.get_noise(seed_b, noise_dtype, height, width)
+
+            noise_batch, embeds_batch = None, None
+            T = np.linspace(0.0, 1.0, num_interpolation_steps)
+            for i, t in enumerate(T):
+                noise = slerp(float(t), noise_a, noise_b)
+                embed = torch.lerp(embed_a, embed_b, t)
+
+                noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise], dim=0)
+                embeds_batch = embed if embeds_batch is None else torch.cat([embeds_batch, embed], dim=0)
+
+                batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
+                if batch_is_ready:
+                    outputs = self(
+                        latents=noise_batch,
+                        text_embeddings=embeds_batch,
+                        height=height,
+                        width=width,
+                        guidance_scale=guidance_scale,
+                        eta=eta,
+                        num_inference_steps=num_inference_steps,
+                    )
+                    noise_batch, embeds_batch = None, None
+
+                    for image in outputs["images"]:
+                        frame_filepath = str(save_path / f"frame_{frame_idx:06d}.png")
+                        image.save(frame_filepath)
+                        frame_filepaths.append(frame_filepath)
+                        frame_idx += 1
+        return frame_filepaths

huggingface_diffusers/examples/community/lpw_stable_diffusion.py

@@ -0,0 +1,1162 @@
+import inspect
+import re
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+
+import diffusers
+import PIL
+from diffusers import SchedulerMixin, StableDiffusionPipeline
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
+from diffusers.utils import deprecate, logging
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+try:
+    from diffusers.utils import PIL_INTERPOLATION
+except ImportError:
+    if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+        PIL_INTERPOLATION = {
+            "linear": PIL.Image.Resampling.BILINEAR,
+            "bilinear": PIL.Image.Resampling.BILINEAR,
+            "bicubic": PIL.Image.Resampling.BICUBIC,
+            "lanczos": PIL.Image.Resampling.LANCZOS,
+            "nearest": PIL.Image.Resampling.NEAREST,
+        }
+    else:
+        PIL_INTERPOLATION = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+            "nearest": PIL.Image.NEAREST,
+        }
+# ------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+re_attention = re.compile(
+    r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""",
+    re.X,
+)
+
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith("\\"):
+            res.append([text[1:], 1.0])
+        elif text == "(":
+            round_brackets.append(len(res))
+        elif text == "[":
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ")" and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == "]" and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            res.append([text, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
+    return res
+
+
+def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], max_length: int):
+    r"""
+    Tokenize a list of prompts and return its tokens with weights of each token.
+
+    No padding, starting or ending token is included.
+    """
+    tokens = []
+    weights = []
+    truncated = False
+    for text in prompt:
+        texts_and_weights = parse_prompt_attention(text)
+        text_token = []
+        text_weight = []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = pipe.tokenizer(word).input_ids[1:-1]
+            text_token += token
+            # copy the weight by length of token
+            text_weight += [weight] * len(token)
+            # stop if the text is too long (longer than truncation limit)
+            if len(text_token) > max_length:
+                truncated = True
+                break
+        # truncate
+        if len(text_token) > max_length:
+            truncated = True
+            text_token = text_token[:max_length]
+            text_weight = text_weight[:max_length]
+        tokens.append(text_token)
+        weights.append(text_weight)
+    if truncated:
+        logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
+    return tokens, weights
+
+
+def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77):
+    r"""
+    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
+    """
+    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
+    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
+    for i in range(len(tokens)):
+        tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
+        if no_boseos_middle:
+            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
+        else:
+            w = []
+            if len(weights[i]) == 0:
+                w = [1.0] * weights_length
+            else:
+                for j in range(max_embeddings_multiples):
+                    w.append(1.0)  # weight for starting token in this chunk
+                    w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
+                    w.append(1.0)  # weight for ending token in this chunk
+                w += [1.0] * (weights_length - len(w))
+            weights[i] = w[:]
+
+    return tokens, weights
+
+
+def get_unweighted_text_embeddings(
+    pipe: StableDiffusionPipeline,
+    text_input: torch.Tensor,
+    chunk_length: int,
+    no_boseos_middle: Optional[bool] = True,
+):
+    """
+    When the length of tokens is a multiple of the capacity of the text encoder,
+    it should be split into chunks and sent to the text encoder individually.
+    """
+    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
+    if max_embeddings_multiples > 1:
+        text_embeddings = []
+        for i in range(max_embeddings_multiples):
+            # extract the i-th chunk
+            text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()
+
+            # cover the head and the tail by the starting and the ending tokens
+            text_input_chunk[:, 0] = text_input[0, 0]
+            text_input_chunk[:, -1] = text_input[0, -1]
+            text_embedding = pipe.text_encoder(text_input_chunk)[0]
+
+            if no_boseos_middle:
+                if i == 0:
+                    # discard the ending token
+                    text_embedding = text_embedding[:, :-1]
+                elif i == max_embeddings_multiples - 1:
+                    # discard the starting token
+                    text_embedding = text_embedding[:, 1:]
+                else:
+                    # discard both starting and ending tokens
+                    text_embedding = text_embedding[:, 1:-1]
+
+            text_embeddings.append(text_embedding)
+        text_embeddings = torch.concat(text_embeddings, axis=1)
+    else:
+        text_embeddings = pipe.text_encoder(text_input)[0]
+    return text_embeddings
+
+
+def get_weighted_text_embeddings(
+    pipe: StableDiffusionPipeline,
+    prompt: Union[str, List[str]],
+    uncond_prompt: Optional[Union[str, List[str]]] = None,
+    max_embeddings_multiples: Optional[int] = 3,
+    no_boseos_middle: Optional[bool] = False,
+    skip_parsing: Optional[bool] = False,
+    skip_weighting: Optional[bool] = False,
+    **kwargs,
+):
+    r"""
+    Prompts can be assigned with local weights using brackets. For example,
+    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
+    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
+
+    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
+
+    Args:
+        pipe (`StableDiffusionPipeline`):
+            Pipe to provide access to the tokenizer and the text encoder.
+        prompt (`str` or `List[str]`):
+            The prompt or prompts to guide the image generation.
+        uncond_prompt (`str` or `List[str]`):
+            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
+            is provided, the embeddings of prompt and uncond_prompt are concatenated.
+        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+            The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        no_boseos_middle (`bool`, *optional*, defaults to `False`):
+            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
+            ending token in each of the chunk in the middle.
+        skip_parsing (`bool`, *optional*, defaults to `False`):
+            Skip the parsing of brackets.
+        skip_weighting (`bool`, *optional*, defaults to `False`):
+            Skip the weighting. When the parsing is skipped, it is forced True.
+    """
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+    if isinstance(prompt, str):
+        prompt = [prompt]
+
+    if not skip_parsing:
+        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
+    else:
+        prompt_tokens = [
+            token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
+        ]
+        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens = [
+                token[1:-1]
+                for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
+            ]
+            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
+
+    # round up the longest length of tokens to a multiple of (model_max_length - 2)
+    max_length = max([len(token) for token in prompt_tokens])
+    if uncond_prompt is not None:
+        max_length = max(max_length, max([len(token) for token in uncond_tokens]))
+
+    max_embeddings_multiples = min(
+        max_embeddings_multiples,
+        (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
+    )
+    max_embeddings_multiples = max(1, max_embeddings_multiples)
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+
+    # pad the length of tokens and weights
+    bos = pipe.tokenizer.bos_token_id
+    eos = pipe.tokenizer.eos_token_id
+    prompt_tokens, prompt_weights = pad_tokens_and_weights(
+        prompt_tokens,
+        prompt_weights,
+        max_length,
+        bos,
+        eos,
+        no_boseos_middle=no_boseos_middle,
+        chunk_length=pipe.tokenizer.model_max_length,
+    )
+    prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
+    if uncond_prompt is not None:
+        uncond_tokens, uncond_weights = pad_tokens_and_weights(
+            uncond_tokens,
+            uncond_weights,
+            max_length,
+            bos,
+            eos,
+            no_boseos_middle=no_boseos_middle,
+            chunk_length=pipe.tokenizer.model_max_length,
+        )
+        uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)
+
+    # get the embeddings
+    text_embeddings = get_unweighted_text_embeddings(
+        pipe,
+        prompt_tokens,
+        pipe.tokenizer.model_max_length,
+        no_boseos_middle=no_boseos_middle,
+    )
+    prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device)
+    if uncond_prompt is not None:
+        uncond_embeddings = get_unweighted_text_embeddings(
+            pipe,
+            uncond_tokens,
+            pipe.tokenizer.model_max_length,
+            no_boseos_middle=no_boseos_middle,
+        )
+        uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device)
+
+    # assign weights to the prompts and normalize in the sense of mean
+    # TODO: should we normalize by chunk or in a whole (current implementation)?
+    if (not skip_parsing) and (not skip_weighting):
+        previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        text_embeddings *= prompt_weights.unsqueeze(-1)
+        current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
+        if uncond_prompt is not None:
+            previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
+            uncond_embeddings *= uncond_weights.unsqueeze(-1)
+            current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
+            uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
+
+    if uncond_prompt is not None:
+        return text_embeddings, uncond_embeddings
+    return text_embeddings, None
+
+
+def preprocess_image(image):
+    w, h = image.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+def preprocess_mask(mask, scale_factor=8):
+    mask = mask.convert("L")
+    w, h = mask.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
+    mask = np.array(mask).astype(np.float32) / 255.0
+    mask = np.tile(mask, (4, 1, 1))
+    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
+    mask = 1 - mask  # repaint white, keep black
+    mask = torch.from_numpy(mask)
+    return mask
+
+
+class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
+    weighting in prompt.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"):
+
+        def __init__(
+            self,
+            vae: AutoencoderKL,
+            text_encoder: CLIPTextModel,
+            tokenizer: CLIPTokenizer,
+            unet: UNet2DConditionModel,
+            scheduler: SchedulerMixin,
+            safety_checker: StableDiffusionSafetyChecker,
+            feature_extractor: CLIPFeatureExtractor,
+            requires_safety_checker: bool = True,
+        ):
+            super().__init__(
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+                requires_safety_checker=requires_safety_checker,
+            )
+            self.__init__additional__()
+
+    else:
+
+        def __init__(
+            self,
+            vae: AutoencoderKL,
+            text_encoder: CLIPTextModel,
+            tokenizer: CLIPTokenizer,
+            unet: UNet2DConditionModel,
+            scheduler: SchedulerMixin,
+            safety_checker: StableDiffusionSafetyChecker,
+            feature_extractor: CLIPFeatureExtractor,
+        ):
+            super().__init__(
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+            )
+            self.__init__additional__()
+
+    def __init__additional__(self):
+        if not hasattr(self, "vae_scale_factor"):
+            setattr(self, "vae_scale_factor", 2 ** (len(self.vae.config.block_out_channels) - 1))
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+        max_embeddings_multiples,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+        elif isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt] * batch_size
+        if batch_size != len(negative_prompt):
+            raise ValueError(
+                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                " the batch size of `prompt`."
+            )
+
+        text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
+            pipe=self,
+            prompt=prompt,
+            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+            max_embeddings_multiples=max_embeddings_multiples,
+        )
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            bs_embed, seq_len, _ = uncond_embeddings.shape
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def check_inputs(self, prompt, height, width, strength, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def get_timesteps(self, num_inference_steps, strength, device, is_text2img):
+        if is_text2img:
+            return self.scheduler.timesteps.to(device), num_inference_steps
+        else:
+            # get the original timestep using init_timestep
+            offset = self.scheduler.config.get("steps_offset", 0)
+            init_timestep = int(num_inference_steps * strength) + offset
+            init_timestep = min(init_timestep, num_inference_steps)
+
+            t_start = max(num_inference_steps - init_timestep + offset, 0)
+            timesteps = self.scheduler.timesteps[t_start:].to(device)
+            return timesteps, num_inference_steps - t_start
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def prepare_latents(self, image, timestep, batch_size, height, width, dtype, device, generator, latents=None):
+        if image is None:
+            shape = (
+                batch_size,
+                self.unet.in_channels,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
+
+            if latents is None:
+                if device.type == "mps":
+                    # randn does not work reproducibly on mps
+                    latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+                else:
+                    latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+            else:
+                if latents.shape != shape:
+                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+                latents = latents.to(device)
+
+            # scale the initial noise by the standard deviation required by the scheduler
+            latents = latents * self.scheduler.init_noise_sigma
+            return latents, None, None
+        else:
+            init_latent_dist = self.vae.encode(image).latent_dist
+            init_latents = init_latent_dist.sample(generator=generator)
+            init_latents = 0.18215 * init_latents
+            init_latents = torch.cat([init_latents] * batch_size, dim=0)
+            init_latents_orig = init_latents
+            shape = init_latents.shape
+
+            # add noise to latents using the timesteps
+            if device.type == "mps":
+                noise = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+            else:
+                noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+            latents = self.scheduler.add_noise(init_latents, noise, timestep)
+            return latents, init_latents_orig, noise
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        strength: float = 0.8,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        message = "Please use `image` instead of `init_image`."
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
+        image = init_image or image
+
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, strength, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            max_embeddings_multiples,
+        )
+        dtype = text_embeddings.dtype
+
+        # 4. Preprocess image and mask
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess_image(image)
+        if image is not None:
+            image = image.to(device=self.device, dtype=dtype)
+        if isinstance(mask_image, PIL.Image.Image):
+            mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
+        if mask_image is not None:
+            mask = mask_image.to(device=self.device, dtype=dtype)
+            mask = torch.cat([mask] * batch_size * num_images_per_prompt)
+        else:
+            mask = None
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device, image is None)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents, init_latents_orig, noise = self.prepare_latents(
+            image,
+            latent_timestep,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            if mask is not None:
+                # masking
+                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
+                latents = (init_latents_proper * mask) + (latents * (1 - mask))
+
+            # call the callback, if provided
+            if i % callback_steps == 0:
+                if callback is not None:
+                    callback(i, t, latents)
+                if is_cancelled_callback is not None and is_cancelled_callback():
+                    return None
+
+        # 9. Post-processing
+        image = self.decode_latents(latents)
+
+        # 10. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+
+        # 11. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return image, has_nsfw_concept
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def text2img(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for text-to-image generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            is_cancelled_callback=is_cancelled_callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    def img2img(
+        self,
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for image-to-image generation.
+        Args:
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter will be modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            is_cancelled_callback=is_cancelled_callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    def inpaint(
+        self,
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for inpaint.
+        Args:
+            image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process. This is the image whose masked region will be inpainted.
+            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
+                is 1, the denoising process will be run on the masked area for the full number of iterations specified
+                in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
+                noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
+                the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            mask_image=mask_image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            is_cancelled_callback=is_cancelled_callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )

huggingface_diffusers/examples/community/lpw_stable_diffusion_onnx.py

@@ -0,0 +1,1148 @@
+import inspect
+import re
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+
+import diffusers
+import PIL
+from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import deprecate, logging
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTokenizer
+
+
+try:
+    from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
+except ImportError:
+    ORT_TO_NP_TYPE = {
+        "tensor(bool)": np.bool_,
+        "tensor(int8)": np.int8,
+        "tensor(uint8)": np.uint8,
+        "tensor(int16)": np.int16,
+        "tensor(uint16)": np.uint16,
+        "tensor(int32)": np.int32,
+        "tensor(uint32)": np.uint32,
+        "tensor(int64)": np.int64,
+        "tensor(uint64)": np.uint64,
+        "tensor(float16)": np.float16,
+        "tensor(float)": np.float32,
+        "tensor(double)": np.float64,
+    }
+
+try:
+    from diffusers.utils import PIL_INTERPOLATION
+except ImportError:
+    if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+        PIL_INTERPOLATION = {
+            "linear": PIL.Image.Resampling.BILINEAR,
+            "bilinear": PIL.Image.Resampling.BILINEAR,
+            "bicubic": PIL.Image.Resampling.BICUBIC,
+            "lanczos": PIL.Image.Resampling.LANCZOS,
+            "nearest": PIL.Image.Resampling.NEAREST,
+        }
+    else:
+        PIL_INTERPOLATION = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+            "nearest": PIL.Image.NEAREST,
+        }
+# ------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+re_attention = re.compile(
+    r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""",
+    re.X,
+)
+
+
+def parse_prompt_attention(text):
+    """
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+    Accepted tokens are:
+      (abc) - increases attention to abc by a multiplier of 1.1
+      (abc:3.12) - increases attention to abc by a multiplier of 3.12
+      [abc] - decreases attention to abc by a multiplier of 1.1
+      \( - literal character '('
+      \[ - literal character '['
+      \) - literal character ')'
+      \] - literal character ']'
+      \\ - literal character '\'
+      anything else - just text
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
+    """
+
+    res = []
+    round_brackets = []
+    square_brackets = []
+
+    round_bracket_multiplier = 1.1
+    square_bracket_multiplier = 1 / 1.1
+
+    def multiply_range(start_position, multiplier):
+        for p in range(start_position, len(res)):
+            res[p][1] *= multiplier
+
+    for m in re_attention.finditer(text):
+        text = m.group(0)
+        weight = m.group(1)
+
+        if text.startswith("\\"):
+            res.append([text[1:], 1.0])
+        elif text == "(":
+            round_brackets.append(len(res))
+        elif text == "[":
+            square_brackets.append(len(res))
+        elif weight is not None and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), float(weight))
+        elif text == ")" and len(round_brackets) > 0:
+            multiply_range(round_brackets.pop(), round_bracket_multiplier)
+        elif text == "]" and len(square_brackets) > 0:
+            multiply_range(square_brackets.pop(), square_bracket_multiplier)
+        else:
+            res.append([text, 1.0])
+
+    for pos in round_brackets:
+        multiply_range(pos, round_bracket_multiplier)
+
+    for pos in square_brackets:
+        multiply_range(pos, square_bracket_multiplier)
+
+    if len(res) == 0:
+        res = [["", 1.0]]
+
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
+    return res
+
+
+def get_prompts_with_weights(pipe, prompt: List[str], max_length: int):
+    r"""
+    Tokenize a list of prompts and return its tokens with weights of each token.
+
+    No padding, starting or ending token is included.
+    """
+    tokens = []
+    weights = []
+    truncated = False
+    for text in prompt:
+        texts_and_weights = parse_prompt_attention(text)
+        text_token = []
+        text_weight = []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = pipe.tokenizer(word, return_tensors="np").input_ids[0, 1:-1]
+            text_token += list(token)
+            # copy the weight by length of token
+            text_weight += [weight] * len(token)
+            # stop if the text is too long (longer than truncation limit)
+            if len(text_token) > max_length:
+                truncated = True
+                break
+        # truncate
+        if len(text_token) > max_length:
+            truncated = True
+            text_token = text_token[:max_length]
+            text_weight = text_weight[:max_length]
+        tokens.append(text_token)
+        weights.append(text_weight)
+    if truncated:
+        logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
+    return tokens, weights
+
+
+def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77):
+    r"""
+    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
+    """
+    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
+    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
+    for i in range(len(tokens)):
+        tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i]))
+        if no_boseos_middle:
+            weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
+        else:
+            w = []
+            if len(weights[i]) == 0:
+                w = [1.0] * weights_length
+            else:
+                for j in range(max_embeddings_multiples):
+                    w.append(1.0)  # weight for starting token in this chunk
+                    w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
+                    w.append(1.0)  # weight for ending token in this chunk
+                w += [1.0] * (weights_length - len(w))
+            weights[i] = w[:]
+
+    return tokens, weights
+
+
+def get_unweighted_text_embeddings(
+    pipe,
+    text_input: np.array,
+    chunk_length: int,
+    no_boseos_middle: Optional[bool] = True,
+):
+    """
+    When the length of tokens is a multiple of the capacity of the text encoder,
+    it should be split into chunks and sent to the text encoder individually.
+    """
+    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
+    if max_embeddings_multiples > 1:
+        text_embeddings = []
+        for i in range(max_embeddings_multiples):
+            # extract the i-th chunk
+            text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].copy()
+
+            # cover the head and the tail by the starting and the ending tokens
+            text_input_chunk[:, 0] = text_input[0, 0]
+            text_input_chunk[:, -1] = text_input[0, -1]
+
+            text_embedding = pipe.text_encoder(input_ids=text_input_chunk)[0]
+
+            if no_boseos_middle:
+                if i == 0:
+                    # discard the ending token
+                    text_embedding = text_embedding[:, :-1]
+                elif i == max_embeddings_multiples - 1:
+                    # discard the starting token
+                    text_embedding = text_embedding[:, 1:]
+                else:
+                    # discard both starting and ending tokens
+                    text_embedding = text_embedding[:, 1:-1]
+
+            text_embeddings.append(text_embedding)
+        text_embeddings = np.concatenate(text_embeddings, axis=1)
+    else:
+        text_embeddings = pipe.text_encoder(input_ids=text_input)[0]
+    return text_embeddings
+
+
+def get_weighted_text_embeddings(
+    pipe,
+    prompt: Union[str, List[str]],
+    uncond_prompt: Optional[Union[str, List[str]]] = None,
+    max_embeddings_multiples: Optional[int] = 4,
+    no_boseos_middle: Optional[bool] = False,
+    skip_parsing: Optional[bool] = False,
+    skip_weighting: Optional[bool] = False,
+    **kwargs,
+):
+    r"""
+    Prompts can be assigned with local weights using brackets. For example,
+    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
+    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
+
+    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
+
+    Args:
+        pipe (`OnnxStableDiffusionPipeline`):
+            Pipe to provide access to the tokenizer and the text encoder.
+        prompt (`str` or `List[str]`):
+            The prompt or prompts to guide the image generation.
+        uncond_prompt (`str` or `List[str]`):
+            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
+            is provided, the embeddings of prompt and uncond_prompt are concatenated.
+        max_embeddings_multiples (`int`, *optional*, defaults to `1`):
+            The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        no_boseos_middle (`bool`, *optional*, defaults to `False`):
+            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
+            ending token in each of the chunk in the middle.
+        skip_parsing (`bool`, *optional*, defaults to `False`):
+            Skip the parsing of brackets.
+        skip_weighting (`bool`, *optional*, defaults to `False`):
+            Skip the weighting. When the parsing is skipped, it is forced True.
+    """
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+    if isinstance(prompt, str):
+        prompt = [prompt]
+
+    if not skip_parsing:
+        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
+    else:
+        prompt_tokens = [
+            token[1:-1]
+            for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True, return_tensors="np").input_ids
+        ]
+        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
+        if uncond_prompt is not None:
+            if isinstance(uncond_prompt, str):
+                uncond_prompt = [uncond_prompt]
+            uncond_tokens = [
+                token[1:-1]
+                for token in pipe.tokenizer(
+                    uncond_prompt,
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="np",
+                ).input_ids
+            ]
+            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
+
+    # round up the longest length of tokens to a multiple of (model_max_length - 2)
+    max_length = max([len(token) for token in prompt_tokens])
+    if uncond_prompt is not None:
+        max_length = max(max_length, max([len(token) for token in uncond_tokens]))
+
+    max_embeddings_multiples = min(
+        max_embeddings_multiples,
+        (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
+    )
+    max_embeddings_multiples = max(1, max_embeddings_multiples)
+    max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+
+    # pad the length of tokens and weights
+    bos = pipe.tokenizer.bos_token_id
+    eos = pipe.tokenizer.eos_token_id
+    prompt_tokens, prompt_weights = pad_tokens_and_weights(
+        prompt_tokens,
+        prompt_weights,
+        max_length,
+        bos,
+        eos,
+        no_boseos_middle=no_boseos_middle,
+        chunk_length=pipe.tokenizer.model_max_length,
+    )
+    prompt_tokens = np.array(prompt_tokens, dtype=np.int32)
+    if uncond_prompt is not None:
+        uncond_tokens, uncond_weights = pad_tokens_and_weights(
+            uncond_tokens,
+            uncond_weights,
+            max_length,
+            bos,
+            eos,
+            no_boseos_middle=no_boseos_middle,
+            chunk_length=pipe.tokenizer.model_max_length,
+        )
+        uncond_tokens = np.array(uncond_tokens, dtype=np.int32)
+
+    # get the embeddings
+    text_embeddings = get_unweighted_text_embeddings(
+        pipe,
+        prompt_tokens,
+        pipe.tokenizer.model_max_length,
+        no_boseos_middle=no_boseos_middle,
+    )
+    prompt_weights = np.array(prompt_weights, dtype=text_embeddings.dtype)
+    if uncond_prompt is not None:
+        uncond_embeddings = get_unweighted_text_embeddings(
+            pipe,
+            uncond_tokens,
+            pipe.tokenizer.model_max_length,
+            no_boseos_middle=no_boseos_middle,
+        )
+        uncond_weights = np.array(uncond_weights, dtype=uncond_embeddings.dtype)
+
+    # assign weights to the prompts and normalize in the sense of mean
+    # TODO: should we normalize by chunk or in a whole (current implementation)?
+    if (not skip_parsing) and (not skip_weighting):
+        previous_mean = text_embeddings.mean(axis=(-2, -1))
+        text_embeddings *= prompt_weights[:, :, None]
+        text_embeddings *= (previous_mean / text_embeddings.mean(axis=(-2, -1)))[:, None, None]
+        if uncond_prompt is not None:
+            previous_mean = uncond_embeddings.mean(axis=(-2, -1))
+            uncond_embeddings *= uncond_weights[:, :, None]
+            uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=(-2, -1)))[:, None, None]
+
+    # For classifier free guidance, we need to do two forward passes.
+    # Here we concatenate the unconditional and text embeddings into a single batch
+    # to avoid doing two forward passes
+    if uncond_prompt is not None:
+        return text_embeddings, uncond_embeddings
+
+    return text_embeddings
+
+
+def preprocess_image(image):
+    w, h = image.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    return 2.0 * image - 1.0
+
+
+def preprocess_mask(mask, scale_factor=8):
+    mask = mask.convert("L")
+    w, h = mask.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
+    mask = np.array(mask).astype(np.float32) / 255.0
+    mask = np.tile(mask, (4, 1, 1))
+    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
+    mask = 1 - mask  # repaint white, keep black
+    return mask
+
+
+class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
+    weighting in prompt.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+    """
+
+    if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"):
+
+        def __init__(
+            self,
+            vae_encoder: OnnxRuntimeModel,
+            vae_decoder: OnnxRuntimeModel,
+            text_encoder: OnnxRuntimeModel,
+            tokenizer: CLIPTokenizer,
+            unet: OnnxRuntimeModel,
+            scheduler: SchedulerMixin,
+            safety_checker: OnnxRuntimeModel,
+            feature_extractor: CLIPFeatureExtractor,
+            requires_safety_checker: bool = True,
+        ):
+            super().__init__(
+                vae_encoder=vae_encoder,
+                vae_decoder=vae_decoder,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+                requires_safety_checker=requires_safety_checker,
+            )
+            self.__init__additional__()
+
+    else:
+
+        def __init__(
+            self,
+            vae_encoder: OnnxRuntimeModel,
+            vae_decoder: OnnxRuntimeModel,
+            text_encoder: OnnxRuntimeModel,
+            tokenizer: CLIPTokenizer,
+            unet: OnnxRuntimeModel,
+            scheduler: SchedulerMixin,
+            safety_checker: OnnxRuntimeModel,
+            feature_extractor: CLIPFeatureExtractor,
+        ):
+            super().__init__(
+                vae_encoder=vae_encoder,
+                vae_decoder=vae_decoder,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=safety_checker,
+                feature_extractor=feature_extractor,
+            )
+            self.__init__additional__()
+
+    def __init__additional__(self):
+        self.unet_in_channels = 4
+        self.vae_scale_factor = 8
+
+    def _encode_prompt(
+        self,
+        prompt,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+        max_embeddings_multiples,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        if negative_prompt is None:
+            negative_prompt = [""] * batch_size
+        elif isinstance(negative_prompt, str):
+            negative_prompt = [negative_prompt] * batch_size
+        if batch_size != len(negative_prompt):
+            raise ValueError(
+                f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                " the batch size of `prompt`."
+            )
+
+        text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
+            pipe=self,
+            prompt=prompt,
+            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+            max_embeddings_multiples=max_embeddings_multiples,
+        )
+
+        text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)
+        if do_classifier_free_guidance:
+            uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)
+            text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def check_inputs(self, prompt, height, width, strength, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def get_timesteps(self, num_inference_steps, strength, is_text2img):
+        if is_text2img:
+            return self.scheduler.timesteps, num_inference_steps
+        else:
+            # get the original timestep using init_timestep
+            offset = self.scheduler.config.get("steps_offset", 0)
+            init_timestep = int(num_inference_steps * strength) + offset
+            init_timestep = min(init_timestep, num_inference_steps)
+
+            t_start = max(num_inference_steps - init_timestep + offset, 0)
+            timesteps = self.scheduler.timesteps[t_start:]
+            return timesteps, num_inference_steps - t_start
+
+    def run_safety_checker(self, image):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(
+                self.numpy_to_pil(image), return_tensors="np"
+            ).pixel_values.astype(image.dtype)
+            # There will throw an error if use safety_checker directly and batchsize>1
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
+        image = np.clip(image / 2 + 0.5, 0, 1)
+        image = image.transpose((0, 2, 3, 1))
+        return image
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def prepare_latents(self, image, timestep, batch_size, height, width, dtype, generator, latents=None):
+        if image is None:
+            shape = (
+                batch_size,
+                self.unet_in_channels,
+                height // self.vae_scale_factor,
+                width // self.vae_scale_factor,
+            )
+
+            if latents is None:
+                latents = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
+            else:
+                if latents.shape != shape:
+                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+
+            # scale the initial noise by the standard deviation required by the scheduler
+            latents = (torch.from_numpy(latents) * self.scheduler.init_noise_sigma).numpy()
+            return latents, None, None
+        else:
+            init_latents = self.vae_encoder(sample=image)[0]
+            init_latents = 0.18215 * init_latents
+            init_latents = np.concatenate([init_latents] * batch_size, axis=0)
+            init_latents_orig = init_latents
+            shape = init_latents.shape
+
+            # add noise to latents using the timesteps
+            noise = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
+            latents = self.scheduler.add_noise(
+                torch.from_numpy(init_latents), torch.from_numpy(noise), timestep
+            ).numpy()
+            return latents, init_latents_orig, noise
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        image: Union[np.ndarray, PIL.Image.Image] = None,
+        mask_image: Union[np.ndarray, PIL.Image.Image] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        strength: float = 0.8,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[np.ndarray] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        is_cancelled_callback: Optional[Callable[[], bool]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process.
+            mask_image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`np.ndarray`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            is_cancelled_callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. If the function returns
+                `True`, the inference will be cancelled.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            `None` if cancelled by `is_cancelled_callback`,
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        message = "Please use `image` instead of `init_image`."
+        init_image = deprecate("init_image", "0.14.0", message, take_from=kwargs)
+        image = init_image or image
+
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, strength, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            max_embeddings_multiples,
+        )
+        dtype = text_embeddings.dtype
+
+        # 4. Preprocess image and mask
+        if isinstance(image, PIL.Image.Image):
+            image = preprocess_image(image)
+        if image is not None:
+            image = image.astype(dtype)
+        if isinstance(mask_image, PIL.Image.Image):
+            mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
+        if mask_image is not None:
+            mask = mask_image.astype(dtype)
+            mask = np.concatenate([mask] * batch_size * num_images_per_prompt)
+        else:
+            mask = None
+
+        # 5. set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, image is None)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents, init_latents_orig, noise = self.prepare_latents(
+            image,
+            latent_timestep,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            dtype,
+            generator,
+            latents,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 8. Denoising loop
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+            latent_model_input = latent_model_input.numpy()
+
+            # predict the noise residual
+            noise_pred = self.unet(
+                sample=latent_model_input,
+                timestep=np.array([t], dtype=timestep_dtype),
+                encoder_hidden_states=text_embeddings,
+            )
+            noise_pred = noise_pred[0]
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            scheduler_output = self.scheduler.step(
+                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+            )
+            latents = scheduler_output.prev_sample.numpy()
+
+            if mask is not None:
+                # masking
+                init_latents_proper = self.scheduler.add_noise(
+                    torch.from_numpy(init_latents_orig),
+                    torch.from_numpy(noise),
+                    t,
+                ).numpy()
+                latents = (init_latents_proper * mask) + (latents * (1 - mask))
+
+            # call the callback, if provided
+            if i % callback_steps == 0:
+                if callback is not None:
+                    callback(i, t, latents)
+                if is_cancelled_callback is not None and is_cancelled_callback():
+                    return None
+
+        # 9. Post-processing
+        image = self.decode_latents(latents)
+
+        # 10. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image)
+
+        # 11. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return image, has_nsfw_concept
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+    def text2img(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[np.ndarray] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for text-to-image generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`np.ndarray`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    def img2img(
+        self,
+        image: Union[np.ndarray, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for image-to-image generation.
+        Args:
+            image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or ndarray representing an image batch, that will be used as the starting point for the
+                process.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+                `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+                number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+                noise will be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter will be modulated by `strength`.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    def inpaint(
+        self,
+        image: Union[np.ndarray, PIL.Image.Image],
+        mask_image: Union[np.ndarray, PIL.Image.Image],
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        max_embeddings_multiples: Optional[int] = 3,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function for inpaint.
+        Args:
+            image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, that will be used as the starting point for the
+                process. This is the image whose masked region will be inpainted.
+            mask_image (`np.ndarray` or `PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
+                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
+                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            strength (`float`, *optional*, defaults to 0.8):
+                Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
+                is 1, the denoising process will be run on the masked area for the full number of iterations specified
+                in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
+                noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
+                the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
+                The max multiple length of prompt embeddings compared to the max output length of text encoder.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        return self.__call__(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            mask_image=mask_image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            max_embeddings_multiples=max_embeddings_multiples,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )

huggingface_diffusers/examples/community/magic_mix.py

@@ -0,0 +1,152 @@
+from typing import Union
+
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from PIL import Image
+from torchvision import transforms as tfms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+
+class MagicMixPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],
+    ):
+        super().__init__()
+
+        self.register_modules(vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
+
+    # convert PIL image to latents
+    def encode(self, img):
+        with torch.no_grad():
+            latent = self.vae.encode(tfms.ToTensor()(img).unsqueeze(0).to(self.device) * 2 - 1)
+            latent = 0.18215 * latent.latent_dist.sample()
+        return latent
+
+    # convert latents to PIL image
+    def decode(self, latent):
+        latent = (1 / 0.18215) * latent
+        with torch.no_grad():
+            img = self.vae.decode(latent).sample
+        img = (img / 2 + 0.5).clamp(0, 1)
+        img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
+        img = (img * 255).round().astype("uint8")
+        return Image.fromarray(img[0])
+
+    # convert prompt into text embeddings, also unconditional embeddings
+    def prep_text(self, prompt):
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_embedding = self.text_encoder(text_input.input_ids.to(self.device))[0]
+
+        uncond_input = self.tokenizer(
+            "",
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        uncond_embedding = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+        return torch.cat([uncond_embedding, text_embedding])
+
+    def __call__(
+        self,
+        img: Image.Image,
+        prompt: str,
+        kmin: float = 0.3,
+        kmax: float = 0.6,
+        mix_factor: float = 0.5,
+        seed: int = 42,
+        steps: int = 50,
+        guidance_scale: float = 7.5,
+    ) -> Image.Image:
+        tmin = steps - int(kmin * steps)
+        tmax = steps - int(kmax * steps)
+
+        text_embeddings = self.prep_text(prompt)
+
+        self.scheduler.set_timesteps(steps)
+
+        width, height = img.size
+        encoded = self.encode(img)
+
+        torch.manual_seed(seed)
+        noise = torch.randn(
+            (1, self.unet.in_channels, height // 8, width // 8),
+        ).to(self.device)
+
+        latents = self.scheduler.add_noise(
+            encoded,
+            noise,
+            timesteps=self.scheduler.timesteps[tmax],
+        )
+
+        input = torch.cat([latents] * 2)
+
+        input = self.scheduler.scale_model_input(input, self.scheduler.timesteps[tmax])
+
+        with torch.no_grad():
+            pred = self.unet(
+                input,
+                self.scheduler.timesteps[tmax],
+                encoder_hidden_states=text_embeddings,
+            ).sample
+
+        pred_uncond, pred_text = pred.chunk(2)
+        pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
+
+        latents = self.scheduler.step(pred, self.scheduler.timesteps[tmax], latents).prev_sample
+
+        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
+            if i > tmax:
+                if i < tmin:  # layout generation phase
+                    orig_latents = self.scheduler.add_noise(
+                        encoded,
+                        noise,
+                        timesteps=t,
+                    )
+
+                    input = (mix_factor * latents) + (
+                        1 - mix_factor
+                    ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
+                    input = torch.cat([input] * 2)
+
+                else:  # content generation phase
+                    input = torch.cat([latents] * 2)
+
+                input = self.scheduler.scale_model_input(input, t)
+
+                with torch.no_grad():
+                    pred = self.unet(
+                        input,
+                        t,
+                        encoder_hidden_states=text_embeddings,
+                    ).sample
+
+                pred_uncond, pred_text = pred.chunk(2)
+                pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
+
+                latents = self.scheduler.step(pred, t, latents).prev_sample
+
+        return self.decode(latents)

huggingface_diffusers/examples/community/multilingual_stable_diffusion.py

@@ -0,0 +1,436 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPTextModel,
+    CLIPTokenizer,
+    MBart50TokenizerFast,
+    MBartForConditionalGeneration,
+    pipeline,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def detect_language(pipe, prompt, batch_size):
+    """helper function to detect language(s) of prompt"""
+
+    if batch_size == 1:
+        preds = pipe(prompt, top_k=1, truncation=True, max_length=128)
+        return preds[0]["label"]
+    else:
+        detected_languages = []
+        for p in prompt:
+            preds = pipe(p, top_k=1, truncation=True, max_length=128)
+            detected_languages.append(preds[0]["label"])
+
+        return detected_languages
+
+
+def translate_prompt(prompt, translation_tokenizer, translation_model, device):
+    """helper function to translate prompt to English"""
+
+    encoded_prompt = translation_tokenizer(prompt, return_tensors="pt").to(device)
+    generated_tokens = translation_model.generate(**encoded_prompt, max_new_tokens=1000)
+    en_trans = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+
+    return en_trans[0]
+
+
+class MultilingualStableDiffusion(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion in different languages.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        detection_pipeline ([`pipeline`]):
+            Transformers pipeline to detect prompt's language.
+        translation_model ([`MBartForConditionalGeneration`]):
+            Model to translate prompt to English, if necessary. Please refer to the
+            [model card](https://huggingface.co/docs/transformers/model_doc/mbart) for details.
+        translation_tokenizer ([`MBart50TokenizerFast`]):
+            Tokenizer of the translation model.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        detection_pipeline: pipeline,
+        translation_model: MBartForConditionalGeneration,
+        translation_tokenizer: MBart50TokenizerFast,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            detection_pipeline=detection_pipeline,
+            translation_model=translation_model,
+            translation_tokenizer=translation_tokenizer,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation. Can be in different languages.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # detect language and translate if necessary
+        prompt_language = detect_language(self.detection_pipeline, prompt, batch_size)
+        if batch_size == 1 and prompt_language != "en":
+            prompt = translate_prompt(prompt, self.translation_tokenizer, self.translation_model, self.device)
+
+        if isinstance(prompt, list):
+            for index in range(batch_size):
+                if prompt_language[index] != "en":
+                    p = translate_prompt(
+                        prompt[index], self.translation_tokenizer, self.translation_model, self.device
+                    )
+                    prompt[index] = p
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                # detect language and translate it if necessary
+                negative_prompt_language = detect_language(self.detection_pipeline, negative_prompt, batch_size)
+                if negative_prompt_language != "en":
+                    negative_prompt = translate_prompt(
+                        negative_prompt, self.translation_tokenizer, self.translation_model, self.device
+                    )
+                if isinstance(negative_prompt, str):
+                    uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                # detect language and translate it if necessary
+                if isinstance(negative_prompt, list):
+                    negative_prompt_languages = detect_language(self.detection_pipeline, negative_prompt, batch_size)
+                    for index in range(batch_size):
+                        if negative_prompt_languages[index] != "en":
+                            p = translate_prompt(
+                                negative_prompt[index], self.translation_tokenizer, self.translation_model, self.device
+                            )
+                            negative_prompt[index] = p
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

huggingface_diffusers/examples/community/one_step_unet.py

@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+import torch
+
+from diffusers import DiffusionPipeline
+
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    def __call__(self):
+        image = torch.randn(
+            (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+        )
+        timestep = 1
+
+        model_output = self.unet(image, timestep).sample
+        scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
+
+        result = scheduler_output - scheduler_output + torch.ones_like(scheduler_output)
+
+        return result

huggingface_diffusers/examples/community/sd_text2img_k_diffusion.py

@@ -0,0 +1,476 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib
+import warnings
+from typing import Callable, List, Optional, Union
+
+import torch
+
+from diffusers import DiffusionPipeline, LMSDiscreteScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import is_accelerate_available, logging
+from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class ModelWrapper:
+    def __init__(self, model, alphas_cumprod):
+        self.model = model
+        self.alphas_cumprod = alphas_cumprod
+
+    def apply_model(self, *args, **kwargs):
+        if len(args) == 3:
+            encoder_hidden_states = args[-1]
+            args = args[:2]
+        if kwargs.get("cond", None) is not None:
+            encoder_hidden_states = kwargs.pop("cond")
+        return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample
+
+
+class StableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae,
+        text_encoder,
+        tokenizer,
+        unet,
+        scheduler,
+        safety_checker,
+        feature_extractor,
+    ):
+        super().__init__()
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        # get correct sigmas from LMS
+        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+        model = ModelWrapper(unet, scheduler.alphas_cumprod)
+        if scheduler.prediction_type == "v_prediction":
+            self.k_diffusion_model = CompVisVDenoiser(model)
+        else:
+            self.k_diffusion_model = CompVisDenoiser(model)
+
+    def set_sampler(self, scheduler_type: str):
+        warnings.warn("The `set_sampler` method is deprecated, please use `set_scheduler` instead.")
+        return self.set_scheduler(scheduler_type)
+
+    def set_scheduler(self, scheduler_type: str):
+        library = importlib.import_module("k_diffusion")
+        sampling = getattr(library, "sampling")
+        self.sampler = getattr(sampling, scheduler_type)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `list(int)`):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+        """
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
+
+        if not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        else:
+            has_nsfw_concept = None
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    def check_inputs(self, prompt, height, width, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (batch_size, num_channels_latents, height // 8, width // 8)
+        if latents is None:
+            if device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # 2. Define call parameters
+        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = True
+        if guidance_scale <= 1.0:
+            raise ValueError("has to use guidance_scale")
+
+        # 3. Encode input prompt
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)
+        sigmas = self.scheduler.sigmas
+        sigmas = sigmas.to(text_embeddings.dtype)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            text_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+        latents = latents * sigmas[0]
+        self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
+        self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
+
+        def model_fn(x, t):
+            latent_model_input = torch.cat([x] * 2)
+
+            noise_pred = self.k_diffusion_model(latent_model_input, t, cond=text_embeddings)
+
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+            return noise_pred
+
+        latents = self.sampler(model_fn, latents, sigmas)
+
+        # 8. Post-processing
+        image = self.decode_latents(latents)
+
+        # 9. Run safety checker
+        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
+
+        # 10. Convert to PIL
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

huggingface_diffusers/examples/community/seed_resize_stable_diffusion.py

@@ -0,0 +1,367 @@
+"""
+modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+"""
+
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+
+from diffusers import DiffusionPipeline
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import logging
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class SeedResizeStableDiffusionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        text_embeddings: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+
+        if text_embeddings is None:
+            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""]
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape_reference = (batch_size * num_images_per_prompt, self.unet.in_channels, 64, 64)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents_reference = torch.randn(
+                    latents_shape_reference, generator=generator, device="cpu", dtype=latents_dtype
+                ).to(self.device)
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents_reference = torch.randn(
+                    latents_shape_reference, generator=generator, device=self.device, dtype=latents_dtype
+                )
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents_reference.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents_reference = latents_reference.to(self.device)
+            latents = latents.to(self.device)
+
+        # This is the key part of the pipeline where we
+        # try to ensure that the generated images w/ the same seed
+        # but different sizes actually result in similar images
+        dx = (latents_shape[3] - latents_shape_reference[3]) // 2
+        dy = (latents_shape[2] - latents_shape_reference[2]) // 2
+        w = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
+        h = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
+        tx = 0 if dx < 0 else dx
+        ty = 0 if dy < 0 else dy
+        dx = max(-dx, 0)
+        dy = max(-dy, 0)
+        # import pdb
+        # pdb.set_trace()
+        latents[:, :, ty : ty + h, tx : tx + w] = latents_reference[:, :, dy : dy + h, dx : dx + w]
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if self.safety_checker is not None:
+            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
+                self.device
+            )
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+            )
+        else:
+            has_nsfw_concept = None
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

huggingface_diffusers/examples/community/speech_to_image_diffusion.py

@@ -0,0 +1,261 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.utils import logging
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPTextModel,
+    CLIPTokenizer,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class SpeechToImagePipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        speech_model: WhisperForConditionalGeneration,
+        speech_processor: WhisperProcessor,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+
+        if safety_checker is None:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        self.register_modules(
+            speech_model=speech_model,
+            speech_processor=speech_processor,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        if slice_size == "auto":
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        audio,
+        sampling_rate=16_000,
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        inputs = self.speech_processor.feature_extractor(
+            audio, return_tensors="pt", sampling_rate=sampling_rate
+        ).input_features.to(self.device)
+        predicted_ids = self.speech_model.generate(inputs, max_length=480_000)
+
+        prompt = self.speech_processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[
+            0
+        ]
+
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not exist on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+
+            # call the callback, if provided
+            if callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return image
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)

huggingface_diffusers/examples/community/stable_diffusion_comparison.py

@@ -0,0 +1,405 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+pipe1_model_id = "CompVis/stable-diffusion-v1-1"
+pipe2_model_id = "CompVis/stable-diffusion-v1-2"
+pipe3_model_id = "CompVis/stable-diffusion-v1-3"
+pipe4_model_id = "CompVis/stable-diffusion-v1-4"
+
+
+class StableDiffusionComparisonPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for parallel comparison of Stable Diffusion v1-v4
+    This pipeline inherits from DiffusionPipeline and depends on the use of an Auth Token for
+    downloading pre-trained checkpoints from Hugging Face Hub.
+    If using Hugging Face Hub, pass the Model ID for Stable Diffusion v1.4 as the previous 3 checkpoints will be loaded
+    automatically.
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super()._init_()
+
+        self.pipe1 = StableDiffusionPipeline.from_pretrained(pipe1_model_id)
+        self.pipe2 = StableDiffusionPipeline.from_pretrained(pipe2_model_id)
+        self.pipe3 = StableDiffusionPipeline.from_pretrained(pipe3_model_id)
+        self.pipe4 = StableDiffusionPipeline(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            requires_safety_checker=requires_safety_checker,
+        )
+
+        self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)
+
+    @property
+    def layers(self) -> Dict[str, Any]:
+        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def text2img_sd1_1(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        return self.pipe1(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def text2img_sd1_2(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        return self.pipe2(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def text2img_sd1_3(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        return self.pipe3(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def text2img_sd1_4(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        return self.pipe4(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+    @torch.no_grad()
+    def _call_(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation. This function will generate 4 results as part
+        of running all the 4 pipelines for SD1.1-1.4 together in a serial-processing, parallel-invocation fashion.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, optional, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, optional, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, optional, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, optional, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            eta (`float`, optional, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, optional):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, optional):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, optional, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, optional, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.to(device)
+
+        # Checks if the height and width are divisible by 8 or not
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}.")
+
+        # Get first result from Stable Diffusion Checkpoint v1.1
+        res1 = self.text2img_sd1_1(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get first result from Stable Diffusion Checkpoint v1.2
+        res2 = self.text2img_sd1_2(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get first result from Stable Diffusion Checkpoint v1.3
+        res3 = self.text2img_sd1_3(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get first result from Stable Diffusion Checkpoint v1.4
+        res4 = self.text2img_sd1_4(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+            **kwargs,
+        )
+
+        # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
+        return StableDiffusionPipelineOutput([res1[0], res2[0], res3[0], res4[0]])

huggingface_diffusers/examples/community/stable_diffusion_mega.py

@@ -0,0 +1,228 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch
+
+import PIL.Image
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionInpaintPipelineLegacy,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.configuration_utils import FrozenDict
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.utils import deprecate, logging
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class StableDiffusionMegaPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionMegaSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    @property
+    def components(self) -> Dict[str, Any]:
+        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    @torch.no_grad()
+    def inpaint(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+    ):
+        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
+        return StableDiffusionInpaintPipelineLegacy(**self.components)(
+            prompt=prompt,
+            image=image,
+            mask_image=mask_image,
+            strength=strength,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+        )
+
+    @torch.no_grad()
+    def img2img(
+        self,
+        prompt: Union[str, List[str]],
+        image: Union[torch.FloatTensor, PIL.Image.Image],
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
+        return StableDiffusionImg2ImgPipeline(**self.components)(
+            prompt=prompt,
+            image=image,
+            strength=strength,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+        )
+
+    @torch.no_grad()
+    def text2img(
+        self,
+        prompt: Union[str, List[str]],
+        height: int = 512,
+        width: int = 512,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+    ):
+        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
+        return StableDiffusionPipeline(**self.components)(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            eta=eta,
+            generator=generator,
+            latents=latents,
+            output_type=output_type,
+            return_dict=return_dict,
+            callback=callback,
+            callback_steps=callback_steps,
+        )