# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import torch from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast from diffusers.image_processor import VaeImageProcessor, PipelineImageInput from diffusers.models.autoencoders import AutoencoderKL from diffusers.models.transformers import FluxTransformer2DModel from diffusers.schedulers import FlowMatchEulerDiscreteScheduler from diffusers.utils import ( is_torch_xla_available, logging, replace_example_docstring, ) from diffusers.utils.torch_utils import randn_tensor from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput from .pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps 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 FluxPipeline >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> prompt = "A cat holding a sign that says hello world" >>> # Depending on the variant being used, the pipeline call will slightly vary. >>> # Refer to the pipeline documentation for more details. >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0] >>> image.save("flux.png") ``` """ # 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") class FluxFillPipeline(FluxPipeline): r""" The Flux pipeline for text-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, ): super().__init__( scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, transformer=transformer ) 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, ) def prepare_mask_latents( self, mask, masked_image, batch_size, num_channels_latents, num_images_per_prompt, height, width, dtype, device, generator, ): # 1. calculate the height and width of the latents # 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) width = 2 * (int(width) // self.vae_scale_factor) # 2. encode the masked image if masked_image.shape[1] == num_channels_latents: 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 masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) # 3. duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method batch_size = batch_size * num_images_per_prompt 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) # 4. pack the masked_image_latents # batch_size, num_channels_latents, height, width -> batch_size, height//2 * width//2 , num_channels_latents*4 masked_image_latents = self._pack_latents( masked_image_latents, batch_size, num_channels_latents, height, width, ) # 5.resize mask to latents shape we we concatenate the mask to the latents mask = mask[:, 0, :, :] # batch_size, 8 * height, 8 * width (mask has not been 8x compressed) mask = mask.view( batch_size, height, self.vae_scale_factor // 2, width, self.vae_scale_factor // 2 ) # batch_size, height, 8, width, 8 mask = mask.permute(0, 2, 4, 1, 3) # batch_size, 8, 8, height, width mask = mask.reshape( batch_size, (self.vae_scale_factor // 2) * (self.vae_scale_factor // 2), height, width ) # batch_size, 8*8, height, width # 6. pack the mask: # batch_size, 64, height, width -> batch_size, height//2 * width//2 , 64*2*2 mask = self._pack_latents( mask, batch_size, (self.vae_scale_factor // 2) * (self.vae_scale_factor // 2), height, width, ) mask = mask.to(device=device, dtype=dtype) return mask, masked_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 get_latents_with_image(self, image, latent_timestep, batch_size, num_channels_latents, height, width, generator, device, dtype): image = image.to(device=device, dtype=dtype) image_latents = self.vae.encode(image).latent_dist.sample(generator) image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor batch_size, num_channels_latents, height, width = image_latents.size() noise = randn_tensor(image_latents.size(), generator=generator, device=device, dtype=dtype) latents = self.scheduler.scale_noise(image_latents, latent_timestep, noise) latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width) return latents @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, img_cond: torch.FloatTensor = None, mask: torch.FloatTensor = None, height: Optional[int] = None, width: Optional[int] = None, strength: float = 1.0, image: PipelineImageInput = None, num_inference_steps: int = 28, timesteps: List[int] = 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, ): 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 height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. 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 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.vae.config.latent_channels latents, latent_image_ids = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 4.5 Prepare masked image latents img_cond = self.image_processor.preprocess(img_cond, height=height, width=width) mask = self.mask_processor.preprocess(mask, height=height, width=width) masked_image = img_cond * (1 - mask) masked_image = masked_image.to(device=device, dtype=prompt_embeds.dtype) height, width = masked_image.shape[-2:] mask, masked_image_latents = self.prepare_mask_latents( mask, masked_image, batch_size, num_channels_latents, num_images_per_prompt, height, width, prompt_embeds.dtype, device, generator, ) masked_image_latents = torch.cat((masked_image_latents, mask), dim=-1) # 5. Prepare timesteps sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) image_seq_len = latents.shape[1] 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, ) if strength != 1.0 and image is not None: timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) latents = self.get_latents_with_image(image, latent_timestep, batch_size, num_channels_latents, height, width, generator, device, latents.dtype) 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 # 6. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # 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=torch.cat((latents, masked_image_latents), dim=-1), 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)