import inspect from typing import Callable, Dict, List, Optional, Union import PIL import PIL.Image import torch from transformers import T5EncoderModel, T5Tokenizer from ...image_processor import VaeImageProcessor from ...loaders import StableDiffusionLoraLoaderMixin from ...models import Kandinsky3UNet, VQModel from ...schedulers import DDPMScheduler from ...utils import ( deprecate, is_torch_xla_available, logging, replace_example_docstring, ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput 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 >>> from diffusers import AutoPipelineForImage2Image >>> from diffusers.utils import load_image >>> import torch >>> pipe = AutoPipelineForImage2Image.from_pretrained( ... "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16 ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = "A painting of the inside of a subway train with tiny raccoons." >>> image = load_image( ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png" ... ) >>> generator = torch.Generator(device="cpu").manual_seed(0) >>> image = pipe(prompt, image=image, strength=0.75, num_inference_steps=25, generator=generator).images[0] ``` """ class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin): model_cpu_offload_seq = "text_encoder->movq->unet->movq" _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", "negative_attention_mask", "attention_mask", ] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, unet: Kandinsky3UNet, scheduler: DDPMScheduler, movq: VQModel, ): super().__init__() self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq ) movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8 movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4 self.image_processor = VaeImageProcessor( vae_scale_factor=movq_scale_factor, vae_latent_channels=movq_latent_channels, resample="bicubic", reducing_gap=1, ) def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start def _process_embeds(self, embeddings, attention_mask, cut_context): # return embeddings, attention_mask if cut_context: embeddings[attention_mask == 0] = torch.zeros_like(embeddings[attention_mask == 0]) max_seq_length = attention_mask.sum(-1).max() + 1 embeddings = embeddings[:, :max_seq_length] attention_mask = attention_mask[:, :max_seq_length] return embeddings, attention_mask @torch.no_grad() def encode_prompt( self, prompt, do_classifier_free_guidance=True, num_images_per_prompt=1, device=None, negative_prompt=None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, _cut_context=False, attention_mask: Optional[torch.Tensor] = None, negative_attention_mask: Optional[torch.Tensor] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`, *optional*): torch device to place the resulting embeddings on num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not 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. 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`). 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. 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. attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask. Must provide if passing `prompt_embeds` directly. negative_attention_mask (`torch.Tensor`, *optional*): Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly. """ if prompt is not None and negative_prompt is not None: if 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)}." ) if device is None: device = self._execution_device 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] max_length = 128 if prompt_embeds is None: text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids.to(device) attention_mask = text_inputs.attention_mask.to(device) prompt_embeds = self.text_encoder( text_input_ids, attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds, attention_mask = self._process_embeds(prompt_embeds, attention_mask, _cut_context) prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2) if self.text_encoder is not None: dtype = self.text_encoder.dtype else: dtype = None prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings 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(bs_embed * num_images_per_prompt, seq_len, -1) attention_mask = attention_mask.repeat(num_images_per_prompt, 1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif isinstance(negative_prompt, str): uncond_tokens = [negative_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`." ) else: uncond_tokens = negative_prompt if negative_prompt is not None: uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=128, truncation=True, return_attention_mask=True, return_tensors="pt", ) text_input_ids = uncond_input.input_ids.to(device) negative_attention_mask = uncond_input.attention_mask.to(device) negative_prompt_embeds = self.text_encoder( text_input_ids, attention_mask=negative_attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]] negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]] negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2) else: negative_prompt_embeds = torch.zeros_like(prompt_embeds) negative_attention_mask = torch.zeros_like(attention_mask) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) if negative_prompt_embeds.shape != prompt_embeds.shape: negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) negative_attention_mask = negative_attention_mask.repeat(num_images_per_prompt, 1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes else: negative_prompt_embeds = None negative_attention_mask = None return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): raise ValueError( f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" ) image = image.to(device=device, dtype=dtype) batch_size = batch_size * num_images_per_prompt if image.shape[1] == 4: init_latents = image else: 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." ) elif isinstance(generator, list): init_latents = [ self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) ] init_latents = torch.cat(init_latents, dim=0) else: init_latents = self.movq.encode(image).latent_dist.sample(generator) init_latents = self.movq.config.scaling_factor * init_latents init_latents = torch.cat([init_latents], dim=0) shape = init_latents.shape noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # get latents init_latents = self.scheduler.add_noise(init_latents, noise, timestep) latents = init_latents return latents # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, attention_mask=None, negative_attention_mask=None, ): if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) 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 negative_prompt_embeds is not None and negative_attention_mask is None: raise ValueError("Please provide `negative_attention_mask` along with `negative_prompt_embeds`") if negative_prompt_embeds is not None and negative_attention_mask is not None: if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape: raise ValueError( "`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but" f" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`" f" {negative_attention_mask.shape}." ) if prompt_embeds is not None and attention_mask is None: raise ValueError("Please provide `attention_mask` along with `prompt_embeds`") if prompt_embeds is not None and attention_mask is not None: if prompt_embeds.shape[:2] != attention_mask.shape: raise ValueError( "`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`" f" {attention_mask.shape}." ) @property def guidance_scale(self): return self._guidance_scale @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 @property def num_timesteps(self): return self._num_timesteps @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None, strength: float = 0.3, num_inference_steps: int = 25, guidance_scale: float = 3.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, negative_attention_mask: Optional[torch.Tensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): """ 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. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. strength (`float`, *optional*, defaults to 0.8): Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`. 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. guidance_scale (`float`, *optional*, defaults to 3.0): 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. 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 less than `1`). 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. 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. 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. attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask. Must provide if passing `prompt_embeds` directly. negative_attention_mask (`torch.Tensor`, *optional*): Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly. 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.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. 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. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple` """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", ) 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]}" ) cut_context = True # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, callback_on_step_end_tensor_inputs, attention_mask, negative_attention_mask, ) self._guidance_scale = guidance_scale if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # 3. Encode input prompt prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt( prompt, self.do_classifier_free_guidance, num_images_per_prompt=num_images_per_prompt, device=device, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, _cut_context=cut_context, attention_mask=attention_mask, negative_attention_mask=negative_attention_mask, ) if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool() if not isinstance(image, list): image = [image] if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image): raise ValueError( f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor" ) image = torch.cat([self.image_processor.preprocess(i) for i in image], dim=0) image = image.to(dtype=prompt_embeds.dtype, device=device) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) # 5. Prepare latents latents = self.movq.encode(image)["latents"] latents = latents.repeat_interleave(num_images_per_prompt, dim=0) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) latents = self.prepare_latents( latents, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator ) if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None: self.text_encoder_offload_hook.offload() # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, encoder_attention_mask=attention_mask, )[0] if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step( noise_pred, t, latents, generator=generator, ).prev_sample 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) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) attention_mask = callback_outputs.pop("attention_mask", attention_mask) negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask) if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) if XLA_AVAILABLE: xm.mark_step() # post-processing if not output_type == "latent": image = self.movq.decode(latents, force_not_quantize=True)["sample"] image = self.image_processor.postprocess(image, output_type) else: image = latents self.maybe_free_model_hooks() if not return_dict: return (image,) return ImagePipelineOutput(images=image)