""" ChromaInpaintPipeline implements a text-guided image inpainting pipeline for the lodestones/Chroma1-HD model, based on the ChromaPipeline from Hugging Face Diffusers:contentReference[oaicite:0]{index=0} and the Stable Diffusion inpainting approach:contentReference[oaicite:1]{index=1}. """ # Copyright 2025 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 PIL.Image import torch from transformers import ( CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5TokenizerFast, ) from ...image_processor import PipelineImageInput, VaeImageProcessor from ...loaders import FluxIPAdapterMixin, FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin from ...models.autoencoders import AutoencoderKL from ...models.transformers import ChromaTransformer2DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import ( USE_PEFT_BACKEND, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from ...utils.torch_utils import randn_tensor from ..chroma.pipeline_output import ChromaPipelineOutput from ..pipeline_utils import DiffusionPipeline 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 ChromaInpaintPipeline >>> from diffusers.utils import load_image >>> pipe = ChromaInpaintPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench" >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" >>> source = load_image(img_url) >>> mask = load_image(mask_url) >>> image = pipe(prompt=prompt, image=source, mask_image=mask).images[0] >>> image.save("chroma_inpainting.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 = 1.15, ): 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: torch.Generator | None = 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: int | None = None, device: str | torch.device | None = None, timesteps: list[int] | None = None, sigmas: list[float] | None = 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 ChromaInpaintPipeline( DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin, TextualInversionLoaderMixin, FluxIPAdapterMixin ): r""" The Flux pipeline for image inpainting. Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ Args: transformer ([`ChromaTransformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`DDIMScheduler`]): 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->image_encoder->transformer->vae" _optional_components = ["image_encoder", "feature_extractor"] _callback_tensor_inputs = ["latents", "prompt_embeds"] def __init__( self, scheduler: FlowMatchEulerDiscreteScheduler, vae: AutoencoderKL, text_encoder: T5EncoderModel, tokenizer: T5TokenizerFast, transformer: ChromaTransformer2DModel, image_encoder: CLIPVisionModelWithProjection = None, feature_extractor: CLIPImageProcessor = None, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, scheduler=scheduler, image_encoder=image_encoder, feature_extractor=feature_extractor, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 self.latent_channels = self.vae.config.latent_channels if getattr(self, "vae", None) else 16 # 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.default_sample_size = 128 self.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor * 2, vae_latent_channels=self.latent_channels, do_normalize=False, do_binarize=True, do_convert_grayscale=True, ) def _get_t5_prompt_embeds( self, prompt: Union[str, List[str], None] = None, num_images_per_prompt: int = 1, max_sequence_length: int = 512, device: torch.device | None = None, dtype: torch.dtype | None = 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) text_inputs = self.tokenizer( 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 tokenizer_mask = text_inputs.attention_mask tokenizer_mask = tokenizer_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids.to(device), output_hidden_states=False, attention_mask=tokenizer_mask, )[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) seq_lengths = tokenizer_mask.sum(dim=1) mask_indices = torch.arange(tokenizer_mask.size(1), device=device).unsqueeze(0).expand(batch_size, -1) attention_mask = (mask_indices <= seq_lengths.unsqueeze(1)).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) attention_mask = attention_mask.repeat(1, num_images_per_prompt) attention_mask = attention_mask.view(batch_size * num_images_per_prompt, seq_len) return prompt_embeds, attention_mask def encode_prompt( self, prompt: Union[str, List[str]], negative_prompt: Union[str, List[str], None] = None, device: torch.device | None = None, num_images_per_prompt: int = 1, prompt_embeds: torch.Tensor | None = None, negative_prompt_embeds: torch.Tensor | None = None, prompt_attention_mask: torch.Tensor | None = None, negative_prompt_attention_mask: torch.Tensor | None = None, do_classifier_free_guidance: bool = True, max_sequence_length: int = 256, lora_scale: bool | None = None, ): r""" Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded negative_prompt (`str` or `List[str]`, *optional*): The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt prompt_embeds (`torch.Tensor`, *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. 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) prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds( prompt=prompt, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, device=device, ) 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, device=device, dtype=dtype) negative_text_ids = None if do_classifier_free_guidance: if negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt = ( batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt ) if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) negative_prompt_embeds, negative_prompt_attention_mask = self._get_t5_prompt_embeds( prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, device=device, ) negative_text_ids = torch.zeros(negative_prompt_embeds.shape[1], 3, device=device, dtype=dtype) 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) return ( prompt_embeds, text_ids, prompt_attention_mask, negative_prompt_embeds, negative_text_ids, negative_prompt_attention_mask, ) # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.encode_image def encode_image(self, image, device, num_images_per_prompt): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeds = self.image_encoder(image).image_embeds image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) return image_embeds # Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_ip_adapter_image_embeds def prepare_ip_adapter_image_embeds( self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt ): image_embeds = [] if ip_adapter_image_embeds is None: if not isinstance(ip_adapter_image, list): ip_adapter_image = [ip_adapter_image] if len(ip_adapter_image) != self.transformer.encoder_hid_proj.num_ip_adapters: raise ValueError( f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {self.transformer.encoder_hid_proj.num_ip_adapters} IP Adapters." ) for single_ip_adapter_image in ip_adapter_image: single_image_embeds = self.encode_image(single_ip_adapter_image, device, 1) image_embeds.append(single_image_embeds[None, :]) else: if not isinstance(ip_adapter_image_embeds, list): ip_adapter_image_embeds = [ip_adapter_image_embeds] if len(ip_adapter_image_embeds) != self.transformer.encoder_hid_proj.num_ip_adapters: raise ValueError( f"`ip_adapter_image_embeds` must have same length as the number of IP Adapters. Got {len(ip_adapter_image_embeds)} image embeds and {self.transformer.encoder_hid_proj.num_ip_adapters} IP Adapters." ) for single_image_embeds in ip_adapter_image_embeds: image_embeds.append(single_image_embeds) ip_adapter_image_embeds = [] for single_image_embeds in image_embeds: single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0) single_image_embeds = single_image_embeds.to(device=device) ip_adapter_image_embeds.append(single_image_embeds) return ip_adapter_image_embeds # 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, image, mask_image, strength, height, width, output_type, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, padding_mask_crop=None, max_sequence_length=None, prompt_attention_mask=None, negative_prompt_attention_mask=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 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)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if prompt_embeds is not None and prompt_attention_mask is None: raise ValueError("Cannot provide `prompt_embeds` without also providing `prompt_attention_mask") if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: raise ValueError( "Cannot provide `negative_prompt_embeds` without also providing `negative_prompt_attention_mask" ) 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 {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 {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, 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 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) image = image.to(device=device, dtype=dtype) if image.shape[1] != self.latent_channels: image_latents = self._encode_vae_image(image=image, generator=generator) else: image_latents = image 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 def prepare_mask_latents( self, mask, masked_image, batch_size, num_channels_latents, num_images_per_prompt, height, width, dtype, device, generator, ): # 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)) # 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 @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 def _prepare_attention_mask( self, batch_size, sequence_length, dtype, attention_mask=None, ): if attention_mask is None: return attention_mask # Extend the prompt attention mask to account for image tokens in the final sequence attention_mask = torch.cat( [attention_mask, torch.ones(batch_size, sequence_length, device=attention_mask.device, dtype=torch.bool)], dim=1, ) attention_mask = attention_mask.to(dtype) return attention_mask @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 @replace_example_docstring(EXAMPLE_DOC_STRING) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, negative_prompt: Union[str, List[str]] = None, true_cfg_scale: float = 1.0, image: PipelineImageInput = None, mask_image: PipelineImageInput = None, masked_image_latents: PipelineImageInput = None, height: int | None = None, width: int | None = None, padding_mask_crop: int | None = None, strength: float = 0.6, num_inference_steps: int = 28, sigmas: list[float] | None = None, guidance_scale: float = 7.0, num_images_per_prompt: Optional[int] = 1, generator: torch.Generator | list[torch.Generator] | None = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, negative_ip_adapter_image: Optional[PipelineImageInput] = None, negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: 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 = 256, prompt_attention_mask: torch.Tensor | None = None, negative_prompt_attention_mask: torch.Tensor | None = None, ): 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. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is not greater than `1`). true_cfg_scale (`float`, *optional*, defaults to 1.0): True classifier-free guidance (guidance scale) is enabled when `true_cfg_scale` > 1 and `negative_prompt` is provided. image (`PipelineImageInput`): The image input for the pipeline. mask_image (`PipelineImageInput`): `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask are repainted while black pixels are preserved. masked_image_latents (`torch.Tensor`, *optional*): Pre-encoded latent representation of the masked image. If not provided, it will be computed from `mask_image` and `image`. 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. padding_mask_crop (`int`, *optional*, defaults to `None`): The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ratio of the image and contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before resizing to the original image size for inpainting. num_inference_steps (`int`, *optional*, defaults to 35): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. guidance_scale (`float`, *optional*, defaults to 3.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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. strength (`float, *optional*, defaults to 0.9): Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image will be used as a starting point, adding more noise to it the larger the strength. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in num_inference_steps. A value of 1, therefore, essentially ignores image. 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.Tensor`, *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 be generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *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. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. negative_ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. prompt_attention_mask (torch.Tensor, *optional*): Attention mask for the prompt embeddings. Used to mask out padding tokens in the prompt sequence. Chroma requires a single padding token remain unmasked. Please refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training negative_prompt_attention_mask (torch.Tensor, *optional*): Attention mask for the negative prompt embeddings. Used to mask out padding tokens in the negative prompt sequence. Chroma requires a single padding token remain unmasked. PLease refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training 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.chroma.ChromaPipelineOutput`] 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.chroma.ChromaPipelineOutput`] or `tuple`: [`~pipelines.chroma.ChromaPipelineOutput`] 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, height=height, width=width, output_type=output_type, strength=strength, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, max_sequence_length=max_sequence_length, image=image, mask_image=mask_image, padding_mask_crop=padding_mask_crop, ) self._guidance_scale = guidance_scale self._joint_attention_kwargs = joint_attention_kwargs self._current_timestep = None self._interrupt = False # 2. Preprocess mask and image if padding_mask_crop is not None: crops_coords = self.mask_processor.get_crop_region(mask_image, width, 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=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode ) init_image = init_image.to(dtype=torch.float32) # 3. 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, text_ids, prompt_attention_mask, negative_prompt_embeds, negative_text_ids, negative_prompt_attention_mask, ) = self.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, do_classifier_free_guidance=self.do_classifier_free_guidance, device=device, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, lora_scale=lora_scale, ) # 4. Prepare timesteps sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas 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.get("base_image_seq_len", 256), self.scheduler.config.get("max_image_seq_len", 4096), self.scheduler.config.get("base_shift", 0.5), self.scheduler.config.get("max_shift", 1.15), ) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, mu=mu, ) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) 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) # 5. Prepare latent variables num_channels_latents = self.transformer.config.in_channels // 4 num_channels_transformer = self.transformer.config.in_channels latents, noise, image_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, ) mask_condition = self.mask_processor.preprocess( mask_image, height=height, width=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, height, width, prompt_embeds.dtype, device, generator, ) 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 if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and ( negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None ): negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8) elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and ( negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None ): ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8) if self.joint_attention_kwargs is None: self._joint_attention_kwargs = {} image_embeds = None negative_image_embeds = None if ip_adapter_image is not None or ip_adapter_image_embeds is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, ) if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None: negative_image_embeds = self.prepare_ip_adapter_image_embeds( negative_ip_adapter_image, negative_ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, ) attention_mask = self._prepare_attention_mask( batch_size=latents.shape[0], sequence_length=image_seq_len, dtype=latents.dtype, attention_mask=prompt_attention_mask, ) negative_attention_mask = self._prepare_attention_mask( batch_size=latents.shape[0], sequence_length=image_seq_len, dtype=latents.dtype, attention_mask=negative_prompt_attention_mask, ) # 6. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue self._current_timestep = t # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latents.shape[0]).to(latents.dtype) if image_embeds is not None: self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds noise_pred = self.transformer( hidden_states=latents, timestep=timestep / 1000, encoder_hidden_states=prompt_embeds, txt_ids=text_ids, img_ids=latent_image_ids, attention_mask=attention_mask, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, )[0] if self.do_classifier_free_guidance: if negative_image_embeds is not None: self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds noise_pred_uncond = self.transformer( hidden_states=latents, timestep=timestep / 1000, encoder_hidden_states=negative_prompt_embeds, txt_ids=negative_text_ids, img_ids=latent_image_ids, attention_mask=negative_attention_mask, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, )[0] noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] # for 64 channel transformer only. 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) 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() self._current_timestep = None 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 ChromaPipelineOutput(images=image)