fix checkpoint, ready to merge to diffusers

config.json

@@ -4,13 +4,6 @@
   "_name_or_path": "./model_hub_tmp_0/.",
   "attention_head_dim": 64,
   "caption_projection_dim": 1536,
-  "conditioning_channels": 3,
-  "conditioning_embedding_out_channels": [
-    16,
-    32,
-    96,
-    256
-  ],
   "in_channels": 16,
   "joint_attention_dim": 4096,
   "num_attention_heads": 24,

controlnet_sd3.py

@@ -1,552 +0,0 @@
-# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX 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.
-
-
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-import diffusers
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
-from diffusers.models.attention import JointTransformerBlock
-from diffusers.models.attention_processor import Attention, AttentionProcessor
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    is_torch_version,
-    logging,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.models.controlnet import BaseOutput, zero_module
-from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
-from diffusers.models.transformers.transformer_2d import Transformer2DModelOutput
-from torch.nn import functional as F
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-from packaging import version
-
-class ControlNetConditioningEmbedding(nn.Module):
-    """
-    Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
-    [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
-    training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
-    convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
-    (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
-    model) to encode image-space conditions ... into feature maps ..."
-    """
-
-    def __init__(
-        self,
-        conditioning_embedding_channels: int,
-        conditioning_channels: int = 3,
-        block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
-    ):
-        super().__init__()
-
-        self.conv_in = nn.Conv2d(
-            conditioning_channels, block_out_channels[0], kernel_size=3, padding=1
-        )
-
-        self.blocks = nn.ModuleList([])
-
-        for i in range(len(block_out_channels) - 1):
-            channel_in = block_out_channels[i]
-            channel_out = block_out_channels[i + 1]
-            self.blocks.append(
-                nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)
-            )
-            self.blocks.append(
-                nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)
-            )
-
-        self.conv_out = zero_module(
-            nn.Conv2d(
-                block_out_channels[-1],
-                conditioning_embedding_channels,
-                kernel_size=3,
-                padding=1,
-            )
-        )
-
-    def forward(self, conditioning):
-        embedding = self.conv_in(conditioning)
-        embedding = F.silu(embedding)
-
-        for block in self.blocks:
-            embedding = block(embedding)
-            embedding = F.silu(embedding)
-
-        embedding = self.conv_out(embedding)
-
-        return embedding
-
-
-@dataclass
-class SD3ControlNetOutput(BaseOutput):
-    controlnet_block_samples: Tuple[torch.Tensor]
-
-
-class SD3ControlNetModel(
-    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin
-):
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: int = 128,
-        patch_size: int = 2,
-        in_channels: int = 16,
-        num_layers: int = 18,
-        attention_head_dim: int = 64,
-        num_attention_heads: int = 18,
-        joint_attention_dim: int = 4096,
-        caption_projection_dim: int = 1152,
-        pooled_projection_dim: int = 2048,
-        out_channels: int = 16,
-        pos_embed_max_size: int = 96,
-        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (
-            16,
-            32,
-            96,
-            256,
-        ),
-        conditioning_channels: int = 3,
-    ):
-        """
-        conditioning_channels: condition image pixel space channels
-        conditioning_embedding_out_channels: intermediate channels
-
-        """
-        super().__init__()
-        default_out_channels = in_channels
-        self.out_channels = (
-            out_channels if out_channels is not None else default_out_channels
-        )
-        self.inner_dim = num_attention_heads * attention_head_dim
-
-        self.pos_embed = PatchEmbed(
-            height=sample_size,
-            width=sample_size,
-            patch_size=patch_size,
-            in_channels=in_channels,
-            embed_dim=self.inner_dim,
-            pos_embed_max_size=pos_embed_max_size,  # hard-code for now.
-        )
-        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
-            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
-        )
-        self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim)
-
-        # control net conditioning embedding
-        # self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
-        #     conditioning_embedding_channels=default_out_channels,
-        #     block_out_channels=conditioning_embedding_out_channels,
-        #     conditioning_channels=conditioning_channels,
-        # )
-
-        # `attention_head_dim` is doubled to account for the mixing.
-        # It needs to crafted when we get the actual checkpoints.
-        self.transformer_blocks = nn.ModuleList(
-            [
-                JointTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim if version.parse(diffusers.__version__) >= version.parse('0.30.0.dev0') else self.inner_dim,
-                    context_pre_only=False,
-                )
-                for _ in range(num_layers)
-            ]
-        )
-
-        # controlnet_blocks
-        self.controlnet_blocks = nn.ModuleList([])
-        for _ in range(len(self.transformer_blocks)):
-            controlnet_block = zero_module(nn.Linear(self.inner_dim, self.inner_dim))
-            self.controlnet_blocks.append(controlnet_block)
-
-        # control condition embedding
-        pos_embed_cond = PatchEmbed(
-            height=sample_size,
-            width=sample_size,
-            patch_size=patch_size,
-            in_channels=in_channels + 1,
-            embed_dim=self.inner_dim,
-            pos_embed_type=None,
-        )
-        # pos_embed_cond = nn.Linear(in_channels + 1, self.inner_dim)
-        self.pos_embed_cond = zero_module(pos_embed_cond)
-
-        self.gradient_checkpointing = False
-
-    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
-    def enable_forward_chunking(
-        self, chunk_size: Optional[int] = None, dim: int = 0
-    ) -> None:
-        """
-        Sets the attention processor to use [feed forward
-        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
-
-        Parameters:
-            chunk_size (`int`, *optional*):
-                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
-                over each tensor of dim=`dim`.
-            dim (`int`, *optional*, defaults to `0`):
-                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
-                or dim=1 (sequence length).
-        """
-        if dim not in [0, 1]:
-            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
-
-        # By default chunk size is 1
-        chunk_size = chunk_size or 1
-
-        def fn_recursive_feed_forward(
-            module: torch.nn.Module, chunk_size: int, dim: int
-        ):
-            if hasattr(module, "set_chunk_feed_forward"):
-                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
-
-            for child in module.children():
-                fn_recursive_feed_forward(child, chunk_size, dim)
-
-        for module in self.children():
-            fn_recursive_feed_forward(module, chunk_size, dim)
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(
-            name: str,
-            module: torch.nn.Module,
-            processors: Dict[str, AttentionProcessor],
-        ):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor(
-                    return_deprecated_lora=True
-                )
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(
-        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
-    ):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
-    def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-        """
-        self.original_attn_processors = None
-
-        for _, attn_processor in self.attn_processors.items():
-            if "Added" in str(attn_processor.__class__.__name__):
-                raise ValueError(
-                    "`fuse_qkv_projections()` is not supported for models having added KV projections."
-                )
-
-        self.original_attn_processors = self.attn_processors
-
-        for module in self.modules():
-            if isinstance(module, Attention):
-                module.fuse_projections(fuse=True)
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
-    def unfuse_qkv_projections(self):
-        """Disables the fused QKV projection if enabled.
-
-        <Tip warning={true}>
-
-        This API is 🧪 experimental.
-
-        </Tip>
-
-        """
-        if self.original_attn_processors is not None:
-            self.set_attn_processor(self.original_attn_processors)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    @classmethod
-    def from_transformer(
-        cls, transformer, num_layers=None, load_weights_from_transformer=True
-    ):
-        config = transformer.config
-        config["num_layers"] = num_layers or config.num_layers
-        controlnet = cls(**config)
-
-        if load_weights_from_transformer:
-            controlnet.pos_embed.load_state_dict(
-                transformer.pos_embed.state_dict(), strict=False
-            )
-            controlnet.time_text_embed.load_state_dict(
-                transformer.time_text_embed.state_dict(), strict=False
-            )
-            controlnet.context_embedder.load_state_dict(
-                transformer.context_embedder.state_dict(), strict=False
-            )
-            controlnet.transformer_blocks.load_state_dict(
-                transformer.transformer_blocks.state_dict(), strict=False
-            )
-
-        return controlnet
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        controlnet_cond: torch.Tensor,
-        conditioning_scale: float = 1.0,
-        encoder_hidden_states: torch.FloatTensor = None,
-        pooled_projections: torch.FloatTensor = None,
-        timestep: torch.LongTensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
-        """
-        The [`SD3Transformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
-                Input `hidden_states`.
-            controlnet_cond (`torch.Tensor`):
-                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
-            conditioning_scale (`float`, defaults to `1.0`):
-                The scale factor for ControlNet outputs.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
-                from the embeddings of input conditions.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            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).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if (
-                joint_attention_kwargs is not None
-                and joint_attention_kwargs.get("scale", None) is not None
-            ):
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
-        height, width = hidden_states.shape[-2:]
-
-        hidden_states = self.pos_embed(
-            hidden_states
-        )  # takes care of adding positional embeddings too. b,c,H,W -> b, N, C
-        temb = self.time_text_embed(timestep, pooled_projections)
-        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
-
-        # add condition
-        hidden_states = hidden_states + self.pos_embed_cond(controlnet_cond)
-
-        block_res_samples = ()
-
-        for block in self.transformer_blocks:
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = (
-                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                )
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    **ckpt_kwargs,
-                )
-
-            else:
-                encoder_hidden_states, hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    temb=temb,
-                )
-
-            block_res_samples = block_res_samples + (hidden_states,)
-
-        controlnet_block_res_samples = ()
-        for block_res_sample, controlnet_block in zip(
-            block_res_samples, self.controlnet_blocks
-        ):
-            block_res_sample = controlnet_block(block_res_sample)
-            controlnet_block_res_samples = controlnet_block_res_samples + (
-                block_res_sample,
-            )
-
-        # 6. scaling
-        controlnet_block_res_samples = [
-            sample * conditioning_scale for sample in controlnet_block_res_samples
-        ]
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (controlnet_block_res_samples,)
-
-        return SD3ControlNetOutput(
-            controlnet_block_samples=controlnet_block_res_samples
-        )
-
-    def invert_copy_paste(self, controlnet_block_samples):
-        controlnet_block_samples = controlnet_block_samples + controlnet_block_samples[::-1]
-        return controlnet_block_samples
-
-class SD3MultiControlNetModel(ModelMixin):
-    r"""
-    `SD3ControlNetModel` wrapper class for Multi-SD3ControlNet
-
-    This module is a wrapper for multiple instances of the `SD3ControlNetModel`. The `forward()` API is designed to be
-    compatible with `SD3ControlNetModel`.
-
-    Args:
-        controlnets (`List[SD3ControlNetModel]`):
-            Provides additional conditioning to the unet during the denoising process. You must set multiple
-            `SD3ControlNetModel` as a list.
-    """
-
-    def __init__(self, controlnets):
-        super().__init__()
-        self.nets = nn.ModuleList(controlnets)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        controlnet_cond: List[torch.tensor],
-        conditioning_scale: List[float],
-        pooled_projections: torch.FloatTensor,
-        encoder_hidden_states: torch.FloatTensor = None,
-        timestep: torch.LongTensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[SD3ControlNetOutput, Tuple]:
-        for i, (image, scale, controlnet) in enumerate(
-            zip(controlnet_cond, conditioning_scale, self.nets)
-        ):
-            block_samples = controlnet(
-                hidden_states=hidden_states,
-                timestep=timestep,
-                encoder_hidden_states=encoder_hidden_states,
-                pooled_projections=pooled_projections,
-                controlnet_cond=image,
-                conditioning_scale=scale,
-                joint_attention_kwargs=joint_attention_kwargs,
-                return_dict=return_dict,
-            )
-
-            # merge samples
-            if i == 0:
-                control_block_samples = block_samples
-            else:
-                control_block_samples = [
-                    control_block_sample + block_sample
-                    for control_block_sample, block_sample in zip(
-                        control_block_samples[0], block_samples[0]
-                    )
-                ]
-                control_block_samples = (tuple(control_block_samples),)
-
-        return control_block_samples

demo.py

@@ -1,53 +0,0 @@
-from diffusers.utils import load_image, check_min_version
-import torch
-
-# Local File
-from pipeline_sd3_controlnet_inpainting import StableDiffusion3ControlNetInpaintingPipeline, one_image_and_mask
-from controlnet_sd3 import SD3ControlNetModel
-
-check_min_version("0.29.2")
-
-# Build model
-controlnet = SD3ControlNetModel.from_pretrained(
-    "alimama-creative/SD3-Controlnet-Inpainting",
-    use_safetensors=True,
-)
-pipe = StableDiffusion3ControlNetInpaintingPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    controlnet=controlnet,
-    torch_dtype=torch.float16,
-)
-pipe.text_encoder.to(torch.float16)
-pipe.controlnet.to(torch.float16)
-pipe.to("cuda")
-
-# Load image
-image = load_image(
-    "https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/blob/main/images/prod.png"
-)
-mask = load_image(
-    "https://huggingface.co/alimama-creative/SD3-Controlnet-Inpainting/blob/main/images/mask.jpeg"
-)
-
-# Set args
-width = 1024
-height = 1024
-prompt="a woman wearing a white jacket, black hat and black pants is standing in a field, the hat writes SD3"
-generator = torch.Generator(device="cuda").manual_seed(24)
-input_dict = one_image_and_mask(image, mask, size=(width, height), latent_scale=pipe.vae_scale_factor, invert_mask = True)
-
-# Inference
-res_image = pipe(
-    negative_prompt='deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW',
-    prompt=prompt,
-    height=height,
-    width=width,
-    control_image= input_dict['pil_masked_image'],  # H, W, C,
-    control_mask=input_dict["mask"] > 0.5,  # B,1,H,W
-    num_inference_steps=28,
-    generator=generator,
-    controlnet_conditioning_scale=0.95,
-    guidance_scale=7,
-).images[0]
-
-res_image.save(f'res.png')

diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:1818c32db9a7541572a5ebb41b82f0fd4859643a1aba87c91d9f352d09e523c7
-size 4160564320
+oid sha256:f7e2bcf98ed0989558dd05d857cc49c0aff14dfa3197050d65e51a9d37008dde
+size 4160564288

pipeline_sd3_controlnet_inpainting.py

@@ -1,1333 +0,0 @@
-# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from transformers import (
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    T5EncoderModel,
-    T5TokenizerFast,
-)
-
-from PIL import Image, ImageOps
-import numpy as np
-import os
-from torchvision.transforms import v2
-
-from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.loaders import FromSingleFileMixin, SD3LoraLoaderMixin
-from diffusers.models.autoencoders import AutoencoderKL
-from diffusers.models.transformers import SD3Transformer2DModel
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import (
-    is_torch_xla_available,
-    logging,
-    replace_example_docstring,
-)
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.stable_diffusion_3.pipeline_output import (
-    StableDiffusion3PipelineOutput,
-)
-from torchvision.transforms.functional import resize, InterpolationMode
-
-from controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel
-
-if is_torch_xla_available():
-    import torch_xla.core.xla_model as xm
-
-    XLA_AVAILABLE = True
-else:
-    XLA_AVAILABLE = False
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import StableDiffusion3ControlNetPipeline
-        >>> from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
-        >>> from diffusers.utils import load_image
-
-        >>> controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)
-
-        >>> pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
-        ... )
-        >>> pipe.to("cuda")
-        >>> control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
-        >>> prompt = "A girl holding a sign that says InstantX"
-        >>> image = pipe(prompt, control_image=control_image, controlnet_conditioning_scale=0.7).images[0]
-        >>> image.save("sd3.png")
-        ```
-"""
-
-def one_image_and_mask(image, mask, size = None, latent_scale = 8 , invert_mask = False):
-    '''
-    Image : PIL Image, Torch Tensor [-1, 1], Path, B,C,H,W
-    Mask : PIL Image , Torch Tensor [0, 1], Path, B,1,H,W
-    '''
-    # size = (W, H)
-    if size is not None:
-        if not ( type(size) == list or type(size) == tuple):
-            size = (size, size)
-        
-    # Get image @ torch tensor
-    if type(image) == str and os.path.exists(image):
-        image = Image.open(image)
-    
-    if isinstance(image, Image.Image): 
-        image = image.convert("RGB")
-        if size is not None:
-            image = image.resize(size, Image.Resampling.LANCZOS)
-        pil_image = image
-        image_arr = np.array(image)
-        assert image_arr.ndim == 3
-        assert image_arr.shape[2] == 3
-        th_image = torch.from_numpy(image_arr).float() / 127. - 1
-        th_image = th_image.permute(2, 0, 1)
-    else:
-        th_image = image
-        pil_image = None
-    
-    # Get BCHW
-    assert isinstance(th_image, torch.Tensor)
-    if len(th_image.shape) == 3:
-        th_image = th_image.unsqueeze(0)
-    H, W = th_image.shape[-2:]
-    assert H % 8 == 0 and W % 8 == 0
-    
-    # Get mask @ torch tensor
-    if type(mask) == str and os.path.exists(mask):
-        mask = Image.open(mask)
-    
-    if isinstance(mask, Image.Image):
-        mask = mask.convert("L")
-        if invert_mask:
-            mask = ImageOps.invert(mask)
-        mask = mask.resize((W, H), Image.Resampling.LANCZOS)
-        pil_mask = mask
-        mask_arr = np.array(mask)
-        if mask_arr.ndim == 3 and mask_arr.shape[2] == 3:
-            mask_arr = mask_arr[:, :, 0] # H, W
-        th_mask = torch.from_numpy(mask_arr).float() / 255.
-        th_mask = th_mask.unsqueeze(0)
-    else:
-        th_mask = mask
-        pil_mask = None
-        
-    assert isinstance(th_mask, torch.Tensor)
-    if len(th_mask.shape) == 3:
-        th_mask = th_mask.unsqueeze(0)
-    
-    # Get mask at latent space
-    th_mask_latent = torch.nn.functional.interpolate(
-            th_mask, size=(H // latent_scale, W // latent_scale), mode="bilinear", antialias=True
-        )
-    
-    # Get masked image for vae-cond
-    masked_image = th_image.clone()
-    masked_image[(th_mask < 0.5).repeat(1,3,1,1)] = - 1. # set 0. like power paint @ https://github.com/open-mmlab/PowerPaint/blob/main/powerpaint/pipelines/pipeline_PowerPaint.py
-    
-    # Get pil masked image
-    pil_masked_image = v2.ToPILImage()((masked_image/2 + 1/2).clip(0, 1).squeeze(0))
-    
-    # Get masked image
-    return {
-        'image': th_image,
-        'mask': th_mask,
-        'mask_latent': th_mask_latent,
-        'masked_image': masked_image,
-        'pil_image': pil_image,
-        'pil_mask': pil_mask,
-        'pil_masked_image': pil_masked_image
-    }
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    """
-    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-    Args:
-        scheduler (`SchedulerMixin`):
-            The scheduler to get timesteps from.
-        num_inference_steps (`int`):
-            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
-            must be `None`.
-        device (`str` or `torch.device`, *optional*):
-            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        timesteps (`List[int]`, *optional*):
-            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-            `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-            `num_inference_steps` and `timesteps` must be `None`.
-
-    Returns:
-        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-        second element is the number of inference steps.
-    """
-    if timesteps is not None and sigmas is not None:
-        raise ValueError(
-            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
-        )
-    if timesteps is not None:
-        accepts_timesteps = "timesteps" in set(
-            inspect.signature(scheduler.set_timesteps).parameters.keys()
-        )
-        if not accepts_timesteps:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" timestep schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(
-            inspect.signature(scheduler.set_timesteps).parameters.keys()
-        )
-        if not accept_sigmas:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" sigmas schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-
-class StableDiffusion3ControlNetInpaintingPipeline(
-    DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin
-):
-    r"""
-    Args:
-        transformer ([`SD3Transformer2DModel`]):
-            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 ([`CLIPTextModelWithProjection`]):
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
-            with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
-            as its dimension.
-        text_encoder_2 ([`CLIPTextModelWithProjection`]):
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the
-            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
-            variant.
-        text_encoder_3 ([`T5EncoderModel`]):
-            Frozen text-encoder. Stable Diffusion 3 uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
-            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_2 (`CLIPTokenizer`):
-            Second Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_3 (`T5TokenizerFast`):
-            Tokenizer of class
-            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-        controlnet ([`SD3ControlNetModel`] or `List[SD3ControlNetModel]` or [`SD3MultiControlNetModel`]):
-            Provides additional conditioning to the `unet` during the denoising process. If you set multiple
-            ControlNets as a list, the outputs from each ControlNet are added together to create one combined
-            additional conditioning.
-    """
-
-    model_cpu_offload_seq = (
-        "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
-    )
-    _optional_components = []
-    _callback_tensor_inputs = [
-        "latents",
-        "prompt_embeds",
-        "negative_prompt_embeds",
-        "negative_pooled_prompt_embeds",
-    ]
-
-    def __init__(
-        self,
-        transformer: SD3Transformer2DModel,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        text_encoder_2: CLIPTextModelWithProjection,
-        tokenizer_2: CLIPTokenizer,
-        text_encoder_3: T5EncoderModel,
-        tokenizer_3: T5TokenizerFast,
-        controlnet: Union[
-            SD3ControlNetModel,
-            List[SD3ControlNetModel],
-            Tuple[SD3ControlNetModel],
-            SD3MultiControlNetModel,
-        ],
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            text_encoder_3=text_encoder_3,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            tokenizer_3=tokenizer_3,
-            transformer=transformer,
-            scheduler=scheduler,
-            controlnet=controlnet,
-        )
-        self.vae_scale_factor = (
-            2 ** (len(self.vae.config.block_out_channels) - 1)
-            if hasattr(self, "vae") and self.vae is not None
-            else 8
-        )
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.control_image_processor = VaeImageProcessor(
-            vae_scale_factor=self.vae_scale_factor,
-            do_convert_rgb=True,
-            do_normalize=False,
-        )
-        self.tokenizer_max_length = (
-            self.tokenizer.model_max_length
-            if hasattr(self, "tokenizer") and self.tokenizer is not None
-            else 77
-        )
-        self.default_sample_size = (
-            self.transformer.config.sample_size
-            if hasattr(self, "transformer") and self.transformer is not None
-            else 128
-        )
-
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
-    def _get_t5_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]] = None,
-        num_images_per_prompt: int = 1,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        device = device or self._execution_device
-        dtype = dtype or self.text_encoder.dtype
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        batch_size = len(prompt)
-
-        if self.text_encoder_3 is None:
-            return torch.zeros(
-                (
-                    batch_size,
-                    self.tokenizer_max_length,
-                    self.transformer.config.joint_attention_dim,
-                ),
-                device=device,
-                dtype=dtype,
-            )
-
-        text_inputs = self.tokenizer_3(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer_max_length,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer_3(
-            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_3.batch_decode(
-                untruncated_ids[:, self.tokenizer_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_max_length} tokens: {removed_text}"
-            )
-
-        prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]
-
-        dtype = self.text_encoder_3.dtype
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        _, seq_len, _ = prompt_embeds.shape
-
-        # duplicate text embeddings and attention mask 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(
-            batch_size * num_images_per_prompt, seq_len, -1
-        )
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_clip_prompt_embeds
-    def _get_clip_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]],
-        num_images_per_prompt: int = 1,
-        device: Optional[torch.device] = None,
-        clip_skip: Optional[int] = None,
-        clip_model_index: int = 0,
-    ):
-        device = device or self._execution_device
-
-        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
-        clip_text_encoders = [self.text_encoder, self.text_encoder_2]
-
-        tokenizer = clip_tokenizers[clip_model_index]
-        text_encoder = clip_text_encoders[clip_model_index]
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        batch_size = len(prompt)
-
-        text_inputs = tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = 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 = tokenizer.batch_decode(
-                untruncated_ids[:, self.tokenizer_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_max_length} tokens: {removed_text}"
-            )
-        prompt_embeds = text_encoder(
-            text_input_ids.to(device), output_hidden_states=True
-        )
-        pooled_prompt_embeds = prompt_embeds[0]
-
-        if clip_skip is None:
-            prompt_embeds = prompt_embeds.hidden_states[-2]
-        else:
-            prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        _, 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(
-            batch_size * num_images_per_prompt, seq_len, -1
-        )
-
-        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        pooled_prompt_embeds = pooled_prompt_embeds.view(
-            batch_size * num_images_per_prompt, -1
-        )
-
-        return prompt_embeds, pooled_prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        prompt_2: Union[str, List[str]],
-        prompt_3: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
-        negative_prompt_3: Optional[Union[str, List[str]]] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in all text-encoders
-            prompt_3 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
-                used in all text-encoders
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            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.
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
-                `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            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.
-            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.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-        """
-        device = device or self._execution_device
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            prompt_2 = prompt_2 or prompt
-            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
-
-            prompt_3 = prompt_3 or prompt
-            prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3
-
-            prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
-                prompt=prompt,
-                device=device,
-                num_images_per_prompt=num_images_per_prompt,
-                clip_skip=clip_skip,
-                clip_model_index=0,
-            )
-            prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
-                prompt=prompt_2,
-                device=device,
-                num_images_per_prompt=num_images_per_prompt,
-                clip_skip=clip_skip,
-                clip_model_index=1,
-            )
-            clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)
-
-            t5_prompt_embed = self._get_t5_prompt_embeds(
-                prompt=prompt_3,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-            )
-
-            clip_prompt_embeds = torch.nn.functional.pad(
-                clip_prompt_embeds,
-                (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1]),
-            )
-
-            prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
-            pooled_prompt_embeds = torch.cat(
-                [pooled_prompt_embed, pooled_prompt_2_embed], dim=-1
-            )
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt_2 = negative_prompt_2 or negative_prompt
-            negative_prompt_3 = negative_prompt_3 or negative_prompt
-
-            # normalize str to list
-            negative_prompt = (
-                batch_size * [negative_prompt]
-                if isinstance(negative_prompt, str)
-                else negative_prompt
-            )
-            negative_prompt_2 = (
-                batch_size * [negative_prompt_2]
-                if isinstance(negative_prompt_2, str)
-                else negative_prompt_2
-            )
-            negative_prompt_3 = (
-                batch_size * [negative_prompt_3]
-                if isinstance(negative_prompt_3, str)
-                else negative_prompt_3
-            )
-
-            if 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 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`."
-                )
-
-            negative_prompt_embed, negative_pooled_prompt_embed = (
-                self._get_clip_prompt_embeds(
-                    negative_prompt,
-                    device=device,
-                    num_images_per_prompt=num_images_per_prompt,
-                    clip_skip=None,
-                    clip_model_index=0,
-                )
-            )
-            negative_prompt_2_embed, negative_pooled_prompt_2_embed = (
-                self._get_clip_prompt_embeds(
-                    negative_prompt_2,
-                    device=device,
-                    num_images_per_prompt=num_images_per_prompt,
-                    clip_skip=None,
-                    clip_model_index=1,
-                )
-            )
-            negative_clip_prompt_embeds = torch.cat(
-                [negative_prompt_embed, negative_prompt_2_embed], dim=-1
-            )
-
-            t5_negative_prompt_embed = self._get_t5_prompt_embeds(
-                prompt=negative_prompt_3,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-            )
-
-            negative_clip_prompt_embeds = torch.nn.functional.pad(
-                negative_clip_prompt_embeds,
-                (
-                    0,
-                    t5_negative_prompt_embed.shape[-1]
-                    - negative_clip_prompt_embeds.shape[-1],
-                ),
-            )
-
-            negative_prompt_embeds = torch.cat(
-                [negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2
-            )
-            negative_pooled_prompt_embeds = torch.cat(
-                [negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1
-            )
-
-        return (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        )
-
-    def check_inputs(
-        self,
-        prompt,
-        prompt_2,
-        prompt_3,
-        height,
-        width,
-        negative_prompt=None,
-        negative_prompt_2=None,
-        negative_prompt_3=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        pooled_prompt_embeds=None,
-        negative_pooled_prompt_embeds=None,
-        callback_on_step_end_tensor_inputs=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(
-                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
-            )
-
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs
-            for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt_2 is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt_3 is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (
-            not isinstance(prompt, str) and not isinstance(prompt, list)
-        ):
-            raise ValueError(
-                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
-            )
-        elif prompt_2 is not None and (
-            not isinstance(prompt_2, str) and not isinstance(prompt_2, list)
-        ):
-            raise ValueError(
-                f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}"
-            )
-        elif prompt_3 is not None and (
-            not isinstance(prompt_3, str) and not isinstance(prompt_3, list)
-        ):
-            raise ValueError(
-                f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}"
-            )
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-        elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        if prompt_embeds is not None and pooled_prompt_embeds is None:
-            raise ValueError(
-                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
-            )
-
-        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
-            raise ValueError(
-                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
-            )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-    ):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            int(height) // self.vae_scale_factor,
-            int(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=device, dtype=dtype)
-
-        return latents
-
-    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
-    def prepare_image(
-        self,
-        image,
-        width,
-        height,
-        batch_size,
-        num_images_per_prompt,
-        device,
-        dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
-    ):
-        image = self.control_image_processor.preprocess(
-            image, height=height, width=width
-        ).to(dtype=torch.float32)
-        image_batch_size = image.shape[0]
-
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-
-        image = image.repeat_interleave(repeat_by, dim=0)
-
-        image = image.to(device=device, dtype=dtype)
-
-        if do_classifier_free_guidance and not guess_mode:
-            image = torch.cat([image] * 2)
-
-        return image
-
-    def prepare_image_with_mask(
-        self,
-        image,
-        mask,
-        width,
-        height,
-        batch_size,
-        num_images_per_prompt,
-        device,
-        dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
-    ):
-
-        if isinstance(image, torch.Tensor):
-            pass
-        else:
-            image = self.image_processor.preprocess(
-                image, height=height, width=width
-            )  # C,H,W
-
-        if isinstance(mask, torch.Tensor):
-            pass
-        else:
-            raise "Control Mask must be tensor"
-
-        image_batch_size = image.shape[0]
-
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-
-        image = image.repeat_interleave(repeat_by, dim=0)
-        mask = mask.repeat_interleave(repeat_by, dim=0)
-
-        image = image.to(device=device, dtype=self.vae.dtype)
-        mask = mask.to(device=device, dtype=dtype)
-
-        image_latents = self.vae.encode(image).latent_dist.sample()
-        image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-        image_latents = image_latents.to(dtype)
-
-        # cat image and mask
-        mask = torch.nn.functional.interpolate(
-            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
-        )
-        
-        control_image = torch.cat([image_latents, mask], dim=1)
-
-        if do_classifier_free_guidance and not guess_mode:
-            control_image = torch.cat([control_image] * 2)
-        return control_image
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def clip_skip(self):
-        return self._clip_skip
-
-    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-    # corresponds to doing no classifier free guidance.
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
-
-    @property
-    def joint_attention_kwargs(self):
-        return self._joint_attention_kwargs
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @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,
-        prompt_3: 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 = 7.0,
-        control_guidance_start: Union[float, List[float]] = 0.0,
-        control_guidance_end: Union[float, List[float]] = 1.0,
-        control_image: Union[
-            PipelineImageInput,
-            List[PipelineImageInput],
-        ] = None,
-        control_mask=None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
-        controlnet_pooled_projections: Optional[torch.FloatTensor] = None,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
-        negative_prompt_3: Optional[Union[str, List[str]]] = None,
-        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,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_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,
-        clip_skip: Optional[int] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-    ):
-        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
-            prompt_3 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. 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 5.0):
-                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.
-            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
-                The percentage of total steps at which the ControlNet starts applying.
-            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
-                The percentage of total steps at which the ControlNet stops applying.
-            control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
-                    `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
-                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
-                specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted
-                as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or
-                width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,
-                images must be passed as a list such that each element of the list can be correctly batched for input
-                to a single ControlNet.
-            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
-                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
-                the corresponding scale as a list.
-            controlnet_pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`):
-                Embeddings projected from the embeddings of controlnet input conditions.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            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 instead
-            negative_prompt_3 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
-                `text_encoder_3`. If not defined, `negative_prompt` is used instead
-            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.
-            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.
-            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.
-            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.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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
-
-        # 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(self.controlnet.nets)
-                if isinstance(self.controlnet, SD3MultiControlNetModel)
-                else 1
-            )
-            control_guidance_start, control_guidance_end = (
-                mult * [control_guidance_start],
-                mult * [control_guidance_end],
-            )
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            prompt_3,
-            height,
-            width,
-            negative_prompt=negative_prompt,
-            negative_prompt_2=negative_prompt_2,
-            negative_prompt_3=negative_prompt_3,
-            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,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._clip_skip = clip_skip
-        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
-        dtype = self.transformer.dtype
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            prompt_3=prompt_3,
-            negative_prompt=negative_prompt,
-            negative_prompt_2=negative_prompt_2,
-            negative_prompt_3=negative_prompt_3,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            device=device,
-            clip_skip=self.clip_skip,
-            num_images_per_prompt=num_images_per_prompt,
-        )
-
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            pooled_prompt_embeds = torch.cat(
-                [negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0
-            )
-
-        # 3. Prepare control image
-        if isinstance(self.controlnet, SD3ControlNetModel):
-            control_image = self.prepare_image_with_mask(
-                image=control_image,
-                mask=control_mask,
-                width=width,
-                height=height,
-                batch_size=batch_size * num_images_per_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-                dtype=self.controlnet.dtype,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-            )
-            height, width = control_image.shape[-2:]
-            height = height * self.vae_scale_factor
-            width = width * self.vae_scale_factor
-        elif isinstance(self.controlnet, SD3MultiControlNetModel):
-            images = []
-            for image_ in control_image:
-                image_ = self.prepare_image_with_mask(
-                    image=image_,
-                    mask=control_mask,
-                    width=width,
-                    height=height,
-                    batch_size=batch_size * num_images_per_prompt,
-                    num_images_per_prompt=num_images_per_prompt,
-                    device=device,
-                    dtype=self.controlnet.dtype,
-                    do_classifier_free_guidance=self.do_classifier_free_guidance,
-                )
-                images.append(image_)
-
-            control_image = images
-            height, width = control_image[0].shape[-2:]
-            height = height * self.vae_scale_factor
-            width = width * self.vae_scale_factor
-        else:
-            raise ValueError("ControlNet must be of type SD3ControlNetModel")
-
-        if controlnet_pooled_projections is None:
-            controlnet_pooled_projections = torch.zeros_like(pooled_prompt_embeds)
-        else:
-            controlnet_pooled_projections = (
-                controlnet_pooled_projections or pooled_prompt_embeds
-            )
-
-        # 4. Prepare timesteps
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, timesteps
-        )
-        num_warmup_steps = max(
-            len(timesteps) - num_inference_steps * self.scheduler.order, 0
-        )
-        self._num_timesteps = len(timesteps)
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. 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(self.controlnet, SD3ControlNetModel) else keeps
-            )
-
-        # 7. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = (
-                    torch.cat([latents] * 2)
-                    if self.do_classifier_free_guidance
-                    else latents
-                )
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0])
-
-                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]
-
-                # controlnet(s) inference
-                control_block_samples = self.controlnet(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    pooled_projections=controlnet_pooled_projections,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    controlnet_cond=control_image,
-                    conditioning_scale=cond_scale,
-                    return_dict=False,
-                )[0]
-
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    pooled_projections=pooled_prompt_embeds,
-                    block_controlnet_hidden_states=control_block_samples,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (
-                        noise_pred_text - noise_pred_uncond
-                    )
-
-                # 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)
-                    negative_prompt_embeds = callback_outputs.pop(
-                        "negative_prompt_embeds", negative_prompt_embeds
-                    )
-                    negative_pooled_prompt_embeds = callback_outputs.pop(
-                        "negative_pooled_prompt_embeds", negative_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
-                ):
-                    progress_bar.update()
-
-                if XLA_AVAILABLE:
-                    xm.mark_step()
-
-        if output_type == "latent":
-            image = latents
-
-        else:
-            latents = (
-                latents / self.vae.config.scaling_factor
-            ) + self.vae.config.shift_factor
-            latents = latents.to(dtype=self.vae.dtype)
-            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 StableDiffusion3PipelineOutput(images=image)