dressing_sd/pipelines/pipeline_sd.py

# -*- coding: utf-8 -*-
# @Time    : 2024/5/31
# @Author  : White Jiang
from diffusers.schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from diffusers.utils import is_accelerate_available
from diffusers.pipelines.controlnet.pipeline_controlnet import *

import os
import sys
from safetensors import safe_open
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(BASE_DIR)

from adapter.resampler import ProjPlusModel
from adapter.attention_processor import RefSAttnProcessor2_0, RefLoraSAttnProcessor2_0,  IPAttnProcessor2_0, LoRAIPAttnProcessor2_0 


class PipIpaControlNet(StableDiffusionControlNetPipeline):
    _optional_components = []

    def __init__(
            self,
            vae,
            reference_unet,
            unet,
            tokenizer,
            text_encoder,
            controlnet,
            image_encoder,
            ImgProj,
            ip_ckpt,
            scheduler: Union[
                DDIMScheduler,
                PNDMScheduler,
                LMSDiscreteScheduler,
                EulerDiscreteScheduler,
                EulerAncestralDiscreteScheduler,
                DPMSolverMultistepScheduler,
            ],
            safety_checker: StableDiffusionSafetyChecker,
            feature_extractor: CLIPImageProcessor,
    ):
        super().__init__(vae, text_encoder, tokenizer, unet, controlnet, scheduler, safety_checker, feature_extractor)

        self.register_modules(
            vae=vae,
            reference_unet=reference_unet,
            unet=unet,
            controlnet=controlnet,
            scheduler=scheduler,
            tokenizer=tokenizer,
            text_encoder=text_encoder,
            image_encoder=image_encoder,
            ImgProj=ImgProj,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.clip_image_processor = CLIPImageProcessor()
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.ref_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False,
        )
        self.cond_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor,
            do_convert_rgb=True,
            do_normalize=False,
        )
        self.ip_ckpt = ip_ckpt
        self.num_tokens = 4
        # image proj model
        self.image_proj_model = self.init_proj()
        self.load_ip_adapter()

    def init_proj(self):
        image_proj_model = ProjPlusModel(
            cross_attention_dim=self.unet.config.cross_attention_dim,
            id_embeddings_dim=512,
            clip_embeddings_dim=self.image_encoder.config.hidden_size,
            num_tokens=self.num_tokens,
        ).to(self.unet.device, dtype=torch.float16)
        return image_proj_model

    def load_ip_adapter(self):
        if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
            state_dict = {"image_proj": {}, "ip_adapter": {}}
            with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
                for key in f.keys():
                    if key.startswith("image_proj."):
                        state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
                    elif key.startswith("ip_adapter."):
                        state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
        else:
            state_dict = torch.load(self.ip_ckpt, map_location="cpu")
        self.image_proj_model.load_state_dict(state_dict["image_proj"])
        ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
        ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)

    @property
    def cross_attention_kwargs(self):
        return self._cross_attention_kwargs

    def enable_vae_slicing(self):
        self.vae.enable_slicing()

    def disable_vae_slicing(self):
        self.vae.disable_slicing()

    def enable_sequential_cpu_offload(self, gpu_id=0):
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("Please install accelerate via `pip install accelerate`")

        device = torch.device(f"cuda:{gpu_id}")

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
            if cpu_offloaded_model is not None:
                cpu_offload(cpu_offloaded_model, device)

    @property
    def _execution_device(self):
        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                    hasattr(module, "_hf_hook")
                    and hasattr(module._hf_hook, "execution_device")
                    and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(
            inspect.signature(self.scheduler.step).parameters.keys()
        )
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(
            inspect.signature(self.scheduler.step).parameters.keys()
        )
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

        # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt

    def encode_prompt(
            self,
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt=None,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
            lora_scale: Optional[float] = None,
            clip_skip: Optional[int] = None,
    ):

        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if not USE_PEFT_BACKEND:
                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
            else:
                scale_lora_layers(self.text_encoder, lora_scale)

        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]

        if prompt_embeds is None:
            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(
                    untruncated_ids[:, self.tokenizer.model_max_length - 1: -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            if clip_skip is None:
                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(
                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
            # Retrieve the original scale by scaling back the LoRA layers
            unscale_lora_layers(self.text_encoder, lora_scale)

        return prompt_embeds, negative_prompt_embeds

    def prepare_latents(
            self,
            batch_size,
            num_channels_latents,
            width,
            height,
            dtype,
            device,
            generator,
            latents=None,
    ):
        shape = (
            batch_size,
            num_channels_latents,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(
                shape, generator=generator, device=device, dtype=dtype
            )
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def prepare_condition(
            self,
            cond_image,
            width,
            height,
            device,
            dtype,
            do_classififer_free_guidance=False,
    ):
        image = self.cond_image_processor.preprocess(
            cond_image, height=height, width=width
        ).to(dtype=torch.float32)

        image = image.to(device=device, dtype=dtype)

        if do_classififer_free_guidance:
            image = torch.cat([image] * 2)

        return image

    def get_image_embeds(self, clip_image=None, faceid_embeds=None):
        with torch.no_grad():
            clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
                                                   output_hidden_states=True).hidden_states[-2]
            uncond_clip_image_embeds = self.image_encoder(
                torch.zeros_like(clip_image).to(self.device, dtype=torch.float16), output_hidden_states=True
            ).hidden_states[-2]

            faceid_embeds = faceid_embeds.to(self.device, dtype=torch.float16)
            image_prompt_embeds = self.image_proj_model(faceid_embeds, clip_image_embeds)
            uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds),uncond_clip_image_embeds)
        return image_prompt_embeds, uncond_image_prompt_embeds

    def set_scale(self, scale, lora_scale):
        for attn_processor in self.unet.attn_processors.values():
            if isinstance(attn_processor, RefLoraSAttnProcessor2_0):
                attn_processor.scale = scale
                attn_processor.lora_scale = lora_scale
            # elif isinstance(attn_processor, RefCAttnProcessor2_0):
            #     attn_processor.scale = scale

    def set_ipa_scale(self, ipa_scale, lora_scale):
        for attn_processor in self.unet.attn_processors.values():
            if isinstance(attn_processor, LoRAIPAttnProcessor2_0):
                attn_processor.scale = ipa_scale
                attn_processor.lora_scale = lora_scale  
            elif isinstance(attn_processor, IPAttnProcessor2_0):
                attn_processor.scale = ipa_scale
                attn_processor.lora_scale = lora_scale

    @torch.no_grad()
    def __call__(
            self,
            prompt,
            null_prompt,
            negative_prompt,
            ref_image,
            width,
            height,
            num_inference_steps,
            guidance_scale,
            pose_image=None,
            ref_clip_image=None,
            face_clip_image=None,
            faceid_embeds=None,
            num_images_per_prompt=1,
            image_scale=1.0,
            ipa_scale=0.0,
            s_lora_scale=0.0,
            c_lora_scale=0.0,
            num_samples=1,
            eta: float = 0.0,
            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
            output_type: Optional[str] = "pil",
            return_dict: bool = True,
            clip_skip: Optional[int] = None,
            callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
            callback_steps: Optional[int] = 1,
            prompt_embeds: Optional[torch.FloatTensor] = None,
            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
            controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
            guess_mode: bool = False,
            control_guidance_start: Union[float, List[float]] = 0.0,
            control_guidance_end: Union[float, List[float]] = 1.0,
            **kwargs,
    ):
        
        if face_clip_image is None:
            self.set_scale(image_scale, lora_scale=0.0)
            self.set_ipa_scale(ipa_scale=0.0, lora_scale=0.0)
        else:
            self.set_scale(image_scale, lora_scale=s_lora_scale)
            self.set_ipa_scale(ipa_scale, lora_scale=c_lora_scale)

        # controlnet
        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
        # align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )
        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

        global_pool_conditions = (
            controlnet.config.global_pool_conditions
            if isinstance(controlnet, ControlNetModel)
            else controlnet.nets[0].config.global_pool_conditions
        )
        guess_mode = guess_mode or global_pool_conditions
        # Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        device = self._execution_device
        self._cross_attention_kwargs = cross_attention_kwargs
        self._clip_skip = clip_skip
        do_classifier_free_guidance = guidance_scale > 1.0

        # Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        batch_size = 1
        if pose_image is not None:
            # Prepare control image
            if isinstance(controlnet, ControlNetModel):
                image = self.prepare_image(
                    image=pose_image,
                    width=width,
                    height=height,
                    batch_size=batch_size * num_images_per_prompt,
                    num_images_per_prompt=num_images_per_prompt,
                    device=device,
                    dtype=controlnet.dtype,
                    do_classifier_free_guidance=do_classifier_free_guidance,
                    guess_mode=guess_mode,
                )
                if do_classifier_free_guidance and not guess_mode:
                    image = image.chunk(2)[0]
                height, width = image.shape[-2:]
            else:
                assert False
            # print(image.shape)

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
            clip_skip=self.clip_skip,
        )

        if face_clip_image is not None:
            # for face image condition
            image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(face_clip_image, faceid_embeds)

            bs_embed, seq_len, _ = image_prompt_embeds.shape
            image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
            image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
            uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
            uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)

        if ref_clip_image is not None:
            with torch.no_grad():
                image_embeds = self.image_encoder(ref_clip_image.to(device, dtype=prompt_embeds.dtype),
                                                  output_hidden_states=True).hidden_states[-2]
                image_null_embeds = \
                    self.image_encoder(torch.zeros_like(ref_clip_image).to(device, dtype=prompt_embeds.dtype),
                                       output_hidden_states=True).hidden_states[-2]
                cloth_proj_embed = self.ImgProj(image_embeds)
                cloth_null_embeds = self.ImgProj(image_null_embeds)
                # cloth_null_embeds = self.ImgProj(torch.zeros_like(image_embeds))
        else:
            null_prompt_embeds, _ = self.encode_prompt(
                null_prompt,
                device,
                num_images_per_prompt,
                do_classifier_free_guidance,
                negative_prompt,
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=negative_prompt_embeds,
                lora_scale=text_encoder_lora_scale,
                clip_skip=self.clip_skip,
            )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        if do_classifier_free_guidance:
            prompt_embeds_control = torch.cat([negative_prompt_embeds, prompt_embeds])
            if ref_clip_image is not None:
                null_prompt_embeds = torch.cat([cloth_null_embeds, cloth_proj_embed])
            else:
                null_prompt_embeds = torch.cat([negative_prompt_embeds, null_prompt_embeds])
            if face_clip_image is not None:
                prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
                negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
            else:
                prompt_embeds = prompt_embeds
                negative_prompt_embeds = negative_prompt_embeds

        num_channels_latents = self.unet.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            width,
            height,
            prompt_embeds.dtype,
            device,
            generator,
        )

        # Prepare extra step kwargs.
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # Prepare ref image latents
        ref_image_tensor = ref_image.to(
            dtype=self.vae.dtype, device=self.vae.device
        )
        ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
        ref_image_latents = ref_image_latents * 0.18215  # (b, 4, h, w)
        if pose_image is not None:
            #  Create tensor stating which controlnets to keep
            controlnet_keep = []
            for i in range(len(timesteps)):
                keeps = [
                    1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                    for s, e in zip(control_guidance_start, control_guidance_end)
                ]
                controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)

        # denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # 1. Forward reference image
                if i == 0:
                    _ = self.reference_unet(
                        ref_image_latents.repeat(
                            (2 if do_classifier_free_guidance else 1), 1, 1, 1
                        ),
                        torch.zeros_like(t),
                        encoder_hidden_states=null_prompt_embeds,
                        return_dict=False,
                    )

                    # get cache tensors
                    sa_hidden_states = {}
                    for name in self.reference_unet.attn_processors.keys():
                        sa_hidden_states[name] = self.reference_unet.attn_processors[name].cache["hidden_states"][
                            1].unsqueeze(0)
                        # sa_hidden_states[name][0, :, :] = 0

                # 3.1 expand the latents if we are doing classifier free guidance
                latent_model_input = (
                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                )
                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t
                )

                # Optionally get Guidance Scale Embedding
                timestep_cond = None
                if self.unet.config.time_cond_proj_dim is not None:
                    guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(
                        batch_size * num_images_per_prompt)
                    timestep_cond = self.get_guidance_scale_embedding(
                        guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
                    ).to(device=device, dtype=latents.dtype)

                # for control
                if pose_image is not None:
                    # controlnet(s) inference
                    if guess_mode and self.do_classifier_free_guidance:
                        # Infer ControlNet only for the conditional batch.
                        control_model_input = latents
                        control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                        controlnet_prompt_embeds = prompt_embeds_control.chunk(2)[1]
                        # controlnet_prompt_embeds = prompt_embeds
                    else:
                        control_model_input = latent_model_input
                        controlnet_prompt_embeds = prompt_embeds_control
                    if isinstance(controlnet_keep[i], list):
                        cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                    else:
                        controlnet_cond_scale = controlnet_conditioning_scale
                        if isinstance(controlnet_cond_scale, list):
                            controlnet_cond_scale = controlnet_cond_scale[0]
                        cond_scale = controlnet_cond_scale * controlnet_keep[i]

                    down_block_res_samples, mid_block_res_sample = self.controlnet(
                        control_model_input,
                        t,
                        encoder_hidden_states=controlnet_prompt_embeds,
                        controlnet_cond=image,
                        conditioning_scale=cond_scale,
                        guess_mode=guess_mode,
                        return_dict=False,
                    )

                    # if do_classifier_free_guidance:
                    down_block_res_samples_con = []
                    down_block_res_samples_uncon = []
                    for down_block in down_block_res_samples:
                        down_block_res_samples_con.append(down_block[1])
                        down_block_res_samples_uncon.append(down_block[0])
                    # for prompt_embeds ref + text
                    noise_pred = self.unet(
                        latent_model_input[0].unsqueeze(0),
                        t,
                        encoder_hidden_states=prompt_embeds,
                        cross_attention_kwargs={
                            "sa_hidden_states": sa_hidden_states,
                        },
                        timestep_cond=timestep_cond,
                        down_block_additional_residuals=down_block_res_samples_con,
                        mid_block_additional_residual=mid_block_res_sample[1],
                        added_cond_kwargs=None,
                        return_dict=False,
                    )[0]
                    # for negative_prompt_embeds non text
                    unc_noise_pred = self.unet(
                        latent_model_input[1].unsqueeze(0),
                        t,
                        encoder_hidden_states=negative_prompt_embeds,
                        timestep_cond=timestep_cond,
                        down_block_additional_residuals=down_block_res_samples_uncon,
                        mid_block_additional_residual=mid_block_res_sample[0],
                        added_cond_kwargs=None,
                        return_dict=False,
                    )[0]
                # for no control
                else:
                    noise_pred = self.unet(
                        latent_model_input[1].unsqueeze(0),
                        t,
                        encoder_hidden_states=prompt_embeds,
                        cross_attention_kwargs={
                            "sa_hidden_states": sa_hidden_states,
                        },
                        timestep_cond=timestep_cond,
                        added_cond_kwargs=None,
                        return_dict=False,
                    )[0]
                    # for negative_prompt_embeds non text
                    unc_noise_pred = self.unet(
                        latent_model_input[0].unsqueeze(0),
                        t,
                        encoder_hidden_states=negative_prompt_embeds,
                        timestep_cond=timestep_cond,
                        added_cond_kwargs=None,
                        return_dict=False,
                    )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    # noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred_uncond, noise_pred_text = unc_noise_pred, noise_pred

                    noise_pred = noise_pred_uncond + guidance_scale * (
                            noise_pred_text - noise_pred_uncond
                    )

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred, t, latents, **extra_step_kwargs, return_dict=False
                )[0]

                # 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, t, latents)

        # Post-processing
        image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
        do_denormalize = [True] * image.shape[0]
        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)