# copied from diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np 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 diffusers.models.controlnets.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel from diffusers.models.transformers import FluxTransformer2DModel from diffusers.schedulers 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 diffusers.pipelines.flux.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__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import FluxControlNetImg2ImgPipeline, FluxControlNetModel >>> from diffusers.utils import load_image >>> device = "cuda" if torch.cuda.is_available() else "cpu" >>> controlnet = FluxControlNetModel.from_pretrained( ... "InstantX/FLUX.1-dev-Controlnet-Canny-alpha", torch_dtype=torch.bfloat16 ... ) >>> pipe = FluxControlNetImg2ImgPipeline.from_pretrained( ... "black-forest-labs/FLUX.1-schnell", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> pipe.text_encoder.to(torch.float16) >>> pipe.controlnet.to(torch.float16) >>> pipe.to("cuda") >>> control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg") >>> init_image = load_image( ... "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" ... ) >>> prompt = "A girl in city, 25 years old, cool, futuristic" >>> image = pipe( ... prompt, ... image=init_image, ... control_image=control_image, ... control_guidance_start=0.2, ... control_guidance_end=0.8, ... controlnet_conditioning_scale=1.0, ... strength=0.7, ... num_inference_steps=2, ... guidance_scale=3.5, ... ).images[0] >>> image.save("flux_controlnet_img2img.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) else: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if accept_sigmas: sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas # 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 FluxControlNetImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin): r""" The Flux controlnet pipeline for image-to-image generation. 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, text_encoder_2: T5EncoderModel, tokenizer_2: T5TokenizerFast, transformer: FluxTransformer2DModel, controlnet: Union[ FluxControlNetModel, List[FluxControlNetModel], Tuple[FluxControlNetModel], FluxMultiControlNetModel ], ): super().__init__() if isinstance(controlnet, (list, tuple)): controlnet = FluxMultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, 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 ) # Flux latents are turned into 2x2 patches and packed. This means the latent width and height has to be divisible # by the patch size. So the vae scale factor is multiplied by the patch size to account for this self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2) 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 = 128 # 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, 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 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, strength, height, width, callback_on_step_end_tensor_inputs, prompt_embeds=None, pooled_prompt_embeds=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 % self.vae_scale_factor * 2 != 0 or width % self.vae_scale_factor * 2 != 0: logger.warning( f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly" ) 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 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, width, 3) latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None] latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[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 # VAE applies 8x compression on images but we must also account for packing which requires # latent height and width to be divisible by 2. height = 2 * (int(height) // (vae_scale_factor * 2)) width = 2 * (int(width) // (vae_scale_factor * 2)) latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2) latents = latents.permute(0, 3, 1, 4, 2, 5) latents = latents.reshape(batch_size, channels // (2 * 2), height, width) return latents # Copied from diffusers.pipelines.flux.pipeline_flux_img2img.FluxImg2ImgPipeline.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." ) # VAE applies 8x compression on images but we must also account for packing which requires # latent height and width to be divisible by 2. height = 2 * (int(height) // (self.vae_scale_factor * 2)) width = 2 * (int(width) // (self.vae_scale_factor * 2)) shape = (batch_size, num_channels_latents, height, width) latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype) if latents is not None: return latents.to(device=device, dtype=dtype), latent_image_ids 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) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = self.scheduler.scale_noise(image_latents, timestep, noise) latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width) return latents, latent_image_ids # 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, control_image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 0.6, num_inference_steps: int = 28, sigmas: Optional[List[float]] = 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_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, 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 modify with the pipeline. 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 transform the reference `image`. Must be between 0 and 1. 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. 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 7.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). 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 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], ) self.check_inputs( prompt, prompt_2, strength, height, width, callback_on_step_end_tensor_inputs, prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, max_sequence_length=max_sequence_length, ) self._guidance_scale = guidance_scale self._joint_attention_kwargs = joint_attention_kwargs self._interrupt = False 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 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, ) init_image = self.image_processor.preprocess(image, height=height, width=width) init_image = init_image.to(dtype=torch.float32) num_channels_latents = self.transformer.config.in_channels // 4 if isinstance(self.controlnet, FluxControlNetModel): 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:] control_image = retrieve_latents(self.vae.encode(control_image), 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, ) 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:] control_image_ = retrieve_latents(self.vae.encode(control_image_), 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, ) control_images.append(control_image_) control_image = control_images 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]) image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2) 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, ) # sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, mu=mu, ) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) latents, latent_image_ids = self.prepare_latents( init_image, latent_timestep, batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) 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) 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) if isinstance(self.controlnet, FluxMultiControlNetModel): use_guidance = self.controlnet.nets[0].config.guidance_embeds else: use_guidance = self.controlnet.config.guidance_embeds guidance = torch.tensor([guidance_scale], device=device) if use_guidance else None guidance = guidance.expand(latents.shape[0]) if guidance is not None else 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, txt_ids=text_ids, img_ids=latent_image_ids, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, ) guidance = ( torch.tensor([guidance_scale], device=device) if self.transformer.config.guidance_embeds else None ) guidance = guidance.expand(latents.shape[0]) if guidance is not None else None noise_pred = self.transformer( hidden_states=latents, timestep=timestep / 1000, guidance=guidance, pooled_projections=pooled_prompt_embeds, encoder_hidden_states=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] 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(): 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) 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) self.maybe_free_model_hooks() if not return_dict: return (image,) return FluxPipelineOutput(images=image)