From f71e5317a2ad968aefd1a18212267a0f05db0561 Mon Sep 17 00:00:00 2001
From: Aflalo <estellea@isl-iam1.rr.intel.com>
Date: Thu, 13 Jul 2023 01:17:13 -0700
Subject: [PATCH 01/15] added ldm3d

---
 .../pipeline_stable_diffusion_ldm3d.py        | 870 ++++++++++++++++++
 1 file changed, 870 insertions(+)
 create mode 100644 optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py

diff --git a/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py b/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
new file mode 100644
index 0000000000..b8a247924c
--- /dev/null
+++ b/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
@@ -0,0 +1,870 @@
+# coding=utf-8
+# 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
+import time
+import warnings
+from dataclasses import dataclass
+from math import ceil
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL
+import torch
+from diffusers.configuration_utils import FrozenDict
+from diffusers.image_processor import VaeImageProcessorLDM3D
+from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import BaseOutput, deprecate
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from optimum.utils import logging
+
+from ....transformers.gaudi_configuration import GaudiConfig
+from ....utils import speed_metrics
+from ..pipeline_utils import GaudiDiffusionPipeline
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class GaudiStableDiffusionLDM3DPipelineOutput(BaseOutput):
+    rgb: Union[List[PIL.Image.Image], np.ndarray]
+    depth: Union[List[PIL.Image.Image], np.ndarray]
+    nsfw_content_detected: Optional[List[bool]]
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+class GaudiStableDiffusionLDM3DPipeline(
+    GaudiDiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
+    """
+    Extends the [`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline) class:
+    - Generation is performed by batches
+    - Two `mark_step()` were added to add support for lazy mode
+    - Added support for HPU graphs
+
+    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 ([`CLIPImageProcessor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+        use_habana (bool, defaults to `False`):
+            Whether to use Gaudi (`True`) or CPU (`False`).
+        use_hpu_graphs (bool, defaults to `False`):
+            Whether to use HPU graphs or not.
+        gaudi_config (Union[str, [`GaudiConfig`]], defaults to `None`):
+            Gaudi configuration to use. Can be a string to download it from the Hub.
+            Or a previously initialized config can be passed.
+        bf16_full_eval (bool, defaults to `False`):
+            Whether to use full bfloat16 evaluation instead of 32-bit.
+            This will be faster and save memory compared to fp32/mixed precision but can harm generated images.
+    """
+
+    _optional_components = ["safety_checker", "feature_extractor"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+        use_habana: bool = False,
+        use_hpu_graphs: bool = False,
+        gaudi_config: Union[str, GaudiConfig] = None,
+        bf16_full_eval: bool = False,
+    ):
+        super().__init__(
+            use_habana,
+            use_hpu_graphs,
+            gaudi_config,
+            bf16_full_eval,
+        )
+
+        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.image_processor = VaeImageProcessorLDM3D(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
+        self.to(self._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 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=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                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]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+        if prompt is not None and isinstance(prompt, str):
+            num_prompts = 1
+        elif prompt is not None and isinstance(prompt, list):
+            num_prompts = len(prompt)
+        else:
+            num_prompts = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            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
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * num_prompts
+            elif prompt is not None and 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 num_prompts != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has {len(negative_prompt)} elements, but `prompt`:"
+                    f" {prompt} has {num_prompts}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.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
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(num_prompts * num_images_per_prompt, seq_len, -1)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            rgb_feature_extractor_input = feature_extractor_input[0]
+            safety_checker_input = self.feature_extractor(rgb_feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    def decode_latents(self, latents):
+        warnings.warn(
+            "The decode_latents method is deprecated and will be removed in a future version. Please"
+            " use VaeImageProcessor instead",
+            FutureWarning,
+        )
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents, return_dict=False)[0]
+        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()
+        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,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        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 prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (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 negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def prepare_latents(self, num_images, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        shape = (num_images, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != num_images:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective number"
+                f" of images of {num_images}. Make sure the number of images matches the length of the generators."
+            )
+
+        if latents is None:
+            # torch.randn is broken on HPU so running it on CPU
+            rand_device = "cpu" if device.type == "hpu" else device
+            if isinstance(generator, list):
+                shape = (1,) + shape[1:]
+                latents = [
+                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
+                    for i in range(num_images)
+                ]
+                latents = torch.cat(latents, dim=0).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
+        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
+
+    @classmethod
+    def _split_inputs_into_batches(cls, batch_size, latents, text_embeddings, uncond_embeddings):
+        # Use torch.split to generate num_batches batches of size batch_size
+        latents_batches = list(torch.split(latents, batch_size))
+        text_embeddings_batches = list(torch.split(text_embeddings, batch_size))
+        if uncond_embeddings is not None:
+            uncond_embeddings_batches = list(torch.split(uncond_embeddings, batch_size))
+
+        # If the last batch has less samples than batch_size, pad it with dummy samples
+        num_dummy_samples = 0
+        if latents_batches[-1].shape[0] < batch_size:
+            num_dummy_samples = batch_size - latents_batches[-1].shape[0]
+            # Pad latents_batches
+            sequence_to_stack = (latents_batches[-1],) + tuple(
+                torch.zeros_like(latents_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+            )
+            latents_batches[-1] = torch.vstack(sequence_to_stack)
+            # Pad text_embeddings_batches
+            sequence_to_stack = (text_embeddings_batches[-1],) + tuple(
+                torch.zeros_like(text_embeddings_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+            )
+            text_embeddings_batches[-1] = torch.vstack(sequence_to_stack)
+            # Pad uncond_embeddings_batches if necessary
+            if uncond_embeddings is not None:
+                sequence_to_stack = (uncond_embeddings_batches[-1],) + tuple(
+                    torch.zeros_like(uncond_embeddings_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                uncond_embeddings_batches[-1] = torch.vstack(sequence_to_stack)
+
+        # Stack batches in the same tensor
+        latents_batches = torch.stack(latents_batches)
+        if uncond_embeddings is not 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
+            for i, (uncond_embeddings_batch, text_embeddings_batch) in enumerate(
+                zip(uncond_embeddings_batches, text_embeddings_batches[:])
+            ):
+                text_embeddings_batches[i] = torch.cat([uncond_embeddings_batch, text_embeddings_batch])
+        text_embeddings_batches = torch.stack(text_embeddings_batches)
+
+        return latents_batches, text_embeddings_batches, num_dummy_samples
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        batch_size: int = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated images.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated images.
+            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 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. If not defined, one has to pass
+                `negative_prompt_embeds` instead. 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.
+            batch_size (`int`, *optional*, defaults to 1):
+                The number of images in a batch.
+            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` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](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 randomly.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            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 [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] 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.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.7):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+
+        Returns:
+            [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] or `tuple`:
+            [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] 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`.
+        """
+        with torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=self.gaudi_config.use_torch_autocast):
+            # 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, negative_prompt, prompt_embeds, negative_prompt_embeds
+            )
+
+            # 2. Define call parameters
+            if prompt is not None and isinstance(prompt, str):
+                num_prompts = 1
+            elif prompt is not None and isinstance(prompt, list):
+                num_prompts = len(prompt)
+            else:
+                num_prompts = prompt_embeds.shape[0]
+            num_batches = ceil((num_images_per_prompt * num_prompts) / batch_size)
+            logger.info(
+                f"{num_prompts} prompt(s) received, {num_images_per_prompt} generation(s) per prompt,"
+                f" {batch_size} sample(s) per batch, {num_batches} total batch(es)."
+            )
+            if num_batches < 3:
+                logger.warning("The first two iterations are slower so it is recommended to feed more batches.")
+            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_encoder_lora_scale = (
+                cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+            )
+            prompt_embeds, negative_prompt_embeds = self._encode_prompt(
+                prompt,
+                device,
+                num_images_per_prompt,
+                do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                lora_scale=text_encoder_lora_scale,
+            )
+
+            # 4. Prepare timesteps
+            self.scheduler.set_timesteps(num_inference_steps, device="cpu")
+            timesteps = self.scheduler.timesteps.to(device)
+
+            # 5. Prepare latent variables
+            num_channels_latents = self.unet.config.in_channels
+            latents = self.prepare_latents(
+                num_prompts * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+            )
+
+            # 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. Split into batches (HPU-specific step)
+            latents_batches, text_embeddings_batches, num_dummy_samples = self._split_inputs_into_batches(
+                batch_size,
+                latents,
+                prompt_embeds,
+                negative_prompt_embeds,
+            )
+
+            outputs = {
+                "images": [],
+                "has_nsfw_concept": [],
+            }
+            t0 = time.time()
+            t1 = t0
+
+            # 8. Denoising loop
+            for j in self.progress_bar(range(num_batches)):
+                # The throughput is calculated from the 3rd iteration
+                # because compilation occurs in the first two iterations
+                if j == 2:
+                    t1 = time.time()
+
+                latents_batch = latents_batches[0]
+                latents_batches = torch.roll(latents_batches, shifts=-1, dims=0)
+                text_embeddings_batch = text_embeddings_batches[0]
+                text_embeddings_batches = torch.roll(text_embeddings_batches, shifts=-1, dims=0)
+
+                for i in range(num_inference_steps):
+                    timestep = timesteps[0]
+                    timesteps = torch.roll(timesteps, shifts=-1, dims=0)
+
+                    capture = True if self.use_hpu_graphs and i < 2 else False
+
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = (
+                        torch.cat([latents_batch] * 2) if do_classifier_free_guidance else latents_batch
+                    )
+                    # latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)
+
+                    # predict the noise residual
+                    noise_pred = self.unet_hpu(
+                        latent_model_input,
+                        timestep,
+                        text_embeddings_batch,
+                        cross_attention_kwargs,
+                        capture,
+                    )
+
+                    # 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)
+
+                    if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                        noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                    # compute the previous noisy sample x_t -> x_t-1
+                    latents_batch = self.scheduler.step(
+                        noise_pred, latents_batch, **extra_step_kwargs, return_dict=False
+                    )[0]
+
+                    if not self.use_hpu_graphs:
+                        self.htcore.mark_step()
+
+                    # call the callback, if provided
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, timestep, latents_batch)
+
+                if not output_type == "latent":
+                    # 8. Post-processing
+                    image = self.vae.decode(latents_batch / self.vae.config.scaling_factor, return_dict=False)[0]
+                else:
+                    image = latents_batch
+                outputs["images"].append(image)
+
+                self.scheduler.reset_timestep_dependent_params()
+
+                if not self.use_hpu_graphs:
+                    self.htcore.mark_step()
+
+            speed_metrics_prefix = "generation"
+            speed_measures = speed_metrics(
+                split=speed_metrics_prefix,
+                start_time=t0,
+                num_samples=num_batches * batch_size if t1 == t0 else (num_batches - 2) * batch_size,
+                num_steps=num_batches,
+                start_time_after_warmup=t1,
+            )
+            logger.info(f"Speed metrics: {speed_measures}")
+
+            # Remove dummy generations if needed
+            if num_dummy_samples > 0:
+                outputs["images"][-1] = outputs["images"][-1][:-num_dummy_samples]
+
+            # Process generated images
+            for i, image in enumerate(outputs["images"][:]):
+                if i == 0:
+                    outputs["images"].clear()
+
+                if output_type == "latent":
+                    has_nsfw_concept = None
+                else:
+                    image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+
+                if has_nsfw_concept is None:
+                    do_denormalize = [True] * image.shape[0]
+                else:
+                    do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+                rgb, depth = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+                if output_type == "pil":
+                    outputs["images"] += image
+                else:
+                    outputs["images"] += [*image]
+
+                if has_nsfw_concept is not None:
+                    outputs["has_nsfw_concept"] += has_nsfw_concept
+                else:
+                    outputs["has_nsfw_concept"] = None
+
+            if not return_dict:
+                return ((rgb, depth), has_nsfw_concept)
+
+            return GaudiStableDiffusionLDM3DPipelineOutput(
+                gb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept,
+                throughput=speed_measures[f"{speed_metrics_prefix}_samples_per_second"],
+            )
+            
+
+    @torch.no_grad()
+    def unet_hpu(self, latent_model_input, timestep, encoder_hidden_states, cross_attention_kwargs, capture):
+        if self.use_hpu_graphs:
+            return self.capture_replay(latent_model_input, timestep, encoder_hidden_states, capture)
+        else:
+            return self.unet(
+                latent_model_input,
+                timestep,
+                encoder_hidden_states=encoder_hidden_states,
+                cross_attention_kwargs=cross_attention_kwargs,
+                return_dict=False,
+            )[0]
+
+    @torch.no_grad()
+    def capture_replay(self, latent_model_input, timestep, encoder_hidden_states, capture):
+        inputs = [latent_model_input, timestep, encoder_hidden_states, False]
+        h = self.ht.hpu.graphs.input_hash(inputs)
+        cached = self.cache.get(h)
+
+        if capture:
+            # Capture the graph and cache it
+            with self.ht.hpu.stream(self.hpu_stream):
+                graph = self.ht.hpu.HPUGraph()
+                graph.capture_begin()
+                outputs = self.unet(inputs[0], inputs[1], inputs[2], inputs[3])[0]
+                graph.capture_end()
+                graph_inputs = inputs
+                graph_outputs = outputs
+                self.cache[h] = self.ht.hpu.graphs.CachedParams(graph_inputs, graph_outputs, graph)
+            return outputs
+
+        # Replay the cached graph with updated inputs
+        self.ht.hpu.graphs.copy_to(cached.graph_inputs, inputs)
+        cached.graph.replay()
+        self.ht.core.hpu.default_stream().synchronize()
+
+        return cached.graph_outputs

From 65ebcbffa1d8e398057cb09e9553da05bc8f70ef Mon Sep 17 00:00:00 2001
From: Aflalo <estellea@isl-iam1.rr.intel.com>
Date: Tue, 25 Jul 2023 02:41:37 -0700
Subject: [PATCH 02/15] added in init

---
 .../text_to_image_generation_ldm3d.py         | 184 ++++++++++++++++++
 optimum/habana/diffusers/__init__.py          |   1 +
 2 files changed, 185 insertions(+)
 create mode 100644 examples/stable-diffusion/text_to_image_generation_ldm3d.py

diff --git a/examples/stable-diffusion/text_to_image_generation_ldm3d.py b/examples/stable-diffusion/text_to_image_generation_ldm3d.py
new file mode 100644
index 0000000000..fb56efb936
--- /dev/null
+++ b/examples/stable-diffusion/text_to_image_generation_ldm3d.py
@@ -0,0 +1,184 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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
+
+import argparse
+import logging
+import sys
+from pathlib import Path
+
+import torch
+
+from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionLDM3DPipeline
+from optimum.habana.utils import set_seed
+
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--model_name_or_path",
+        default="runwayml/stable-diffusion-v1-5",
+        type=str,
+        help="Path to pre-trained model",
+    )
+
+    # Pipeline arguments
+    parser.add_argument(
+        "--prompts",
+        type=str,
+        nargs="*",
+        default="An image of a squirrel in Picasso style",
+        help="The prompt or prompts to guide the image generation.",
+    )
+    parser.add_argument(
+        "--num_images_per_prompt", type=int, default=1, help="The number of images to generate per prompt."
+    )
+    parser.add_argument("--batch_size", type=int, default=1, help="The number of images in a batch.")
+    parser.add_argument("--height", type=int, default=512, help="The height in pixels of the generated images.")
+    parser.add_argument("--width", type=int, default=512, help="The width in pixels of the generated images.")
+    parser.add_argument(
+        "--num_inference_steps",
+        type=int,
+        default=50,
+        help=(
+            "The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense"
+            " of slower inference."
+        ),
+    )
+    parser.add_argument(
+        "--guidance_scale",
+        type=float,
+        default=5.0,
+        help=(
+            "Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598)."
+            " Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,"
+            " usually at the expense of lower image quality."
+        ),
+    )
+    parser.add_argument(
+        "--negative_prompts",
+        type=str,
+        nargs="*",
+        default=None,
+        help="The prompt or prompts not to guide the image generation.",
+    )
+    parser.add_argument(
+        "--eta",
+        type=float,
+        default=0.0,
+        help="Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502.",
+    )
+    parser.add_argument(
+        "--output_type",
+        type=str,
+        choices=["pil", "np"],
+        default="pil",
+        help="Whether to return PIL images or Numpy arrays.",
+    )
+
+    parser.add_argument(
+        "--pipeline_save_dir",
+        type=str,
+        default=None,
+        help="The directory where the generation pipeline will be saved.",
+    )
+    parser.add_argument(
+        "--image_save_dir",
+        type=str,
+        default="./stable-diffusion-generated-images",
+        help="The directory where images will be saved.",
+    )
+
+    parser.add_argument("--seed", type=int, default=42, help="Random seed for initialization.")
+
+    # HPU-specific arguments
+    parser.add_argument("--use_habana", action="store_true", help="Use HPU.")
+    parser.add_argument(
+        "--use_hpu_graphs", action="store_true", help="Use HPU graphs on HPU. This should lead to faster generations."
+    )
+    parser.add_argument(
+        "--gaudi_config_name",
+        type=str,
+        default="Habana/stable-diffusion",
+        help=(
+            "Name or path of the Gaudi configuration. In particular, it enables to specify how to apply Habana Mixed"
+            " Precision."
+        ),
+    )
+    parser.add_argument("--bf16", action="store_true", help="Whether to perform generation in bf16 precision.")
+
+    args = parser.parse_args()
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO)
+
+    # Initialize the scheduler and the generation pipeline
+    scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler")
+    kwargs = {
+        "scheduler": scheduler,
+        "use_habana": args.use_habana,
+        "use_hpu_graphs": args.use_hpu_graphs,
+        "gaudi_config": args.gaudi_config_name,
+    }
+    if args.bf16:
+        kwargs["torch_dtype"] = torch.bfloat16
+    pipeline = GaudiStableDiffusionLDM3DPipeline.from_pretrained(
+        args.model_name_or_path,
+        **kwargs,
+    )
+
+    # Set seed before running the model
+    set_seed(args.seed)
+
+    # Generate images
+    outputs = pipeline(
+        prompt=args.prompts,
+        num_images_per_prompt=args.num_images_per_prompt,
+        batch_size=args.batch_size,
+        height=args.height,
+        width=args.width,
+        num_inference_steps=args.num_inference_steps,
+        guidance_scale=args.guidance_scale,
+        negative_prompt=args.negative_prompts,
+        eta=args.eta,
+        output_type=args.output_type,
+    )
+
+    # Save the pipeline in the specified directory if not None
+    if args.pipeline_save_dir is not None:
+        pipeline.save_pretrained(args.pipeline_save_dir)
+
+    # Save images in the specified directory if not None and if they are in PIL format
+    if args.image_save_dir is not None:
+        if args.output_type == "pil":
+            image_save_dir = Path(args.image_save_dir)
+            image_save_dir.mkdir(parents=True, exist_ok=True)
+            logger.info(f"Saving images in {image_save_dir.resolve()}...")
+            for i, image in enumerate(outputs.images):
+                image.save(image_save_dir / f"image_{i+1}.png")
+        else:
+            logger.warning("--output_type should be equal to 'pil' to save images in --image_save_dir.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/optimum/habana/diffusers/__init__.py b/optimum/habana/diffusers/__init__.py
index 0df716baa0..add6a7fa8a 100644
--- a/optimum/habana/diffusers/__init__.py
+++ b/optimum/habana/diffusers/__init__.py
@@ -1,3 +1,4 @@
 from .pipelines.pipeline_utils import GaudiDiffusionPipeline
 from .pipelines.stable_diffusion.pipeline_stable_diffusion import GaudiStableDiffusionPipeline
+from .pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d import GaudiStableDiffusionLDM3DPipeline
 from .schedulers import GaudiDDIMScheduler

From 58afa1a9b97b79b7b4f92a3dd964d2997d3bdea2 Mon Sep 17 00:00:00 2001
From: Phillip Howard <phillip.r.howard@intel.com>
Date: Tue, 25 Jul 2023 12:14:06 +0000
Subject: [PATCH 03/15] added fixes

---
 .../stable-diffusion/text_to_image_generation_ldm3d.py    | 8 +++++---
 .../stable_diffusion/pipeline_stable_diffusion_ldm3d.py   | 3 ++-
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation_ldm3d.py b/examples/stable-diffusion/text_to_image_generation_ldm3d.py
index fb56efb936..c27f16b9a9 100644
--- a/examples/stable-diffusion/text_to_image_generation_ldm3d.py
+++ b/examples/stable-diffusion/text_to_image_generation_ldm3d.py
@@ -32,7 +32,7 @@ def main():
 
     parser.add_argument(
         "--model_name_or_path",
-        default="runwayml/stable-diffusion-v1-5",
+        default="Intel/ldm3d-4c",
         type=str,
         help="Path to pre-trained model",
     )
@@ -174,8 +174,10 @@ def main():
             image_save_dir = Path(args.image_save_dir)
             image_save_dir.mkdir(parents=True, exist_ok=True)
             logger.info(f"Saving images in {image_save_dir.resolve()}...")
-            for i, image in enumerate(outputs.images):
-                image.save(image_save_dir / f"image_{i+1}.png")
+            for i, rgb in enumerate(outputs.rgb):
+                rgb.save(image_save_dir / f"rgb_{i+1}.png")
+            for i, depth in enumerate(outputs.depth):
+                depth.save(image_save_dir / f"depth_{i+1}.png")
         else:
             logger.warning("--output_type should be equal to 'pil' to save images in --image_save_dir.")
 
diff --git a/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py b/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
index b8a247924c..f650f61874 100644
--- a/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
+++ b/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
@@ -47,6 +47,7 @@
 class GaudiStableDiffusionLDM3DPipelineOutput(BaseOutput):
     rgb: Union[List[PIL.Image.Image], np.ndarray]
     depth: Union[List[PIL.Image.Image], np.ndarray]
+    throughput: float
     nsfw_content_detected: Optional[List[bool]]
 
 
@@ -826,7 +827,7 @@ def __call__(
                 return ((rgb, depth), has_nsfw_concept)
 
             return GaudiStableDiffusionLDM3DPipelineOutput(
-                gb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept,
+                rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept,
                 throughput=speed_measures[f"{speed_metrics_prefix}_samples_per_second"],
             )
             

From c2b530205af8d4d09aacceb29636fe915a34201d Mon Sep 17 00:00:00 2001
From: Phillip Howard <phillip.r.howard@intel.com>
Date: Tue, 25 Jul 2023 13:22:33 +0000
Subject: [PATCH 04/15] doc

---
 .../tutorials/stable_diffusion_ldm3d.mdx      | 67 +++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 docs/source/tutorials/stable_diffusion_ldm3d.mdx

diff --git a/docs/source/tutorials/stable_diffusion_ldm3d.mdx b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
new file mode 100644
index 0000000000..39b6528dfd
--- /dev/null
+++ b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
@@ -0,0 +1,67 @@
+<!---
+Copyright 2022 The Intel authors Team and 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.
+-->
+
+# Text-to-(RGB, depth)
+
+LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./stable_diffusion/overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. 
+
+The abstract from the paper is:
+
+*This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at [this url](https://t.ly/tdi2).*
+
+
+## How to generate rgb and depth images?
+
+To generate rgb and depth images with Stable Diffusion LDM3D on Gaudi, you need to instantiate two instances:
+- A pipeline with [`GaudiStableDiffusionLDM3DPipeline`]. This pipeline supports *text-to-(rgb, depth) generation*.
+- A scheduler with [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler). This scheduler has been optimized for Gaudi.
+
+When initializing the pipeline, you have to specify `use_habana=True` to deploy it on HPUs.
+Furthermore, to get the fastest possible generations you should enable **HPU graphs** with `use_hpu_graphs=True`.
+Finally, you will need to specify a [Gaudi configuration](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config) which can be downloaded from the Hugging Face Hub.
+
+```python
+from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionLDM3DPipeline
+from optimum.habana.utils import set_seed
+
+model_name = "Intel/ldm3d-4c"
+
+scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
+
+set_seed(42)
+
+pipeline = GaudiStableDiffusionLDM3DPipeline.from_pretrained(
+    model_name,
+    scheduler=scheduler,
+    use_habana=True,
+    use_hpu_graphs=True,
+    gaudi_config="Habana/stable-diffusion",
+)
+outputs = pipeline(
+    prompt=["High quality photo of an astronaut riding a horse in space"],
+    num_images_per_prompt=1,
+    batch_size=1,
+    output_type="pil",
+    num_inference_steps=40,
+    guidance_scale=5.0,
+    negative_prompt=None
+)
+
+
+rgb_image, depth_image = outputs.rgb, outputs.depth
+rgb_image[0].save("astronaut_ldm3d_rgb.png")
+depth_image[0].save("astronaut_ldm3d_depth.png")
+```

From 6f22238873ce898c9d03b3de2fc8ebe3b592fbd9 Mon Sep 17 00:00:00 2001
From: Aflalo <estellea@isl-iam1.rr.intel.com>
Date: Sun, 30 Jul 2023 03:54:25 -0700
Subject: [PATCH 05/15] updated existing text2img gen and added test

---
 .../text_to_image_generation.py               | 14 +++++++-
 tests/test_diffusers.py                       | 36 ++++++++++++++++++-
 2 files changed, 48 insertions(+), 2 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 747834e2c5..f78d09a77f 100644
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -20,7 +20,7 @@
 
 import torch
 
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
+from optimum.habana.diffusers import GaudiDDIMScheduler
 from optimum.habana.utils import set_seed
 
 
@@ -121,9 +121,21 @@ def main():
         ),
     )
     parser.add_argument("--bf16", action="store_true", help="Whether to perform generation in bf16 precision.")
+    parser.add_argument("--ldm3d", action="store_true", help="Use LDM3D to generate an image and a depth map from a given text prompt.")    parser.add_argument(
+        "--ldm3d_model_name_or_path",
+        default="Intel/ldm3d-4c",
+        type=str,
+        help="Path to pre-trained model",
+    )
 
     args = parser.parse_args()
 
+    if args.ldm3d:
+        from optimum.habana.diffusers import GaudiStableDiffusionLDM3DPipeline as GaudiStableDiffusionPipeline
+        args.model_name_or_path = args.ldm3d_model_name_or_path
+    else:
+        from optimum.habana.diffusers import GaudiStableDiffusionPipeline
+
     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
diff --git a/tests/test_diffusers.py b/tests/test_diffusers.py
index 2eab7623da..73e82c6ad9 100644
--- a/tests/test_diffusers.py
+++ b/tests/test_diffusers.py
@@ -27,7 +27,7 @@
 from transformers.testing_utils import slow
 
 from optimum.habana import GaudiConfig
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiDiffusionPipeline, GaudiStableDiffusionPipeline
+from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiDiffusionPipeline, GaudiStableDiffusionPipeline, GaudiStableDiffusionLDM3DPipeline
 from optimum.habana.utils import set_seed
 
 
@@ -609,3 +609,37 @@ def test_no_generation_regression(self):
 
             self.assertEqual(image.shape, (512, 512, 3))
             self.assertLess(np.abs(expected_slice - image[-3:, -3:, -1].flatten()).max(), 5e-3)
+
+    @slow
+    def test_no_generation_regression_ldm3d(self):
+        model_name = "Intel/ldm3d-4c"
+        # fp32
+        with hmp.disable_casts():
+            scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
+            pipeline = GaudiStableDiffusionLDM3DPipeline.from_pretrained(
+                model_name,
+                scheduler=scheduler,
+                safety_checker=None,
+                use_habana=True,
+                use_hpu_graphs=True,
+                gaudi_config=GaudiConfig(use_habana_mixed_precision=False),
+            )
+            set_seed(27)
+            outputs = pipeline(
+                prompt="An image of a squirrel in Picasso style",
+                output_type="np",
+            )
+
+            expected_slice_rgb = np.array(
+                [0.70760196, 0.7136303, 0.7000798, 0.714934, 0.6776865, 0.6800843, 0.6923707, 0.6653969, 0.6408076]
+            )
+            expected_slice_depth = np.array(
+                [0.70760196, 0.7136303, 0.7000798, 0.714934, 0.6776865, 0.6800843, 0.6923707, 0.6653969, 0.6408076]
+            )
+            rgb = outputs.rgb[0]
+            depth = outputs.depth[0]
+
+            self.assertEqual(rgb.shape, (512, 512, 3))
+            self.assertEqual(depth.shape, (512, 512, 1))
+            self.assertLess(np.abs(expected_slice_rgb - rgb[-3:, -3:, -1].flatten()).max(), 5e-3)
+            self.assertLess(np.abs(expected_slice_depth - depth[-3:, -3:, -1].flatten()).max(), 5e-3)
\ No newline at end of file

From e3d52408a713a338386b4aa36f14730bde41fcfa Mon Sep 17 00:00:00 2001
From: estelleafl <estelle.aflalo@intel.com>
Date: Sun, 30 Jul 2023 14:20:58 +0300
Subject: [PATCH 06/15] Update docs/source/tutorials/stable_diffusion_ldm3d.mdx

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
---
 docs/source/tutorials/stable_diffusion_ldm3d.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorials/stable_diffusion_ldm3d.mdx b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
index 39b6528dfd..6301ddea9d 100644
--- a/docs/source/tutorials/stable_diffusion_ldm3d.mdx
+++ b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
@@ -25,7 +25,7 @@ The abstract from the paper is:
 
 ## How to generate rgb and depth images?
 
-To generate rgb and depth images with Stable Diffusion LDM3D on Gaudi, you need to instantiate two instances:
+To generate RGB and depth images with Stable Diffusion LDM3D on Gaudi, you need to instantiate two instances:
 - A pipeline with [`GaudiStableDiffusionLDM3DPipeline`]. This pipeline supports *text-to-(rgb, depth) generation*.
 - A scheduler with [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler). This scheduler has been optimized for Gaudi.
 

From d966b8160abd922a243ec8249b863b62362ee195 Mon Sep 17 00:00:00 2001
From: estelleafl <estelle.aflalo@intel.com>
Date: Sun, 30 Jul 2023 14:21:09 +0300
Subject: [PATCH 07/15] Update docs/source/tutorials/stable_diffusion_ldm3d.mdx

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
---
 docs/source/tutorials/stable_diffusion_ldm3d.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorials/stable_diffusion_ldm3d.mdx b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
index 6301ddea9d..6411215651 100644
--- a/docs/source/tutorials/stable_diffusion_ldm3d.mdx
+++ b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
@@ -23,7 +23,7 @@ The abstract from the paper is:
 *This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at [this url](https://t.ly/tdi2).*
 
 
-## How to generate rgb and depth images?
+## How to generate RGB and depth images?
 
 To generate RGB and depth images with Stable Diffusion LDM3D on Gaudi, you need to instantiate two instances:
 - A pipeline with [`GaudiStableDiffusionLDM3DPipeline`]. This pipeline supports *text-to-(rgb, depth) generation*.

From c8a701bd12b0af9ba059317d25e78af531388887 Mon Sep 17 00:00:00 2001
From: Phillip Howard <phillip.r.howard@intel.com>
Date: Sun, 30 Jul 2023 12:04:36 +0000
Subject: [PATCH 08/15] added test, updated readme, other changes

---
 docs/source/_toctree.yml                      |  2 +
 examples/stable-diffusion/README.md           | 29 +++++++++
 .../text_to_image_generation.py               | 16 ++++-
 .../pipeline_stable_diffusion_ldm3d.py        | 62 +++----------------
 tests/test_diffusers.py                       | 15 +++--
 5 files changed, 61 insertions(+), 63 deletions(-)

diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index 34db5f524f..3d60858c53 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -16,6 +16,8 @@
       title: Run Inference
     - local: tutorials/stable_diffusion
       title: Stable Diffusion
+    - local: tutorials/ldm3d
+      title: LDM3D
     title: Tutorials
   - sections:
     - local: usage_guides/overview
diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md
index 45c4dd7dc0..cf75c1c511 100644
--- a/examples/stable-diffusion/README.md
+++ b/examples/stable-diffusion/README.md
@@ -86,3 +86,32 @@ python text_to_image_generation.py \
 > There are two different checkpoints for Stable Diffusion 2:
 > - use [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) for generating 768x768 images
 > - use [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) for generating 512x512 images
+
+
+### Latent Diffusion Model for 3D (LDM3D)
+
+[LDM3D](https://arxiv.org/abs/2305.10853) generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. 
+
+[Original checkpoint](https://huggingface.co/Intel/ldm3d) and [latest checkpoint](https://huggingface.co/Intel/ldm3d-4c) are open source.
+
+
+```python
+python text_to_image_generation.py \
+    --ldm3d_model_name_or_path "Intel/ldm3d-4c" \
+    --prompts "An image of a squirrel in Picasso style" \
+    --num_images_per_prompt 10 \
+    --batch_size 2 \
+    --height 768 \
+    --width 768 \
+    --image_save_dir /tmp/stable_diffusion_images \
+    --use_habana \
+    --use_hpu_graphs \
+    --gaudi_config Habana/stable-diffusion-2
+    --ldm3d
+```
+
+> There are three different checkpoints for LDM3D:
+> - use [original checkpoint](https://huggingface.co/Intel/ldm3d) to generate outputs from the paper
+> - use [the latest checkpoint](https://huggingface.co/Intel/ldm3d-4c) for generating improved results
+> - use [the pano checkpoint](https://huggingface.co/Intel/ldm3d-pano) to generate panoramic view
+
diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index f78d09a77f..7fe9fd8a2e 100644
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -121,7 +121,10 @@ def main():
         ),
     )
     parser.add_argument("--bf16", action="store_true", help="Whether to perform generation in bf16 precision.")
-    parser.add_argument("--ldm3d", action="store_true", help="Use LDM3D to generate an image and a depth map from a given text prompt.")    parser.add_argument(
+    parser.add_argument(
+        "--ldm3d", action="store_true", help="Use LDM3D to generate an image and a depth map from a given text prompt."
+    )
+    parser.add_argument(
         "--ldm3d_model_name_or_path",
         default="Intel/ldm3d-4c",
         type=str,
@@ -132,6 +135,7 @@ def main():
 
     if args.ldm3d:
         from optimum.habana.diffusers import GaudiStableDiffusionLDM3DPipeline as GaudiStableDiffusionPipeline
+
         args.model_name_or_path = args.ldm3d_model_name_or_path
     else:
         from optimum.habana.diffusers import GaudiStableDiffusionPipeline
@@ -186,8 +190,14 @@ def main():
             image_save_dir = Path(args.image_save_dir)
             image_save_dir.mkdir(parents=True, exist_ok=True)
             logger.info(f"Saving images in {image_save_dir.resolve()}...")
-            for i, image in enumerate(outputs.images):
-                image.save(image_save_dir / f"image_{i+1}.png")
+            if args.ldm3d:
+                for i, rgb in enumerate(outputs.rgb):
+                    rgb.save(image_save_dir / f"rgb_{i+1}.png")
+                for i, depth in enumerate(outputs.depth):
+                    depth.save(image_save_dir / f"depth_{i+1}.png")
+            else:
+                for i, image in enumerate(outputs.images):
+                    image.save(image_save_dir / f"image_{i+1}.png")
         else:
             logger.warning("--output_type should be equal to 'pil' to save images in --image_save_dir.")
 
diff --git a/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py b/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
index f650f61874..5af3ed5bc7 100644
--- a/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
+++ b/optimum/habana/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py
@@ -23,14 +23,12 @@
 import numpy as np
 import PIL
 import torch
-from diffusers.configuration_utils import FrozenDict
 from diffusers.image_processor import VaeImageProcessorLDM3D
 from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import BaseOutput, deprecate
-from packaging import version
+from diffusers.utils import BaseOutput
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from optimum.utils import logging
@@ -73,6 +71,7 @@ class GaudiStableDiffusionLDM3DPipeline(
     - Generation is performed by batches
     - Two `mark_step()` were added to add support for lazy mode
     - Added support for HPU graphs
+    - Adjusted original Stable Diffusion to match with the LDM3D implementation (input and output being different)
 
     Args:
         vae ([`AutoencoderKL`]):
@@ -129,33 +128,6 @@ def __init__(
             bf16_full_eval,
         )
 
-        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"
@@ -172,27 +144,6 @@ def __init__(
                 " 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,
@@ -811,7 +762,9 @@ def __call__(
                 else:
                     do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
 
-                rgb, depth = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+                rgb, depth = self.image_processor.postprocess(
+                    image, output_type=output_type, do_denormalize=do_denormalize
+                )
 
                 if output_type == "pil":
                     outputs["images"] += image
@@ -827,10 +780,11 @@ def __call__(
                 return ((rgb, depth), has_nsfw_concept)
 
             return GaudiStableDiffusionLDM3DPipelineOutput(
-                rgb=rgb, depth=depth, nsfw_content_detected=has_nsfw_concept,
+                rgb=rgb,
+                depth=depth,
+                nsfw_content_detected=has_nsfw_concept,
                 throughput=speed_measures[f"{speed_metrics_prefix}_samples_per_second"],
             )
-            
 
     @torch.no_grad()
     def unet_hpu(self, latent_model_input, timestep, encoder_hidden_states, cross_attention_kwargs, capture):
diff --git a/tests/test_diffusers.py b/tests/test_diffusers.py
index 73e82c6ad9..dc93c7def7 100644
--- a/tests/test_diffusers.py
+++ b/tests/test_diffusers.py
@@ -27,7 +27,12 @@
 from transformers.testing_utils import slow
 
 from optimum.habana import GaudiConfig
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiDiffusionPipeline, GaudiStableDiffusionPipeline, GaudiStableDiffusionLDM3DPipeline
+from optimum.habana.diffusers import (
+    GaudiDDIMScheduler,
+    GaudiDiffusionPipeline,
+    GaudiStableDiffusionLDM3DPipeline,
+    GaudiStableDiffusionPipeline,
+)
 from optimum.habana.utils import set_seed
 
 
@@ -630,11 +635,9 @@ def test_no_generation_regression_ldm3d(self):
                 output_type="np",
             )
 
-            expected_slice_rgb = np.array(
-                [0.70760196, 0.7136303, 0.7000798, 0.714934, 0.6776865, 0.6800843, 0.6923707, 0.6653969, 0.6408076]
-            )
+            expected_slice_rgb = np.array([0.7083766, 1.0, 1.0, 0.70610344, 0.9867363, 1.0, 0.7214538, 1.0, 1.0])
             expected_slice_depth = np.array(
-                [0.70760196, 0.7136303, 0.7000798, 0.714934, 0.6776865, 0.6800843, 0.6923707, 0.6653969, 0.6408076]
+                [0.919621, 0.92072034, 0.9184986, 0.91994286, 0.9242079, 0.93387043, 0.92345214, 0.93558526, 0.9223714]
             )
             rgb = outputs.rgb[0]
             depth = outputs.depth[0]
@@ -642,4 +645,4 @@ def test_no_generation_regression_ldm3d(self):
             self.assertEqual(rgb.shape, (512, 512, 3))
             self.assertEqual(depth.shape, (512, 512, 1))
             self.assertLess(np.abs(expected_slice_rgb - rgb[-3:, -3:, -1].flatten()).max(), 5e-3)
-            self.assertLess(np.abs(expected_slice_depth - depth[-3:, -3:, -1].flatten()).max(), 5e-3)
\ No newline at end of file
+            self.assertLess(np.abs(expected_slice_depth - depth[-3:, -3:, -1].flatten()).max(), 5e-3)

From ea3b42948f695bdec86684fef0c979a7451956b2 Mon Sep 17 00:00:00 2001
From: Phillip Howard <phillip.r.howard@intel.com>
Date: Sun, 30 Jul 2023 12:05:01 +0000
Subject: [PATCH 09/15] removed text2img gen ldm3d, now in standard with flag

---
 .../text_to_image_generation_ldm3d.py         | 186 ------------------
 1 file changed, 186 deletions(-)
 delete mode 100644 examples/stable-diffusion/text_to_image_generation_ldm3d.py

diff --git a/examples/stable-diffusion/text_to_image_generation_ldm3d.py b/examples/stable-diffusion/text_to_image_generation_ldm3d.py
deleted file mode 100644
index c27f16b9a9..0000000000
--- a/examples/stable-diffusion/text_to_image_generation_ldm3d.py
+++ /dev/null
@@ -1,186 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. 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
-
-import argparse
-import logging
-import sys
-from pathlib import Path
-
-import torch
-
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionLDM3DPipeline
-from optimum.habana.utils import set_seed
-
-
-logger = logging.getLogger(__name__)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--model_name_or_path",
-        default="Intel/ldm3d-4c",
-        type=str,
-        help="Path to pre-trained model",
-    )
-
-    # Pipeline arguments
-    parser.add_argument(
-        "--prompts",
-        type=str,
-        nargs="*",
-        default="An image of a squirrel in Picasso style",
-        help="The prompt or prompts to guide the image generation.",
-    )
-    parser.add_argument(
-        "--num_images_per_prompt", type=int, default=1, help="The number of images to generate per prompt."
-    )
-    parser.add_argument("--batch_size", type=int, default=1, help="The number of images in a batch.")
-    parser.add_argument("--height", type=int, default=512, help="The height in pixels of the generated images.")
-    parser.add_argument("--width", type=int, default=512, help="The width in pixels of the generated images.")
-    parser.add_argument(
-        "--num_inference_steps",
-        type=int,
-        default=50,
-        help=(
-            "The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense"
-            " of slower inference."
-        ),
-    )
-    parser.add_argument(
-        "--guidance_scale",
-        type=float,
-        default=5.0,
-        help=(
-            "Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598)."
-            " Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,"
-            " usually at the expense of lower image quality."
-        ),
-    )
-    parser.add_argument(
-        "--negative_prompts",
-        type=str,
-        nargs="*",
-        default=None,
-        help="The prompt or prompts not to guide the image generation.",
-    )
-    parser.add_argument(
-        "--eta",
-        type=float,
-        default=0.0,
-        help="Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502.",
-    )
-    parser.add_argument(
-        "--output_type",
-        type=str,
-        choices=["pil", "np"],
-        default="pil",
-        help="Whether to return PIL images or Numpy arrays.",
-    )
-
-    parser.add_argument(
-        "--pipeline_save_dir",
-        type=str,
-        default=None,
-        help="The directory where the generation pipeline will be saved.",
-    )
-    parser.add_argument(
-        "--image_save_dir",
-        type=str,
-        default="./stable-diffusion-generated-images",
-        help="The directory where images will be saved.",
-    )
-
-    parser.add_argument("--seed", type=int, default=42, help="Random seed for initialization.")
-
-    # HPU-specific arguments
-    parser.add_argument("--use_habana", action="store_true", help="Use HPU.")
-    parser.add_argument(
-        "--use_hpu_graphs", action="store_true", help="Use HPU graphs on HPU. This should lead to faster generations."
-    )
-    parser.add_argument(
-        "--gaudi_config_name",
-        type=str,
-        default="Habana/stable-diffusion",
-        help=(
-            "Name or path of the Gaudi configuration. In particular, it enables to specify how to apply Habana Mixed"
-            " Precision."
-        ),
-    )
-    parser.add_argument("--bf16", action="store_true", help="Whether to perform generation in bf16 precision.")
-
-    args = parser.parse_args()
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
-    logger.setLevel(logging.INFO)
-
-    # Initialize the scheduler and the generation pipeline
-    scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler")
-    kwargs = {
-        "scheduler": scheduler,
-        "use_habana": args.use_habana,
-        "use_hpu_graphs": args.use_hpu_graphs,
-        "gaudi_config": args.gaudi_config_name,
-    }
-    if args.bf16:
-        kwargs["torch_dtype"] = torch.bfloat16
-    pipeline = GaudiStableDiffusionLDM3DPipeline.from_pretrained(
-        args.model_name_or_path,
-        **kwargs,
-    )
-
-    # Set seed before running the model
-    set_seed(args.seed)
-
-    # Generate images
-    outputs = pipeline(
-        prompt=args.prompts,
-        num_images_per_prompt=args.num_images_per_prompt,
-        batch_size=args.batch_size,
-        height=args.height,
-        width=args.width,
-        num_inference_steps=args.num_inference_steps,
-        guidance_scale=args.guidance_scale,
-        negative_prompt=args.negative_prompts,
-        eta=args.eta,
-        output_type=args.output_type,
-    )
-
-    # Save the pipeline in the specified directory if not None
-    if args.pipeline_save_dir is not None:
-        pipeline.save_pretrained(args.pipeline_save_dir)
-
-    # Save images in the specified directory if not None and if they are in PIL format
-    if args.image_save_dir is not None:
-        if args.output_type == "pil":
-            image_save_dir = Path(args.image_save_dir)
-            image_save_dir.mkdir(parents=True, exist_ok=True)
-            logger.info(f"Saving images in {image_save_dir.resolve()}...")
-            for i, rgb in enumerate(outputs.rgb):
-                rgb.save(image_save_dir / f"rgb_{i+1}.png")
-            for i, depth in enumerate(outputs.depth):
-                depth.save(image_save_dir / f"depth_{i+1}.png")
-        else:
-            logger.warning("--output_type should be equal to 'pil' to save images in --image_save_dir.")
-
-
-if __name__ == "__main__":
-    main()

From 4bb94af9ae78ed22469efc2fea01e2d463850f55 Mon Sep 17 00:00:00 2001
From: estelleafl <estelle.aflalo@intel.com>
Date: Wed, 2 Aug 2023 18:26:02 +0300
Subject: [PATCH 10/15] Update README.md

---
 examples/stable-diffusion/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md
index cf75c1c511..0a21732845 100644
--- a/examples/stable-diffusion/README.md
+++ b/examples/stable-diffusion/README.md
@@ -93,7 +93,7 @@ python text_to_image_generation.py \
 [LDM3D](https://arxiv.org/abs/2305.10853) generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. 
 
 [Original checkpoint](https://huggingface.co/Intel/ldm3d) and [latest checkpoint](https://huggingface.co/Intel/ldm3d-4c) are open source.
-
+A [demo](https://huggingface.co/spaces/Intel/ldm3d) is also available
 
 ```python
 python text_to_image_generation.py \

From ea1a37dd12e1f1bb0fa4a05f4d697546f6602264 Mon Sep 17 00:00:00 2001
From: Phillip Howard <phillip.r.howard@intel.com>
Date: Thu, 3 Aug 2023 14:02:15 +0000
Subject: [PATCH 11/15] toctree

---
 docs/source/_toctree.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index 3d60858c53..aa79f0df2e 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -16,7 +16,7 @@
       title: Run Inference
     - local: tutorials/stable_diffusion
       title: Stable Diffusion
-    - local: tutorials/ldm3d
+    - local: tutorials/stable_diffusion_ldm3d
       title: LDM3D
     title: Tutorials
   - sections:

From 4003ab29745dde11dfc05ebf039b9b2bfa7f9048 Mon Sep 17 00:00:00 2001
From: estelleafl <estelle.aflalo@intel.com>
Date: Mon, 14 Aug 2023 13:29:52 +0300
Subject: [PATCH 12/15] Update docs/source/tutorials/stable_diffusion_ldm3d.mdx

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
---
 docs/source/tutorials/stable_diffusion_ldm3d.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/tutorials/stable_diffusion_ldm3d.mdx b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
index 6411215651..d7c975ceb6 100644
--- a/docs/source/tutorials/stable_diffusion_ldm3d.mdx
+++ b/docs/source/tutorials/stable_diffusion_ldm3d.mdx
@@ -16,7 +16,7 @@ limitations under the License.
 
 # Text-to-(RGB, depth)
 
-LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./stable_diffusion/overview) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. 
+LDM3D was proposed in [LDM3D: Latent Diffusion Model for 3D](https://huggingface.co/papers/2305.10853) by Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Muller, and Vasudev Lal. LDM3D generates an image and a depth map from a given text prompt unlike the existing text-to-image diffusion models such as [Stable Diffusion](./stable_diffusion) which only generates an image. With almost the same number of parameters, LDM3D achieves to create a latent space that can compress both the RGB images and the depth maps. 
 
 The abstract from the paper is:
 

From fb69de3a0996efb924a2bea857d1aa423422dad3 Mon Sep 17 00:00:00 2001
From: estelleafl <estelle.aflalo@intel.com>
Date: Mon, 14 Aug 2023 13:30:13 +0300
Subject: [PATCH 13/15] Update examples/stable-diffusion/README.md

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
---
 examples/stable-diffusion/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md
index 0a21732845..93bce3137b 100644
--- a/examples/stable-diffusion/README.md
+++ b/examples/stable-diffusion/README.md
@@ -106,7 +106,7 @@ python text_to_image_generation.py \
     --image_save_dir /tmp/stable_diffusion_images \
     --use_habana \
     --use_hpu_graphs \
-    --gaudi_config Habana/stable-diffusion-2
+    --gaudi_config Habana/stable-diffusion-2 \
     --ldm3d
 ```
 

From 32dc181382f54941b981b99870ebb2a3c7aeed7f Mon Sep 17 00:00:00 2001
From: estelleafl <estelle.aflalo@intel.com>
Date: Mon, 14 Aug 2023 13:30:28 +0300
Subject: [PATCH 14/15] Update examples/stable-diffusion/README.md

Co-authored-by: regisss <15324346+regisss@users.noreply.github.com>
---
 examples/stable-diffusion/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md
index 93bce3137b..e1c92e8fcb 100644
--- a/examples/stable-diffusion/README.md
+++ b/examples/stable-diffusion/README.md
@@ -93,7 +93,7 @@ python text_to_image_generation.py \
 [LDM3D](https://arxiv.org/abs/2305.10853) generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. 
 
 [Original checkpoint](https://huggingface.co/Intel/ldm3d) and [latest checkpoint](https://huggingface.co/Intel/ldm3d-4c) are open source.
-A [demo](https://huggingface.co/spaces/Intel/ldm3d) is also available
+A [demo](https://huggingface.co/spaces/Intel/ldm3d) is also available. Here is how to run this model:
 
 ```python
 python text_to_image_generation.py \

From 9c6709780e82c129e07ca51b3f80d835b5b1b181 Mon Sep 17 00:00:00 2001
From: Phillip Howard <phillip.r.howard@intel.com>
Date: Mon, 14 Aug 2023 11:41:28 +0000
Subject: [PATCH 15/15] updated ldm3d model flag

---
 examples/stable-diffusion/text_to_image_generation.py | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 7fe9fd8a2e..042bfa34a7 100644
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -124,19 +124,14 @@ def main():
     parser.add_argument(
         "--ldm3d", action="store_true", help="Use LDM3D to generate an image and a depth map from a given text prompt."
     )
-    parser.add_argument(
-        "--ldm3d_model_name_or_path",
-        default="Intel/ldm3d-4c",
-        type=str,
-        help="Path to pre-trained model",
-    )
 
     args = parser.parse_args()
 
     if args.ldm3d:
         from optimum.habana.diffusers import GaudiStableDiffusionLDM3DPipeline as GaudiStableDiffusionPipeline
 
-        args.model_name_or_path = args.ldm3d_model_name_or_path
+        if args.model_name_or_path == "runwayml/stable-diffusion-v1-5":
+            args.model_name_or_path = "Intel/ldm3d-4c"
     else:
         from optimum.habana.diffusers import GaudiStableDiffusionPipeline