Update pipeline.py

pipeline.py

@@ -19,6 +19,7 @@ import numpy as np
 import inspect
 from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
 from diffusers.image_processor import PipelineImageInput
+from diffusers import FluxPipeline
 from typing import Any, Callable, Dict, List, Optional, Union
 
 from diffusers.utils import (
@@ -141,375 +142,376 @@ def prepare_latent_image_ids(batch_size, height, width, device, dtype):
 
     return latent_image_ids.to(device=device, dtype=dtype)
 
-
-@torch.no_grad()
-def run(
-        self,
-        prompt: Union[str, List[str]] = None,
-        prompt_2: Optional[Union[str, List[str]]] = None,
-        negative_prompt: Union[str, List[str]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
-        true_cfg_scale: float = 1.0,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 28,
-        sigmas: Optional[List[float]] = None,
-        timesteps: Optional[List[float]] = None,
-        scales: List[float] = 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,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
-        negative_ip_adapter_image: Optional[PipelineImageInput] = None,
-        negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_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,
-):
-    r"""
-    Function invoked when calling the pipeline for generation.
-
-    Args:
-        prompt (`str` or `List[str]`, *optional*):
-            The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-            instead.
-        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.
-        negative_prompt (`str` or `List[str]`, *optional*):
-            The prompt or prompts not to guide the image generation. If not defined, one has to pass
-            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
-            not greater than `1`).
-        negative_prompt_2 (`str` or `List[str]`, *optional*):
-            The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-            `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
-        true_cfg_scale (`float`, *optional*, defaults to 1.0):
-            When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
-        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.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
-            their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
-            will be used.
-        guidance_scale (`float`, *optional*, defaults to 3.5):
-            Guidance scale as defined in [Classifier-Free Diffusion
-            Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
-            of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
-        ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
-        ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
-            Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
-            IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
-            provided, embeddings are computed from the `ip_adapter_image` input argument.
-        negative_ip_adapter_image:
-            (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
-        negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
-            Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
-            IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
-            provided, embeddings are computed from the `ip_adapter_image` input argument.
-        negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-            Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-            weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-            argument.
-        negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-            Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-            weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-            input argument.
-        output_type (`str`, *optional*, defaults to `"pil"`):
-            The output format of the generate image. Choose between
-            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-        return_dict (`bool`, *optional*, defaults to `True`):
-            Whether or not to return a [`~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`.
-
-    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.
-    """
-
-    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,
-        negative_prompt=negative_prompt,
-        negative_prompt_2=negative_prompt_2,
-        prompt_embeds=prompt_embeds,
-        negative_prompt_embeds=negative_prompt_embeds,
-        pooled_prompt_embeds=pooled_prompt_embeds,
-        negative_pooled_prompt_embeds=negative_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._current_timestep = None
-    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
-
-    lora_scale = (
-        self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
-    )
-    has_neg_prompt = negative_prompt is not None or (
-            negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
-    )
-    do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
-    (
-        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,
-        lora_scale=lora_scale,
-    )
-    if do_true_cfg:
+class SwDPipeline(FluxPipeline):
+    
+    @torch.no_grad()
+    def __call__(
+            self,
+            prompt: Union[str, List[str]] = None,
+            prompt_2: Optional[Union[str, List[str]]] = None,
+            negative_prompt: Union[str, List[str]] = None,
+            negative_prompt_2: Optional[Union[str, List[str]]] = None,
+            true_cfg_scale: float = 1.0,
+            height: Optional[int] = None,
+            width: Optional[int] = None,
+            num_inference_steps: int = 28,
+            sigmas: Optional[List[float]] = None,
+            timesteps: Optional[List[float]] = None,
+            scales: List[float] = 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,
+            ip_adapter_image: Optional[PipelineImageInput] = None,
+            ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+            negative_ip_adapter_image: Optional[PipelineImageInput] = None,
+            negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_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,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+    
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            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.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
+            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.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 3.5):
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            negative_ip_adapter_image:
+                (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~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`.
+    
+        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.
+        """
+    
+        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,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_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._current_timestep = None
+        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
+    
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        has_neg_prompt = negative_prompt is not None or (
+                negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
+        )
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
         (
-            negative_prompt_embeds,
-            negative_pooled_prompt_embeds,
-            negative_text_ids,
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
         ) = self.encode_prompt(
-            prompt=negative_prompt,
-            prompt_2=negative_prompt_2,
-            prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            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,
             lora_scale=lora_scale,
         )
-
-    # 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) if sigmas is None else sigmas
-    image_seq_len = latents.shape[1]
-    mu = calculate_shift(
-        image_seq_len,
-        self.scheduler.config.get("base_image_seq_len", 256),
-        self.scheduler.config.get("max_image_seq_len", 4096),
-        self.scheduler.config.get("base_shift", 0.5),
-        self.scheduler.config.get("max_shift", 1.15),
-    )
-    timesteps, num_inference_steps = retrieve_timesteps(
-        self.scheduler,
-        num_inference_steps,
-        device,
-        sigmas=sigmas,
-        mu=mu,
-    ) if timesteps is None else (timesteps, len(timesteps))
-    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
-
-    if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and (
-            negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None
-    ):
-        negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
-        negative_ip_adapter_image = [negative_ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
-
-    elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and (
-            negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None
-    ):
-        ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
-        ip_adapter_image = [ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
-
-    if self.joint_attention_kwargs is None:
-        self._joint_attention_kwargs = {}
-
-    image_embeds = None
-    negative_image_embeds = None
-    if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
-        image_embeds = self.prepare_ip_adapter_image_embeds(
-            ip_adapter_image,
-            ip_adapter_image_embeds,
-            device,
+        if do_true_cfg:
+            (
+                negative_prompt_embeds,
+                negative_pooled_prompt_embeds,
+                negative_text_ids,
+            ) = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_2=negative_prompt_2,
+                prompt_embeds=negative_prompt_embeds,
+                pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
+            )
+    
+        # 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,
-        )
-    if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None:
-        negative_image_embeds = self.prepare_ip_adapter_image_embeds(
-            negative_ip_adapter_image,
-            negative_ip_adapter_image_embeds,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
             device,
-            batch_size * num_images_per_prompt,
+            generator,
+            latents,
         )
-
-    # 6. Denoising loop
-    with self.progress_bar(total=num_inference_steps) as progress_bar:
-        for i, t in enumerate(timesteps):
-            if self.interrupt:
-                continue
-
-            self._current_timestep = t
-            if image_embeds is not None:
-                self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
-            # 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]
-
-            if do_true_cfg:
-                if negative_image_embeds is not None:
-                    self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
-                neg_noise_pred = self.transformer(
+    
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            mu=mu,
+        ) if timesteps is None else (timesteps, len(timesteps))
+        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
+    
+        if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and (
+                negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None
+        ):
+            negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
+            negative_ip_adapter_image = [negative_ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
+    
+        elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and (
+                negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None
+        ):
+            ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
+            ip_adapter_image = [ip_adapter_image] * self.transformer.encoder_hid_proj.num_ip_adapters
+    
+        if self.joint_attention_kwargs is None:
+            self._joint_attention_kwargs = {}
+    
+        image_embeds = None
+        negative_image_embeds = None
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+            )
+        if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None:
+            negative_image_embeds = self.prepare_ip_adapter_image_embeds(
+                negative_ip_adapter_image,
+                negative_ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+            )
+    
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+    
+                self._current_timestep = t
+                if image_embeds is not None:
+                    self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
+                # 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=negative_pooled_prompt_embeds,
-                    encoder_hidden_states=negative_prompt_embeds,
-                    txt_ids=negative_text_ids,
+                    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]
-                noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            if scales is None:
-                latents_dtype = latents.dtype
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-            else:
-                latents_dtype = latents.dtype
-                sigma = sigmas[i]
-                sigma_next = sigmas[i + 1]
-                x0_pred = (latents - sigma * noise_pred)
-                x0_pred = unpack_latents(x0_pred, scales[i], scales[i])
-                if scales and i + 1 < len(scales):
-                    x0_pred = torch.nn.functional.interpolate(x0_pred, size=scales[i + 1], mode='bicubic')
-                    latent_image_ids = prepare_latent_image_ids(batch_size, scales[i + 1] // 2, scales[i + 1] // 2, device, prompt_embeds.dtype)
-                x0_pred = pack_latents(x0_pred, *x0_pred.shape)
-                noise = torch.randn(x0_pred.shape, generator=generator, dtype=x0_pred.dtype).to(x0_pred.device)
-                latents = (1 - sigma_next) * x0_pred + sigma_next * noise
-
-            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()
-
-            if XLA_AVAILABLE:
-                xm.mark_step()
-
-    self._current_timestep = None
-
-    if output_type == "latent":
-        image = latents
-    else:
-        if scales is not None:
-            height, width = int(scales[-1] * 8), int(scales[-1] * 8)
-        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 FluxPipelineOutput(images=image)
+    
+                if do_true_cfg:
+                    if negative_image_embeds is not None:
+                        self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
+                    neg_noise_pred = self.transformer(
+                        hidden_states=latents,
+                        timestep=timestep / 1000,
+                        guidance=guidance,
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        txt_ids=negative_text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                    noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+    
+                # compute the previous noisy sample x_t -> x_t-1
+                if scales is None:
+                    latents_dtype = latents.dtype
+                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                else:
+                    latents_dtype = latents.dtype
+                    sigma = sigmas[i]
+                    sigma_next = sigmas[i + 1]
+                    x0_pred = (latents - sigma * noise_pred)
+                    x0_pred = unpack_latents(x0_pred, scales[i], scales[i])
+                    if scales and i + 1 < len(scales):
+                        x0_pred = torch.nn.functional.interpolate(x0_pred, size=scales[i + 1], mode='bicubic')
+                        latent_image_ids = prepare_latent_image_ids(batch_size, scales[i + 1] // 2, scales[i + 1] // 2, device, prompt_embeds.dtype)
+                    x0_pred = pack_latents(x0_pred, *x0_pred.shape)
+                    noise = torch.randn(x0_pred.shape, generator=generator, dtype=x0_pred.dtype).to(x0_pred.device)
+                    latents = (1 - sigma_next) * x0_pred + sigma_next * noise
+    
+                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()
+    
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+    
+        self._current_timestep = None
+    
+        if output_type == "latent":
+            image = latents
+        else:
+            if scales is not None:
+                height, width = int(scales[-1] * 8), int(scales[-1] * 8)
+            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 FluxPipelineOutput(images=image)