Handle multi control inputs for control lora training

extensions_built_in/sd_trainer/SDTrainer.py

@@ -1063,7 +1063,6 @@ class SDTrainer(BaseSDTrainProcess):
         if self.adapter and isinstance(self.adapter, CustomAdapter):
             # condition the prompt
             # todo handle more than one adapter image
-            self.adapter.num_control_images = 1
             conditioned_prompts = self.adapter.condition_prompt(conditioned_prompts)
 
         network_weight_list = batch.get_network_weight_list()

toolkit/config_modules.py

@@ -241,6 +241,9 @@ class AdapterConfig:
             self.lora_config: NetworkConfig = NetworkConfig(**lora_config)
         else:
             self.lora_config = None
+        self.num_control_images: int = kwargs.get('num_control_images', 1)
+        # decimal for how often the control is dropped out and replaced with noise 1.0 is 100%
+        self.control_image_dropout: float = kwargs.get('control_image_dropout', 0.0)
 
 
 class EmbeddingConfig:
@@ -710,7 +713,7 @@ class DatasetConfig:
         self.flip_x: bool = kwargs.get('flip_x', False)
         self.flip_y: bool = kwargs.get('flip_y', False)
         self.augments: List[str] = kwargs.get('augments', [])
-        self.control_path: str = kwargs.get('control_path', None)  # depth maps, etc
+        self.control_path: Union[str,List[str]] = kwargs.get('control_path', None)  # depth maps, etc
         # instead of cropping ot match image, it will serve the full size control image (clip images ie for ip adapters)
         self.full_size_control_images: bool = kwargs.get('full_size_control_images', False)
         self.alpha_mask: bool = kwargs.get('alpha_mask', False)  # if true, will use alpha channel as mask
@@ -833,6 +836,7 @@ class GenerateImageConfig:
             logger: Optional[EmptyLogger] = None,
             num_frames: int = 1,
             fps: int = 15,
+            ctrl_idx: int = 0
     ):
         self.width: int = width
         self.height: int = height
@@ -863,6 +867,7 @@ class GenerateImageConfig:
         self.extra_values = extra_values if extra_values is not None else []
         self.num_frames = num_frames
         self.fps = fps
+        self.ctrl_idx = ctrl_idx
         
 
         # prompt string will override any settings above
@@ -1056,6 +1061,8 @@ class GenerateImageConfig:
                         self.num_frames = int(content)
                     elif flag == 'fps':
                         self.fps = int(content)
+                    elif flag == 'ctrl_idx':
+                        self.ctrl_idx = int(content)
 
     def post_process_embeddings(
             self,

toolkit/custom_adapter.py

@@ -82,7 +82,7 @@ class CustomAdapter(torch.nn.Module):
 
         self.position_ids: Optional[List[int]] = None
 
-        self.num_control_images = 1
+        self.num_control_images = self.config.num_control_images
         self.token_mask: Optional[torch.Tensor] = None
 
         # setup clip
@@ -575,19 +575,53 @@ class CustomAdapter(torch.nn.Module):
                     # concat random normal noise onto the latents
                     # check dimension, this is before they are rearranged
                     # it is latent_model_input = torch.cat([latents, control_image], dim=2) after rearranging
-                    latents = torch.cat((latents, torch.randn_like(latents)), dim=1)
+                    ctrl = torch.randn(
+                        latents.shape[0], # bs
+                        latents.shape[1] * self.num_control_images, # ch
+                        latents.shape[2], 
+                        latents.shape[3], 
+                        device=latents.device, 
+                        dtype=latents.dtype
+                    )
+                    latents = torch.cat((latents, ctrl), dim=1)
                     return latents.detach()
-                # it is 0-1 need to convert to -1 to 1
+                # if we have multiple control tensors, they come in like [bs, num_control_images, ch, h, w]
-                control_tensor = control_tensor * 2 - 1
+                # if we have 1, it comes in like [bs, ch, h, w]
+                # stack out control tensors to be [bs, ch * num_control_images, h, w]
+                
+                control_tensor_list = []
+                if len(control_tensor.shape) == 4:
+                    control_tensor_list.append(control_tensor)
+                else:
+                    # reshape
+                    control_tensor = control_tensor.view(
+                        control_tensor.shape[0], 
+                        control_tensor.shape[1] * control_tensor.shape[2], 
+                        control_tensor.shape[3], 
+                        control_tensor.shape[4]
+                    )
+                    control_tensor_list = control_tensor.chunk(self.num_control_images, dim=1)
+                control_latent_list = []
+                for control_tensor in control_tensor_list:
+                    do_dropout = random.random() < self.config.control_image_dropout
+                    if do_dropout:
+                        # dropout with noise
+                        control_latent_list.append(torch.zeros_like(batch.latents))
+                    else:
+                        # it is 0-1 need to convert to -1 to 1
+                        control_tensor = control_tensor * 2 - 1
 
-                control_tensor = control_tensor.to(sd.vae_device_torch, dtype=sd.torch_dtype)
+                        control_tensor = control_tensor.to(sd.vae_device_torch, dtype=sd.torch_dtype)
-                
+                        
-                # if it is not the size of batch.tensor, (bs,ch,h,w) then we need to resize it
+                        # if it is not the size of batch.tensor, (bs,ch,h,w) then we need to resize it
-                if control_tensor.shape[2] != batch.tensor.shape[2] or control_tensor.shape[3] != batch.tensor.shape[3]:
+                        if control_tensor.shape[2] != batch.tensor.shape[2] or control_tensor.shape[3] != batch.tensor.shape[3]:
-                    control_tensor = F.interpolate(control_tensor, size=(batch.tensor.shape[2], batch.tensor.shape[3]), mode='bicubic')
+                            control_tensor = F.interpolate(control_tensor, size=(batch.tensor.shape[2], batch.tensor.shape[3]), mode='bicubic')
-                
+                        
-                # encode it
+                        # encode it
-                control_latent = sd.encode_images(control_tensor).to(latents.device, latents.dtype)
+                        control_latent = sd.encode_images(control_tensor).to(latents.device, latents.dtype)
+                        control_latent_list.append(control_latent)
+                # stack them on the channel dimension
+                control_latent = torch.cat(control_latent_list, dim=1)
                 # concat it onto the latents
                 latents = torch.cat((latents, control_latent), dim=1)
                 return latents.detach()

toolkit/dataloader_mixins.py

@@ -743,75 +743,100 @@ class ControlFileItemDTOMixin:
         if hasattr(super(), '__init__'):
             super().__init__(*args, **kwargs)
         self.has_control_image = False
-        self.control_path: Union[str, None] = None
+        self.control_path: Union[str, List[str], None] = None
         self.control_tensor: Union[torch.Tensor, None] = None
         dataset_config: 'DatasetConfig' = kwargs.get('dataset_config', None)
         self.full_size_control_images = False
         if dataset_config.control_path is not None:
             # find the control image path
-            control_path = dataset_config.control_path
+            control_path_list = dataset_config.control_path
+            if not isinstance(control_path_list, list):
+                control_path_list = [control_path_list]
             self.full_size_control_images = dataset_config.full_size_control_images
             # we are using control images
             img_path = kwargs.get('path', None)
             img_ext_list = ['.jpg', '.jpeg', '.png', '.webp']
             file_name_no_ext = os.path.splitext(os.path.basename(img_path))[0]
-            for ext in img_ext_list:
+            
-                if os.path.exists(os.path.join(control_path, file_name_no_ext + ext)):
+            found_control_images = []
-                    self.control_path = os.path.join(control_path, file_name_no_ext + ext)
+            for control_path in control_path_list:
-                    self.has_control_image = True
+                for ext in img_ext_list:
-                    break
+                    if os.path.exists(os.path.join(control_path, file_name_no_ext + ext)):
+                        found_control_images.append(os.path.join(control_path, file_name_no_ext + ext))
+                        self.has_control_image = True
+                        break
+            self.control_path = found_control_images
+            if len(self.control_path) == 0:
+                self.control_path = None
+            elif len(self.control_path) == 1:
+                # only do one
+                self.control_path = self.control_path[0]
 
     def load_control_image(self: 'FileItemDTO'):
-        try:
+        control_tensors = []
-            img = Image.open(self.control_path).convert('RGB')
+        control_path_list = self.control_path
-            img = exif_transpose(img)
+        if not isinstance(self.control_path, list):
-        except Exception as e:
+            control_path_list = [self.control_path]
-            print_acc(f"Error: {e}")
+        
-            print_acc(f"Error loading image: {self.control_path}")
+        for control_path in control_path_list:
+            try:
+                img = Image.open(control_path).convert('RGB')
+                img = exif_transpose(img)
+            except Exception as e:
+                print_acc(f"Error: {e}")
+                print_acc(f"Error loading image: {control_path}")
 
-        if self.full_size_control_images:
+            if self.full_size_control_images:
-            # we just scale them to 512x512:
+                # we just scale them to 512x512:
-            w, h = img.size
+                w, h = img.size
-            img = img.resize((512, 512), Image.BICUBIC)
+                img = img.resize((512, 512), Image.BICUBIC)
 
-        else:
-            w, h = img.size
-            if w > h and self.scale_to_width < self.scale_to_height:
-                # throw error, they should match
-                raise ValueError(
-                    f"unexpected values: w={w}, h={h}, file_item.scale_to_width={self.scale_to_width}, file_item.scale_to_height={self.scale_to_height}, file_item.path={self.path}")
-            elif h > w and self.scale_to_height < self.scale_to_width:
-                # throw error, they should match
-                raise ValueError(
-                    f"unexpected values: w={w}, h={h}, file_item.scale_to_width={self.scale_to_width}, file_item.scale_to_height={self.scale_to_height}, file_item.path={self.path}")
-
-            if self.flip_x:
-                # do a flip
-                img = img.transpose(Image.FLIP_LEFT_RIGHT)
-            if self.flip_y:
-                # do a flip
-                img = img.transpose(Image.FLIP_TOP_BOTTOM)
-
-            if self.dataset_config.buckets:
-                # scale and crop based on file item
-                img = img.resize((self.scale_to_width, self.scale_to_height), Image.BICUBIC)
-                # img = transforms.CenterCrop((self.crop_height, self.crop_width))(img)
-                # crop
-                img = img.crop((
-                    self.crop_x,
-                    self.crop_y,
-                    self.crop_x + self.crop_width,
-                    self.crop_y + self.crop_height
-                ))
             else:
-                raise Exception("Control images not supported for non-bucket datasets")
+                w, h = img.size
-        transform = transforms.Compose([
+                if w > h and self.scale_to_width < self.scale_to_height:
-            transforms.ToTensor(),
+                    # throw error, they should match
-        ])
+                    raise ValueError(
-        if self.aug_replay_spatial_transforms:
+                        f"unexpected values: w={w}, h={h}, file_item.scale_to_width={self.scale_to_width}, file_item.scale_to_height={self.scale_to_height}, file_item.path={self.path}")
-            self.control_tensor = self.augment_spatial_control(img, transform=transform)
+                elif h > w and self.scale_to_height < self.scale_to_width:
+                    # throw error, they should match
+                    raise ValueError(
+                        f"unexpected values: w={w}, h={h}, file_item.scale_to_width={self.scale_to_width}, file_item.scale_to_height={self.scale_to_height}, file_item.path={self.path}")
+
+                if self.flip_x:
+                    # do a flip
+                    img = img.transpose(Image.FLIP_LEFT_RIGHT)
+                if self.flip_y:
+                    # do a flip
+                    img = img.transpose(Image.FLIP_TOP_BOTTOM)
+
+                if self.dataset_config.buckets:
+                    # scale and crop based on file item
+                    img = img.resize((self.scale_to_width, self.scale_to_height), Image.BICUBIC)
+                    # img = transforms.CenterCrop((self.crop_height, self.crop_width))(img)
+                    # crop
+                    img = img.crop((
+                        self.crop_x,
+                        self.crop_y,
+                        self.crop_x + self.crop_width,
+                        self.crop_y + self.crop_height
+                    ))
+                else:
+                    raise Exception("Control images not supported for non-bucket datasets")
+            transform = transforms.Compose([
+                transforms.ToTensor(),
+            ])
+            if self.aug_replay_spatial_transforms:
+                tensor = self.augment_spatial_control(img, transform=transform)
+            else:
+                tensor = transform(img)
+            control_tensors.append(tensor)
+            
+        if len(control_tensors) == 0:
+            self.control_tensor = None
+        elif len(control_tensors) == 1:
+            self.control_tensor = control_tensors[0]
         else:
-            self.control_tensor = transform(img)
+            self.control_tensor = torch.stack(control_tensors, dim=0)
 
     def cleanup_control(self: 'FileItemDTO'):
         self.control_tensor = None

toolkit/models/control_lora_adapter.py

@@ -46,14 +46,14 @@ class ImgEmbedder(torch.nn.Module):
         cls, 
         model: FluxTransformer2DModel, 
         adapter: 'ControlLoraAdapter', 
-        num_channel_multiplier=2
+        num_control_images=1
     ):
         if model.__class__.__name__ == 'FluxTransformer2DModel':
             x_embedder: torch.nn.Linear = model.x_embedder
             img_embedder = cls(
                 adapter, 
                 orig_layer=x_embedder,
-                in_channels=x_embedder.in_features * (num_channel_multiplier - 1), # only our new channels
+                in_channels=x_embedder.in_features * num_control_images,
                 out_channels=x_embedder.out_features, 
             )
             
@@ -62,7 +62,7 @@ class ImgEmbedder(torch.nn.Module):
             x_embedder.forward = img_embedder.forward
 
             # update the config of the transformer
-            model.config.in_channels = model.config.in_channels * num_channel_multiplier
+            model.config.in_channels = model.config.in_channels * (num_control_images + 1)
             model.config["in_channels"] = model.config.in_channels
             
             return img_embedder
@@ -178,7 +178,11 @@ class ControlLoraAdapter(torch.nn.Module):
             if self.train_config.gradient_checkpointing:
                 self.control_lora.enable_gradient_checkpointing()
             
-        self.x_embedder = ImgEmbedder.from_model(sd.unet, self)
+        self.x_embedder = ImgEmbedder.from_model(
+            sd.unet, 
+            self,
+            num_control_images=config.num_control_images
+        )
         self.x_embedder.to(self.device_torch)
 
     def get_params(self):
@@ -230,6 +234,16 @@ class ControlLoraAdapter(torch.nn.Module):
         # todo process state dict before loading
         if self.control_lora is not None:
             self.control_lora.load_weights(lora_sd)
+        # automatically upgrade the x imbedder if more dims are added
+        if self.x_embedder.weight.shape[1] > img_embedder_sd['weight'].shape[1]:
+            print("Upgrading x_embedder from {} to {}".format(
+                img_embedder_sd['weight'].shape[1], 
+                self.x_embedder.weight.shape[1]
+            ))
+            while img_embedder_sd['weight'].shape[1] < self.x_embedder.weight.shape[1]:
+                img_embedder_sd['weight'] = torch.cat([img_embedder_sd['weight'] ] * 2, dim=1)
+            if img_embedder_sd['weight'].shape[1] > self.x_embedder.weight.shape[1]:
+                img_embedder_sd['weight'] = img_embedder_sd['weight'][:, :self.x_embedder.weight.shape[1]]
         self.x_embedder.load_state_dict(img_embedder_sd, strict=False)
         
     def get_state_dict(self):

toolkit/pipelines.py

@@ -4,7 +4,7 @@ from typing import Union, List, Optional, Dict, Any, Tuple, Callable
 
 import numpy as np
 import torch
-from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline, LMSDiscreteScheduler, FluxPipeline
+from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline, LMSDiscreteScheduler, FluxPipeline, FluxControlPipeline
 from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
 from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
@@ -15,6 +15,7 @@ from diffusers.utils import is_torch_xla_available
 from k_diffusion.external import CompVisVDenoiser, CompVisDenoiser
 from k_diffusion.sampling import get_sigmas_karras, BrownianTreeNoiseSampler
 from toolkit.models.flux import bypass_flux_guidance, restore_flux_guidance
+from diffusers.image_processor import PipelineImageInput
 
 
 if is_torch_xla_available():
@@ -1423,4 +1424,293 @@ class FluxWithCFGPipeline(FluxPipeline):
         if not return_dict:
             return (image,)
 
-        return FluxPipelineOutput(images=image)
+        return FluxPipelineOutput(images=image)
+    
+    
+class FluxAdvancedControlPipeline(FluxControlPipeline):
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        control_image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        control_image_idx: int = 0,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            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
+            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.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 3.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Prepare text embeddings
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables
+        # num_channels_latents = self.transformer.config.in_channels // 8
+        num_channels_latents = 128 // 8
+
+        control_image = self.prepare_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=self.vae.dtype,
+        )
+
+        if control_image.ndim == 4:
+            control_image = self.vae.encode(control_image).latent_dist.sample(generator=generator)
+            control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+            height_control_image, width_control_image = control_image.shape[2:]
+            control_image = self._pack_latents(
+                control_image,
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height_control_image,
+                width_control_image,
+            )
+
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+        
+        # flux has 64 input channels. 
+        total_controls = (self.transformer.config.in_channels // 64) - 1
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                
+                control_image_list = [torch.zeros_like(latents) for _ in range(total_controls)]
+                control_image_list[control_image_idx] = control_image
+
+                latent_model_input = torch.cat([latents] + control_image_list, dim=2)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+    

toolkit/stable_diffusion_model.py

@@ -43,7 +43,8 @@ from toolkit.train_tools import get_torch_dtype, apply_noise_offset
 from einops import rearrange, repeat
 import torch
 from toolkit.pipelines import CustomStableDiffusionXLPipeline, CustomStableDiffusionPipeline, \
-    StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline, FluxWithCFGPipeline
+    StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline, FluxWithCFGPipeline, \
+    FluxAdvancedControlPipeline
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, DDPMScheduler, \
     StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline, DiffusionPipeline, PixArtTransformer2DModel, \
     StableDiffusionXLImg2ImgPipeline, LCMScheduler, Transformer2DModel, AutoencoderTiny, ControlNetModel, \
@@ -1244,7 +1245,7 @@ class StableDiffusion:
                     if self.adapter is not None and isinstance(self.adapter, CustomAdapter):
                         # see if it is a control lora
                         if self.adapter.control_lora is not None:
-                            Pipe = FluxControlPipeline
+                            Pipe = FluxAdvancedControlPipeline
                     
                     pipeline = Pipe(
                         vae=self.vae,
@@ -1367,6 +1368,7 @@ class StableDiffusion:
                         if isinstance(self.adapter, CustomAdapter) and self.adapter.control_lora is not None:
                             validation_image = validation_image.resize((gen_config.width, gen_config.height))
                             extra['control_image'] = validation_image
+                            extra['control_image_idx'] = gen_config.ctrl_idx
                         if isinstance(self.adapter, IPAdapter) or isinstance(self.adapter, ClipVisionAdapter):
                             transform = transforms.Compose([
                                 transforms.ToTensor(),