From 9991f09f9bd350ae29904c80fe7181f8334a33da Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Sat, 14 Sep 2024 11:12:40 +0800
Subject: [PATCH 01/30] upgrade diffusers

---
 optimum/habana/diffusers/__init__.py          |   1 +
 .../diffusers/pipelines/flux/pipeline_flux.py | 434 ++++++++++++++++++
 setup.py                                      |   2 +-
 3 files changed, 436 insertions(+), 1 deletion(-)
 create mode 100644 optimum/habana/diffusers/pipelines/flux/pipeline_flux.py

diff --git a/optimum/habana/diffusers/__init__.py b/optimum/habana/diffusers/__init__.py
index d3ec347d07..4ca2933e91 100644
--- a/optimum/habana/diffusers/__init__.py
+++ b/optimum/habana/diffusers/__init__.py
@@ -1,6 +1,7 @@
 from .pipelines.auto_pipeline import AutoPipelineForInpainting, AutoPipelineForText2Image
 from .pipelines.controlnet.pipeline_controlnet import GaudiStableDiffusionControlNetPipeline
 from .pipelines.ddpm.pipeline_ddpm import GaudiDDPMPipeline
+from .pipelines.flux.pipeline_flux import GaudiFluxPipeline
 from .pipelines.pipeline_utils import GaudiDiffusionPipeline
 from .pipelines.stable_diffusion.pipeline_stable_diffusion import GaudiStableDiffusionPipeline
 from .pipelines.stable_diffusion.pipeline_stable_diffusion_depth2img import GaudiStableDiffusionDepth2ImgPipeline
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
new file mode 100644
index 0000000000..7c620c222b
--- /dev/null
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -0,0 +1,434 @@
+# Copyright 2024 Black Forest Labs and 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 time
+import math
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+
+import torch
+
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+
+from diffusers.utils import BaseOutput, replace_example_docstring
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.models.transformers import FluxTransformer2DModel
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+
+from optimum.utils import logging
+
+from ....transformers.gaudi_configuration import GaudiConfig
+from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
+from ..pipeline_utils import GaudiDiffusionPipeline
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class GaudiFluxPipelineOutput(BaseOutput):
+    """
+    Output class for Stable Diffusion pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from optimum.habana.diffusers import GaudiFluxPipeline
+
+        >>> pipe = GaudiFluxPipeline.from_pretrained(
+        ...    "black-forest-labs/FLUX.1-schnell",
+        ...     torch_dtype=torch.bfloat16,
+        ...     use_habana=True,
+        ...     use_hpu_graphs=True,
+        ...     gaudi_config="Habana/stable-diffusion",
+        ... )
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
+        >>> image.save("flux.png")
+        ```
+"""
+
+
+class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
+    r"""
+    Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L140
+
+    The Flux pipeline for text-to-image generation.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Args:
+        transformer ([`FluxTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            [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.
+        text_encoder_2 ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`T5TokenizerFast`):
+            Second Tokenizer of class
+            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: T5TokenizerFast,
+        transformer: FluxTransformer2DModel,
+        use_habana: bool = False,
+        use_hpu_graphs: bool = False,
+        gaudi_config: Union[str, GaudiConfig] = None,
+        bf16_full_eval: bool = False,
+    ):
+        GaudiDiffusionPipeline.__init__(
+            self,
+            use_habana,
+            use_hpu_graphs,
+            gaudi_config,
+            bf16_full_eval,
+        )
+        FluxPipeline.__init__(
+            self,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_encoder_2=text_encoder_2,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+        )
+        self.to(self._device)
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        profiling_warmup_steps: Optional[int] = 0,
+        profiling_steps: Optional[int] = 0,
+        **kwargs
+    ):
+        r"""
+        Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L531
+        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.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            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 by sampling using the supplied random `generator`.
+            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.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `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 [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+            profiling_warmup_steps (`int`, *optional*):
+                Number of steps to ignore for profling.
+            profiling_steps (`int`, *optional*):
+                Number of steps to be captured when enabling profiling.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        import habana_frameworks.torch.core as htcore
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        # 5-1. 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 = math.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.")
+
+        throughput_warmup_steps = kwargs.get("throughput_warmup_steps", 3)
+
+        t0 = time.time()
+        t1 = t0
+
+        hb_profiler = HabanaProfile(
+            warmup=profiling_warmup_steps,
+            active=profiling_steps,
+            record_shapes=False,
+        )
+        hb_profiler.start()
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # because compilation occurs in the first two iterations
+                if i == throughput_warmup_steps:
+                    t1 = time.time()
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                hb_profiler.step()
+                htcore.mark_step(sync=True)
+
+            hb_profiler.stop()
+            t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
+            speed_metrics_prefix = "generation"
+            speed_measures = speed_metrics(
+                split=speed_metrics_prefix,
+                start_time=t0,
+                num_samples=num_batches * batch_size,
+                num_steps=num_batches * batch_size * num_inference_steps,
+                start_time_after_warmup=t1,
+            )
+            logger.info(f"Speed metrics: {speed_measures}")
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return GaudiFluxPipelineOutput(images=image)
diff --git a/setup.py b/setup.py
index cea680353e..c77a241717 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
     "optimum",
     "torch",
     "accelerate >= 0.33.0, < 0.34.0",
-    "diffusers == 0.29.2",
+    "diffusers >= 0.30.2",
     "huggingface_hub >= 0.23.2",
     "sentence-transformers[train] == 3.0.1",
 ]

From 9bbcc1b7c649550c83149ae58f79dbe1a6d76099 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Wed, 18 Sep 2024 10:06:45 +0800
Subject: [PATCH 02/30] replace schduler

---
 .../stable-diffusion/run_flux_pipeline.py     | 32 +++++++++++++++++++
 .../diffusers/pipelines/flux/pipeline_flux.py | 10 ++----
 .../habana/diffusers/schedulers/__init__.py   |  1 +
 .../scheduling_flow_mactch_euler_discrete.py  | 25 +++++++++++++++
 4 files changed, 60 insertions(+), 8 deletions(-)
 create mode 100644 examples/stable-diffusion/run_flux_pipeline.py
 create mode 100644 optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py

diff --git a/examples/stable-diffusion/run_flux_pipeline.py b/examples/stable-diffusion/run_flux_pipeline.py
new file mode 100644
index 0000000000..e41ba633a9
--- /dev/null
+++ b/examples/stable-diffusion/run_flux_pipeline.py
@@ -0,0 +1,32 @@
+import argparse
+import torch
+from optimum.habana.diffusers import GaudiFluxPipeline
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--warmup", type=int, default=3, help="warmup iterations")
+parser.add_argument("--iterations", type=int, default=3, help="warmup iterations")
+parser.add_argument("--use_hpu_graph", action='store_true', help="use hpu graph")
+args = parser.parse_args()
+
+
+pipe = GaudiFluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=torch.bfloat16,
+    use_habana=True,
+    use_hpu_graphs=args.use_hpu_graph,
+    gaudi_config="Habana/stable-diffusion",
+)
+
+if args.use_hpu_graph:
+    from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+    pipe.transformer = wrap_in_hpu_graph(pipe.transformer)
+prompt = "A cat in a bin, and holding a sign '/bin/cat' "
+# Depending on the variant being used, the pipeline call will slightly vary.
+# Refer to the pipeline documentation for more details.
+print("warmuping...")
+for i in range(args.warmup):
+    image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0).images[0]
+torch.hpu.synchronize()
+image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0, profiling_warmup_steps=3, profiling_steps=3).images[0]
+image.save("flux.png")
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 7c620c222b..0b5cfc2db0 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -25,7 +25,6 @@
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from diffusers.utils import BaseOutput, replace_example_docstring
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
 from diffusers.models.autoencoders import AutoencoderKL
 from diffusers.models.transformers import FluxTransformer2DModel
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
@@ -35,6 +34,7 @@
 from ....transformers.gaudi_configuration import GaudiConfig
 from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
 from ..pipeline_utils import GaudiDiffusionPipeline
+from ...schedulers import GaudiFlowMatchEulerDiscreteScheduler
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -110,7 +110,7 @@ class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
 
     def __init__(
         self,
-        scheduler: FlowMatchEulerDiscreteScheduler,
+        scheduler: GaudiFlowMatchEulerDiscreteScheduler,
         vae: AutoencoderKL,
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
@@ -380,14 +380,8 @@ def __call__(
                 )[0]
 
                 # compute the previous noisy sample x_t -> x_t-1
-                latents_dtype = latents.dtype
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
 
-                if latents.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents = latents.to(latents_dtype)
-
                 if callback_on_step_end is not None:
                     callback_kwargs = {}
                     for k in callback_on_step_end_tensor_inputs:
diff --git a/optimum/habana/diffusers/schedulers/__init__.py b/optimum/habana/diffusers/schedulers/__init__.py
index 37eb80b1a6..48bf0bd8e9 100644
--- a/optimum/habana/diffusers/schedulers/__init__.py
+++ b/optimum/habana/diffusers/schedulers/__init__.py
@@ -1,3 +1,4 @@
 from .scheduling_ddim import GaudiDDIMScheduler
 from .scheduling_euler_ancestral_discrete import GaudiEulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import GaudiEulerDiscreteScheduler
+from .scheduling_flow_mactch_euler_discrete import GaudiFlowMatchEulerDiscreteScheduler
diff --git a/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py b/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py
new file mode 100644
index 0000000000..ccc597fa07
--- /dev/null
+++ b/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py
@@ -0,0 +1,25 @@
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+
+
+class GaudiFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
+    # TODO: overwrite orginal func with following one to fix dyn error in gaudi lazy mode
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        # indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        # pos = 1 if len(indices) > 1 else 0
+
+        # return indices[pos].item()
+
+        masked = (schedule_timesteps == timestep)
+        tmp = masked.cumsum(dim=0)
+        pos = (tmp == 0).sum().item()
+        if masked.sum() > 1:
+            pos += (tmp == 1).sum().item()
+        return pos

From cb2aaf0f896dc400ed66b2f8ec125e05292022aa Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Wed, 18 Sep 2024 16:36:02 +0800
Subject: [PATCH 03/30] update wkld entrypoint

---
 examples/stable-diffusion/README.md           | 55 +++++++++++++++++--
 .../text_to_image_generation.py               | 34 ++++++++++--
 optimum/habana/diffusers/__init__.py          |  2 +-
 3 files changed, 80 insertions(+), 11 deletions(-)

diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md
index 38ca7ae9d7..f3040b653d 100644
--- a/examples/stable-diffusion/README.md
+++ b/examples/stable-diffusion/README.md
@@ -20,12 +20,12 @@ This directory contains a script that showcases how to perform text-to-image gen
 
 Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team.
 
-
 ## Text-to-image Generation
 
 ### Single Prompt
 
 Here is how to generate images with one prompt:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path CompVis/stable-diffusion-v1-4 \
@@ -43,10 +43,10 @@ python text_to_image_generation.py \
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
-
 ### Multiple Prompts
 
 Here is how to generate images with several prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path CompVis/stable-diffusion-v1-4 \
@@ -61,7 +61,9 @@ python text_to_image_generation.py \
 ```
 
 ### Distributed inference with multiple HPUs
+
 Here is how to generate images with two prompts on two HPUs:
+
 ```bash
 python ../gaudi_spawn.py \
     --world_size 2 text_to_image_generation.py \
@@ -101,10 +103,10 @@ 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.
@@ -127,7 +129,9 @@ python text_to_image_generation.py \
     --ldm3d \
     --bf16
 ```
+
 Here is how to generate images and depth maps with two prompts on two HPUs:
+
 ```bash
 python ../gaudi_spawn.py \
     --world_size 2 text_to_image_generation.py \
@@ -146,6 +150,7 @@ python ../gaudi_spawn.py \
 ```
 
 > 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
@@ -155,6 +160,7 @@ python ../gaudi_spawn.py \
 Stable Diffusion XL was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/pdf/2307.01952.pdf) by the Stability AI team.
 
 Here is how to generate SDXL images with a single prompt:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
@@ -174,6 +180,7 @@ python text_to_image_generation.py \
 > You can enable this mode with `--use_hpu_graphs`.
 
 Here is how to generate SDXL images with several prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
@@ -191,6 +198,7 @@ python text_to_image_generation.py \
 SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly
 increase the number of parameters. Here is how to generate images with several prompts for both `prompt`
 and `prompt_2` (2nd text encoder), as well as their negative prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
@@ -209,6 +217,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate SDXL images with two prompts on two HPUs:
+
 ```bash
 python ../gaudi_spawn.py \
     --world_size 2 text_to_image_generation.py \
@@ -227,14 +236,17 @@ python ../gaudi_spawn.py \
     --bf16 \
     --distributed
 ```
+
 > HPU graphs are recommended when generating images by batches to get the fastest possible generations.
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
 ### SDXL-Turbo
+
 SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis.
 
 Here is how to generate images with multiple prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/sdxl-turbo \
@@ -267,11 +279,13 @@ Before running SD3 pipeline, you need to:
 
 1. Agree to the Terms and Conditions for using SD3 model at [HuggingFace model page](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
 2. Authenticate with HuggingFace using your HF Token. For authentication, run:
+
 ```bash
 huggingface-cli login
 ```
 
 Here is how to generate SD3 images with a single prompt:
+
 ```bash
 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \
 python text_to_image_generation.py \
@@ -291,12 +305,32 @@ python text_to_image_generation.py \
 > For improved performance of the SD3 pipeline on Gaudi, it is recommended to configure the environment
 > by setting PT_HPU_MAX_COMPOUND_OP_SIZE to 1.
 
+### FLUX.1
+
+FLUX.1 was was introduced by Black Forest Labs [here](https://blackforestlabs.ai/announcing-black-forest-labs/)
+
+```bash
+python text_to_image_generation.py \
+    --model_name_or_path black-forest-labs/FLUX.1-schnell \
+    --prompts "A cat holding a sign that says hello world" \
+    --num_images_per_prompt 10 \
+    --batch_size 1 \
+    --num_inference_steps 28 \
+    --image_save_dir /tmp/flux_1_images \
+    --scheduler flow_match_euler_discrete\
+    --use_habana \
+    --use_hpu_graphs \
+    --gaudi_config Habana/stable-diffusion \
+    --bf16
+```
+
 ## ControlNet
 
-ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models ](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
 It is a type of model for controlling StableDiffusion by conditioning the model with an additional input image.
 
 Here is how to generate images conditioned by canny edge model:
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -314,6 +348,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate images conditioned by canny edge model and with multiple prompts:
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -331,6 +366,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate images conditioned by canny edge model and with two prompts on two HPUs:
+
 ```bash
 pip install -r requirements.txt
 python ../gaudi_spawn.py \
@@ -350,6 +386,7 @@ python ../gaudi_spawn.py \
 ```
 
 Here is how to generate images conditioned by open pose model:
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -368,6 +405,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate images with conditioned by canny edge model using Stable Diffusion 2
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -392,6 +430,7 @@ Inpainting replaces or edits specific areas of an image. For more details,
 please refer to [Hugging Face Diffusers doc](https://huggingface.co/docs/diffusers/en/using-diffusers/inpaint).
 
 ### Stable Diffusion Inpainting
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path  stabilityai/stable-diffusion-2-inpainting \
@@ -409,6 +448,7 @@ python text_to_image_generation.py \
 ```
 
 ### Stable Diffusion XL Inpainting
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path  diffusers/stable-diffusion-xl-1.0-inpainting-0.1\
@@ -455,10 +495,10 @@ python image_to_image_generation.py \
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
-
 ### Multiple Prompts
 
 Here is how to generate images with several prompts and one image.
+
 ```bash
 pip install -r requirements.txt
 python image_to_image_generation.py \
@@ -481,10 +521,10 @@ python image_to_image_generation.py \
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
-
 ### Stable Diffusion XL Refiner
 
 Here is how to generate SDXL images with a single prompt and one image:
+
 ```bash
 pip install -r requirements.txt
 python image_to_image_generation.py \
@@ -505,6 +545,7 @@ python image_to_image_generation.py \
 ### Stable Diffusion Image Variations
 
 Here is how to generate images with one image, it does not accept prompt input
+
 ```bash
 pip install -r requirements.txt
 python image_to_image_generation.py \
@@ -565,6 +606,7 @@ Script `image_to_video_generation.py` showcases how to perform image-to-video ge
 ### Single Image Prompt
 
 Here is how to generate video with one image prompt:
+
 ```bash
 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \
 python image_to_video_generation.py \
@@ -585,6 +627,7 @@ python image_to_video_generation.py \
 ### Multiple Image Prompts
 
 Here is how to generate videos with several image prompts:
+
 ```bash
 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \
 python image_to_video_generation.py \
diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 8caa659ca6..02168b080f 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -26,6 +26,7 @@
     GaudiDDIMScheduler,
     GaudiEulerAncestralDiscreteScheduler,
     GaudiEulerDiscreteScheduler,
+    GaudiFlowMatchEulerDiscreteScheduler
 )
 from optimum.habana.utils import set_seed
 
@@ -65,7 +66,7 @@ def main():
     parser.add_argument(
         "--scheduler",
         default="ddim",
-        choices=["default", "euler_discrete", "euler_ancestral_discrete", "ddim"],
+        choices=["default", "euler_discrete", "euler_ancestral_discrete", "ddim", "flow_match_euler_discrete"],
         type=str,
         help="Name of scheduler",
     )
@@ -275,13 +276,16 @@ def main():
     # Select stable diffuson pipeline based on input
     sdxl_models = ["stable-diffusion-xl", "sdxl"]
     sd3_models = ["stable-diffusion-3"]
+    flux_models = ["FLUX.1-dev", "FLUX.1-schnell"]
     sdxl = True if any(model in args.model_name_or_path for model in sdxl_models) else False
     sd3 = True if any(model in args.model_name_or_path for model in sd3_models) else False
+    flux = True if any(model in args.model_name_or_path for model in flux_models) else False
     controlnet = True if args.control_image is not None else False
     inpainting = True if (args.base_image is not None) and (args.mask_image is not None) else False
 
     # Set the scheduler
     kwargs = {"timestep_spacing": args.timestep_spacing}
+
     if args.scheduler == "euler_discrete":
         scheduler = GaudiEulerDiscreteScheduler.from_pretrained(
             args.model_name_or_path, subfolder="scheduler", **kwargs
@@ -292,6 +296,10 @@ def main():
         )
     elif args.scheduler == "ddim":
         scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler", **kwargs)
+    elif args.scheduler == "flow_match_euler_discrete":
+        scheduler = GaudiFlowMatchEulerDiscreteScheduler.from_pretrained(
+            args.model_name_or_path, subfolder="scheduler", **kwargs
+        )
     else:
         scheduler = None
 
@@ -340,16 +348,18 @@ def main():
                 negative_prompts = negative_prompt
     kwargs_call["negative_prompt"] = negative_prompts
 
-    if sdxl or sd3:
+    if sdxl or sd3 or flux:
         prompts_2 = args.prompts_2
-        negative_prompts_2 = args.negative_prompts_2
         if args.distributed and args.prompts_2 is not None:
             with distributed_state.split_between_processes(args.prompts_2) as prompt_2:
                 prompts_2 = prompt_2
+        kwargs_call["prompt_2"] = prompts_2
+
+    if sdxl or sd3:
+        negative_prompts_2 = args.negative_prompts_2
         if args.distributed and args.negative_prompts_2 is not None:
             with distributed_state.split_between_processes(args.negative_prompts_2) as negative_prompt_2:
                 negative_prompts_2 = negative_prompt_2
-        kwargs_call["prompt_2"] = prompts_2
         kwargs_call["negative_prompt_2"] = negative_prompts_2
 
     if sd3:
@@ -428,6 +438,22 @@ def main():
                 args.model_name_or_path,
                 **kwargs,
             )
+    elif flux:
+        # SD3 pipelines
+        if controlnet:
+            # Import SD3+ControlNet pipeline
+            raise ValueError("SD3+ControlNet pipeline is not currenly supported")
+        elif inpainting:
+            # Import SD3 Inpainting pipeline
+            raise ValueError("SD3 Inpainting pipeline is not currenly supported")
+        else:
+            # Import SD3 pipeline
+            from optimum.habana.diffusers import GaudiFluxPipeline
+
+            pipeline = GaudiFluxPipeline.from_pretrained(
+                args.model_name_or_path,
+                **kwargs,
+            )
 
     else:
         # SD pipelines (SD1.x, SD2.x)
diff --git a/optimum/habana/diffusers/__init__.py b/optimum/habana/diffusers/__init__.py
index 4ca2933e91..3be8537f45 100644
--- a/optimum/habana/diffusers/__init__.py
+++ b/optimum/habana/diffusers/__init__.py
@@ -21,4 +21,4 @@
 from .pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import GaudiStableDiffusionXLInpaintPipeline
 from .pipelines.stable_video_diffusion.pipeline_stable_video_diffusion import GaudiStableVideoDiffusionPipeline
 from .pipelines.text_to_video_synthesis.pipeline_text_to_video_synth import GaudiTextToVideoSDPipeline
-from .schedulers import GaudiDDIMScheduler, GaudiEulerAncestralDiscreteScheduler, GaudiEulerDiscreteScheduler
+from .schedulers import GaudiDDIMScheduler, GaudiEulerAncestralDiscreteScheduler, GaudiEulerDiscreteScheduler, GaudiFlowMatchEulerDiscreteScheduler

From 4fcf181e77b663341ef4a256adebfae1066a05c0 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Wed, 18 Sep 2024 16:36:53 +0800
Subject: [PATCH 04/30] rem demo wkld entrypoint

---
 .../stable-diffusion/run_flux_pipeline.py     | 32 -------------------
 1 file changed, 32 deletions(-)
 delete mode 100644 examples/stable-diffusion/run_flux_pipeline.py

diff --git a/examples/stable-diffusion/run_flux_pipeline.py b/examples/stable-diffusion/run_flux_pipeline.py
deleted file mode 100644
index e41ba633a9..0000000000
--- a/examples/stable-diffusion/run_flux_pipeline.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import argparse
-import torch
-from optimum.habana.diffusers import GaudiFluxPipeline
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--warmup", type=int, default=3, help="warmup iterations")
-parser.add_argument("--iterations", type=int, default=3, help="warmup iterations")
-parser.add_argument("--use_hpu_graph", action='store_true', help="use hpu graph")
-args = parser.parse_args()
-
-
-pipe = GaudiFluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-schnell",
-    torch_dtype=torch.bfloat16,
-    use_habana=True,
-    use_hpu_graphs=args.use_hpu_graph,
-    gaudi_config="Habana/stable-diffusion",
-)
-
-if args.use_hpu_graph:
-    from habana_frameworks.torch.hpu import wrap_in_hpu_graph
-    pipe.transformer = wrap_in_hpu_graph(pipe.transformer)
-prompt = "A cat in a bin, and holding a sign '/bin/cat' "
-# Depending on the variant being used, the pipeline call will slightly vary.
-# Refer to the pipeline documentation for more details.
-print("warmuping...")
-for i in range(args.warmup):
-    image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0).images[0]
-torch.hpu.synchronize()
-image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0, profiling_warmup_steps=3, profiling_steps=3).images[0]
-image.save("flux.png")

From 8e0a02f3670735e4385fa6a33cec12c3dec6992b Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Mon, 23 Sep 2024 14:03:05 +0800
Subject: [PATCH 05/30] add warp in hpu graph

---
 .../stable-diffusion/text_to_image_generation.py     | 12 ++++++------
 optimum/habana/diffusers/pipelines/auto_pipeline.py  |  4 ++++
 .../habana/diffusers/pipelines/flux/pipeline_flux.py |  3 +++
 optimum/habana/diffusers/pipelines/pipeline_utils.py |  1 +
 4 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 02168b080f..8425389b4b 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -439,15 +439,15 @@ def main():
                 **kwargs,
             )
     elif flux:
-        # SD3 pipelines
+        # Flux pipelines
         if controlnet:
-            # Import SD3+ControlNet pipeline
-            raise ValueError("SD3+ControlNet pipeline is not currenly supported")
+            # Import Flux+ControlNet pipeline
+            raise ValueError("Flux+ControlNet pipeline is not currenly supported")
         elif inpainting:
-            # Import SD3 Inpainting pipeline
-            raise ValueError("SD3 Inpainting pipeline is not currenly supported")
+            # Import FLux Inpainting pipeline
+            raise ValueError("Flux Inpainting pipeline is not currenly supported")
         else:
-            # Import SD3 pipeline
+            # Import Flux pipeline
             from optimum.habana.diffusers import GaudiFluxPipeline
 
             pipeline = GaudiFluxPipeline.from_pretrained(
diff --git a/optimum/habana/diffusers/pipelines/auto_pipeline.py b/optimum/habana/diffusers/pipelines/auto_pipeline.py
index 77171c9502..a7fb5431ca 100644
--- a/optimum/habana/diffusers/pipelines/auto_pipeline.py
+++ b/optimum/habana/diffusers/pipelines/auto_pipeline.py
@@ -33,6 +33,8 @@
 from .stable_diffusion.pipeline_stable_diffusion_inpaint import GaudiStableDiffusionInpaintPipeline
 from .stable_diffusion_xl.pipeline_stable_diffusion_xl import GaudiStableDiffusionXLPipeline
 from .stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import GaudiStableDiffusionXLInpaintPipeline
+from .stable_diffusion_3.pipeline_stable_diffusion_3 import GaudiStableDiffusion3Pipeline
+from .flux.pipeline_flux import GaudiFluxPipeline
 
 
 GAUDI_PREFIX_NAME = "Gaudi"
@@ -42,6 +44,8 @@
         ("stable-diffusion", GaudiStableDiffusionPipeline),
         ("stable-diffusion-xl", GaudiStableDiffusionXLPipeline),
         ("stable-diffusion-controlnet", GaudiStableDiffusionControlNetPipeline),
+        ("stable-diffusion-3", GaudiStableDiffusion3Pipeline),
+        ("flux", GaudiFluxPipeline),
     ]
 )
 
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 0b5cfc2db0..e2f432c6ac 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -140,6 +140,9 @@ def __init__(
             transformer=transformer,
         )
         self.to(self._device)
+        if use_hpu_graphs:
+            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+            transformer = wrap_in_hpu_graph(transformer)
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
diff --git a/optimum/habana/diffusers/pipelines/pipeline_utils.py b/optimum/habana/diffusers/pipelines/pipeline_utils.py
index 7f36b90ae4..6e659edff4 100644
--- a/optimum/habana/diffusers/pipelines/pipeline_utils.py
+++ b/optimum/habana/diffusers/pipelines/pipeline_utils.py
@@ -55,6 +55,7 @@
     "optimum.habana.diffusers.schedulers": {
         "GaudiDDIMScheduler": ["save_pretrained", "from_pretrained"],
         "GaudiEulerDiscreteScheduler": ["save_pretrained", "from_pretrained"],
+        "GaudiFlowMatchEulerDiscreteScheduler": ["save_pretrained", "from_pretrained"],
         "GaudiEulerAncestralDiscreteScheduler": ["save_pretrained", "from_pretrained"],
     },
 }

From 154101e4988433b9919aa34add0617c36a30d2ba Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Sat, 14 Sep 2024 11:12:40 +0800
Subject: [PATCH 06/30] upgrade diffusers

---
 optimum/habana/diffusers/__init__.py          |   1 +
 .../diffusers/pipelines/flux/pipeline_flux.py | 434 ++++++++++++++++++
 setup.py                                      |   2 +-
 3 files changed, 436 insertions(+), 1 deletion(-)
 create mode 100644 optimum/habana/diffusers/pipelines/flux/pipeline_flux.py

diff --git a/optimum/habana/diffusers/__init__.py b/optimum/habana/diffusers/__init__.py
index d3ec347d07..4ca2933e91 100644
--- a/optimum/habana/diffusers/__init__.py
+++ b/optimum/habana/diffusers/__init__.py
@@ -1,6 +1,7 @@
 from .pipelines.auto_pipeline import AutoPipelineForInpainting, AutoPipelineForText2Image
 from .pipelines.controlnet.pipeline_controlnet import GaudiStableDiffusionControlNetPipeline
 from .pipelines.ddpm.pipeline_ddpm import GaudiDDPMPipeline
+from .pipelines.flux.pipeline_flux import GaudiFluxPipeline
 from .pipelines.pipeline_utils import GaudiDiffusionPipeline
 from .pipelines.stable_diffusion.pipeline_stable_diffusion import GaudiStableDiffusionPipeline
 from .pipelines.stable_diffusion.pipeline_stable_diffusion_depth2img import GaudiStableDiffusionDepth2ImgPipeline
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
new file mode 100644
index 0000000000..7c620c222b
--- /dev/null
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -0,0 +1,434 @@
+# Copyright 2024 Black Forest Labs and 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 time
+import math
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import PIL.Image
+
+import torch
+
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+
+from diffusers.utils import BaseOutput, replace_example_docstring
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.models.transformers import FluxTransformer2DModel
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+
+from optimum.utils import logging
+
+from ....transformers.gaudi_configuration import GaudiConfig
+from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
+from ..pipeline_utils import GaudiDiffusionPipeline
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class GaudiFluxPipelineOutput(BaseOutput):
+    """
+    Output class for Stable Diffusion pipelines.
+
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from optimum.habana.diffusers import GaudiFluxPipeline
+
+        >>> pipe = GaudiFluxPipeline.from_pretrained(
+        ...    "black-forest-labs/FLUX.1-schnell",
+        ...     torch_dtype=torch.bfloat16,
+        ...     use_habana=True,
+        ...     use_hpu_graphs=True,
+        ...     gaudi_config="Habana/stable-diffusion",
+        ... )
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
+        >>> image.save("flux.png")
+        ```
+"""
+
+
+class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
+    r"""
+    Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L140
+
+    The Flux pipeline for text-to-image generation.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Args:
+        transformer ([`FluxTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            [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.
+        text_encoder_2 ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`T5TokenizerFast`):
+            Second Tokenizer of class
+            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: T5TokenizerFast,
+        transformer: FluxTransformer2DModel,
+        use_habana: bool = False,
+        use_hpu_graphs: bool = False,
+        gaudi_config: Union[str, GaudiConfig] = None,
+        bf16_full_eval: bool = False,
+    ):
+        GaudiDiffusionPipeline.__init__(
+            self,
+            use_habana,
+            use_hpu_graphs,
+            gaudi_config,
+            bf16_full_eval,
+        )
+        FluxPipeline.__init__(
+            self,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_encoder_2=text_encoder_2,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+        )
+        self.to(self._device)
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        profiling_warmup_steps: Optional[int] = 0,
+        profiling_steps: Optional[int] = 0,
+        **kwargs
+    ):
+        r"""
+        Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L531
+        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.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            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 by sampling using the supplied random `generator`.
+            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.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `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 [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+            profiling_warmup_steps (`int`, *optional*):
+                Number of steps to ignore for profling.
+            profiling_steps (`int`, *optional*):
+                Number of steps to be captured when enabling profiling.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        import habana_frameworks.torch.core as htcore
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        # 5-1. 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 = math.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.")
+
+        throughput_warmup_steps = kwargs.get("throughput_warmup_steps", 3)
+
+        t0 = time.time()
+        t1 = t0
+
+        hb_profiler = HabanaProfile(
+            warmup=profiling_warmup_steps,
+            active=profiling_steps,
+            record_shapes=False,
+        )
+        hb_profiler.start()
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # because compilation occurs in the first two iterations
+                if i == throughput_warmup_steps:
+                    t1 = time.time()
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                hb_profiler.step()
+                htcore.mark_step(sync=True)
+
+            hb_profiler.stop()
+            t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
+            speed_metrics_prefix = "generation"
+            speed_measures = speed_metrics(
+                split=speed_metrics_prefix,
+                start_time=t0,
+                num_samples=num_batches * batch_size,
+                num_steps=num_batches * batch_size * num_inference_steps,
+                start_time_after_warmup=t1,
+            )
+            logger.info(f"Speed metrics: {speed_measures}")
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return GaudiFluxPipelineOutput(images=image)
diff --git a/setup.py b/setup.py
index cea680353e..c77a241717 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
     "optimum",
     "torch",
     "accelerate >= 0.33.0, < 0.34.0",
-    "diffusers == 0.29.2",
+    "diffusers >= 0.30.2",
     "huggingface_hub >= 0.23.2",
     "sentence-transformers[train] == 3.0.1",
 ]

From 875926435e96aff23fd840c526b4928f20f06e76 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Wed, 18 Sep 2024 10:06:45 +0800
Subject: [PATCH 07/30] replace schduler

---
 .../stable-diffusion/run_flux_pipeline.py     | 32 +++++++++++++++++++
 .../diffusers/pipelines/flux/pipeline_flux.py | 10 ++----
 .../habana/diffusers/schedulers/__init__.py   |  1 +
 .../scheduling_flow_mactch_euler_discrete.py  | 25 +++++++++++++++
 4 files changed, 60 insertions(+), 8 deletions(-)
 create mode 100644 examples/stable-diffusion/run_flux_pipeline.py
 create mode 100644 optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py

diff --git a/examples/stable-diffusion/run_flux_pipeline.py b/examples/stable-diffusion/run_flux_pipeline.py
new file mode 100644
index 0000000000..e41ba633a9
--- /dev/null
+++ b/examples/stable-diffusion/run_flux_pipeline.py
@@ -0,0 +1,32 @@
+import argparse
+import torch
+from optimum.habana.diffusers import GaudiFluxPipeline
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--warmup", type=int, default=3, help="warmup iterations")
+parser.add_argument("--iterations", type=int, default=3, help="warmup iterations")
+parser.add_argument("--use_hpu_graph", action='store_true', help="use hpu graph")
+args = parser.parse_args()
+
+
+pipe = GaudiFluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=torch.bfloat16,
+    use_habana=True,
+    use_hpu_graphs=args.use_hpu_graph,
+    gaudi_config="Habana/stable-diffusion",
+)
+
+if args.use_hpu_graph:
+    from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+    pipe.transformer = wrap_in_hpu_graph(pipe.transformer)
+prompt = "A cat in a bin, and holding a sign '/bin/cat' "
+# Depending on the variant being used, the pipeline call will slightly vary.
+# Refer to the pipeline documentation for more details.
+print("warmuping...")
+for i in range(args.warmup):
+    image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0).images[0]
+torch.hpu.synchronize()
+image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0, profiling_warmup_steps=3, profiling_steps=3).images[0]
+image.save("flux.png")
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 7c620c222b..0b5cfc2db0 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -25,7 +25,6 @@
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from diffusers.utils import BaseOutput, replace_example_docstring
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
 from diffusers.models.autoencoders import AutoencoderKL
 from diffusers.models.transformers import FluxTransformer2DModel
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
@@ -35,6 +34,7 @@
 from ....transformers.gaudi_configuration import GaudiConfig
 from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
 from ..pipeline_utils import GaudiDiffusionPipeline
+from ...schedulers import GaudiFlowMatchEulerDiscreteScheduler
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -110,7 +110,7 @@ class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
 
     def __init__(
         self,
-        scheduler: FlowMatchEulerDiscreteScheduler,
+        scheduler: GaudiFlowMatchEulerDiscreteScheduler,
         vae: AutoencoderKL,
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
@@ -380,14 +380,8 @@ def __call__(
                 )[0]
 
                 # compute the previous noisy sample x_t -> x_t-1
-                latents_dtype = latents.dtype
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
 
-                if latents.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents = latents.to(latents_dtype)
-
                 if callback_on_step_end is not None:
                     callback_kwargs = {}
                     for k in callback_on_step_end_tensor_inputs:
diff --git a/optimum/habana/diffusers/schedulers/__init__.py b/optimum/habana/diffusers/schedulers/__init__.py
index 37eb80b1a6..48bf0bd8e9 100644
--- a/optimum/habana/diffusers/schedulers/__init__.py
+++ b/optimum/habana/diffusers/schedulers/__init__.py
@@ -1,3 +1,4 @@
 from .scheduling_ddim import GaudiDDIMScheduler
 from .scheduling_euler_ancestral_discrete import GaudiEulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import GaudiEulerDiscreteScheduler
+from .scheduling_flow_mactch_euler_discrete import GaudiFlowMatchEulerDiscreteScheduler
diff --git a/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py b/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py
new file mode 100644
index 0000000000..ccc597fa07
--- /dev/null
+++ b/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py
@@ -0,0 +1,25 @@
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+
+
+class GaudiFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
+    # TODO: overwrite orginal func with following one to fix dyn error in gaudi lazy mode
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        # indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        # pos = 1 if len(indices) > 1 else 0
+
+        # return indices[pos].item()
+
+        masked = (schedule_timesteps == timestep)
+        tmp = masked.cumsum(dim=0)
+        pos = (tmp == 0).sum().item()
+        if masked.sum() > 1:
+            pos += (tmp == 1).sum().item()
+        return pos

From 16848b3639270b8007bd1b701ba68faa01d18b36 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Wed, 18 Sep 2024 16:36:02 +0800
Subject: [PATCH 08/30] update wkld entrypoint

---
 examples/stable-diffusion/README.md           | 55 +++++++++++++++++--
 .../text_to_image_generation.py               | 34 ++++++++++--
 optimum/habana/diffusers/__init__.py          |  2 +-
 3 files changed, 80 insertions(+), 11 deletions(-)

diff --git a/examples/stable-diffusion/README.md b/examples/stable-diffusion/README.md
index 38ca7ae9d7..f3040b653d 100644
--- a/examples/stable-diffusion/README.md
+++ b/examples/stable-diffusion/README.md
@@ -20,12 +20,12 @@ This directory contains a script that showcases how to perform text-to-image gen
 
 Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team.
 
-
 ## Text-to-image Generation
 
 ### Single Prompt
 
 Here is how to generate images with one prompt:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path CompVis/stable-diffusion-v1-4 \
@@ -43,10 +43,10 @@ python text_to_image_generation.py \
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
-
 ### Multiple Prompts
 
 Here is how to generate images with several prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path CompVis/stable-diffusion-v1-4 \
@@ -61,7 +61,9 @@ python text_to_image_generation.py \
 ```
 
 ### Distributed inference with multiple HPUs
+
 Here is how to generate images with two prompts on two HPUs:
+
 ```bash
 python ../gaudi_spawn.py \
     --world_size 2 text_to_image_generation.py \
@@ -101,10 +103,10 @@ 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.
@@ -127,7 +129,9 @@ python text_to_image_generation.py \
     --ldm3d \
     --bf16
 ```
+
 Here is how to generate images and depth maps with two prompts on two HPUs:
+
 ```bash
 python ../gaudi_spawn.py \
     --world_size 2 text_to_image_generation.py \
@@ -146,6 +150,7 @@ python ../gaudi_spawn.py \
 ```
 
 > 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
@@ -155,6 +160,7 @@ python ../gaudi_spawn.py \
 Stable Diffusion XL was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/pdf/2307.01952.pdf) by the Stability AI team.
 
 Here is how to generate SDXL images with a single prompt:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
@@ -174,6 +180,7 @@ python text_to_image_generation.py \
 > You can enable this mode with `--use_hpu_graphs`.
 
 Here is how to generate SDXL images with several prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
@@ -191,6 +198,7 @@ python text_to_image_generation.py \
 SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly
 increase the number of parameters. Here is how to generate images with several prompts for both `prompt`
 and `prompt_2` (2nd text encoder), as well as their negative prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
@@ -209,6 +217,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate SDXL images with two prompts on two HPUs:
+
 ```bash
 python ../gaudi_spawn.py \
     --world_size 2 text_to_image_generation.py \
@@ -227,14 +236,17 @@ python ../gaudi_spawn.py \
     --bf16 \
     --distributed
 ```
+
 > HPU graphs are recommended when generating images by batches to get the fastest possible generations.
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
 ### SDXL-Turbo
+
 SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis.
 
 Here is how to generate images with multiple prompts:
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path stabilityai/sdxl-turbo \
@@ -267,11 +279,13 @@ Before running SD3 pipeline, you need to:
 
 1. Agree to the Terms and Conditions for using SD3 model at [HuggingFace model page](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
 2. Authenticate with HuggingFace using your HF Token. For authentication, run:
+
 ```bash
 huggingface-cli login
 ```
 
 Here is how to generate SD3 images with a single prompt:
+
 ```bash
 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \
 python text_to_image_generation.py \
@@ -291,12 +305,32 @@ python text_to_image_generation.py \
 > For improved performance of the SD3 pipeline on Gaudi, it is recommended to configure the environment
 > by setting PT_HPU_MAX_COMPOUND_OP_SIZE to 1.
 
+### FLUX.1
+
+FLUX.1 was was introduced by Black Forest Labs [here](https://blackforestlabs.ai/announcing-black-forest-labs/)
+
+```bash
+python text_to_image_generation.py \
+    --model_name_or_path black-forest-labs/FLUX.1-schnell \
+    --prompts "A cat holding a sign that says hello world" \
+    --num_images_per_prompt 10 \
+    --batch_size 1 \
+    --num_inference_steps 28 \
+    --image_save_dir /tmp/flux_1_images \
+    --scheduler flow_match_euler_discrete\
+    --use_habana \
+    --use_hpu_graphs \
+    --gaudi_config Habana/stable-diffusion \
+    --bf16
+```
+
 ## ControlNet
 
-ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models ](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
+ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
 It is a type of model for controlling StableDiffusion by conditioning the model with an additional input image.
 
 Here is how to generate images conditioned by canny edge model:
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -314,6 +348,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate images conditioned by canny edge model and with multiple prompts:
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -331,6 +366,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate images conditioned by canny edge model and with two prompts on two HPUs:
+
 ```bash
 pip install -r requirements.txt
 python ../gaudi_spawn.py \
@@ -350,6 +386,7 @@ python ../gaudi_spawn.py \
 ```
 
 Here is how to generate images conditioned by open pose model:
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -368,6 +405,7 @@ python text_to_image_generation.py \
 ```
 
 Here is how to generate images with conditioned by canny edge model using Stable Diffusion 2
+
 ```bash
 pip install -r requirements.txt
 python text_to_image_generation.py \
@@ -392,6 +430,7 @@ Inpainting replaces or edits specific areas of an image. For more details,
 please refer to [Hugging Face Diffusers doc](https://huggingface.co/docs/diffusers/en/using-diffusers/inpaint).
 
 ### Stable Diffusion Inpainting
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path  stabilityai/stable-diffusion-2-inpainting \
@@ -409,6 +448,7 @@ python text_to_image_generation.py \
 ```
 
 ### Stable Diffusion XL Inpainting
+
 ```bash
 python text_to_image_generation.py \
     --model_name_or_path  diffusers/stable-diffusion-xl-1.0-inpainting-0.1\
@@ -455,10 +495,10 @@ python image_to_image_generation.py \
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
-
 ### Multiple Prompts
 
 Here is how to generate images with several prompts and one image.
+
 ```bash
 pip install -r requirements.txt
 python image_to_image_generation.py \
@@ -481,10 +521,10 @@ python image_to_image_generation.py \
 > The first batch of images entails a performance penalty. All subsequent batches will be generated much faster.
 > You can enable this mode with `--use_hpu_graphs`.
 
-
 ### Stable Diffusion XL Refiner
 
 Here is how to generate SDXL images with a single prompt and one image:
+
 ```bash
 pip install -r requirements.txt
 python image_to_image_generation.py \
@@ -505,6 +545,7 @@ python image_to_image_generation.py \
 ### Stable Diffusion Image Variations
 
 Here is how to generate images with one image, it does not accept prompt input
+
 ```bash
 pip install -r requirements.txt
 python image_to_image_generation.py \
@@ -565,6 +606,7 @@ Script `image_to_video_generation.py` showcases how to perform image-to-video ge
 ### Single Image Prompt
 
 Here is how to generate video with one image prompt:
+
 ```bash
 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \
 python image_to_video_generation.py \
@@ -585,6 +627,7 @@ python image_to_video_generation.py \
 ### Multiple Image Prompts
 
 Here is how to generate videos with several image prompts:
+
 ```bash
 PT_HPU_MAX_COMPOUND_OP_SIZE=1 \
 python image_to_video_generation.py \
diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 8caa659ca6..02168b080f 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -26,6 +26,7 @@
     GaudiDDIMScheduler,
     GaudiEulerAncestralDiscreteScheduler,
     GaudiEulerDiscreteScheduler,
+    GaudiFlowMatchEulerDiscreteScheduler
 )
 from optimum.habana.utils import set_seed
 
@@ -65,7 +66,7 @@ def main():
     parser.add_argument(
         "--scheduler",
         default="ddim",
-        choices=["default", "euler_discrete", "euler_ancestral_discrete", "ddim"],
+        choices=["default", "euler_discrete", "euler_ancestral_discrete", "ddim", "flow_match_euler_discrete"],
         type=str,
         help="Name of scheduler",
     )
@@ -275,13 +276,16 @@ def main():
     # Select stable diffuson pipeline based on input
     sdxl_models = ["stable-diffusion-xl", "sdxl"]
     sd3_models = ["stable-diffusion-3"]
+    flux_models = ["FLUX.1-dev", "FLUX.1-schnell"]
     sdxl = True if any(model in args.model_name_or_path for model in sdxl_models) else False
     sd3 = True if any(model in args.model_name_or_path for model in sd3_models) else False
+    flux = True if any(model in args.model_name_or_path for model in flux_models) else False
     controlnet = True if args.control_image is not None else False
     inpainting = True if (args.base_image is not None) and (args.mask_image is not None) else False
 
     # Set the scheduler
     kwargs = {"timestep_spacing": args.timestep_spacing}
+
     if args.scheduler == "euler_discrete":
         scheduler = GaudiEulerDiscreteScheduler.from_pretrained(
             args.model_name_or_path, subfolder="scheduler", **kwargs
@@ -292,6 +296,10 @@ def main():
         )
     elif args.scheduler == "ddim":
         scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler", **kwargs)
+    elif args.scheduler == "flow_match_euler_discrete":
+        scheduler = GaudiFlowMatchEulerDiscreteScheduler.from_pretrained(
+            args.model_name_or_path, subfolder="scheduler", **kwargs
+        )
     else:
         scheduler = None
 
@@ -340,16 +348,18 @@ def main():
                 negative_prompts = negative_prompt
     kwargs_call["negative_prompt"] = negative_prompts
 
-    if sdxl or sd3:
+    if sdxl or sd3 or flux:
         prompts_2 = args.prompts_2
-        negative_prompts_2 = args.negative_prompts_2
         if args.distributed and args.prompts_2 is not None:
             with distributed_state.split_between_processes(args.prompts_2) as prompt_2:
                 prompts_2 = prompt_2
+        kwargs_call["prompt_2"] = prompts_2
+
+    if sdxl or sd3:
+        negative_prompts_2 = args.negative_prompts_2
         if args.distributed and args.negative_prompts_2 is not None:
             with distributed_state.split_between_processes(args.negative_prompts_2) as negative_prompt_2:
                 negative_prompts_2 = negative_prompt_2
-        kwargs_call["prompt_2"] = prompts_2
         kwargs_call["negative_prompt_2"] = negative_prompts_2
 
     if sd3:
@@ -428,6 +438,22 @@ def main():
                 args.model_name_or_path,
                 **kwargs,
             )
+    elif flux:
+        # SD3 pipelines
+        if controlnet:
+            # Import SD3+ControlNet pipeline
+            raise ValueError("SD3+ControlNet pipeline is not currenly supported")
+        elif inpainting:
+            # Import SD3 Inpainting pipeline
+            raise ValueError("SD3 Inpainting pipeline is not currenly supported")
+        else:
+            # Import SD3 pipeline
+            from optimum.habana.diffusers import GaudiFluxPipeline
+
+            pipeline = GaudiFluxPipeline.from_pretrained(
+                args.model_name_or_path,
+                **kwargs,
+            )
 
     else:
         # SD pipelines (SD1.x, SD2.x)
diff --git a/optimum/habana/diffusers/__init__.py b/optimum/habana/diffusers/__init__.py
index 4ca2933e91..3be8537f45 100644
--- a/optimum/habana/diffusers/__init__.py
+++ b/optimum/habana/diffusers/__init__.py
@@ -21,4 +21,4 @@
 from .pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import GaudiStableDiffusionXLInpaintPipeline
 from .pipelines.stable_video_diffusion.pipeline_stable_video_diffusion import GaudiStableVideoDiffusionPipeline
 from .pipelines.text_to_video_synthesis.pipeline_text_to_video_synth import GaudiTextToVideoSDPipeline
-from .schedulers import GaudiDDIMScheduler, GaudiEulerAncestralDiscreteScheduler, GaudiEulerDiscreteScheduler
+from .schedulers import GaudiDDIMScheduler, GaudiEulerAncestralDiscreteScheduler, GaudiEulerDiscreteScheduler, GaudiFlowMatchEulerDiscreteScheduler

From 073b6d0d27eea0d7adc8224f1ff050fceb0d767e Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Wed, 18 Sep 2024 16:36:53 +0800
Subject: [PATCH 09/30] rem demo wkld entrypoint

---
 .../stable-diffusion/run_flux_pipeline.py     | 32 -------------------
 1 file changed, 32 deletions(-)
 delete mode 100644 examples/stable-diffusion/run_flux_pipeline.py

diff --git a/examples/stable-diffusion/run_flux_pipeline.py b/examples/stable-diffusion/run_flux_pipeline.py
deleted file mode 100644
index e41ba633a9..0000000000
--- a/examples/stable-diffusion/run_flux_pipeline.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import argparse
-import torch
-from optimum.habana.diffusers import GaudiFluxPipeline
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--warmup", type=int, default=3, help="warmup iterations")
-parser.add_argument("--iterations", type=int, default=3, help="warmup iterations")
-parser.add_argument("--use_hpu_graph", action='store_true', help="use hpu graph")
-args = parser.parse_args()
-
-
-pipe = GaudiFluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-schnell",
-    torch_dtype=torch.bfloat16,
-    use_habana=True,
-    use_hpu_graphs=args.use_hpu_graph,
-    gaudi_config="Habana/stable-diffusion",
-)
-
-if args.use_hpu_graph:
-    from habana_frameworks.torch.hpu import wrap_in_hpu_graph
-    pipe.transformer = wrap_in_hpu_graph(pipe.transformer)
-prompt = "A cat in a bin, and holding a sign '/bin/cat' "
-# Depending on the variant being used, the pipeline call will slightly vary.
-# Refer to the pipeline documentation for more details.
-print("warmuping...")
-for i in range(args.warmup):
-    image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0).images[0]
-torch.hpu.synchronize()
-image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0, profiling_warmup_steps=3, profiling_steps=3).images[0]
-image.save("flux.png")

From 60c5de3ce2ba2437735ec00a27c1fee433417f7c Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Mon, 23 Sep 2024 14:03:05 +0800
Subject: [PATCH 10/30] add warp in hpu graph

---
 .../stable-diffusion/text_to_image_generation.py     | 12 ++++++------
 optimum/habana/diffusers/pipelines/auto_pipeline.py  |  4 ++++
 .../habana/diffusers/pipelines/flux/pipeline_flux.py |  3 +++
 optimum/habana/diffusers/pipelines/pipeline_utils.py |  1 +
 4 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 02168b080f..8425389b4b 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -439,15 +439,15 @@ def main():
                 **kwargs,
             )
     elif flux:
-        # SD3 pipelines
+        # Flux pipelines
         if controlnet:
-            # Import SD3+ControlNet pipeline
-            raise ValueError("SD3+ControlNet pipeline is not currenly supported")
+            # Import Flux+ControlNet pipeline
+            raise ValueError("Flux+ControlNet pipeline is not currenly supported")
         elif inpainting:
-            # Import SD3 Inpainting pipeline
-            raise ValueError("SD3 Inpainting pipeline is not currenly supported")
+            # Import FLux Inpainting pipeline
+            raise ValueError("Flux Inpainting pipeline is not currenly supported")
         else:
-            # Import SD3 pipeline
+            # Import Flux pipeline
             from optimum.habana.diffusers import GaudiFluxPipeline
 
             pipeline = GaudiFluxPipeline.from_pretrained(
diff --git a/optimum/habana/diffusers/pipelines/auto_pipeline.py b/optimum/habana/diffusers/pipelines/auto_pipeline.py
index 77171c9502..a7fb5431ca 100644
--- a/optimum/habana/diffusers/pipelines/auto_pipeline.py
+++ b/optimum/habana/diffusers/pipelines/auto_pipeline.py
@@ -33,6 +33,8 @@
 from .stable_diffusion.pipeline_stable_diffusion_inpaint import GaudiStableDiffusionInpaintPipeline
 from .stable_diffusion_xl.pipeline_stable_diffusion_xl import GaudiStableDiffusionXLPipeline
 from .stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import GaudiStableDiffusionXLInpaintPipeline
+from .stable_diffusion_3.pipeline_stable_diffusion_3 import GaudiStableDiffusion3Pipeline
+from .flux.pipeline_flux import GaudiFluxPipeline
 
 
 GAUDI_PREFIX_NAME = "Gaudi"
@@ -42,6 +44,8 @@
         ("stable-diffusion", GaudiStableDiffusionPipeline),
         ("stable-diffusion-xl", GaudiStableDiffusionXLPipeline),
         ("stable-diffusion-controlnet", GaudiStableDiffusionControlNetPipeline),
+        ("stable-diffusion-3", GaudiStableDiffusion3Pipeline),
+        ("flux", GaudiFluxPipeline),
     ]
 )
 
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 0b5cfc2db0..e2f432c6ac 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -140,6 +140,9 @@ def __init__(
             transformer=transformer,
         )
         self.to(self._device)
+        if use_hpu_graphs:
+            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+            transformer = wrap_in_hpu_graph(transformer)
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
diff --git a/optimum/habana/diffusers/pipelines/pipeline_utils.py b/optimum/habana/diffusers/pipelines/pipeline_utils.py
index 7f36b90ae4..6e659edff4 100644
--- a/optimum/habana/diffusers/pipelines/pipeline_utils.py
+++ b/optimum/habana/diffusers/pipelines/pipeline_utils.py
@@ -55,6 +55,7 @@
     "optimum.habana.diffusers.schedulers": {
         "GaudiDDIMScheduler": ["save_pretrained", "from_pretrained"],
         "GaudiEulerDiscreteScheduler": ["save_pretrained", "from_pretrained"],
+        "GaudiFlowMatchEulerDiscreteScheduler": ["save_pretrained", "from_pretrained"],
         "GaudiEulerAncestralDiscreteScheduler": ["save_pretrained", "from_pretrained"],
     },
 }

From e1879304c12f46dd817369a9fd425c46a7fbcb0a Mon Sep 17 00:00:00 2001
From: Daniel Socek <daniel.socek@intel.com>
Date: Thu, 26 Sep 2024 12:53:13 +0000
Subject: [PATCH 11/30] Add fp8 to flux and fix timing

Signed-off-by: Daniel Socek <daniel.socek@intel.com>
---
 .../measure_all/fp8_hooks_maxabs.json         | 8071 +++++++++++++++++
 .../quantize/measure_all/fp8_hooks_maxabs.npz |  Bin 0 -> 97750 bytes
 ...fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json | 7567 ++++++++++++++++
 .../fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz |  Bin 0 -> 124974 bytes
 .../fp8_hooks_maxabs_mod_list.json            |  506 ++
 .../measure_all_500/fp8_hooks_maxabs.json     | 8071 +++++++++++++++++
 .../measure_all_500/fp8_hooks_maxabs.npz      |  Bin 0 -> 97750 bytes
 ...fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json | 7567 ++++++++++++++++
 .../fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz |  Bin 0 -> 124974 bytes
 .../fp8_hooks_maxabs_mod_list.json            |  506 ++
 .../quantize/measure_config.json              |    5 +
 .../quantize/quant_config.json                |    6 +
 .../quantize/quant_config_500.json            |    6 +
 .../quantize/quant_config_bmm.json            |    7 +
 examples/stable-diffusion/readme.txt          |   12 +
 examples/stable-diffusion/run_bf16.sh         |   13 +
 examples/stable-diffusion/run_fp8.sh          |   16 +
 examples/stable-diffusion/run_fp8_500.sh      |   16 +
 .../text_to_image_generation.py               |    9 +
 .../unconditional_image_generation.py         |    0
 .../diffusers/pipelines/flux/pipeline_flux.py |   47 +-
 21 files changed, 32414 insertions(+), 11 deletions(-)
 create mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json
 create mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.npz
 create mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
 create mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
 create mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json
 create mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json
 create mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.npz
 create mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
 create mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
 create mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json
 create mode 100755 examples/stable-diffusion/quantize/measure_config.json
 create mode 100755 examples/stable-diffusion/quantize/quant_config.json
 create mode 100755 examples/stable-diffusion/quantize/quant_config_500.json
 create mode 100755 examples/stable-diffusion/quantize/quant_config_bmm.json
 create mode 100644 examples/stable-diffusion/readme.txt
 create mode 100755 examples/stable-diffusion/run_bf16.sh
 create mode 100755 examples/stable-diffusion/run_fp8.sh
 create mode 100755 examples/stable-diffusion/run_fp8_500.sh
 mode change 100644 => 100755 examples/stable-diffusion/unconditional_image_generation.py

diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json
new file mode 100644
index 0000000000..8e4c0fb98e
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json
@@ -0,0 +1,8071 @@
+{
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+}
\ No newline at end of file
diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.npz b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
new file mode 100644
index 0000000000..87012fbef2
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json
new file mode 100644
index 0000000000..4cd94ad59e
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json
@@ -0,0 +1,506 @@
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+    "time_text_embed.timestep_embedder.linear_2",
+    "time_text_embed.guidance_embedder.linear_1",
+    "time_text_embed.guidance_embedder.linear_2",
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json
new file mode 100644
index 0000000000..775d712618
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json
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HcmV?d00001

diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
new file mode 100644
index 0000000000..78408ef56a
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
@@ -0,0 +1,7567 @@
+{
+    "GlobalRank": null,
+    "LocalRank": null,
+    "Mode": "Scale",
+    "Nodes": {
+        "time_text_embed.timestep_embedder.linear_1": {
+            "inputs": [
+                0.0625
+            ],
+            "outputs": [
+                [
+                    0.000244140625
+                ]
+            ],
+            "params": {
+                "weight": [
+                    0.00390625
+                ]
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json
new file mode 100644
index 0000000000..4cd94ad59e
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json
@@ -0,0 +1,506 @@
+[
+    "time_text_embed.timestep_embedder.linear_1",
+    "time_text_embed.timestep_embedder.linear_2",
+    "time_text_embed.guidance_embedder.linear_1",
+    "time_text_embed.guidance_embedder.linear_2",
+    "time_text_embed.text_embedder.linear_1",
+    "time_text_embed.text_embedder.linear_2",
+    "context_embedder",
+    "x_embedder",
+    "transformer_blocks.0.norm1.linear",
+    "transformer_blocks.0.norm1_context.linear",
+    "transformer_blocks.0.attn.to_q",
+    "transformer_blocks.0.attn.to_k",
+    "transformer_blocks.0.attn.to_v",
+    "transformer_blocks.0.attn.add_k_proj",
+    "transformer_blocks.0.attn.add_v_proj",
+    "transformer_blocks.0.attn.add_q_proj",
+    "transformer_blocks.0.attn.to_out.0",
+    "transformer_blocks.0.attn.to_add_out",
+    "transformer_blocks.0.ff.net.0.proj",
+    "transformer_blocks.0.ff.net.2",
+    "transformer_blocks.0.ff_context.net.0.proj",
+    "transformer_blocks.0.ff_context.net.2",
+    "transformer_blocks.1.norm1.linear",
+    "transformer_blocks.1.norm1_context.linear",
+    "transformer_blocks.1.attn.to_q",
+    "transformer_blocks.1.attn.to_k",
+    "transformer_blocks.1.attn.to_v",
+    "transformer_blocks.1.attn.add_k_proj",
+    "transformer_blocks.1.attn.add_v_proj",
+    "transformer_blocks.1.attn.add_q_proj",
+    "transformer_blocks.1.attn.to_out.0",
+    "transformer_blocks.1.attn.to_add_out",
+    "transformer_blocks.1.ff.net.0.proj",
+    "transformer_blocks.1.ff.net.2",
+    "transformer_blocks.1.ff_context.net.0.proj",
+    "transformer_blocks.1.ff_context.net.2",
+    "transformer_blocks.2.norm1.linear",
+    "transformer_blocks.2.norm1_context.linear",
+    "transformer_blocks.2.attn.to_q",
+    "transformer_blocks.2.attn.to_k",
+    "transformer_blocks.2.attn.to_v",
+    "transformer_blocks.2.attn.add_k_proj",
+    "transformer_blocks.2.attn.add_v_proj",
+    "transformer_blocks.2.attn.add_q_proj",
+    "transformer_blocks.2.attn.to_out.0",
+    "transformer_blocks.2.attn.to_add_out",
+    "transformer_blocks.2.ff.net.0.proj",
+    "transformer_blocks.2.ff.net.2",
+    "transformer_blocks.2.ff_context.net.0.proj",
+    "transformer_blocks.2.ff_context.net.2",
+    "transformer_blocks.3.norm1.linear",
+    "transformer_blocks.3.norm1_context.linear",
+    "transformer_blocks.3.attn.to_q",
+    "transformer_blocks.3.attn.to_k",
+    "transformer_blocks.3.attn.to_v",
+    "transformer_blocks.3.attn.add_k_proj",
+    "transformer_blocks.3.attn.add_v_proj",
+    "transformer_blocks.3.attn.add_q_proj",
+    "transformer_blocks.3.attn.to_out.0",
+    "transformer_blocks.3.attn.to_add_out",
+    "transformer_blocks.3.ff.net.0.proj",
+    "transformer_blocks.3.ff.net.2",
+    "transformer_blocks.3.ff_context.net.0.proj",
+    "transformer_blocks.3.ff_context.net.2",
+    "transformer_blocks.4.norm1.linear",
+    "transformer_blocks.4.norm1_context.linear",
+    "transformer_blocks.4.attn.to_q",
+    "transformer_blocks.4.attn.to_k",
+    "transformer_blocks.4.attn.to_v",
+    "transformer_blocks.4.attn.add_k_proj",
+    "transformer_blocks.4.attn.add_v_proj",
+    "transformer_blocks.4.attn.add_q_proj",
+    "transformer_blocks.4.attn.to_out.0",
+    "transformer_blocks.4.attn.to_add_out",
+    "transformer_blocks.4.ff.net.0.proj",
+    "transformer_blocks.4.ff.net.2",
+    "transformer_blocks.4.ff_context.net.0.proj",
+    "transformer_blocks.4.ff_context.net.2",
+    "transformer_blocks.5.norm1.linear",
+    "transformer_blocks.5.norm1_context.linear",
+    "transformer_blocks.5.attn.to_q",
+    "transformer_blocks.5.attn.to_k",
+    "transformer_blocks.5.attn.to_v",
+    "transformer_blocks.5.attn.add_k_proj",
+    "transformer_blocks.5.attn.add_v_proj",
+    "transformer_blocks.5.attn.add_q_proj",
+    "transformer_blocks.5.attn.to_out.0",
+    "transformer_blocks.5.attn.to_add_out",
+    "transformer_blocks.5.ff.net.0.proj",
+    "transformer_blocks.5.ff.net.2",
+    "transformer_blocks.5.ff_context.net.0.proj",
+    "transformer_blocks.5.ff_context.net.2",
+    "transformer_blocks.6.norm1.linear",
+    "transformer_blocks.6.norm1_context.linear",
+    "transformer_blocks.6.attn.to_q",
+    "transformer_blocks.6.attn.to_k",
+    "transformer_blocks.6.attn.to_v",
+    "transformer_blocks.6.attn.add_k_proj",
+    "transformer_blocks.6.attn.add_v_proj",
+    "transformer_blocks.6.attn.add_q_proj",
+    "transformer_blocks.6.attn.to_out.0",
+    "transformer_blocks.6.attn.to_add_out",
+    "transformer_blocks.6.ff.net.0.proj",
+    "transformer_blocks.6.ff.net.2",
+    "transformer_blocks.6.ff_context.net.0.proj",
+    "transformer_blocks.6.ff_context.net.2",
+    "transformer_blocks.7.norm1.linear",
+    "transformer_blocks.7.norm1_context.linear",
+    "transformer_blocks.7.attn.to_q",
+    "transformer_blocks.7.attn.to_k",
+    "transformer_blocks.7.attn.to_v",
+    "transformer_blocks.7.attn.add_k_proj",
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+    "single_transformer_blocks.37.attn.to_k",
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+    "norm_out.linear",
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+]
\ No newline at end of file
diff --git a/examples/stable-diffusion/quantize/measure_config.json b/examples/stable-diffusion/quantize/measure_config.json
new file mode 100755
index 0000000000..19d5e988d4
--- /dev/null
+++ b/examples/stable-diffusion/quantize/measure_config.json
@@ -0,0 +1,5 @@
+{
+    "method": "HOOKS",
+    "mode": "MEASURE",
+    "dump_stats_path": "quantize/measure_all/fp8"
+}
diff --git a/examples/stable-diffusion/quantize/quant_config.json b/examples/stable-diffusion/quantize/quant_config.json
new file mode 100755
index 0000000000..eab3011a5e
--- /dev/null
+++ b/examples/stable-diffusion/quantize/quant_config.json
@@ -0,0 +1,6 @@
+{
+    "method": "HOOKS",
+    "mode": "QUANTIZE",
+    "scale_method": "maxabs_hw_opt_weight",
+    "dump_stats_path": "quantize/measure_all/fp8"
+}
diff --git a/examples/stable-diffusion/quantize/quant_config_500.json b/examples/stable-diffusion/quantize/quant_config_500.json
new file mode 100755
index 0000000000..173f93772c
--- /dev/null
+++ b/examples/stable-diffusion/quantize/quant_config_500.json
@@ -0,0 +1,6 @@
+{
+    "method": "HOOKS",
+    "mode": "QUANTIZE",
+    "scale_method": "maxabs_hw_opt_weight",
+    "dump_stats_path": "quantize/measure_all_500/fp8"
+}
diff --git a/examples/stable-diffusion/quantize/quant_config_bmm.json b/examples/stable-diffusion/quantize/quant_config_bmm.json
new file mode 100755
index 0000000000..5cbc2bac9f
--- /dev/null
+++ b/examples/stable-diffusion/quantize/quant_config_bmm.json
@@ -0,0 +1,7 @@
+{
+    "method": "HOOKS",
+    "mode": "QUANTIZE",
+    "scale_method": "maxabs_hw_opt_weight",
+    "dump_stats_path": "quantize/measure_all/fp8",
+    "blocklist": {"types": ["Linear", "Conv2d", "LoRACompatibleLinear", "LoRACompatibleConv"]}
+}
diff --git a/examples/stable-diffusion/readme.txt b/examples/stable-diffusion/readme.txt
new file mode 100644
index 0000000000..d57f39a7f1
--- /dev/null
+++ b/examples/stable-diffusion/readme.txt
@@ -0,0 +1,12 @@
+This is experimental PR for Fal.ai ask based on current FLUX PR https://github.com/huggingface/optimum-habana/pull/1331
+* PR is fixed with timing (HPU device sync and include VAD into timing measure)
+* Added FP8 quantization support
+
+To run sample with 1 image 1 batch in BF16 precision:
+./run_bf16.sh
+
+To run sample with 1 image 1 batch in FP8 precision (quant weights were tuned with 1 prompt):
+./run_fp8.sh
+
+To run sample with 1 image 1 batch in FP8 precision (quant weights were tuned with 500 prompts):
+./run_fp8_500.sh
diff --git a/examples/stable-diffusion/run_bf16.sh b/examples/stable-diffusion/run_bf16.sh
new file mode 100755
index 0000000000..e8b74d3312
--- /dev/null
+++ b/examples/stable-diffusion/run_bf16.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts "A cat holding a sign that says hello world" \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16
diff --git a/examples/stable-diffusion/run_fp8.sh b/examples/stable-diffusion/run_fp8.sh
new file mode 100755
index 0000000000..e914a3421d
--- /dev/null
+++ b/examples/stable-diffusion/run_fp8.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+QUANT_CONFIG=quantize/quant_config.json \
+PT_HPU_WEIGHT_SHARING=0 \
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts "A cat holding a sign that says hello world" \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images_fp8 \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16 \
+     --quant_mode quantize
diff --git a/examples/stable-diffusion/run_fp8_500.sh b/examples/stable-diffusion/run_fp8_500.sh
new file mode 100755
index 0000000000..50ffc36b66
--- /dev/null
+++ b/examples/stable-diffusion/run_fp8_500.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+QUANT_CONFIG=quantize/quant_config_500.json \
+PT_HPU_WEIGHT_SHARING=0 \
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts "A cat holding a sign that says hello world" \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images_fp8_500 \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16 \
+     --quant_mode quantize
diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 8425389b4b..70be07ee3b 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -271,6 +271,12 @@ def main():
         action="store_true",
         help="Enable deterministic generation using CPU Generator",
     )
+    parser.add_argument(
+        "--quant_mode",
+        default="disable",
+        type=str,
+        help="Quantization mode 'measure', 'quantize' or 'disable'",
+    )
     args = parser.parse_args()
 
     # Select stable diffuson pipeline based on input
@@ -398,7 +404,10 @@ def main():
             control_image = Image.fromarray(image)
         kwargs_call["image"] = control_image
 
+    kwargs_call["quant_mode"] = args.quant_mode
+
     # Instantiate a Stable Diffusion pipeline class
+    import habana_frameworks.torch.core as htcore
     if sdxl:
         # SDXL pipelines
         if controlnet:
diff --git a/examples/stable-diffusion/unconditional_image_generation.py b/examples/stable-diffusion/unconditional_image_generation.py
old mode 100644
new mode 100755
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index e2f432c6ac..78aff5d4ff 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -244,9 +244,24 @@ def __call__(
             is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
             images.
         """
-
+        import habana_frameworks.torch as ht
         import habana_frameworks.torch.core as htcore
 
+        quant_mode=kwargs["quant_mode"]
+        if quant_mode == "measure" or quant_mode == "quantize":
+            import os
+            quant_config_path = os.getenv('QUANT_CONFIG')
+
+            htcore.hpu_set_env()
+
+            from neural_compressor.torch.quantization import FP8Config, convert, prepare
+            config = FP8Config.from_json_file(quant_config_path)
+            if config.measure:
+                self.transformer = prepare(self.transformer, config)
+            elif config.quantize:
+                self.transformer = convert(self.transformer, config)
+            htcore.hpu_initialize(self.transformer, mark_only_scales_as_const=True)
+
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
 
@@ -276,6 +291,7 @@ def __call__(
 
         device = self._execution_device
 
+        # 3. Run text encoder
         (
             prompt_embeds,
             pooled_prompt_embeds,
@@ -348,6 +364,7 @@ def __call__(
 
         throughput_warmup_steps = kwargs.get("throughput_warmup_steps", 3)
 
+        ht.hpu.synchronize()
         t0 = time.time()
         t1 = t0
 
@@ -363,6 +380,7 @@ def __call__(
             for i, t in enumerate(timesteps):
                 # because compilation occurs in the first two iterations
                 if i == throughput_warmup_steps:
+                    ht.hpu.synchronize()
                     t1 = time.time()
                 if self.interrupt:
                     continue
@@ -402,16 +420,10 @@ def __call__(
                 htcore.mark_step(sync=True)
 
             hb_profiler.stop()
-            t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
-            speed_metrics_prefix = "generation"
-            speed_measures = speed_metrics(
-                split=speed_metrics_prefix,
-                start_time=t0,
-                num_samples=num_batches * batch_size,
-                num_steps=num_batches * batch_size * num_inference_steps,
-                start_time_after_warmup=t1,
-            )
-            logger.info(f"Speed metrics: {speed_measures}")
+
+            if quant_mode == "measure":
+                from neural_compressor.torch.quantization import finalize_calibration
+                finalize_calibration(self.transformer)
 
         if output_type == "latent":
             image = latents
@@ -422,6 +434,19 @@ def __call__(
             image = self.vae.decode(latents, return_dict=False)[0]
             image = self.image_processor.postprocess(image, output_type=output_type)
 
+        # Synchronize and measure performance
+        ht.hpu.synchronize()
+        t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
+        speed_metrics_prefix = "generation"
+        speed_measures = speed_metrics(
+            split=speed_metrics_prefix,
+            start_time=t0,
+            num_samples=num_batches * batch_size,
+            num_steps=num_batches * batch_size * num_inference_steps,
+            start_time_after_warmup=t1,
+        )
+        logger.info(f"Speed metrics: {speed_measures}")
+
         # Offload all models
         self.maybe_free_model_hooks()
 

From 66098ff2e911dcad039258797123e22afd83bfce Mon Sep 17 00:00:00 2001
From: Daniel Socek <daniel.socek@intel.com>
Date: Thu, 26 Sep 2024 22:27:30 +0000
Subject: [PATCH 12/30] Enable batching for flux inference

Signed-off-by: Daniel Socek <daniel.socek@intel.com>
Co-authored-by: Deepak Narayana <deepak.narayana@intel.com>
---
 examples/stable-diffusion/prompts_100.txt     | 100 ++++++
 examples/stable-diffusion/prompts_5.txt       |   5 +
 examples/stable-diffusion/readme.txt          |   9 +
 .../stable-diffusion/run_bf16_prompts_100.sh  |  13 +
 .../stable-diffusion/run_bf16_prompts_5.sh    |  13 +
 .../text_to_image_generation.py               |  14 +
 .../diffusers/pipelines/flux/pipeline_flux.py | 294 +++++++++++++++---
 7 files changed, 401 insertions(+), 47 deletions(-)
 create mode 100644 examples/stable-diffusion/prompts_100.txt
 create mode 100644 examples/stable-diffusion/prompts_5.txt
 create mode 100755 examples/stable-diffusion/run_bf16_prompts_100.sh
 create mode 100755 examples/stable-diffusion/run_bf16_prompts_5.sh

diff --git a/examples/stable-diffusion/prompts_100.txt b/examples/stable-diffusion/prompts_100.txt
new file mode 100644
index 0000000000..09a94f1b9a
--- /dev/null
+++ b/examples/stable-diffusion/prompts_100.txt
@@ -0,0 +1,100 @@
+A women playing tennis on a blue tennis court.
+Two surfers in wetsuits carrying surfboards along the beach.
+People are flying their kites in a large field.
+A statue that is in front of a building.
+A man attempting to do a skateboard trick on an outdoor halfpipe.
+A cream bathroom with red accents and open window.
+A woman in a white dress and a man in gray stand near a cake on a white table under a white canopy.
+A tour bus downtown with yoga ads all over it.
+Three people riding horses on a beach next to the ocean..
+A bear lying in its den on a pile of wood.
+A herd of cattle is feeding at the river's edge.
+there is a male snowboarder that is in the air
+A statue of an elephant with tattoos and a target drawing
+a woman is standing over a white cake
+A grey motorcycle parked in a tropical setting.
+Two dogs playing in the grass with a frisbee.
+A group of people standing around a table full of food.
+A person holding a piece of broccoli with an insect on it. 
+A man in a gray suit and a red tie.
+Someone with skis on his back walking up a snow covered mountain.
+a woman in a blue shirt holding a pair of large scissors
+A fluffy white cat has a frowning look on it's face.
+A bus that is sitting on the street.
+Sinks in the washroom that is public and white.
+this lady is walking along the shore on a beach
+A woman helping a man to do his tie.
+A tray with coffee and a pastry on it.
+A man with a bald head and a bear wearing a bow tie.
+A cat eating a birthday cake on top of the table. 
+A man at a party talking on a cell phone.
+Three commuter buses sitting outside of a building.
+Food truck with customers ordering them with friends.
+A couple of bikes in front of a small stone wall.
+Someone is enjoying a small slice of pie. 
+there is a small tv and coffee table in the living room
+A plate with two sandwiches, cup and knife on the table
+A group of people venturing out on a horseback ride.
+A horse walking through a grassy field while two cows eat hay. 
+A bathroom scene with focus on the toilet.
+A man on a field swinging a baseball bat.
+Some people are standing on a crowd crowded sidewalk
+A pregnant women taking a picture of herself in the mirror.
+A line of police offices riding horses down a street.
+A buffet styled restaurant without self service but a server.
+A tview of a living room with fold out bed.
+A surfboard advertising offerings as people check them out.
+a bath room with a sink a mirror and towel racks
+A desk with a computer on in and a key board
+A large clock mounted on the wall of a stone building
+Three men sitting around a table with wine on it. 
+Man with glasses and a mustache standing in front of a door.
+A woman in a black dress holding a racquet.
+This hotel room has a king size bed. 
+A severely injured man hooked up to machines  in the hospital
+A black and white picture of an old store.
+Fingers keep a meatball sub from falling apart.
+A plate of fish covered in marinara, cheese, carrots, a fork, next to bread.
+A anal filled with boats and the street above it filled with people under umbrellas. 
+A woman standing on a tennis court holding a racquet.
+An elephant is standing next to a tree and a fence.
+People in a street with birds all over.
+THERE ARE PEOPLE THAT ARE STANDING IN THE GRASS
+a man standing by a fence while throwing a frisbee
+A pot that is on the stove with some food in it.
+A stuffed animal is inside of a microwave.
+Group of parents watching small children on a baseball field. 
+The kite is flying  high in the air 
+This cat is playing on a fuzzy white blanket.
+A lone giraffe at a zoo with trees behind it.
+A train engine carrying carts down a track.
+a young man brushes his teeth in the bathroom
+Two children playing baseball in red uniforms and hats.
+Men lined up an a runway in a desert greet an arriving jet plane.
+The American flag flies next to the clock tower on a snowy day. 
+A man takes a selfie of himself in the mirror. 
+A baseball game where a player is running to 3rd base.
+A man and a woman standing in front of a bus.
+A plate of dessert sitting beside a drink in a cafe.
+A man on the couch is petting the dog
+A cat is sleeping with a remote control on a couch.
+A European fighter jet flying above the tree tops.
+Snowman's head has a carrot for a nose and lemon slices for eyes.
+A man standing on a tennis court holding a tennis racquet.
+Two large green and white jumbo jet planes on the tarmac.
+The bedroom is is decorated in various zebra prints.
+Dog displaying skills near disc in open grassy area.
+A toilet facility in a stone cell on a plank floor.
+A picture done by Independent Expression Photography of a girl posing in an empty road sitting on her suit cases.
+Pair of colorful stuffed bears hanging on line in backyard.
+a large train is on the track going by the ocean
+There is a pizza with olives, peppers, meat, and cheese on the table.
+A man standing in front of microphones. 
+A woman that is holding a book sitting on a bed.
+A man showing a ring at a formal event.
+A concrete building with towers, a steep in the middle and a clock underneath.
+A white dog standing on top of a wooden bench.
+A woman holds a plate with rainbow cake.
+A women who is taking a picture of her food.
+A man with long hair and in a towel holding a toothbrush.
+a woman holding a tennis racket in the air 
diff --git a/examples/stable-diffusion/prompts_5.txt b/examples/stable-diffusion/prompts_5.txt
new file mode 100644
index 0000000000..6254d2714e
--- /dev/null
+++ b/examples/stable-diffusion/prompts_5.txt
@@ -0,0 +1,5 @@
+A women playing tennis on a blue tennis court.
+Two surfers in wetsuits carrying surfboards along the beach.
+People are flying their kites in a large field.
+A statue that is in front of a building.
+A man attempting to do a skateboard trick on an outdoor halfpipe.
diff --git a/examples/stable-diffusion/readme.txt b/examples/stable-diffusion/readme.txt
index d57f39a7f1..17d9b726cb 100644
--- a/examples/stable-diffusion/readme.txt
+++ b/examples/stable-diffusion/readme.txt
@@ -10,3 +10,12 @@ To run sample with 1 image 1 batch in FP8 precision (quant weights were tuned wi
 
 To run sample with 1 image 1 batch in FP8 precision (quant weights were tuned with 500 prompts):
 ./run_fp8_500.sh
+
+* Added batching
+* Added --prompt_file option for large number of input prompts
+
+To run sample with 5 prompts (batch size 1) in BF16 precision:
+./run_bf16_prompts_5.sh
+
+To run sample with 100 prompts (batch size 1) in BF16 precision:
+./run_bf16_prompts_100.sh
diff --git a/examples/stable-diffusion/run_bf16_prompts_100.sh b/examples/stable-diffusion/run_bf16_prompts_100.sh
new file mode 100755
index 0000000000..d229d5fe68
--- /dev/null
+++ b/examples/stable-diffusion/run_bf16_prompts_100.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts_file prompts_100.txt \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16
diff --git a/examples/stable-diffusion/run_bf16_prompts_5.sh b/examples/stable-diffusion/run_bf16_prompts_5.sh
new file mode 100755
index 0000000000..22cc40dc0c
--- /dev/null
+++ b/examples/stable-diffusion/run_bf16_prompts_5.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts_file prompts_5.txt \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16
diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 70be07ee3b..74816f0d10 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -277,6 +277,12 @@ def main():
         type=str,
         help="Quantization mode 'measure', 'quantize' or 'disable'",
     )
+    parser.add_argument(
+        "--prompts_file",
+        type=str,
+        default=None,
+        help="The file with prompts (for large number of images generation).",
+    )
     args = parser.parse_args()
 
     # Select stable diffuson pipeline based on input
@@ -534,6 +540,14 @@ def main():
     # Set RNG seed
     set_seed(args.seed)
 
+    # If prompts file is specified override prompts from the file
+    if args.prompts_file is not None:
+        lines = []
+        with open(args.prompts_file, "r") as file:
+            lines = file.readlines()
+        lines = [line.strip() for line in lines]
+        args.prompts = lines
+
     # Generate Images using a Stable Diffusion pipeline
     if args.distributed:
         with distributed_state.split_between_processes(args.prompts) as prompt:
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 78aff5d4ff..cbbcfcd185 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -51,6 +51,7 @@ class GaudiFluxPipelineOutput(BaseOutput):
     """
 
     images: Union[List[PIL.Image.Image], np.ndarray]
+    throughput: float
 
 
 EXAMPLE_DOC_STRING = """
@@ -144,6 +145,86 @@ def __init__(
             from habana_frameworks.torch.hpu import wrap_in_hpu_graph
             transformer = wrap_in_hpu_graph(transformer)
 
+
+    @classmethod
+    def _split_inputs_into_batches(
+        cls,
+        batch_size,
+        latents,
+        prompt_embeds,
+        pooled_prompt_embeds,
+        text_ids,
+        latent_image_ids,
+        guidance
+    ):
+        # Use torch.split to generate num_batches batches of size batch_size
+        latents_batches = list(torch.split(latents, batch_size))
+        prompt_embeds_batches = list(torch.split(prompt_embeds, batch_size))
+        if pooled_prompt_embeds is not None:
+            pooled_prompt_embeds_batches = list(torch.split(pooled_prompt_embeds, batch_size))
+        if text_ids is not None:
+            text_ids_batches = list(torch.split(text_ids, batch_size))
+        if latent_image_ids is not None:
+            latent_image_ids_batches = list(torch.split(latent_image_ids, batch_size))
+        if guidance is not None:
+            guidance_batches = list(torch.split(guidance, 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 prompt_embeds_batches
+            sequence_to_stack = (prompt_embeds_batches[-1],) + tuple(
+                torch.zeros_like(prompt_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+            )
+            prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack)
+
+            # Pad pooled_prompt_embeds if necessary
+            if pooled_prompt_embeds is not None:
+                sequence_to_stack = (pooled_prompt_embeds_batches[-1],) + tuple(
+                    torch.zeros_like(pooled_prompt_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                pooled_prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack)
+
+            # Pad text_ids_batches if necessary
+            if text_ids is not None:
+                sequence_to_stack = (text_ids_batches[-1],) + tuple(
+                    torch.zeros_like(text_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                text_ids_batches[-1] = torch.vstack(sequence_to_stack)
+
+            # Pad latent_image_ids if necessary
+            if latent_image_ids is not None:
+                sequence_to_stack = (latent_image_ids_batches[-1],) + tuple(
+                    torch.zeros_like(latent_image_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                latent_image_ids_batches[-1] = torch.vstack(sequence_to_stack)
+
+            # Pad guidance if necessary
+            if guidance is not None:
+                sequence_to_stack = (guidance_batches[-1],) + tuple(
+                    torch.zeros_like(guidance_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                guidance_batches[-1] = torch.vstack(sequence_to_stack)
+
+        # Stack batches in the same tensor
+        latents_batches = torch.stack(latents_batches)
+        prompt_embeds_batches = torch.stack(prompt_embeds_batches)
+        pooled_prompt_embeds_batches = torch.stack(pooled_prompt_embeds_batches)
+        text_ids_batches = torch.stack(text_ids_batches)
+        latent_image_ids_batches = torch.stack(latent_image_ids_batches)
+        guidance_batches = torch.stack(guidance_batches)
+
+        return latents_batches, prompt_embeds_batches, pooled_prompt_embeds_batches, text_ids_batches, latent_image_ids_batches, guidance_batches, num_dummy_samples
+
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -155,6 +236,7 @@ def __call__(
         num_inference_steps: int = 28,
         timesteps: List[int] = None,
         guidance_scale: float = 3.5,
+        batch_size: int = 1,
         num_images_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.FloatTensor] = None,
@@ -244,6 +326,23 @@ def __call__(
             is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
             images.
         """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
         import habana_frameworks.torch as ht
         import habana_frameworks.torch.core as htcore
 
@@ -283,11 +382,12 @@ def __call__(
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
+            num_prompts = 1
         elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
+            num_prompts = len(prompt)
         else:
-            batch_size = prompt_embeds.shape[0]
+            num_prompts = prompt_embeds.shape[0]
+        num_batches = math.ceil((num_images_per_prompt * num_prompts) / batch_size)
 
         device = self._execution_device
 
@@ -309,7 +409,7 @@ def __call__(
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels // 4
         latents, latent_image_ids = self.prepare_latents(
-            batch_size * num_images_per_prompt,
+            num_prompts * num_images_per_prompt,
             num_channels_latents,
             height,
             width,
@@ -347,14 +447,6 @@ def __call__(
         else:
             guidance = None
 
-        # 5-1. 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 = math.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)."
@@ -363,6 +455,9 @@ def __call__(
             logger.warning("The first two iterations are slower so it is recommended to feed more batches.")
 
         throughput_warmup_steps = kwargs.get("throughput_warmup_steps", 3)
+        use_warmup_inference_steps = (
+            num_batches <= throughput_warmup_steps and num_inference_steps > throughput_warmup_steps
+        )
 
         ht.hpu.synchronize()
         t0 = time.time()
@@ -375,82 +470,187 @@ def __call__(
         )
         hb_profiler.start()
 
+        # 5.1. Split Input data to batches (HPU-specific step)
+        (
+            latents_batches,
+            text_embeddings_batches,
+            pooled_prompt_embeddings_batches,
+            text_ids_batches,
+            latent_image_ids_batches,
+            guidance_batches,
+            num_dummy_samples
+        ) = self._split_inputs_into_batches(
+            batch_size,
+            latents,
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+            latent_image_ids,
+            guidance
+        )
+
+        outputs = {
+            "images": [],
+        }
+
         # 6. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # because compilation occurs in the first two iterations
-                if i == throughput_warmup_steps:
+        for j in range(num_batches):
+
+            # The throughput is calculated from the 4th iteration
+            # because compilation occurs in the first 2-3 iterations
+            if j == throughput_warmup_steps:
+                ht.hpu.synchronize()
+                t1 = time.time()
+            if use_warmup_inference_steps:
+                ht.hpu.synchronize()
+                t0_inf = 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)
+            pooled_prompt_embeddings_batch = pooled_prompt_embeddings_batches[0]
+            pooled_prompt_embeddings_batches = torch.roll(pooled_prompt_embeddings_batches, shifts=-1, dims=0)
+            text_ids_batch = text_ids_batches[0]
+            text_ids_batches = torch.roll(text_ids_batches, shifts=-1, dims=0)
+            latent_image_ids_batch = latent_image_ids_batches[0]
+            latent_image_ids_batches = torch.roll(latent_image_ids_batches, shifts=-1, dims=0)
+            guidance_batch = guidance_batches[0]
+            guidance_batches = torch.roll(guidance_batches, shifts=-1, dims=0)
+
+            if hasattr(self.scheduler, "_init_step_index"):
+                # Reset scheduler step index for next batch
+                self.scheduler.timesteps = timesteps
+                self.scheduler._init_step_index(timesteps[0])
+
+            for i in self.progress_bar(range(len(timesteps))):
+                if use_warmup_inference_steps and i == throughput_warmup_steps:
                     ht.hpu.synchronize()
-                    t1 = time.time()
+                    t1_inf = time.time()
+                    t1 += t1_inf - t0_inf
+
                 if self.interrupt:
-                    continue
+                   continue
 
+                timestep = timesteps[0]
+                timesteps = torch.roll(timesteps, shifts=-1, dims=0)
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+                timestep = timestep.expand(latents_batch.shape[0]).to(latents_batch.dtype)
 
                 noise_pred = self.transformer(
-                    hidden_states=latents,
+                    hidden_states=latents_batch,
                     timestep=timestep / 1000,
-                    guidance=guidance,
-                    pooled_projections=pooled_prompt_embeds,
-                    encoder_hidden_states=prompt_embeds,
-                    txt_ids=text_ids,
-                    img_ids=latent_image_ids,
+                    guidance=guidance_batch,
+                    pooled_projections=pooled_prompt_embeddings_batch,
+                    encoder_hidden_states=text_embeddings_batch,
+                    txt_ids=text_ids_batch,
+                    img_ids=latent_image_ids_batch,
                     joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
                 )[0]
 
                 # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                latents_dtype = latents_batch.dtype
+                latents_batch = self.scheduler.step(noise_pred, timestep, latents_batch, return_dict=False)[0]
+
+                if latents_batch.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents_batch = latents_batch.to(latents_dtype)
 
                 if callback_on_step_end is not None:
                     callback_kwargs = {}
                     for k in callback_on_step_end_tensor_inputs:
                         callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+                    callback_outputs = callback_on_step_end(self, i, timestep, callback_kwargs)
+
+                    latents_batch = callback_outputs.pop("latents", latents_batch)
+
+                    _prompt_embeds = callback_outputs.pop("prompt_embeds", None)
+                    _negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", None)
+                    if _prompt_embeds is not None and _negative_prompt_embeds is not None:
+                        text_embeddings_batch = torch.cat([_negative_prompt_embeds, _prompt_embeds])
+                    _pooled_prompt_embeds = callback_outputs.pop("pooled_prompt_embeds", None)
+                    _negative_pooled_prompt_embeds = callback_outputs.pop("negative_pooled_prompt_embeds", None)
+                    if _pooled_prompt_embeds is not None and _negative_pooled_prompt_embeds is not None:
+                        pooled_prompt_embeddings_batch = torch.cat([_negative_pooled_prompt_embeds, _pooled_prompt_embeds])
 
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, timestep, latents)
 
                 hb_profiler.step()
-                htcore.mark_step(sync=True)
+                #htcore.mark_step(sync=True)
 
-            hb_profiler.stop()
+            if use_warmup_inference_steps:
+                t1 = warmup_inference_steps_time_adjustment(
+                    t1, t1_inf, num_inference_steps, throughput_warmup_steps
+                )
 
-            if quant_mode == "measure":
-                from neural_compressor.torch.quantization import finalize_calibration
-                finalize_calibration(self.transformer)
+            if not output_type == "latent":
+                latents_batch = self._unpack_latents(latents_batch, height, width, self.vae_scale_factor)
+                latents_batch = (latents_batch / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+                image = self.vae.decode(latents_batch, return_dict=False)[0]
+                image = self.image_processor.postprocess(image, output_type=output_type)
+            else:
+                image = latents_batch
 
-        if output_type == "latent":
-            image = latents
+            outputs["images"].append(image)
+            #htcore.mark_step(sync=True)
 
-        else:
-            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-            image = self.vae.decode(latents, return_dict=False)[0]
-            image = self.image_processor.postprocess(image, output_type=output_type)
+        # Stage after denoising
+        hb_profiler.stop()
+
+        if quant_mode == "measure":
+            from neural_compressor.torch.quantization import finalize_calibration
+            finalize_calibration(self.transformer)
 
-        # Synchronize and measure performance
         ht.hpu.synchronize()
-        t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
         speed_metrics_prefix = "generation"
         speed_measures = speed_metrics(
             split=speed_metrics_prefix,
             start_time=t0,
-            num_samples=num_batches * batch_size,
+            num_samples=num_batches * batch_size
+            if t1 == t0 or use_warmup_inference_steps
+            else (num_batches - throughput_warmup_steps) * batch_size,
             num_steps=num_batches * batch_size * num_inference_steps,
             start_time_after_warmup=t1,
         )
         logger.info(f"Speed metrics: {speed_measures}")
 
+        # 8 Output Images
+        # 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 == "pil" and isinstance(image, list):
+                outputs["images"] += image
+            elif output_type in ["np", "numpy"] and isinstance(image, np.ndarray):
+                if len(outputs["images"]) == 0:
+                    outputs["images"] = image
+                else:
+                    outputs["images"] = np.concatenate((outputs["images"], image), axis=0)
+            else:
+                if len(outputs["images"]) == 0:
+                    outputs["images"] = image
+                else:
+                    outputs["images"] = torch.cat((outputs["images"], image), 0)
+
         # Offload all models
         self.maybe_free_model_hooks()
 
         if not return_dict:
-            return (image,)
+            return outputs["images"]
 
-        return GaudiFluxPipelineOutput(images=image)
+        return GaudiFluxPipelineOutput(
+            images=outputs["images"],
+            throughput=speed_measures[f"{speed_metrics_prefix}_samples_per_second"],
+        )

From 0615ce154c654be434d08f8eb6c48f008b935e22 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Fri, 27 Sep 2024 13:16:36 +0800
Subject: [PATCH 13/30] update diffusers to adopt rope changes

---
 .../habana/diffusers/pipelines/flux/pipeline_flux.py  | 11 +++++------
 optimum/habana/utils.py                               |  3 ++-
 setup.py                                              |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index e2f432c6ac..0819f76a3d 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -141,8 +141,7 @@ def __init__(
         )
         self.to(self._device)
         if use_hpu_graphs:
-            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
-            transformer = wrap_in_hpu_graph(transformer)
+            transformer = self.ht.wrap_in_hpu_graph(transformer)
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
@@ -245,8 +244,6 @@ def __call__(
             images.
         """
 
-        import habana_frameworks.torch.core as htcore
-
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
 
@@ -355,6 +352,7 @@ def __call__(
             warmup=profiling_warmup_steps,
             active=profiling_steps,
             record_shapes=False,
+            with_stack=True
         )
         hb_profiler.start()
 
@@ -397,9 +395,10 @@ def __call__(
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
-
+                if profiling_warmup_steps and profiling_steps:
+                    self.ht.hpu.synchronize()
                 hb_profiler.step()
-                htcore.mark_step(sync=True)
+                self.ht.core.mark_step(sync=True)
 
             hb_profiler.stop()
             t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
diff --git a/optimum/habana/utils.py b/optimum/habana/utils.py
index 3da3f11872..5434344246 100755
--- a/optimum/habana/utils.py
+++ b/optimum/habana/utils.py
@@ -285,6 +285,7 @@ def __init__(
         warmup: int = 0,
         active: int = 0,
         record_shapes: bool = True,
+        with_stack: bool = False,
         output_dir: str = "./hpu_profile",
         wait: int = 0,
     ):
@@ -306,7 +307,7 @@ def noop():
                 activities=activities,
                 on_trace_ready=torch.profiler.tensorboard_trace_handler(output_dir),
                 record_shapes=record_shapes,
-                with_stack=False,
+                with_stack=with_stack
             )
             self.start = profiler.start
             self.stop = profiler.stop
diff --git a/setup.py b/setup.py
index c77a241717..55510eb4c3 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
     "optimum",
     "torch",
     "accelerate >= 0.33.0, < 0.34.0",
-    "diffusers >= 0.30.2",
+    "diffusers @ git+https://github.com/huggingface/diffusers.git@main",
     "huggingface_hub >= 0.23.2",
     "sentence-transformers[train] == 3.0.1",
 ]

From 97a6dd549fbae0dd517c44855f26acda502a8329 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Fri, 27 Sep 2024 13:48:07 +0800
Subject: [PATCH 14/30] fix import error

---
 optimum/habana/diffusers/pipelines/flux/pipeline_flux.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 0819f76a3d..34a70cb069 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -141,7 +141,7 @@ def __init__(
         )
         self.to(self._device)
         if use_hpu_graphs:
-            transformer = self.ht.wrap_in_hpu_graph(transformer)
+            transformer = self.ht.hpu.wrap_in_hpu_graph(transformer)
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)

From 9aefc5fd51b650872205f881c86f297bd277c19a Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Fri, 27 Sep 2024 14:31:34 +0800
Subject: [PATCH 15/30] fix time clac drift

---
 optimum/habana/diffusers/pipelines/flux/pipeline_flux.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 34a70cb069..cf6d758ccf 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -361,6 +361,8 @@ def __call__(
             for i, t in enumerate(timesteps):
                 # because compilation occurs in the first two iterations
                 if i == throughput_warmup_steps:
+                    # clac acc time `end - t1`
+                    self.ht.hpu.synchronize()
                     t1 = time.time()
                 if self.interrupt:
                     continue
@@ -401,6 +403,8 @@ def __call__(
                 self.ht.core.mark_step(sync=True)
 
             hb_profiler.stop()
+            # clac acc time `end - t1`
+            self.ht.hpu.synchronize()
             t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
             speed_metrics_prefix = "generation"
             speed_measures = speed_metrics(

From 1bc593a8c8b1bfcb4ff5a0b8708c293a1f7507d2 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Fri, 27 Sep 2024 15:08:20 +0800
Subject: [PATCH 16/30] fix import error in lazy mode

---
 optimum/habana/diffusers/pipelines/flux/pipeline_flux.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index cf6d758ccf..fe7380dbeb 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -362,7 +362,7 @@ def __call__(
                 # because compilation occurs in the first two iterations
                 if i == throughput_warmup_steps:
                     # clac acc time `end - t1`
-                    self.ht.hpu.synchronize()
+                    torch.hpu.synchronize()
                     t1 = time.time()
                 if self.interrupt:
                     continue
@@ -398,13 +398,13 @@ def __call__(
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                 if profiling_warmup_steps and profiling_steps:
-                    self.ht.hpu.synchronize()
+                    torch.hpu.synchronize()
                 hb_profiler.step()
-                self.ht.core.mark_step(sync=True)
+                self.htcore.mark_step(sync=True)
 
             hb_profiler.stop()
             # clac acc time `end - t1`
-            self.ht.hpu.synchronize()
+            torch.hpu.synchronize()
             t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
             speed_metrics_prefix = "generation"
             speed_measures = speed_metrics(

From 3a53c95fab843e4e7f72841fd17b429a37db165d Mon Sep 17 00:00:00 2001
From: Daniel Socek <daniel.socek@intel.com>
Date: Fri, 27 Sep 2024 18:22:39 +0000
Subject: [PATCH 17/30] Add hybrid fp8 and bf16 denoising to flux

Signed-off-by: Daniel Socek <daniel.socek@intel.com>
---
 examples/stable-diffusion/readme.txt          |  8 +++
 .../stable-diffusion/run_fp8_500_hybrid.sh    | 16 +++++
 .../run_fp8_500_hybrid_prompts_5.sh           | 16 +++++
 .../text_to_image_generation.py               |  2 +-
 .../diffusers/pipelines/flux/pipeline_flux.py | 59 +++++++++++++------
 5 files changed, 83 insertions(+), 18 deletions(-)
 create mode 100755 examples/stable-diffusion/run_fp8_500_hybrid.sh
 create mode 100755 examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh

diff --git a/examples/stable-diffusion/readme.txt b/examples/stable-diffusion/readme.txt
index 17d9b726cb..e72ebcd497 100644
--- a/examples/stable-diffusion/readme.txt
+++ b/examples/stable-diffusion/readme.txt
@@ -19,3 +19,11 @@ To run sample with 5 prompts (batch size 1) in BF16 precision:
 
 To run sample with 100 prompts (batch size 1) in BF16 precision:
 ./run_bf16_prompts_100.sh
+
+* Added hybrid (mixed fp9 and bf16) precision denoising 
+
+To run sample with 1 image 1 batch in hybrid precision:
+./run_fp8_500_hybrid.sh
+
+To run sample with 5 prompts (batch size 1) in hybrid precision:
+./run_fp8_500_hybrid_prompts_5.sh
diff --git a/examples/stable-diffusion/run_fp8_500_hybrid.sh b/examples/stable-diffusion/run_fp8_500_hybrid.sh
new file mode 100755
index 0000000000..6de1397574
--- /dev/null
+++ b/examples/stable-diffusion/run_fp8_500_hybrid.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+QUANT_CONFIG=quantize/quant_config_500.json \
+PT_HPU_WEIGHT_SHARING=0 \
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts "A cat holding a sign that says hello world" \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images_fp8_500 \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16 \
+     --quant_mode quantize-mixed
diff --git a/examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh b/examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh
new file mode 100755
index 0000000000..29d4ac1d22
--- /dev/null
+++ b/examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+QUANT_CONFIG=quantize/quant_config_500.json \
+PT_HPU_WEIGHT_SHARING=0 \
+python text_to_image_generation.py \
+     --model_name_or_path black-forest-labs/FLUX.1-dev \
+     --prompts_file prompts_5.txt \
+     --num_images_per_prompt 1 \
+     --batch_size 1 \
+     --num_inference_steps 30 \
+     --image_save_dir /tmp/flux_1_images \
+     --scheduler flow_match_euler_discrete \
+     --use_habana \
+     --use_hpu_graphs \
+     --gaudi_config Habana/stable-diffusion \
+     --bf16 \
+     --quant_mode quantize-mixed
diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 74816f0d10..7e862386f5 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -275,7 +275,7 @@ def main():
         "--quant_mode",
         default="disable",
         type=str,
-        help="Quantization mode 'measure', 'quantize' or 'disable'",
+        help="Quantization mode 'measure', 'quantize', 'quantize-mixed' or 'disable'",
     )
     parser.add_argument(
         "--prompts_file",
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index cbbcfcd185..999a15bdcc 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -346,8 +346,13 @@ def __call__(
         import habana_frameworks.torch as ht
         import habana_frameworks.torch.core as htcore
 
-        quant_mode=kwargs["quant_mode"]
-        if quant_mode == "measure" or quant_mode == "quantize":
+        quant_mode = kwargs["quant_mode"]
+
+        if quant_mode == "quantize-mixed":
+            import copy
+            transformer_bf16 = copy.deepcopy(self.transformer).to(self._execution_device)
+
+        if quant_mode == "measure" or quant_mode.startswith("quantize"):
             import os
             quant_config_path = os.getenv('QUANT_CONFIG')
 
@@ -523,6 +528,14 @@ def __call__(
                 self.scheduler.timesteps = timesteps
                 self.scheduler._init_step_index(timesteps[0])
 
+            # Mixed quantization
+            quant_mixed_step = len(timesteps)
+            if quant_mode == "quantize-mixed":
+                # 10% of steps use higher precision in mixed quant mode
+                quant_mixed_step = quant_mixed_step - (quant_mixed_step // 10)
+                print(f"Use FP8  Transformer at steps 0 to {quant_mixed_step - 1}")
+                print(f"Use BF16 Transformer at steps {quant_mixed_step} to {len(timesteps) - 1}")
+
             for i in self.progress_bar(range(len(timesteps))):
                 if use_warmup_inference_steps and i == throughput_warmup_steps:
                     ht.hpu.synchronize()
@@ -537,17 +550,31 @@ def __call__(
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = timestep.expand(latents_batch.shape[0]).to(latents_batch.dtype)
 
-                noise_pred = self.transformer(
-                    hidden_states=latents_batch,
-                    timestep=timestep / 1000,
-                    guidance=guidance_batch,
-                    pooled_projections=pooled_prompt_embeddings_batch,
-                    encoder_hidden_states=text_embeddings_batch,
-                    txt_ids=text_ids_batch,
-                    img_ids=latent_image_ids_batch,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                )[0]
+                if i >= quant_mixed_step:
+                    # Mixed quantization
+                    noise_pred = transformer_bf16(
+                        hidden_states=latents_batch,
+                        timestep=timestep / 1000,
+                        guidance=guidance_batch,
+                        pooled_projections=pooled_prompt_embeddings_batch,
+                        encoder_hidden_states=text_embeddings_batch,
+                        txt_ids=text_ids_batch,
+                        img_ids=latent_image_ids_batch,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                else:
+                    noise_pred = self.transformer(
+                        hidden_states=latents_batch,
+                        timestep=timestep / 1000,
+                        guidance=guidance_batch,
+                        pooled_projections=pooled_prompt_embeddings_batch,
+                        encoder_hidden_states=text_embeddings_batch,
+                        txt_ids=text_ids_batch,
+                        img_ids=latent_image_ids_batch,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                    )[0]
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents_dtype = latents_batch.dtype
@@ -567,11 +594,9 @@ def __call__(
                     latents_batch = callback_outputs.pop("latents", latents_batch)
 
                     _prompt_embeds = callback_outputs.pop("prompt_embeds", None)
-                    _negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", None)
                     if _prompt_embeds is not None and _negative_prompt_embeds is not None:
                         text_embeddings_batch = torch.cat([_negative_prompt_embeds, _prompt_embeds])
                     _pooled_prompt_embeds = callback_outputs.pop("pooled_prompt_embeds", None)
-                    _negative_pooled_prompt_embeds = callback_outputs.pop("negative_pooled_prompt_embeds", None)
                     if _pooled_prompt_embeds is not None and _negative_pooled_prompt_embeds is not None:
                         pooled_prompt_embeddings_batch = torch.cat([_negative_pooled_prompt_embeds, _pooled_prompt_embeds])
 
@@ -601,7 +626,7 @@ def __call__(
             outputs["images"].append(image)
             #htcore.mark_step(sync=True)
 
-        # Stage after denoising
+        # 7. Stage after denoising
         hb_profiler.stop()
 
         if quant_mode == "measure":
@@ -622,8 +647,8 @@ def __call__(
         logger.info(f"Speed metrics: {speed_measures}")
 
         # 8 Output Images
-        # Remove dummy generations if needed
         if num_dummy_samples > 0:
+            # Remove dummy generations if needed
             outputs["images"][-1] = outputs["images"][-1][:-num_dummy_samples]
 
         # Process generated images

From 0b80a6a7e1fe15d33367ea059dd3ba20747f07dc Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Sun, 29 Sep 2024 10:40:32 +0800
Subject: [PATCH 18/30] use default scheduler from upstream diffusers

---
 .../text_to_image_generation.py               |  4 ---
 optimum/habana/diffusers/__init__.py          |  2 +-
 .../diffusers/pipelines/flux/pipeline_flux.py | 10 +++++---
 .../diffusers/pipelines/pipeline_utils.py     |  1 -
 .../habana/diffusers/schedulers/__init__.py   |  1 -
 .../scheduling_flow_mactch_euler_discrete.py  | 25 -------------------
 6 files changed, 7 insertions(+), 36 deletions(-)
 delete mode 100644 optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 8425389b4b..67360aec3c 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -296,10 +296,6 @@ def main():
         )
     elif args.scheduler == "ddim":
         scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler", **kwargs)
-    elif args.scheduler == "flow_match_euler_discrete":
-        scheduler = GaudiFlowMatchEulerDiscreteScheduler.from_pretrained(
-            args.model_name_or_path, subfolder="scheduler", **kwargs
-        )
     else:
         scheduler = None
 
diff --git a/optimum/habana/diffusers/__init__.py b/optimum/habana/diffusers/__init__.py
index 3be8537f45..4ca2933e91 100644
--- a/optimum/habana/diffusers/__init__.py
+++ b/optimum/habana/diffusers/__init__.py
@@ -21,4 +21,4 @@
 from .pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import GaudiStableDiffusionXLInpaintPipeline
 from .pipelines.stable_video_diffusion.pipeline_stable_video_diffusion import GaudiStableVideoDiffusionPipeline
 from .pipelines.text_to_video_synthesis.pipeline_text_to_video_synth import GaudiTextToVideoSDPipeline
-from .schedulers import GaudiDDIMScheduler, GaudiEulerAncestralDiscreteScheduler, GaudiEulerDiscreteScheduler, GaudiFlowMatchEulerDiscreteScheduler
+from .schedulers import GaudiDDIMScheduler, GaudiEulerAncestralDiscreteScheduler, GaudiEulerDiscreteScheduler
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index fe7380dbeb..9d9caece02 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -21,12 +21,12 @@
 import PIL.Image
 
 import torch
-
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from diffusers.utils import BaseOutput, replace_example_docstring
 from diffusers.models.autoencoders import AutoencoderKL
 from diffusers.models.transformers import FluxTransformer2DModel
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
 
 from optimum.utils import logging
@@ -34,7 +34,6 @@
 from ....transformers.gaudi_configuration import GaudiConfig
 from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
 from ..pipeline_utils import GaudiDiffusionPipeline
-from ...schedulers import GaudiFlowMatchEulerDiscreteScheduler
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -110,7 +109,7 @@ class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
 
     def __init__(
         self,
-        scheduler: GaudiFlowMatchEulerDiscreteScheduler,
+        scheduler: FlowMatchEulerDiscreteScheduler,
         vae: AutoencoderKL,
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
@@ -140,6 +139,9 @@ def __init__(
             transformer=transformer,
         )
         self.to(self._device)
+        if use_habana:
+            import habana_frameworks.torch as ht
+            self.ht = ht
         if use_hpu_graphs:
             transformer = self.ht.hpu.wrap_in_hpu_graph(transformer)
 
@@ -400,7 +402,7 @@ def __call__(
                 if profiling_warmup_steps and profiling_steps:
                     torch.hpu.synchronize()
                 hb_profiler.step()
-                self.htcore.mark_step(sync=True)
+                self.ht.core.mark_step(sync=True)
 
             hb_profiler.stop()
             # clac acc time `end - t1`
diff --git a/optimum/habana/diffusers/pipelines/pipeline_utils.py b/optimum/habana/diffusers/pipelines/pipeline_utils.py
index 6e659edff4..7f36b90ae4 100644
--- a/optimum/habana/diffusers/pipelines/pipeline_utils.py
+++ b/optimum/habana/diffusers/pipelines/pipeline_utils.py
@@ -55,7 +55,6 @@
     "optimum.habana.diffusers.schedulers": {
         "GaudiDDIMScheduler": ["save_pretrained", "from_pretrained"],
         "GaudiEulerDiscreteScheduler": ["save_pretrained", "from_pretrained"],
-        "GaudiFlowMatchEulerDiscreteScheduler": ["save_pretrained", "from_pretrained"],
         "GaudiEulerAncestralDiscreteScheduler": ["save_pretrained", "from_pretrained"],
     },
 }
diff --git a/optimum/habana/diffusers/schedulers/__init__.py b/optimum/habana/diffusers/schedulers/__init__.py
index 48bf0bd8e9..37eb80b1a6 100644
--- a/optimum/habana/diffusers/schedulers/__init__.py
+++ b/optimum/habana/diffusers/schedulers/__init__.py
@@ -1,4 +1,3 @@
 from .scheduling_ddim import GaudiDDIMScheduler
 from .scheduling_euler_ancestral_discrete import GaudiEulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import GaudiEulerDiscreteScheduler
-from .scheduling_flow_mactch_euler_discrete import GaudiFlowMatchEulerDiscreteScheduler
diff --git a/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py b/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py
deleted file mode 100644
index ccc597fa07..0000000000
--- a/optimum/habana/diffusers/schedulers/scheduling_flow_mactch_euler_discrete.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-
-
-class GaudiFlowMatchEulerDiscreteScheduler(FlowMatchEulerDiscreteScheduler):
-    # TODO: overwrite orginal func with following one to fix dyn error in gaudi lazy mode
-    def index_for_timestep(self, timestep, schedule_timesteps=None):
-        if schedule_timesteps is None:
-            schedule_timesteps = self.timesteps
-
-        # indices = (schedule_timesteps == timestep).nonzero()
-
-        # The sigma index that is taken for the **very** first `step`
-        # is always the second index (or the last index if there is only 1)
-        # This way we can ensure we don't accidentally skip a sigma in
-        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
-        # pos = 1 if len(indices) > 1 else 0
-
-        # return indices[pos].item()
-
-        masked = (schedule_timesteps == timestep)
-        tmp = masked.cumsum(dim=0)
-        pos = (tmp == 0).sum().item()
-        if masked.sum() > 1:
-            pos += (tmp == 1).sum().item()
-        return pos

From f26795889b64166b3e613e79cb327c9830d39ee9 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Sun, 29 Sep 2024 10:49:28 +0800
Subject: [PATCH 19/30] fix import error

---
 examples/stable-diffusion/text_to_image_generation.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index 67360aec3c..69b7f3a897 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -25,8 +25,7 @@
 from optimum.habana.diffusers import (
     GaudiDDIMScheduler,
     GaudiEulerAncestralDiscreteScheduler,
-    GaudiEulerDiscreteScheduler,
-    GaudiFlowMatchEulerDiscreteScheduler
+    GaudiEulerDiscreteScheduler
 )
 from optimum.habana.utils import set_seed
 

From 9bcc65f1070b51173060bca2914b3a14512bcf00 Mon Sep 17 00:00:00 2001
From: Daniel Socek <daniel.socek@intel.com>
Date: Tue, 1 Oct 2024 14:57:52 +0000
Subject: [PATCH 20/30] Fix timing issue with batching

Signed-off-by: Daniel Socek <daniel.socek@intel.com>
---
 .../diffusers/pipelines/flux/pipeline_flux.py | 120 +++++-------------
 1 file changed, 35 insertions(+), 85 deletions(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 999a15bdcc..dd180da8b1 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -12,29 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import time
 import math
+import time
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
 import PIL.Image
-
 import torch
-
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
-
-from diffusers.utils import BaseOutput, replace_example_docstring
 from diffusers.models.autoencoders import AutoencoderKL
 from diffusers.models.transformers import FluxTransformer2DModel
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+from diffusers.utils import BaseOutput, replace_example_docstring
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from optimum.utils import logging
 
 from ....transformers.gaudi_configuration import GaudiConfig
 from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
-from ..pipeline_utils import GaudiDiffusionPipeline
 from ...schedulers import GaudiFlowMatchEulerDiscreteScheduler
+from ..pipeline_utils import GaudiDiffusionPipeline
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -143,19 +141,12 @@ def __init__(
         self.to(self._device)
         if use_hpu_graphs:
             from habana_frameworks.torch.hpu import wrap_in_hpu_graph
-            transformer = wrap_in_hpu_graph(transformer)
 
+            transformer = wrap_in_hpu_graph(transformer)
 
     @classmethod
     def _split_inputs_into_batches(
-        cls,
-        batch_size,
-        latents,
-        prompt_embeds,
-        pooled_prompt_embeds,
-        text_ids,
-        latent_image_ids,
-        guidance
+        cls, batch_size, latents, prompt_embeds, pooled_prompt_embeds, text_ids, latent_image_ids, guidance
     ):
         # Use torch.split to generate num_batches batches of size batch_size
         latents_batches = list(torch.split(latents, batch_size))
@@ -222,8 +213,15 @@ def _split_inputs_into_batches(
         latent_image_ids_batches = torch.stack(latent_image_ids_batches)
         guidance_batches = torch.stack(guidance_batches)
 
-        return latents_batches, prompt_embeds_batches, pooled_prompt_embeds_batches, text_ids_batches, latent_image_ids_batches, guidance_batches, num_dummy_samples
-
+        return (
+            latents_batches,
+            prompt_embeds_batches,
+            pooled_prompt_embeds_batches,
+            text_ids_batches,
+            latent_image_ids_batches,
+            guidance_batches,
+            num_dummy_samples,
+        )
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
@@ -250,7 +248,7 @@ def __call__(
         max_sequence_length: int = 512,
         profiling_warmup_steps: Optional[int] = 0,
         profiling_steps: Optional[int] = 0,
-        **kwargs
+        **kwargs,
     ):
         r"""
         Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L531
@@ -326,23 +324,6 @@ def __call__(
             is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
             images.
         """
-
-        callback = kwargs.pop("callback", None)
-        callback_steps = kwargs.pop("callback_steps", None)
-
-        if callback is not None:
-            deprecate(
-                "callback",
-                "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
-            )
-        if callback_steps is not None:
-            deprecate(
-                "callback_steps",
-                "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
-            )
-
         import habana_frameworks.torch as ht
         import habana_frameworks.torch.core as htcore
 
@@ -350,15 +331,18 @@ def __call__(
 
         if quant_mode == "quantize-mixed":
             import copy
+
             transformer_bf16 = copy.deepcopy(self.transformer).to(self._execution_device)
 
         if quant_mode == "measure" or quant_mode.startswith("quantize"):
             import os
-            quant_config_path = os.getenv('QUANT_CONFIG')
+
+            quant_config_path = os.getenv("QUANT_CONFIG")
 
             htcore.hpu_set_env()
 
             from neural_compressor.torch.quantization import FP8Config, convert, prepare
+
             config = FP8Config.from_json_file(quant_config_path)
             if config.measure:
                 self.transformer = prepare(self.transformer, config)
@@ -442,7 +426,6 @@ def __call__(
             sigmas,
             mu=mu,
         )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
 
         # handle guidance
@@ -483,15 +466,9 @@ def __call__(
             text_ids_batches,
             latent_image_ids_batches,
             guidance_batches,
-            num_dummy_samples
+            num_dummy_samples,
         ) = self._split_inputs_into_batches(
-            batch_size,
-            latents,
-            prompt_embeds,
-            pooled_prompt_embeds,
-            text_ids,
-            latent_image_ids,
-            guidance
+            batch_size, latents, prompt_embeds, pooled_prompt_embeds, text_ids, latent_image_ids, guidance
         )
 
         outputs = {
@@ -500,15 +477,11 @@ def __call__(
 
         # 6. Denoising loop
         for j in range(num_batches):
-
             # The throughput is calculated from the 4th iteration
             # because compilation occurs in the first 2-3 iterations
             if j == throughput_warmup_steps:
                 ht.hpu.synchronize()
                 t1 = time.time()
-            if use_warmup_inference_steps:
-                ht.hpu.synchronize()
-                t0_inf = time.time()
 
             latents_batch = latents_batches[0]
             latents_batches = torch.roll(latents_batches, shifts=-1, dims=0)
@@ -537,13 +510,12 @@ def __call__(
                 print(f"Use BF16 Transformer at steps {quant_mixed_step} to {len(timesteps) - 1}")
 
             for i in self.progress_bar(range(len(timesteps))):
-                if use_warmup_inference_steps and i == throughput_warmup_steps:
+                if use_warmup_inference_steps and i == throughput_warmup_steps and j == num_batches - 1:
                     ht.hpu.synchronize()
-                    t1_inf = time.time()
-                    t1 += t1_inf - t0_inf
+                    t1 = time.time()
 
                 if self.interrupt:
-                   continue
+                    continue
 
                 timestep = timesteps[0]
                 timesteps = torch.roll(timesteps, shifts=-1, dims=0)
@@ -585,35 +557,8 @@ def __call__(
                         # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                         latents_batch = latents_batch.to(latents_dtype)
 
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, timestep, callback_kwargs)
-
-                    latents_batch = callback_outputs.pop("latents", latents_batch)
-
-                    _prompt_embeds = callback_outputs.pop("prompt_embeds", None)
-                    if _prompt_embeds is not None and _negative_prompt_embeds is not None:
-                        text_embeddings_batch = torch.cat([_negative_prompt_embeds, _prompt_embeds])
-                    _pooled_prompt_embeds = callback_outputs.pop("pooled_prompt_embeds", None)
-                    if _pooled_prompt_embeds is not None and _negative_pooled_prompt_embeds is not None:
-                        pooled_prompt_embeddings_batch = torch.cat([_negative_pooled_prompt_embeds, _pooled_prompt_embeds])
-
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    if callback is not None and i % callback_steps == 0:
-                        step_idx = i // getattr(self.scheduler, "order", 1)
-                        callback(step_idx, timestep, latents)
-
                 hb_profiler.step()
-                #htcore.mark_step(sync=True)
-
-            if use_warmup_inference_steps:
-                t1 = warmup_inference_steps_time_adjustment(
-                    t1, t1_inf, num_inference_steps, throughput_warmup_steps
-                )
+                # htcore.mark_step(sync=True)
 
             if not output_type == "latent":
                 latents_batch = self._unpack_latents(latents_batch, height, width, self.vae_scale_factor)
@@ -624,24 +569,29 @@ def __call__(
                 image = latents_batch
 
             outputs["images"].append(image)
-            #htcore.mark_step(sync=True)
+            # htcore.mark_step(sync=True)
 
         # 7. Stage after denoising
         hb_profiler.stop()
 
         if quant_mode == "measure":
             from neural_compressor.torch.quantization import finalize_calibration
+
             finalize_calibration(self.transformer)
 
         ht.hpu.synchronize()
         speed_metrics_prefix = "generation"
+        if use_warmup_inference_steps:
+            t1 = warmup_inference_steps_time_adjustment(t1, t1, num_inference_steps, throughput_warmup_steps)
         speed_measures = speed_metrics(
             split=speed_metrics_prefix,
             start_time=t0,
-            num_samples=num_batches * batch_size
+            num_samples=batch_size
             if t1 == t0 or use_warmup_inference_steps
             else (num_batches - throughput_warmup_steps) * batch_size,
-            num_steps=num_batches * batch_size * num_inference_steps,
+            num_steps=batch_size * num_inference_steps
+            if use_warmup_inference_steps
+            else (num_batches - throughput_warmup_steps) * batch_size * num_inference_steps,
             start_time_after_warmup=t1,
         )
         logger.info(f"Speed metrics: {speed_measures}")

From 1144815b12047292c8185b0c8ba62dbf3d2ab40a Mon Sep 17 00:00:00 2001
From: Sergey Plotnikov <sergey.plotnikov@intel.com>
Date: Wed, 9 Oct 2024 14:29:12 -0700
Subject: [PATCH 21/30] Add FusedSDPA

---
 .../diffusers/pipelines/flux/pipeline_flux.py | 186 ++++++++++++++++++
 1 file changed, 186 insertions(+)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index dd180da8b1..571a435f4e 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -73,6 +73,186 @@ class GaudiFluxPipelineOutput(BaseOutput):
         ```
 """
 
+#GaudiFluxSingleAttnProcessor2_0 and GaudiFluxAttnProcessor2_0 are based on FluxSingleAttnProcessor2_0 and FluxAttnProcessor2_0
+#from //github.com/huggingface/diffusers/blob/v0.30.3/src/diffusers/models/attention_processor.py and have been
+#modified to support FusedSDPA 
+import torch.nn.functional as F
+from diffusers.models.attention_processor import Attention
+
+def apply_rope(xq, xk, freqs_cis):
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+class GaudiFluxSingleAttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+
+        query = attn.to_q(hidden_states)
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            # YiYi to-do: update uising apply_rotary_emb
+            # from ..embeddings import apply_rotary_emb
+            # query = apply_rotary_emb(query, image_rotary_emb)
+            # key = apply_rotary_emb(key, image_rotary_emb)
+            query, key = apply_rope(query, key, image_rotary_emb)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        from habana_frameworks.torch.hpex.kernels import FusedSDPA
+        import habana_frameworks.torch.hpu as ht
+        hidden_states = FusedSDPA.apply(query, key, value,      None,      0.0, False,    None,      'fast',   None)
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        return hidden_states
+
+
+class GaudiFluxAttnProcessor2_0:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+        context_input_ndim = encoder_hidden_states.ndim
+        if context_input_ndim == 4:
+            batch_size, channel, height, width = encoder_hidden_states.shape
+            encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # `context` projections.
+        encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+        encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+            batch_size, -1, attn.heads, head_dim
+        ).transpose(1, 2)
+        encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+            batch_size, -1, attn.heads, head_dim
+        ).transpose(1, 2)
+        encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+            batch_size, -1, attn.heads, head_dim
+        ).transpose(1, 2)
+
+        if attn.norm_added_q is not None:
+            encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+        if attn.norm_added_k is not None:
+            encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+        # attention
+        query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+        key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+        value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+            # YiYi to-do: update uising apply_rotary_emb
+            # from ..embeddings import apply_rotary_emb
+            # query = apply_rotary_emb(query, image_rotary_emb)
+            # key = apply_rotary_emb(key, image_rotary_emb)
+            query, key = apply_rope(query, key, image_rotary_emb)
+
+        from habana_frameworks.torch.hpex.kernels import FusedSDPA
+        import habana_frameworks.torch.hpu as ht
+        hidden_states = FusedSDPA.apply(query, key, value,      None,      0.0, False,    None,      'fast',   None)
+        
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        encoder_hidden_states, hidden_states = (
+            hidden_states[:, : encoder_hidden_states.shape[1]],
+            hidden_states[:, encoder_hidden_states.shape[1] :],
+        )
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+        encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+        if context_input_ndim == 4:
+            encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        return hidden_states, encoder_hidden_states
 
 class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
     r"""
@@ -138,6 +318,12 @@ def __init__(
             tokenizer_2=tokenizer_2,
             transformer=transformer,
         )
+
+        for block in self.transformer.single_transformer_blocks:
+            block.attn.processor = GaudiFluxSingleAttnProcessor2_0()
+        for block in self.transformer.transformer_blocks:
+            block.attn.processor = GaudiFluxAttnProcessor2_0()
+        
         self.to(self._device)
         if use_hpu_graphs:
             from habana_frameworks.torch.hpu import wrap_in_hpu_graph

From 479dc96c022774e4454cb7fcfc23d1aea7748ce8 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Thu, 10 Oct 2024 12:39:18 +0800
Subject: [PATCH 22/30] use latest attn rope

---
 .../diffusers/models/attention_processor.py   | 190 +++++++++++++++++-
 .../diffusers/pipelines/flux/pipeline_flux.py | 189 +----------------
 2 files changed, 191 insertions(+), 188 deletions(-)

diff --git a/optimum/habana/diffusers/models/attention_processor.py b/optimum/habana/diffusers/models/attention_processor.py
index b0461a272b..2202f517ec 100755
--- a/optimum/habana/diffusers/models/attention_processor.py
+++ b/optimum/habana/diffusers/models/attention_processor.py
@@ -19,6 +19,7 @@
 import torch
 import torch.nn.functional as F
 from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import apply_rotary_emb
 from diffusers.utils import USE_PEFT_BACKEND, logging
 from diffusers.utils.import_utils import is_xformers_available
 from torch import nn
@@ -186,4 +187,191 @@ def __call__(
         return hidden_states
 
 
-AttentionProcessor = Union[AttnProcessor2_0,]
+class GaudiFluxAttnProcessor2_0:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        if encoder_hidden_states is not None:
+            # `context` projections.
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        # hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        from habana_frameworks.torch.hpex.kernels import FusedSDPA
+        import habana_frameworks.torch.hpu as ht
+        hidden_states = FusedSDPA.apply(query, key, value, None, 0.0, False, None, 'fast', None)
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            encoder_hidden_states, hidden_states = (
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+                hidden_states[:, encoder_hidden_states.shape[1]:],
+            )
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            return hidden_states, encoder_hidden_states
+        else:
+            return hidden_states
+
+
+class GaudiFusedFluxAttnProcessor2_0:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FusedFluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+
+        # `sample` projections.
+        qkv = attn.to_qkv(hidden_states)
+        split_size = qkv.shape[-1] // 3
+        query, key, value = torch.split(qkv, split_size, dim=-1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        # `context` projections.
+        if encoder_hidden_states is not None:
+            encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
+            split_size = encoder_qkv.shape[-1] // 3
+            (
+                encoder_hidden_states_query_proj,
+                encoder_hidden_states_key_proj,
+                encoder_hidden_states_value_proj,
+            ) = torch.split(encoder_qkv, split_size, dim=-1)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        # hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        from habana_frameworks.torch.hpex.kernels import FusedSDPA
+        import habana_frameworks.torch.hpu as ht
+        hidden_states = FusedSDPA.apply(query, key, value, None, 0.0, False, None, 'fast', None)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            encoder_hidden_states, hidden_states = (
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+                hidden_states[:, encoder_hidden_states.shape[1]:],
+            )
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            return hidden_states, encoder_hidden_states
+        else:
+            return hidden_states
+
+
+AttentionProcessor = Union[AttnProcessor2_0, GaudiFluxAttnProcessor2_0, GaudiFusedFluxAttnProcessor2_0]
diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 07abd518fb..d4e4968cd0 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -36,6 +36,7 @@
 from ....transformers.gaudi_configuration import GaudiConfig
 from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment
 from ..pipeline_utils import GaudiDiffusionPipeline
+from ...models.attention_processor import GaudiFluxAttnProcessor2_0, GaudiFusedFluxAttnProcessor2_0
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -76,188 +77,6 @@ class GaudiFluxPipelineOutput(BaseOutput):
         ```
 """
 
-# GaudiFluxSingleAttnProcessor2_0 and GaudiFluxAttnProcessor2_0 are based on FluxSingleAttnProcessor2_0 and FluxAttnProcessor2_0
-# from //github.com/huggingface/diffusers/blob/v0.30.3/src/diffusers/models/attention_processor.py and have been
-# modified to support FusedSDPA
-
-
-def apply_rope(xq, xk, freqs_cis):
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
-    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
-
-
-class GaudiFluxSingleAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-
-        query = attn.to_q(hidden_states)
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        # Apply RoPE if needed
-        if image_rotary_emb is not None:
-            # YiYi to-do: update uising apply_rotary_emb
-            # from ..embeddings import apply_rotary_emb
-            # query = apply_rotary_emb(query, image_rotary_emb)
-            # key = apply_rotary_emb(key, image_rotary_emb)
-            query, key = apply_rope(query, key, image_rotary_emb)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        from habana_frameworks.torch.hpex.kernels import FusedSDPA
-        import habana_frameworks.torch.hpu as ht
-        hidden_states = FusedSDPA.apply(query, key, value, None, 0.0, False, None, 'fast', None)
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        return hidden_states
-
-
-class GaudiFluxAttnProcessor2_0:
-    """Attention processor used typically in processing the SD3-like self-attention projections."""
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: torch.FloatTensor = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-    ) -> torch.FloatTensor:
-        input_ndim = hidden_states.ndim
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-        context_input_ndim = encoder_hidden_states.ndim
-        if context_input_ndim == 4:
-            batch_size, channel, height, width = encoder_hidden_states.shape
-            encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size = encoder_hidden_states.shape[0]
-
-        # `sample` projections.
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-
-        # `context` projections.
-        encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
-        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
-        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
-
-        encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
-            batch_size, -1, attn.heads, head_dim
-        ).transpose(1, 2)
-        encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
-            batch_size, -1, attn.heads, head_dim
-        ).transpose(1, 2)
-        encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
-            batch_size, -1, attn.heads, head_dim
-        ).transpose(1, 2)
-
-        if attn.norm_added_q is not None:
-            encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
-        if attn.norm_added_k is not None:
-            encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
-
-        # attention
-        query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
-        key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
-        value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
-
-        if image_rotary_emb is not None:
-            # YiYi to-do: update uising apply_rotary_emb
-            # from ..embeddings import apply_rotary_emb
-            # query = apply_rotary_emb(query, image_rotary_emb)
-            # key = apply_rotary_emb(key, image_rotary_emb)
-            query, key = apply_rope(query, key, image_rotary_emb)
-
-        from habana_frameworks.torch.hpex.kernels import FusedSDPA
-        import habana_frameworks.torch.hpu as ht
-        hidden_states = FusedSDPA.apply(query, key, value, None, 0.0, False, None, 'fast', None)
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        encoder_hidden_states, hidden_states = (
-            hidden_states[:, : encoder_hidden_states.shape[1]],
-            hidden_states[:, encoder_hidden_states.shape[1]:],
-        )
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-        encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-        if context_input_ndim == 4:
-            encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        return hidden_states, encoder_hidden_states
-
-
 class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
     r"""
     Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L140
@@ -324,7 +143,7 @@ def __init__(
         )
 
         for block in self.transformer.single_transformer_blocks:
-            block.attn.processor = GaudiFluxSingleAttnProcessor2_0()
+            block.attn.processor = GaudiFluxAttnProcessor2_0()
         for block in self.transformer.transformer_blocks:
             block.attn.processor = GaudiFluxAttnProcessor2_0()
 
@@ -742,10 +561,6 @@ def __call__(
                 latents_dtype = latents_batch.dtype
                 latents_batch = self.scheduler.step(noise_pred, timestep, latents_batch, return_dict=False)[0]
 
-                if latents_batch.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents_batch = latents_batch.to(latents_dtype)
 
                 hb_profiler.step()
                 # htcore.mark_step(sync=True)

From 18c59609e9c910b4fdcd603c66ea06d17f295118 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Thu, 10 Oct 2024 15:53:52 +0800
Subject: [PATCH 23/30] fix scheduler

---
 examples/stable-diffusion/text_to_image_generation.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index d14548b534..de796d177a 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -322,10 +322,6 @@ def main():
         )
     elif args.scheduler == "ddim":
         scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler", **kwargs)
-    elif args.scheduler == "flow_match_euler_discrete":
-        scheduler = GaudiFlowMatchEulerDiscreteScheduler.from_pretrained(
-            args.model_name_or_path, subfolder="scheduler", **kwargs
-        )
     else:
         scheduler = None
 

From eee48c7d4d6a809e659e78c51343b0965d2b616b Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Fri, 11 Oct 2024 11:05:42 +0800
Subject: [PATCH 24/30] add OenFLUX.1

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

diff --git a/examples/stable-diffusion/text_to_image_generation.py b/examples/stable-diffusion/text_to_image_generation.py
index de796d177a..fed390fc12 100755
--- a/examples/stable-diffusion/text_to_image_generation.py
+++ b/examples/stable-diffusion/text_to_image_generation.py
@@ -303,7 +303,7 @@ def main():
     # Select stable diffuson pipeline based on input
     sdxl_models = ["stable-diffusion-xl", "sdxl"]
     sd3_models = ["stable-diffusion-3"]
-    flux_models = ["FLUX.1-dev", "FLUX.1-schnell"]
+    flux_models = ["FLUX.1-dev", "FLUX.1-schnell","OpenFLUX.1"]
     sdxl = True if any(model in args.model_name_or_path for model in sdxl_models) else False
     sd3 = True if any(model in args.model_name_or_path for model in sd3_models) else False
     flux = True if any(model in args.model_name_or_path for model in flux_models) else False

From c299caa4c0bd16a3e882b388cb27e32871bcd18b Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Mon, 14 Oct 2024 11:34:58 +0800
Subject: [PATCH 25/30] fix errors

---
 .../diffusers/pipelines/flux/pipeline_flux.py | 46 ++++---------------
 1 file changed, 10 insertions(+), 36 deletions(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index d4e4968cd0..7606b6a2b5 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -77,6 +77,7 @@ class GaudiFluxPipelineOutput(BaseOutput):
         ```
 """
 
+
 class GaudiFluxPipeline(GaudiDiffusionPipeline, FluxPipeline):
     r"""
     Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py#L140
@@ -154,20 +155,16 @@ def __init__(
             transformer = wrap_in_hpu_graph(transformer)
 
     @classmethod
-    def _split_inputs_into_batches(
-        cls, batch_size, latents, prompt_embeds, pooled_prompt_embeds, text_ids, latent_image_ids, guidance
-    ):
+    def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, pooled_prompt_embeds, guidance):
         # Use torch.split to generate num_batches batches of size batch_size
         latents_batches = list(torch.split(latents, batch_size))
         prompt_embeds_batches = list(torch.split(prompt_embeds, batch_size))
         if pooled_prompt_embeds is not None:
             pooled_prompt_embeds_batches = list(torch.split(pooled_prompt_embeds, batch_size))
-        if text_ids is not None:
-            text_ids_batches = list(torch.split(text_ids, batch_size))
-        if latent_image_ids is not None:
-            latent_image_ids_batches = list(torch.split(latent_image_ids, batch_size))
         if guidance is not None:
             guidance_batches = list(torch.split(guidance, batch_size))
+        else:
+            guidance_batches = [torch.tensor(float('nan')),] * len(latents_batches)
 
         # If the last batch has less samples than batch_size, pad it with dummy samples
         num_dummy_samples = 0
@@ -193,20 +190,6 @@ def _split_inputs_into_batches(
                 )
                 pooled_prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack)
 
-            # Pad text_ids_batches if necessary
-            if text_ids is not None:
-                sequence_to_stack = (text_ids_batches[-1],) + tuple(
-                    torch.zeros_like(text_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
-                )
-                text_ids_batches[-1] = torch.vstack(sequence_to_stack)
-
-            # Pad latent_image_ids if necessary
-            if latent_image_ids is not None:
-                sequence_to_stack = (latent_image_ids_batches[-1],) + tuple(
-                    torch.zeros_like(latent_image_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
-                )
-                latent_image_ids_batches[-1] = torch.vstack(sequence_to_stack)
-
             # Pad guidance if necessary
             if guidance is not None:
                 sequence_to_stack = (guidance_batches[-1],) + tuple(
@@ -226,8 +209,6 @@ def _split_inputs_into_batches(
             latents_batches,
             prompt_embeds_batches,
             pooled_prompt_embeds_batches,
-            text_ids_batches,
-            latent_image_ids_batches,
             guidance_batches,
             num_dummy_samples,
         )
@@ -472,12 +453,10 @@ def __call__(
             latents_batches,
             text_embeddings_batches,
             pooled_prompt_embeddings_batches,
-            text_ids_batches,
-            latent_image_ids_batches,
             guidance_batches,
             num_dummy_samples,
         ) = self._split_inputs_into_batches(
-            batch_size, latents, prompt_embeds, pooled_prompt_embeds, text_ids, latent_image_ids, guidance
+            batch_size, latents, prompt_embeds, pooled_prompt_embeds, guidance
         )
 
         outputs = {
@@ -498,11 +477,7 @@ def __call__(
             text_embeddings_batches = torch.roll(text_embeddings_batches, shifts=-1, dims=0)
             pooled_prompt_embeddings_batch = pooled_prompt_embeddings_batches[0]
             pooled_prompt_embeddings_batches = torch.roll(pooled_prompt_embeddings_batches, shifts=-1, dims=0)
-            text_ids_batch = text_ids_batches[0]
-            text_ids_batches = torch.roll(text_ids_batches, shifts=-1, dims=0)
-            latent_image_ids_batch = latent_image_ids_batches[0]
-            latent_image_ids_batches = torch.roll(latent_image_ids_batches, shifts=-1, dims=0)
-            guidance_batch = guidance_batches[0]
+            guidance_batch = None if guidance_batches[0].isnan() else guidance_batches[0]
             guidance_batches = torch.roll(guidance_batches, shifts=-1, dims=0)
 
             if hasattr(self.scheduler, "_init_step_index"):
@@ -539,8 +514,8 @@ def __call__(
                         guidance=guidance_batch,
                         pooled_projections=pooled_prompt_embeddings_batch,
                         encoder_hidden_states=text_embeddings_batch,
-                        txt_ids=text_ids_batch,
-                        img_ids=latent_image_ids_batch,
+                        txt_ids=text_ids,
+                        img_ids=latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,
                     )[0]
@@ -551,8 +526,8 @@ def __call__(
                         guidance=guidance_batch,
                         pooled_projections=pooled_prompt_embeddings_batch,
                         encoder_hidden_states=text_embeddings_batch,
-                        txt_ids=text_ids_batch,
-                        img_ids=latent_image_ids_batch,
+                        txt_ids=text_ids,
+                        img_ids=latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,
                     )[0]
@@ -561,7 +536,6 @@ def __call__(
                 latents_dtype = latents_batch.dtype
                 latents_batch = self.scheduler.step(noise_pred, timestep, latents_batch, return_dict=False)[0]
 
-
                 hb_profiler.step()
                 # htcore.mark_step(sync=True)
 

From c0d391e50ee93510d4fe811835e75b387c2964da Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Mon, 14 Oct 2024 13:05:45 +0800
Subject: [PATCH 26/30] rem quant files

---
 .../measure_all/fp8_hooks_maxabs.json         | 8071 -----------------
 .../quantize/measure_all/fp8_hooks_maxabs.npz |  Bin 97750 -> 0 bytes
 ...fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json | 7567 ----------------
 .../fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz |  Bin 124974 -> 0 bytes
 .../fp8_hooks_maxabs_mod_list.json            |  506 --
 .../measure_all_500/fp8_hooks_maxabs.json     | 8071 -----------------
 .../measure_all_500/fp8_hooks_maxabs.npz      |  Bin 97750 -> 0 bytes
 ...fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json | 7567 ----------------
 .../fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz |  Bin 124974 -> 0 bytes
 .../fp8_hooks_maxabs_mod_list.json            |  506 --
 10 files changed, 32288 deletions(-)
 delete mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json
 delete mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.npz
 delete mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
 delete mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
 delete mode 100644 examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json
 delete mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json
 delete mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.npz
 delete mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
 delete mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
 delete mode 100644 examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json

diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json
deleted file mode 100644
index 8e4c0fb98e..0000000000
--- a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.json
+++ /dev/null
@@ -1,8071 +0,0 @@
-{
-    "GlobalRank": null,
-    "LocalRank": null,
-    "Mode": "DynamicRange",
-    "Nodes": {
-        "time_text_embed.timestep_embedder.linear_1": {
-            "inputs": [
-                [
-                    [
-                        1.0
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.28515625
-                    ]
-                ]
-            }
-        },
-        "time_text_embed.timestep_embedder.linear_2": {
-            "inputs": [
-                [
-                    [
-                        3.28125
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        1.1484375
-                    ]
-                ]
-            }
-        },
-        "time_text_embed.guidance_embedder.linear_1": {
-            "inputs": [
-                [
-                    [
-                        1.0
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.10400390625
-                    ]
-                ]
-            }
-        },
-        "time_text_embed.guidance_embedder.linear_2": {
-            "inputs": [
-                [
-                    [
-                        0.60546875
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.1201171875
-                    ]
-                ]
-            }
-        },
-        "time_text_embed.text_embedder.linear_1": {
-            "inputs": [
-                [
-                    [
-                        4.0625
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.263671875
-                    ]
-                ]
-            }
-        },
-        "time_text_embed.text_embedder.linear_2": {
-            "inputs": [
-                [
-                    [
-                        0.373046875
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        1.171875
-                    ]
-                ]
-            }
-        },
-        "context_embedder": {
-            "inputs": [
-                [
-                    [
-                        6.4375
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.72265625
-                    ]
-                ]
-            }
-        },
-        "x_embedder": {
-            "inputs": [
-                [
-                    [
-                        5.0
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.55859375
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.norm1.linear": {
-            "inputs": [
-                [
-                    [
-                        6.4375
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.396484375
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.norm1_context.linear": {
-            "inputs": [
-                [
-                    [
-                        6.4375
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.546875
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.attn.to_q": {
-            "inputs": [
-                [
-                    [
-                        15.5
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        2.4375
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.attn.to_k": {
-            "inputs": [
-                [
-                    [
-                        15.5
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.5390625
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.attn.to_v": {
-            "inputs": [
-                [
-                    [
-                        15.5
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.38671875
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.attn.add_k_proj": {
-            "inputs": [
-                [
-                    [
-                        14.25
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.5390625
-                    ]
-                ]
-            }
-        },
-        "transformer_blocks.0.attn.add_v_proj": {
-            "inputs": [
-                [
-                    [
-                        14.25
-                    ]
-                ]
-            ],
-            "params": {
-                "weight": [
-                    [
-                        0.236328125
-                    ]
-                ]
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.npz b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
deleted file mode 100644
index 87012fbef2..0000000000
--- a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
+++ /dev/null
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json b/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json
deleted file mode 100644
index 4cd94ad59e..0000000000
--- a/examples/stable-diffusion/quantize/measure_all/fp8_hooks_maxabs_mod_list.json
+++ /dev/null
@@ -1,506 +0,0 @@
-[
-    "time_text_embed.timestep_embedder.linear_1",
-    "time_text_embed.timestep_embedder.linear_2",
-    "time_text_embed.guidance_embedder.linear_1",
-    "time_text_embed.guidance_embedder.linear_2",
-    "time_text_embed.text_embedder.linear_1",
-    "time_text_embed.text_embedder.linear_2",
-    "context_embedder",
-    "x_embedder",
-    "transformer_blocks.0.norm1.linear",
-    "transformer_blocks.0.norm1_context.linear",
-    "transformer_blocks.0.attn.to_q",
-    "transformer_blocks.0.attn.to_k",
-    "transformer_blocks.0.attn.to_v",
-    "transformer_blocks.0.attn.add_k_proj",
-    "transformer_blocks.0.attn.add_v_proj",
-    "transformer_blocks.0.attn.add_q_proj",
-    "transformer_blocks.0.attn.to_out.0",
-    "transformer_blocks.0.attn.to_add_out",
-    "transformer_blocks.0.ff.net.0.proj",
-    "transformer_blocks.0.ff.net.2",
-    "transformer_blocks.0.ff_context.net.0.proj",
-    "transformer_blocks.0.ff_context.net.2",
-    "transformer_blocks.1.norm1.linear",
-    "transformer_blocks.1.norm1_context.linear",
-    "transformer_blocks.1.attn.to_q",
-    "transformer_blocks.1.attn.to_k",
-    "transformer_blocks.1.attn.to_v",
-    "transformer_blocks.1.attn.add_k_proj",
-    "transformer_blocks.1.attn.add_v_proj",
-    "transformer_blocks.1.attn.add_q_proj",
-    "transformer_blocks.1.attn.to_out.0",
-    "transformer_blocks.1.attn.to_add_out",
-    "transformer_blocks.1.ff.net.0.proj",
-    "transformer_blocks.1.ff.net.2",
-    "transformer_blocks.1.ff_context.net.0.proj",
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-    "norm_out.linear",
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\ No newline at end of file
diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json
deleted file mode 100644
index 775d712618..0000000000
--- a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.json
+++ /dev/null
@@ -1,8071 +0,0 @@
-{
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.npz b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
deleted file mode 100644
index 78408ef56a..0000000000
--- a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.json
+++ /dev/null
@@ -1,7567 +0,0 @@
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_MAXABS_HW_OPT_WEIGHT.npz
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diff --git a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json b/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json
deleted file mode 100644
index 4cd94ad59e..0000000000
--- a/examples/stable-diffusion/quantize/measure_all_500/fp8_hooks_maxabs_mod_list.json
+++ /dev/null
@@ -1,506 +0,0 @@
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-    "single_transformer_blocks.3.attn.to_q",
-    "single_transformer_blocks.3.attn.to_k",
-    "single_transformer_blocks.3.attn.to_v",
-    "single_transformer_blocks.4.norm.linear",
-    "single_transformer_blocks.4.proj_mlp",
-    "single_transformer_blocks.4.proj_out",
-    "single_transformer_blocks.4.attn.to_q",
-    "single_transformer_blocks.4.attn.to_k",
-    "single_transformer_blocks.4.attn.to_v",
-    "single_transformer_blocks.5.norm.linear",
-    "single_transformer_blocks.5.proj_mlp",
-    "single_transformer_blocks.5.proj_out",
-    "single_transformer_blocks.5.attn.to_q",
-    "single_transformer_blocks.5.attn.to_k",
-    "single_transformer_blocks.5.attn.to_v",
-    "single_transformer_blocks.6.norm.linear",
-    "single_transformer_blocks.6.proj_mlp",
-    "single_transformer_blocks.6.proj_out",
-    "single_transformer_blocks.6.attn.to_q",
-    "single_transformer_blocks.6.attn.to_k",
-    "single_transformer_blocks.6.attn.to_v",
-    "single_transformer_blocks.7.norm.linear",
-    "single_transformer_blocks.7.proj_mlp",
-    "single_transformer_blocks.7.proj_out",
-    "single_transformer_blocks.7.attn.to_q",
-    "single_transformer_blocks.7.attn.to_k",
-    "single_transformer_blocks.7.attn.to_v",
-    "single_transformer_blocks.8.norm.linear",
-    "single_transformer_blocks.8.proj_mlp",
-    "single_transformer_blocks.8.proj_out",
-    "single_transformer_blocks.8.attn.to_q",
-    "single_transformer_blocks.8.attn.to_k",
-    "single_transformer_blocks.8.attn.to_v",
-    "single_transformer_blocks.9.norm.linear",
-    "single_transformer_blocks.9.proj_mlp",
-    "single_transformer_blocks.9.proj_out",
-    "single_transformer_blocks.9.attn.to_q",
-    "single_transformer_blocks.9.attn.to_k",
-    "single_transformer_blocks.9.attn.to_v",
-    "single_transformer_blocks.10.norm.linear",
-    "single_transformer_blocks.10.proj_mlp",
-    "single_transformer_blocks.10.proj_out",
-    "single_transformer_blocks.10.attn.to_q",
-    "single_transformer_blocks.10.attn.to_k",
-    "single_transformer_blocks.10.attn.to_v",
-    "single_transformer_blocks.11.norm.linear",
-    "single_transformer_blocks.11.proj_mlp",
-    "single_transformer_blocks.11.proj_out",
-    "single_transformer_blocks.11.attn.to_q",
-    "single_transformer_blocks.11.attn.to_k",
-    "single_transformer_blocks.11.attn.to_v",
-    "single_transformer_blocks.12.norm.linear",
-    "single_transformer_blocks.12.proj_mlp",
-    "single_transformer_blocks.12.proj_out",
-    "single_transformer_blocks.12.attn.to_q",
-    "single_transformer_blocks.12.attn.to_k",
-    "single_transformer_blocks.12.attn.to_v",
-    "single_transformer_blocks.13.norm.linear",
-    "single_transformer_blocks.13.proj_mlp",
-    "single_transformer_blocks.13.proj_out",
-    "single_transformer_blocks.13.attn.to_q",
-    "single_transformer_blocks.13.attn.to_k",
-    "single_transformer_blocks.13.attn.to_v",
-    "single_transformer_blocks.14.norm.linear",
-    "single_transformer_blocks.14.proj_mlp",
-    "single_transformer_blocks.14.proj_out",
-    "single_transformer_blocks.14.attn.to_q",
-    "single_transformer_blocks.14.attn.to_k",
-    "single_transformer_blocks.14.attn.to_v",
-    "single_transformer_blocks.15.norm.linear",
-    "single_transformer_blocks.15.proj_mlp",
-    "single_transformer_blocks.15.proj_out",
-    "single_transformer_blocks.15.attn.to_q",
-    "single_transformer_blocks.15.attn.to_k",
-    "single_transformer_blocks.15.attn.to_v",
-    "single_transformer_blocks.16.norm.linear",
-    "single_transformer_blocks.16.proj_mlp",
-    "single_transformer_blocks.16.proj_out",
-    "single_transformer_blocks.16.attn.to_q",
-    "single_transformer_blocks.16.attn.to_k",
-    "single_transformer_blocks.16.attn.to_v",
-    "single_transformer_blocks.17.norm.linear",
-    "single_transformer_blocks.17.proj_mlp",
-    "single_transformer_blocks.17.proj_out",
-    "single_transformer_blocks.17.attn.to_q",
-    "single_transformer_blocks.17.attn.to_k",
-    "single_transformer_blocks.17.attn.to_v",
-    "single_transformer_blocks.18.norm.linear",
-    "single_transformer_blocks.18.proj_mlp",
-    "single_transformer_blocks.18.proj_out",
-    "single_transformer_blocks.18.attn.to_q",
-    "single_transformer_blocks.18.attn.to_k",
-    "single_transformer_blocks.18.attn.to_v",
-    "single_transformer_blocks.19.norm.linear",
-    "single_transformer_blocks.19.proj_mlp",
-    "single_transformer_blocks.19.proj_out",
-    "single_transformer_blocks.19.attn.to_q",
-    "single_transformer_blocks.19.attn.to_k",
-    "single_transformer_blocks.19.attn.to_v",
-    "single_transformer_blocks.20.norm.linear",
-    "single_transformer_blocks.20.proj_mlp",
-    "single_transformer_blocks.20.proj_out",
-    "single_transformer_blocks.20.attn.to_q",
-    "single_transformer_blocks.20.attn.to_k",
-    "single_transformer_blocks.20.attn.to_v",
-    "single_transformer_blocks.21.norm.linear",
-    "single_transformer_blocks.21.proj_mlp",
-    "single_transformer_blocks.21.proj_out",
-    "single_transformer_blocks.21.attn.to_q",
-    "single_transformer_blocks.21.attn.to_k",
-    "single_transformer_blocks.21.attn.to_v",
-    "single_transformer_blocks.22.norm.linear",
-    "single_transformer_blocks.22.proj_mlp",
-    "single_transformer_blocks.22.proj_out",
-    "single_transformer_blocks.22.attn.to_q",
-    "single_transformer_blocks.22.attn.to_k",
-    "single_transformer_blocks.22.attn.to_v",
-    "single_transformer_blocks.23.norm.linear",
-    "single_transformer_blocks.23.proj_mlp",
-    "single_transformer_blocks.23.proj_out",
-    "single_transformer_blocks.23.attn.to_q",
-    "single_transformer_blocks.23.attn.to_k",
-    "single_transformer_blocks.23.attn.to_v",
-    "single_transformer_blocks.24.norm.linear",
-    "single_transformer_blocks.24.proj_mlp",
-    "single_transformer_blocks.24.proj_out",
-    "single_transformer_blocks.24.attn.to_q",
-    "single_transformer_blocks.24.attn.to_k",
-    "single_transformer_blocks.24.attn.to_v",
-    "single_transformer_blocks.25.norm.linear",
-    "single_transformer_blocks.25.proj_mlp",
-    "single_transformer_blocks.25.proj_out",
-    "single_transformer_blocks.25.attn.to_q",
-    "single_transformer_blocks.25.attn.to_k",
-    "single_transformer_blocks.25.attn.to_v",
-    "single_transformer_blocks.26.norm.linear",
-    "single_transformer_blocks.26.proj_mlp",
-    "single_transformer_blocks.26.proj_out",
-    "single_transformer_blocks.26.attn.to_q",
-    "single_transformer_blocks.26.attn.to_k",
-    "single_transformer_blocks.26.attn.to_v",
-    "single_transformer_blocks.27.norm.linear",
-    "single_transformer_blocks.27.proj_mlp",
-    "single_transformer_blocks.27.proj_out",
-    "single_transformer_blocks.27.attn.to_q",
-    "single_transformer_blocks.27.attn.to_k",
-    "single_transformer_blocks.27.attn.to_v",
-    "single_transformer_blocks.28.norm.linear",
-    "single_transformer_blocks.28.proj_mlp",
-    "single_transformer_blocks.28.proj_out",
-    "single_transformer_blocks.28.attn.to_q",
-    "single_transformer_blocks.28.attn.to_k",
-    "single_transformer_blocks.28.attn.to_v",
-    "single_transformer_blocks.29.norm.linear",
-    "single_transformer_blocks.29.proj_mlp",
-    "single_transformer_blocks.29.proj_out",
-    "single_transformer_blocks.29.attn.to_q",
-    "single_transformer_blocks.29.attn.to_k",
-    "single_transformer_blocks.29.attn.to_v",
-    "single_transformer_blocks.30.norm.linear",
-    "single_transformer_blocks.30.proj_mlp",
-    "single_transformer_blocks.30.proj_out",
-    "single_transformer_blocks.30.attn.to_q",
-    "single_transformer_blocks.30.attn.to_k",
-    "single_transformer_blocks.30.attn.to_v",
-    "single_transformer_blocks.31.norm.linear",
-    "single_transformer_blocks.31.proj_mlp",
-    "single_transformer_blocks.31.proj_out",
-    "single_transformer_blocks.31.attn.to_q",
-    "single_transformer_blocks.31.attn.to_k",
-    "single_transformer_blocks.31.attn.to_v",
-    "single_transformer_blocks.32.norm.linear",
-    "single_transformer_blocks.32.proj_mlp",
-    "single_transformer_blocks.32.proj_out",
-    "single_transformer_blocks.32.attn.to_q",
-    "single_transformer_blocks.32.attn.to_k",
-    "single_transformer_blocks.32.attn.to_v",
-    "single_transformer_blocks.33.norm.linear",
-    "single_transformer_blocks.33.proj_mlp",
-    "single_transformer_blocks.33.proj_out",
-    "single_transformer_blocks.33.attn.to_q",
-    "single_transformer_blocks.33.attn.to_k",
-    "single_transformer_blocks.33.attn.to_v",
-    "single_transformer_blocks.34.norm.linear",
-    "single_transformer_blocks.34.proj_mlp",
-    "single_transformer_blocks.34.proj_out",
-    "single_transformer_blocks.34.attn.to_q",
-    "single_transformer_blocks.34.attn.to_k",
-    "single_transformer_blocks.34.attn.to_v",
-    "single_transformer_blocks.35.norm.linear",
-    "single_transformer_blocks.35.proj_mlp",
-    "single_transformer_blocks.35.proj_out",
-    "single_transformer_blocks.35.attn.to_q",
-    "single_transformer_blocks.35.attn.to_k",
-    "single_transformer_blocks.35.attn.to_v",
-    "single_transformer_blocks.36.norm.linear",
-    "single_transformer_blocks.36.proj_mlp",
-    "single_transformer_blocks.36.proj_out",
-    "single_transformer_blocks.36.attn.to_q",
-    "single_transformer_blocks.36.attn.to_k",
-    "single_transformer_blocks.36.attn.to_v",
-    "single_transformer_blocks.37.norm.linear",
-    "single_transformer_blocks.37.proj_mlp",
-    "single_transformer_blocks.37.proj_out",
-    "single_transformer_blocks.37.attn.to_q",
-    "single_transformer_blocks.37.attn.to_k",
-    "single_transformer_blocks.37.attn.to_v",
-    "norm_out.linear",
-    "proj_out"
-]
\ No newline at end of file

From b5aee78273b35db3052630bc5ac2f5cebc3f5d19 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Mon, 14 Oct 2024 13:07:27 +0800
Subject: [PATCH 27/30] rem tmp tests files

---
 examples/stable-diffusion/readme.txt          | 29 -------------------
 examples/stable-diffusion/run_bf16.sh         | 13 ---------
 .../stable-diffusion/run_bf16_prompts_100.sh  | 13 ---------
 .../stable-diffusion/run_bf16_prompts_5.sh    | 13 ---------
 examples/stable-diffusion/run_fp8.sh          | 16 ----------
 examples/stable-diffusion/run_fp8_500.sh      | 16 ----------
 .../stable-diffusion/run_fp8_500_hybrid.sh    | 16 ----------
 .../run_fp8_500_hybrid_prompts_5.sh           | 16 ----------
 8 files changed, 132 deletions(-)
 delete mode 100644 examples/stable-diffusion/readme.txt
 delete mode 100755 examples/stable-diffusion/run_bf16.sh
 delete mode 100755 examples/stable-diffusion/run_bf16_prompts_100.sh
 delete mode 100755 examples/stable-diffusion/run_bf16_prompts_5.sh
 delete mode 100755 examples/stable-diffusion/run_fp8.sh
 delete mode 100755 examples/stable-diffusion/run_fp8_500.sh
 delete mode 100755 examples/stable-diffusion/run_fp8_500_hybrid.sh
 delete mode 100755 examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh

diff --git a/examples/stable-diffusion/readme.txt b/examples/stable-diffusion/readme.txt
deleted file mode 100644
index e72ebcd497..0000000000
--- a/examples/stable-diffusion/readme.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-This is experimental PR for Fal.ai ask based on current FLUX PR https://github.com/huggingface/optimum-habana/pull/1331
-* PR is fixed with timing (HPU device sync and include VAD into timing measure)
-* Added FP8 quantization support
-
-To run sample with 1 image 1 batch in BF16 precision:
-./run_bf16.sh
-
-To run sample with 1 image 1 batch in FP8 precision (quant weights were tuned with 1 prompt):
-./run_fp8.sh
-
-To run sample with 1 image 1 batch in FP8 precision (quant weights were tuned with 500 prompts):
-./run_fp8_500.sh
-
-* Added batching
-* Added --prompt_file option for large number of input prompts
-
-To run sample with 5 prompts (batch size 1) in BF16 precision:
-./run_bf16_prompts_5.sh
-
-To run sample with 100 prompts (batch size 1) in BF16 precision:
-./run_bf16_prompts_100.sh
-
-* Added hybrid (mixed fp9 and bf16) precision denoising 
-
-To run sample with 1 image 1 batch in hybrid precision:
-./run_fp8_500_hybrid.sh
-
-To run sample with 5 prompts (batch size 1) in hybrid precision:
-./run_fp8_500_hybrid_prompts_5.sh
diff --git a/examples/stable-diffusion/run_bf16.sh b/examples/stable-diffusion/run_bf16.sh
deleted file mode 100755
index e8b74d3312..0000000000
--- a/examples/stable-diffusion/run_bf16.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts "A cat holding a sign that says hello world" \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16
diff --git a/examples/stable-diffusion/run_bf16_prompts_100.sh b/examples/stable-diffusion/run_bf16_prompts_100.sh
deleted file mode 100755
index d229d5fe68..0000000000
--- a/examples/stable-diffusion/run_bf16_prompts_100.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts_file prompts_100.txt \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16
diff --git a/examples/stable-diffusion/run_bf16_prompts_5.sh b/examples/stable-diffusion/run_bf16_prompts_5.sh
deleted file mode 100755
index 22cc40dc0c..0000000000
--- a/examples/stable-diffusion/run_bf16_prompts_5.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts_file prompts_5.txt \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16
diff --git a/examples/stable-diffusion/run_fp8.sh b/examples/stable-diffusion/run_fp8.sh
deleted file mode 100755
index e914a3421d..0000000000
--- a/examples/stable-diffusion/run_fp8.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-QUANT_CONFIG=quantize/quant_config.json \
-PT_HPU_WEIGHT_SHARING=0 \
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts "A cat holding a sign that says hello world" \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images_fp8 \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16 \
-     --quant_mode quantize
diff --git a/examples/stable-diffusion/run_fp8_500.sh b/examples/stable-diffusion/run_fp8_500.sh
deleted file mode 100755
index 50ffc36b66..0000000000
--- a/examples/stable-diffusion/run_fp8_500.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-QUANT_CONFIG=quantize/quant_config_500.json \
-PT_HPU_WEIGHT_SHARING=0 \
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts "A cat holding a sign that says hello world" \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images_fp8_500 \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16 \
-     --quant_mode quantize
diff --git a/examples/stable-diffusion/run_fp8_500_hybrid.sh b/examples/stable-diffusion/run_fp8_500_hybrid.sh
deleted file mode 100755
index 6de1397574..0000000000
--- a/examples/stable-diffusion/run_fp8_500_hybrid.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-QUANT_CONFIG=quantize/quant_config_500.json \
-PT_HPU_WEIGHT_SHARING=0 \
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts "A cat holding a sign that says hello world" \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images_fp8_500 \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16 \
-     --quant_mode quantize-mixed
diff --git a/examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh b/examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh
deleted file mode 100755
index 29d4ac1d22..0000000000
--- a/examples/stable-diffusion/run_fp8_500_hybrid_prompts_5.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-QUANT_CONFIG=quantize/quant_config_500.json \
-PT_HPU_WEIGHT_SHARING=0 \
-python text_to_image_generation.py \
-     --model_name_or_path black-forest-labs/FLUX.1-dev \
-     --prompts_file prompts_5.txt \
-     --num_images_per_prompt 1 \
-     --batch_size 1 \
-     --num_inference_steps 30 \
-     --image_save_dir /tmp/flux_1_images \
-     --scheduler flow_match_euler_discrete \
-     --use_habana \
-     --use_hpu_graphs \
-     --gaudi_config Habana/stable-diffusion \
-     --bf16 \
-     --quant_mode quantize-mixed

From 44a48c72105d2e9c89fafdbdd42f1140f94204e3 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Mon, 14 Oct 2024 14:02:06 +0800
Subject: [PATCH 28/30] rem text_ids image_ids from split into batches

---
 optimum/habana/diffusers/pipelines/flux/pipeline_flux.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 7606b6a2b5..7727f5fedf 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -201,8 +201,6 @@ def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, pooled_p
         latents_batches = torch.stack(latents_batches)
         prompt_embeds_batches = torch.stack(prompt_embeds_batches)
         pooled_prompt_embeds_batches = torch.stack(pooled_prompt_embeds_batches)
-        text_ids_batches = torch.stack(text_ids_batches)
-        latent_image_ids_batches = torch.stack(latent_image_ids_batches)
         guidance_batches = torch.stack(guidance_batches)
 
         return (

From 6bd7351ea4c0f0bb1e88bda26a7af513c30b572c Mon Sep 17 00:00:00 2001
From: "Zhou, Huijuan" <huijuan.zhou@intel.com>
Date: Mon, 21 Oct 2024 23:11:24 -0700
Subject: [PATCH 29/30] fix guidance nan boolean tensor ambiguous err keep
 text_ids latent_image_ids split for diffuser 0.30.x

---
 .../diffusers/pipelines/flux/pipeline_flux.py | 46 ++++++++++++++++---
 1 file changed, 39 insertions(+), 7 deletions(-)

diff --git a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
index 7727f5fedf..d0d9fd7c35 100644
--- a/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
+++ b/optimum/habana/diffusers/pipelines/flux/pipeline_flux.py
@@ -155,12 +155,16 @@ def __init__(
             transformer = wrap_in_hpu_graph(transformer)
 
     @classmethod
-    def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, pooled_prompt_embeds, guidance):
+    def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, pooled_prompt_embeds, text_ids, latent_image_ids, guidance):
         # Use torch.split to generate num_batches batches of size batch_size
         latents_batches = list(torch.split(latents, batch_size))
         prompt_embeds_batches = list(torch.split(prompt_embeds, batch_size))
         if pooled_prompt_embeds is not None:
             pooled_prompt_embeds_batches = list(torch.split(pooled_prompt_embeds, batch_size))
+        if text_ids is not None and text_ids.ndim == 3:
+            text_ids_batches = list(torch.split(text_ids, batch_size))
+        if latent_image_ids is not None and latent_image_ids.ndim == 3:
+            latent_image_ids_batches = list(torch.split(latent_image_ids, batch_size))
         if guidance is not None:
             guidance_batches = list(torch.split(guidance, batch_size))
         else:
@@ -190,6 +194,20 @@ def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, pooled_p
                 )
                 pooled_prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack)
 
+            # Pad text_ids_batches if necessary
+            if text_ids is not None and text_ids.ndim == 3:
+                sequence_to_stack = (text_ids_batches[-1],) + tuple(
+                    torch.zeros_like(text_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                text_ids_batches[-1] = torch.vstack(sequence_to_stack)
+
+            # Pad latent_image_ids if necessary
+            if latent_image_ids is not None and latent_image_ids.ndim == 3:
+                sequence_to_stack = (latent_image_ids_batches[-1],) + tuple(
+                    torch.zeros_like(latent_image_ids_batches[-1][0][None, :]) for _ in range(num_dummy_samples)
+                )
+                latent_image_ids_batches[-1] = torch.vstack(sequence_to_stack)
+
             # Pad guidance if necessary
             if guidance is not None:
                 sequence_to_stack = (guidance_batches[-1],) + tuple(
@@ -201,12 +219,18 @@ def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, pooled_p
         latents_batches = torch.stack(latents_batches)
         prompt_embeds_batches = torch.stack(prompt_embeds_batches)
         pooled_prompt_embeds_batches = torch.stack(pooled_prompt_embeds_batches)
+        if text_ids is not None and text_ids.ndim == 3:
+            text_ids_batches = torch.stack(text_ids_batches)
+        if latent_image_ids is not None and latent_image_ids.ndim == 3:
+            latent_image_ids_batches = torch.stack(latent_image_ids_batches)
         guidance_batches = torch.stack(guidance_batches)
 
         return (
             latents_batches,
             prompt_embeds_batches,
             pooled_prompt_embeds_batches,
+            text_ids_batches if text_ids.ndim == 3 else text_ids,
+            latent_image_ids_batches if latent_image_ids.ndim == 3 else latent_image_ids,
             guidance_batches,
             num_dummy_samples,
         )
@@ -451,10 +475,12 @@ def __call__(
             latents_batches,
             text_embeddings_batches,
             pooled_prompt_embeddings_batches,
+            text_ids_batches,
+            latent_image_ids_batches,
             guidance_batches,
             num_dummy_samples,
         ) = self._split_inputs_into_batches(
-            batch_size, latents, prompt_embeds, pooled_prompt_embeds, guidance
+            batch_size, latents, prompt_embeds, pooled_prompt_embeds, text_ids, latent_image_ids, guidance
         )
 
         outputs = {
@@ -475,7 +501,13 @@ def __call__(
             text_embeddings_batches = torch.roll(text_embeddings_batches, shifts=-1, dims=0)
             pooled_prompt_embeddings_batch = pooled_prompt_embeddings_batches[0]
             pooled_prompt_embeddings_batches = torch.roll(pooled_prompt_embeddings_batches, shifts=-1, dims=0)
-            guidance_batch = None if guidance_batches[0].isnan() else guidance_batches[0]
+            if text_ids.ndim == 3:
+                text_ids_batch = text_ids_batches[0]
+                text_ids_batches = torch.roll(text_ids_batches, shifts=-1, dims=0)
+            if latent_image_ids.ndim == 3:
+                latent_image_ids_batch = latent_image_ids_batches[0]
+                latent_image_ids_batches = torch.roll(latent_image_ids_batches, shifts=-1, dims=0)
+            guidance_batch = None if guidance_batches[0].isnan().any() else guidance_batches[0]
             guidance_batches = torch.roll(guidance_batches, shifts=-1, dims=0)
 
             if hasattr(self.scheduler, "_init_step_index"):
@@ -512,8 +544,8 @@ def __call__(
                         guidance=guidance_batch,
                         pooled_projections=pooled_prompt_embeddings_batch,
                         encoder_hidden_states=text_embeddings_batch,
-                        txt_ids=text_ids,
-                        img_ids=latent_image_ids,
+                        txt_ids=text_ids_batch if text_ids.ndim == 3 else text_ids,
+                        img_ids=latent_image_ids_batch if latent_image_ids.ndim ==3 else latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,
                     )[0]
@@ -524,8 +556,8 @@ def __call__(
                         guidance=guidance_batch,
                         pooled_projections=pooled_prompt_embeddings_batch,
                         encoder_hidden_states=text_embeddings_batch,
-                        txt_ids=text_ids,
-                        img_ids=latent_image_ids,
+                        txt_ids=text_ids_batch if text_ids.ndim == 3 else text_ids,
+                        img_ids=latent_image_ids_batch if latent_image_ids.ndim == 3 else latent_image_ids,
                         joint_attention_kwargs=self.joint_attention_kwargs,
                         return_dict=False,
                     )[0]

From 40d12e797425a8c63c9b597780dec0c19f31dd29 Mon Sep 17 00:00:00 2001
From: baocheny <baochen.yang@intel.com>
Date: Thu, 24 Oct 2024 11:38:35 +0800
Subject: [PATCH 30/30] upgrade diffusers to 0.31.0 relese version inrequ

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 388df57fcf..e8e693a108 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
     "optimum",
     "torch",
     "accelerate >= 0.33.0, < 0.34.0",
-    "diffusers @ git+https://github.com/huggingface/diffusers.git@main",
+    "diffusers == 0.31.0",
     "huggingface_hub >= 0.24.7",
     "sentence-transformers[train] == 3.0.1",
 ]