import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL import torch from transformers import ( CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast, ) from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin from diffusers.models.autoencoders import AutoencoderKL from .controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel from .transformer_flux import FluxTransformer2DModel from .scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler from diffusers.utils import ( USE_PEFT_BACKEND, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from diffusers.utils.torch_utils import randn_tensor from diffusers.pipelines.pipeline_utils import DiffusionPipeline from .pipeline_output import FluxPipelineOutput 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__) EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import FluxControlNetInpaintPipeline >>> from diffusers.models import FluxControlNetModel >>> from diffusers.utils import load_image >>> controlnet = FluxControlNetModel.from_pretrained( ... "InstantX/FLUX.1-dev-controlnet-canny", torch_dtype=torch.float16 ... ) >>> pipe = FluxControlNetInpaintPipeline.from_pretrained( ... "black-forest-labs/FLUX.1-schnell", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> pipe.to("cuda") >>> control_image = load_image( ... "https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny-alpha/resolve/main/canny.jpg" ... ) >>> init_image = load_image( ... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" ... ) >>> mask_image = load_image( ... "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" ... ) >>> prompt = "A girl holding a sign that says InstantX" >>> image = pipe( ... prompt, ... image=init_image, ... mask_image=mask_image, ... control_image=control_image, ... control_guidance_start=0.2, ... control_guidance_end=0.8, ... controlnet_conditioning_scale=0.7, ... strength=0.7, ... num_inference_steps=28, ... guidance_scale=3.5, ... ).images[0] >>> image.save("flux_controlnet_inpaint.png") ``` """ # Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift def calculate_shift( image_seq_len, base_seq_len: int = 256, max_seq_len: int = 4096, base_shift: float = 0.5, max_shift: float = 1.16, ): m = (max_shift - base_shift) / (max_seq_len - base_seq_len) b = base_shift - m * base_seq_len mu = image_seq_len * m + b return mu # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents def retrieve_latents( encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") # 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, ): r""" 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 FluxControlNetInpaintPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin): r""" The Flux controlnet pipeline for inpainting. Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ Args: transformer ([`FluxTransformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. text_encoder_2 ([`T5EncoderModel`]): [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 (`T5TokenizerFast`): Second Tokenizer of class [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast). """ model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" _optional_components = [] _callback_tensor_inputs = ["latents", "prompt_embeds"] def __init__( self, scheduler: FlowMatchEulerDiscreteScheduler, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, transformer: FluxTransformer2DModel, controlnet: Union[ FluxControlNetModel, List[FluxControlNetModel], Tuple[FluxControlNetModel], FluxMultiControlNetModel ], text_encoder_2: T5EncoderModel | None = None, tokenizer_2: T5TokenizerFast | None = None, ): super().__init__() if isinstance(controlnet, (list, tuple)): controlnet = FluxMultiControlNetModel(controlnet) self.register_modules( scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, transformer=transformer, controlnet=controlnet, ) self.vae_scale_factor = ( 2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16 ) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, vae_latent_channels=self.vae.config.latent_channels, do_normalize=False, do_binarize=True, do_convert_grayscale=True, ) 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 = 64 # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_t5_prompt_embeds def _get_t5_prompt_embeds( self, prompt: Union[str, List[str]] = None, num_images_per_prompt: int = 1, max_sequence_length: int = 512, 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 isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer_2) text_inputs = self.tokenizer_2( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, return_length=False, return_overflowing_tokens=False, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer_2(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_2.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because `max_sequence_length` is set to " f" {max_sequence_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0] dtype = self.text_encoder_2.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.flux.pipeline_flux.FluxPipeline._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, ): device = device or self._execution_device prompt = [prompt] if isinstance(prompt, str) else prompt batch_size = len(prompt) if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer_max_length, truncation=True, return_overflowing_tokens=False, return_length=False, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_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(text_input_ids.to(device), output_hidden_states=False) # Use pooled output of CLIPTextModel prompt_embeds = prompt_embeds.pooler_output prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt) prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1) return prompt_embeds # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.encode_prompt def encode_prompt( self, prompt: Union[str, List[str]], prompt_2: Union[str, List[str]], device: Optional[torch.device] = None, num_images_per_prompt: int = 1, prompt_embeds: Optional[torch.FloatTensor] = None, t5_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, max_sequence_length: int = 512, lora_scale: Optional[float] = 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 device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt 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. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. """ device = device or self._execution_device # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if self.text_encoder is not None and USE_PEFT_BACKEND: scale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None and USE_PEFT_BACKEND: scale_lora_layers(self.text_encoder_2, lora_scale) prompt = [prompt] if isinstance(prompt, str) else prompt if prompt_embeds is None: prompt_2 = prompt_2 or prompt prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 # We only use the pooled prompt output from the CLIPTextModel pooled_prompt_embeds = self._get_clip_prompt_embeds( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, ) prompt_embeds = self._get_t5_prompt_embeds( prompt=prompt_2, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, device=device, ) if self.text_encoder is not None: if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder_2, lora_scale) dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype if t5_prompt_embeds is not None: text_ids = torch.zeros(prompt_embeds.shape[1] + t5_prompt_embeds.shape[1], 3).to(device=device, dtype=dtype) else: text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype) return prompt_embeds, pooled_prompt_embeds, text_ids # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_inpaint.StableDiffusion3InpaintPipeline._encode_vae_image def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator): if isinstance(generator, list): image_latents = [ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(image.shape[0]) ] image_latents = torch.cat(image_latents, dim=0) else: image_latents = retrieve_latents(self.vae.encode(image), generator=generator) image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor return image_latents # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(num_inference_steps * strength, num_inference_steps) t_start = int(max(num_inference_steps - init_timestep, 0)) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start * self.scheduler.order) return timesteps, num_inference_steps - t_start def check_inputs( self, prompt, prompt_2, image, mask_image, strength, height, width, output_type, prompt_embeds=None, pooled_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, padding_mask_crop=None, max_sequence_length=None, ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") 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 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)}") 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 padding_mask_crop is not None: if not isinstance(image, PIL.Image.Image): raise ValueError( f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}." ) if not isinstance(mask_image, PIL.Image.Image): raise ValueError( f"The mask image should be a PIL image when inpainting mask crop, but is of type" f" {type(mask_image)}." ) if output_type != "pil": raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.") if max_sequence_length is not None and max_sequence_length > 512: raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}") @staticmethod # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids def _prepare_latent_image_ids(batch_size, height, width, device, dtype): latent_image_ids = torch.zeros(height // 2, width // 2, 3) latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None] latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :] latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape latent_image_ids = latent_image_ids.reshape( latent_image_id_height * latent_image_id_width, latent_image_id_channels ) return latent_image_ids.to(device=device, dtype=dtype) @staticmethod # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents def _pack_latents(latents, batch_size, num_channels_latents, height, width): latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2) latents = latents.permute(0, 2, 4, 1, 3, 5) latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4) return latents @staticmethod # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._unpack_latents def _unpack_latents(latents, height, width, vae_scale_factor): batch_size, num_patches, channels = latents.shape height = height // vae_scale_factor width = width // vae_scale_factor latents = latents.view(batch_size, height, width, channels // 4, 2, 2) latents = latents.permute(0, 3, 1, 4, 2, 5) latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2) return latents # Copied from diffusers.pipelines.flux.pipeline_flux_inpaint.FluxInpaintPipeline.prepare_latents def prepare_latents( self, image, timestep, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, ): 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." ) height = 2 * (int(height) // self.vae_scale_factor) width = 2 * (int(width) // self.vae_scale_factor) shape = (batch_size, num_channels_latents, height, width) latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype) image = image.to(device=device, dtype=dtype) image_latents = self._encode_vae_image(image=image, generator=generator) if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: # expand init_latents for batch_size additional_image_per_prompt = batch_size // image_latents.shape[0] image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." ) else: image_latents = torch.cat([image_latents], dim=0) if latents is None: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = self.scheduler.scale_noise(image_latents, timestep, noise) else: noise = latents.to(device) latents = noise noise = self._pack_latents(noise, batch_size, num_channels_latents, height, width) image_latents = self._pack_latents(image_latents, batch_size, num_channels_latents, height, width) latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width) return latents, noise, image_latents, latent_image_ids # Copied from diffusers.pipelines.flux.pipeline_flux_inpaint.FluxInpaintPipeline.prepare_mask_latents def prepare_mask_latents( self, mask, masked_image, batch_size, num_channels_latents, num_images_per_prompt, height, width, dtype, device, generator, ): height = 2 * (int(height) // self.vae_scale_factor) width = 2 * (int(width) // self.vae_scale_factor) # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask = torch.nn.functional.interpolate(mask, size=(height, width)) mask = mask.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt masked_image = masked_image.to(device=device, dtype=dtype) if masked_image.shape[1] == 16: masked_image_latents = masked_image else: masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator) masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if mask.shape[0] < batch_size: if not batch_size % mask.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) masked_image_latents = self._pack_latents( masked_image_latents, batch_size, num_channels_latents, height, width, ) mask = self._pack_latents( mask.repeat(1, num_channels_latents, 1, 1), batch_size, num_channels_latents, height, width, ) return mask, masked_image_latents # Copied from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet.StableDiffusion3ControlNetPipeline.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, ): if isinstance(image, torch.Tensor): pass else: image = self.image_processor.preprocess(image, height=height, width=width) 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 @property def guidance_scale(self): return self._guidance_scale @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, image: PipelineImageInput = None, mask_image: PipelineImageInput = None, masked_image_latents: PipelineImageInput = None, control_image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 0.6, padding_mask_crop: Optional[int] = None, timesteps: List[int] = None, num_inference_steps: int = 28, guidance_scale: float = 7.0, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, control_mode: Optional[Union[int, List[int]]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, 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, t5_prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds_control: 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, ): """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The image(s) to inpaint. mask_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The mask image(s) to use for inpainting. White pixels in the mask will be repainted, while black pixels will be preserved. masked_image_latents (`torch.FloatTensor`, *optional*): Pre-generated masked image latents. control_image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): The ControlNet input condition. Image to control the generation. height (`int`, *optional*, defaults to self.default_sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.default_sample_size * self.vae_scale_factor): The width in pixels of the generated image. strength (`float`, *optional*, defaults to 0.6): Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. padding_mask_crop (`int`, *optional*): The size of the padding to use when cropping the mask. num_inference_steps (`int`, *optional*, defaults to 28): 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. guidance_scale (`float`, *optional*, defaults to 7.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). 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_mode (`int` or `List[int]`, *optional*): The mode for the ControlNet. If multiple ControlNets are used, this should be a list. 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 transformer. 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 more [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. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between `PIL.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*): Additional keyword arguments to be passed to the joint attention mechanism. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising step during the inference. callback_on_step_end_tensor_inputs (`List[str]`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. max_sequence_length (`int`, *optional*, defaults to 512): The maximum length of the sequence to be generated. 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 global_height = height global_width = width 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, FluxMultiControlNetModel) else 1 control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 1. Check inputs self.check_inputs( prompt, prompt_2, image, mask_image, strength, height, width, output_type=output_type, prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, padding_mask_crop=padding_mask_crop, 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 dtype = self.transformer.dtype # 3. Encode input prompt 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, t5_prompt_embeds=t5_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. Preprocess mask and image if padding_mask_crop is not None: crops_coords = self.mask_processor.get_crop_region( mask_image, global_width, global_height, pad=padding_mask_crop ) resize_mode = "fill" else: crops_coords = None resize_mode = "default" original_image = image init_image = self.image_processor.preprocess( image, height=global_height, width=global_width, crops_coords=crops_coords, resize_mode=resize_mode ) init_image = init_image.to(dtype=torch.float32) # 5. Prepare control image num_channels_latents = self.transformer.config.in_channels // 4 if isinstance(self.controlnet, FluxControlNetModel): control_image = self.prepare_image( image=control_image, width=height, height=width, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=self.vae.dtype, ) height, width = control_image.shape[-2:] # vae encode control_image = self.vae.encode(control_image).latent_dist.sample() control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor # pack 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, ) # set control mode if control_mode is not None: control_mode = torch.tensor(control_mode).to(device, dtype=torch.long) control_mode = control_mode.reshape([-1, 1]) elif isinstance(self.controlnet, FluxMultiControlNetModel): control_images = [] for control_image_ in control_image: 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, ) height, width = control_image_.shape[-2:] # vae encode control_image_ = self.vae.encode(control_image_).latent_dist.sample() control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor # pack 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, ) control_images.append(control_image_) control_image = control_images ## set control mode #control_mode_ = [] #if isinstance(control_mode, list): # for cmode in control_mode: # if cmode is None: # control_mode_.append(-1) # else: # control_mode_.append(cmode) #control_mode = torch.tensor(control_mode_).to(device, dtype=torch.long) #control_mode = control_mode.reshape([-1, 1]) control_modes = [] for cmode in control_mode: if cmode is None: cmode = -1 control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long) control_modes.append(control_mode) control_mode = control_modes # 6. Prepare timesteps sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) image_seq_len = (int(global_height) // self.vae_scale_factor) * (int(global_width) // self.vae_scale_factor) mu = calculate_shift( image_seq_len, self.scheduler.config.base_image_seq_len, self.scheduler.config.max_image_seq_len, self.scheduler.config.base_shift, self.scheduler.config.max_shift, ) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas, mu=mu, ) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) if num_inference_steps < 1: raise ValueError( f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline" f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline." ) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # 7. Prepare latent variables latents, noise, image_latents, latent_image_ids = self.prepare_latents( init_image, latent_timestep, batch_size * num_images_per_prompt, num_channels_latents, global_height, global_width, prompt_embeds.dtype, device, generator, latents, ) # 8. Prepare mask latents mask_condition = self.mask_processor.preprocess( mask_image, height=global_height, width=global_width, resize_mode=resize_mode, crops_coords=crops_coords ) if masked_image_latents is None: masked_image = init_image * (mask_condition < 0.5) else: masked_image = masked_image_latents mask, masked_image_latents = self.prepare_mask_latents( mask_condition, masked_image, batch_size, num_channels_latents, num_images_per_prompt, global_height, global_width, prompt_embeds.dtype, device, generator, ) 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, FluxControlNetModel) else keeps) # 9. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue timestep = t.expand(latents.shape[0]).to(latents.dtype) # predict the noise residual #if self.controlnet.config.guidance_embeds: guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32) guidance = guidance.expand(latents.shape[0]) #else: # guidance = None 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_block_samples, controlnet_single_block_samples = self.controlnet( hidden_states=latents, controlnet_cond=control_image, controlnet_mode=control_mode, conditioning_scale=cond_scale, timestep=timestep / 1000, guidance=guidance, pooled_projections=pooled_prompt_embeds, encoder_hidden_states=prompt_embeds_control, t5_encoder_hidden_states=t5_prompt_embeds, txt_ids=text_ids, img_ids=latent_image_ids, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, ) 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 noise_pred = self.transformer( hidden_states=latents, timestep=timestep / 1000, guidance=guidance, pooled_projections=pooled_prompt_embeds, encoder_hidden_states=prompt_embeds, t5_encoder_hidden_states=t5_prompt_embeds, controlnet_block_samples=controlnet_block_samples, controlnet_single_block_samples=controlnet_single_block_samples, 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] # For inpainting, we need to apply the mask and add the masked image latents init_latents_proper = image_latents init_mask = mask if i < len(timesteps) - 1: noise_timestep = timesteps[i + 1] init_latents_proper = self.scheduler.scale_noise( init_latents_proper, torch.tensor([noise_timestep]), noise ) latents = (1 - init_mask) * init_latents_proper + init_mask * latents 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) # call the callback, if provided 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) 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() # Post-processing if output_type == "latent": image = latents else: latents = self._unpack_latents(latents, global_height, global_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)