1691 lines
76 KiB
Python
1691 lines
76 KiB
Python
import inspect
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import PIL.Image
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import torch
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import torch.nn.functional as F
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from diffusers import StableDiffusionMixin, UNet2DConditionModel
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from transformers import (
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CLIPImageProcessor,
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CLIPTextModel,
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CLIPTokenizer,
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CLIPVisionModelWithProjection,
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)
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from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
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from diffusers.loaders import (
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FromSingleFileMixin,
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IPAdapterMixin,
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LoraLoaderMixin,
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TextualInversionLoaderMixin,
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)
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from diffusers.models import AutoencoderKL, ImageProjection
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from diffusers.models.lora import adjust_lora_scale_text_encoder
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from diffusers.schedulers import KarrasDiffusionSchedulers
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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deprecate,
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logging,
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replace_example_docstring,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from diffusers.utils.torch_utils import (
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is_compiled_module,
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is_torch_version,
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randn_tensor,
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)
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion.pipeline_output import (
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StableDiffusionPipelineOutput,
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)
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from diffusers.pipelines.stable_diffusion.safety_checker import (
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StableDiffusionSafetyChecker,
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)
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from .BrushNet_CA import BrushNetModel
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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from diffusers import StableDiffusionBrushNetPipeline, BrushNetModel, UniPCMultistepScheduler
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from diffusers.utils import load_image
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import torch
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import cv2
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import numpy as np
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from PIL import Image
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base_model_path = "runwayml/stable-diffusion-v1-5"
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brushnet_path = "ckpt_path"
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brushnet = BrushNetModel.from_pretrained(brushnet_path, torch_dtype=torch.float16)
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pipe = StableDiffusionBrushNetPipeline.from_pretrained(
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base_model_path, brushnet=brushnet, torch_dtype=torch.float16, low_cpu_mem_usage=False
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)
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# speed up diffusion process with faster scheduler and memory optimization
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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# remove following line if xformers is not installed or when using Torch 2.0.
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# pipe.enable_xformers_memory_efficient_attention()
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# memory optimization.
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pipe.enable_model_cpu_offload()
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image_path="examples/brushnet/src/test_image.jpg"
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mask_path="examples/brushnet/src/test_mask.jpg"
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caption="A cake on the table."
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init_image = cv2.imread(image_path)
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mask_image = 1.*(cv2.imread(mask_path).sum(-1)>255)[:,:,np.newaxis]
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init_image = init_image * (1-mask_image)
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init_image = Image.fromarray(init_image.astype(np.uint8)).convert("RGB")
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mask_image = Image.fromarray(mask_image.astype(np.uint8).repeat(3,-1)*255).convert("RGB")
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generator = torch.Generator("cuda").manual_seed(1234)
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image = pipe(
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caption,
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init_image,
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mask_image,
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num_inference_steps=50,
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generator=generator,
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paintingnet_conditioning_scale=1.0
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).images[0]
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image.save("output.png")
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```
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"""
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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**kwargs,
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):
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"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used,
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`timesteps` must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
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timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
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must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(
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inspect.signature(scheduler.set_timesteps).parameters.keys()
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)
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if not accepts_timesteps:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class StableDiffusionPowerPaintBrushNetPipeline(
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DiffusionPipeline,
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StableDiffusionMixin,
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TextualInversionLoaderMixin,
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LoraLoaderMixin,
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IPAdapterMixin,
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FromSingleFileMixin,
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):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion with BrushNet guidance.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
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- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
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text_encoder ([`~transformers.CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer ([`~transformers.CLIPTokenizer`]):
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A `CLIPTokenizer` to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` to denoise the encoded image latents.
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brushnet ([`BrushNetModel`]`):
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Provides additional conditioning to the `unet` during the denoising process.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
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about a model's potential harms.
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feature_extractor ([`~transformers.CLIPImageProcessor`]):
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A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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"""
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model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
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_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
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_exclude_from_cpu_offload = ["safety_checker"]
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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text_encoder_brushnet: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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brushnet: BrushNetModel,
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scheduler: KarrasDiffusionSchedulers,
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPImageProcessor,
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image_encoder: CLIPVisionModelWithProjection = None,
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requires_safety_checker: bool = True,
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):
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super().__init__()
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if safety_checker is None and requires_safety_checker:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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if safety_checker is not None and feature_extractor is None:
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raise ValueError(
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"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
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" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
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)
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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text_encoder_brushnet=text_encoder_brushnet,
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tokenizer=tokenizer,
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unet=unet,
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brushnet=brushnet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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image_encoder=image_encoder,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(
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vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
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)
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self.register_to_config(requires_safety_checker=requires_safety_checker)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
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def _encode_prompt(
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self,
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promptA,
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promptB,
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t,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_promptA=None,
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negative_promptB=None,
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t_nag=None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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lora_scale: Optional[float] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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lora_scale (`float`, *optional*):
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A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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"""
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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if lora_scale is not None and isinstance(self, LoraLoaderMixin):
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self._lora_scale = lora_scale
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prompt = promptA
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negative_prompt = negative_promptA
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if promptA is not None and isinstance(promptA, str):
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batch_size = 1
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elif promptA is not None and isinstance(promptA, list):
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batch_size = len(promptA)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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promptA = self.maybe_convert_prompt(promptA, self.tokenizer)
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text_inputsA = self.tokenizer(
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promptA,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_inputsB = self.tokenizer(
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promptB,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_idsA = text_inputsA.input_ids
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text_input_idsB = text_inputsB.input_ids
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untruncated_ids = self.tokenizer(
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promptA, padding="longest", return_tensors="pt"
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).input_ids
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if untruncated_ids.shape[-1] >= text_input_idsA.shape[
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-1
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] and not torch.equal(text_input_idsA, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if (
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hasattr(self.text_encoder_brushnet.config, "use_attention_mask")
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and self.text_encoder_brushnet.config.use_attention_mask
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):
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attention_mask = text_inputsA.attention_mask.to(device)
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else:
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attention_mask = None
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# print("text_input_idsA: ",text_input_idsA)
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# print("text_input_idsB: ",text_input_idsB)
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# print('t: ',t)
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prompt_embedsA = self.text_encoder_brushnet(
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text_input_idsA.to(device),
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attention_mask=attention_mask,
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)
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prompt_embedsA = prompt_embedsA[0]
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prompt_embedsB = self.text_encoder_brushnet(
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text_input_idsB.to(device),
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attention_mask=attention_mask,
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)
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prompt_embedsB = prompt_embedsB[0]
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prompt_embeds = prompt_embedsA * (t) + (1 - t) * prompt_embedsB
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# print("prompt_embeds: ",prompt_embeds)
|
|
|
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if self.text_encoder_brushnet is not None:
|
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prompt_embeds_dtype = self.text_encoder_brushnet.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
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# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
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prompt_embeds = prompt_embeds.view(
|
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bs_embed * num_images_per_prompt, seq_len, -1
|
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)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
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uncond_tokensA: List[str]
|
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uncond_tokensB: List[str]
|
|
if negative_prompt is None:
|
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uncond_tokensA = [""] * batch_size
|
|
uncond_tokensB = [""] * batch_size
|
|
elif 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 isinstance(negative_prompt, str):
|
|
uncond_tokensA = [negative_promptA]
|
|
uncond_tokensB = [negative_promptB]
|
|
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_tokensA = negative_promptA
|
|
uncond_tokensB = negative_promptB
|
|
|
|
# textual inversion: procecss multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokensA = self.maybe_convert_prompt(
|
|
uncond_tokensA, self.tokenizer
|
|
)
|
|
uncond_tokensB = self.maybe_convert_prompt(
|
|
uncond_tokensB, self.tokenizer
|
|
)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_inputA = self.tokenizer(
|
|
uncond_tokensA,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
uncond_inputB = self.tokenizer(
|
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uncond_tokensB,
|
|
padding="max_length",
|
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max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
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)
|
|
|
|
if (
|
|
hasattr(self.text_encoder_brushnet.config, "use_attention_mask")
|
|
and self.text_encoder_brushnet.config.use_attention_mask
|
|
):
|
|
attention_mask = uncond_inputA.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
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negative_prompt_embedsA = self.text_encoder_brushnet(
|
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uncond_inputA.input_ids.to(device),
|
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attention_mask=attention_mask,
|
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)
|
|
negative_prompt_embedsB = self.text_encoder_brushnet(
|
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uncond_inputB.input_ids.to(device),
|
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attention_mask=attention_mask,
|
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)
|
|
negative_prompt_embeds = (
|
|
negative_prompt_embedsA[0] * (t_nag)
|
|
+ (1 - t_nag) * negative_prompt_embedsB[0]
|
|
)
|
|
|
|
# negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
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=prompt_embeds_dtype, device=device
|
|
)
|
|
|
|
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
|
|
)
|
|
|
|
# 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
|
|
# print("prompt_embeds: ",prompt_embeds)
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
return prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
|
|
def encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
lora_scale: Optional[float] = None,
|
|
clip_skip: Optional[int] = None,
|
|
):
|
|
r"""
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
prompt to be encoded
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
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.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *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.
|
|
lora_scale (`float`, *optional*):
|
|
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
"""
|
|
# set lora scale so that monkey patched LoRA
|
|
# function of text encoder can correctly access it
|
|
# print('1 ',prompt,negative_prompt)
|
|
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
# dynamically adjust the LoRA scale
|
|
if not USE_PEFT_BACKEND:
|
|
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
|
|
else:
|
|
scale_lora_layers(self.text_encoder, lora_scale)
|
|
# print('2 ',prompt,negative_prompt)
|
|
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]
|
|
# print('3 ',prompt,negative_prompt)
|
|
if prompt_embeds is None:
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
# print('4 ',prompt,negative_prompt)
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
|
|
|
# print('5 ',prompt,negative_prompt)
|
|
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
# print(prompt, text_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.model_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.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
if (
|
|
hasattr(self.text_encoder.config, "use_attention_mask")
|
|
and self.text_encoder.config.use_attention_mask
|
|
):
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
if clip_skip is None:
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device), attention_mask=attention_mask
|
|
)
|
|
prompt_embeds = prompt_embeds[0]
|
|
else:
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
output_hidden_states=True,
|
|
)
|
|
# Access the `hidden_states` first, that contains a tuple of
|
|
# all the hidden states from the encoder layers. Then index into
|
|
# the tuple to access the hidden states from the desired layer.
|
|
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
|
# We also need to apply the final LayerNorm here to not mess with the
|
|
# representations. The `last_hidden_states` that we typically use for
|
|
# obtaining the final prompt representations passes through the LayerNorm
|
|
# layer.
|
|
prompt_embeds = self.text_encoder.text_model.final_layer_norm(
|
|
prompt_embeds
|
|
)
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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
|
|
)
|
|
|
|
# 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 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 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
|
|
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
# print("neg: ", uncond_input.input_ids)
|
|
|
|
if (
|
|
hasattr(self.text_encoder.config, "use_attention_mask")
|
|
and self.text_encoder.config.use_attention_mask
|
|
):
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
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=prompt_embeds_dtype, device=device
|
|
)
|
|
|
|
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
|
|
)
|
|
|
|
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
return prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
|
|
def encode_image(
|
|
self, image, device, num_images_per_prompt, output_hidden_states=None
|
|
):
|
|
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)
|
|
if output_hidden_states:
|
|
image_enc_hidden_states = self.image_encoder(
|
|
image, output_hidden_states=True
|
|
).hidden_states[-2]
|
|
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(
|
|
num_images_per_prompt, dim=0
|
|
)
|
|
uncond_image_enc_hidden_states = self.image_encoder(
|
|
torch.zeros_like(image), output_hidden_states=True
|
|
).hidden_states[-2]
|
|
uncond_image_enc_hidden_states = (
|
|
uncond_image_enc_hidden_states.repeat_interleave(
|
|
num_images_per_prompt, dim=0
|
|
)
|
|
)
|
|
return image_enc_hidden_states, uncond_image_enc_hidden_states
|
|
else:
|
|
image_embeds = self.image_encoder(image).image_embeds
|
|
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_embeds = torch.zeros_like(image_embeds)
|
|
|
|
return image_embeds, uncond_image_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
|
|
def prepare_ip_adapter_image_embeds(
|
|
self,
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
):
|
|
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) != len(
|
|
self.unet.encoder_hid_proj.image_projection_layers
|
|
):
|
|
raise ValueError(
|
|
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
|
|
)
|
|
|
|
image_embeds = []
|
|
for single_ip_adapter_image, image_proj_layer in zip(
|
|
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
|
|
):
|
|
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
|
|
single_image_embeds, single_negative_image_embeds = self.encode_image(
|
|
single_ip_adapter_image, device, 1, output_hidden_state
|
|
)
|
|
single_image_embeds = torch.stack(
|
|
[single_image_embeds] * num_images_per_prompt, dim=0
|
|
)
|
|
single_negative_image_embeds = torch.stack(
|
|
[single_negative_image_embeds] * num_images_per_prompt, dim=0
|
|
)
|
|
|
|
if do_classifier_free_guidance:
|
|
single_image_embeds = torch.cat(
|
|
[single_negative_image_embeds, single_image_embeds]
|
|
)
|
|
single_image_embeds = single_image_embeds.to(device)
|
|
|
|
image_embeds.append(single_image_embeds)
|
|
else:
|
|
repeat_dims = [1]
|
|
image_embeds = []
|
|
for single_image_embeds in ip_adapter_image_embeds:
|
|
if do_classifier_free_guidance:
|
|
single_negative_image_embeds, single_image_embeds = (
|
|
single_image_embeds.chunk(2)
|
|
)
|
|
single_image_embeds = single_image_embeds.repeat(
|
|
num_images_per_prompt,
|
|
*(repeat_dims * len(single_image_embeds.shape[1:])),
|
|
)
|
|
single_negative_image_embeds = single_negative_image_embeds.repeat(
|
|
num_images_per_prompt,
|
|
*(repeat_dims * len(single_negative_image_embeds.shape[1:])),
|
|
)
|
|
single_image_embeds = torch.cat(
|
|
[single_negative_image_embeds, single_image_embeds]
|
|
)
|
|
else:
|
|
single_image_embeds = single_image_embeds.repeat(
|
|
num_images_per_prompt,
|
|
*(repeat_dims * len(single_image_embeds.shape[1:])),
|
|
)
|
|
image_embeds.append(single_image_embeds)
|
|
|
|
return image_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
|
def run_safety_checker(self, image, device, dtype):
|
|
if self.safety_checker is None:
|
|
has_nsfw_concept = None
|
|
else:
|
|
if torch.is_tensor(image):
|
|
feature_extractor_input = self.image_processor.postprocess(
|
|
image, output_type="pil"
|
|
)
|
|
else:
|
|
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
|
safety_checker_input = self.feature_extractor(
|
|
feature_extractor_input, return_tensors="pt"
|
|
).to(device)
|
|
image, has_nsfw_concept = self.safety_checker(
|
|
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
|
)
|
|
return image, has_nsfw_concept
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
|
def decode_latents(self, latents):
|
|
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
|
|
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
|
|
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
image = (image / 2 + 0.5).clamp(0, 1)
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
|
return image
|
|
|
|
# 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://arxiv.org/abs/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,
|
|
image,
|
|
mask,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
ip_adapter_image=None,
|
|
ip_adapter_image_embeds=None,
|
|
brushnet_conditioning_scale=1.0,
|
|
control_guidance_start=0.0,
|
|
control_guidance_end=1.0,
|
|
callback_on_step_end_tensor_inputs=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}."
|
|
)
|
|
|
|
# Check `image`
|
|
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
|
|
self.brushnet, torch._dynamo.eval_frame.OptimizedModule
|
|
)
|
|
if (
|
|
isinstance(self.brushnet, BrushNetModel)
|
|
or is_compiled
|
|
and isinstance(self.brushnet._orig_mod, BrushNetModel)
|
|
):
|
|
self.check_image(image, mask, prompt, prompt_embeds)
|
|
else:
|
|
assert False
|
|
|
|
# Check `brushnet_conditioning_scale`
|
|
if (
|
|
isinstance(self.brushnet, BrushNetModel)
|
|
or is_compiled
|
|
and isinstance(self.brushnet._orig_mod, BrushNetModel)
|
|
):
|
|
if not isinstance(brushnet_conditioning_scale, float):
|
|
raise TypeError(
|
|
"For single brushnet: `brushnet_conditioning_scale` must be type `float`."
|
|
)
|
|
else:
|
|
assert False
|
|
|
|
if not isinstance(control_guidance_start, (tuple, list)):
|
|
control_guidance_start = [control_guidance_start]
|
|
|
|
if not isinstance(control_guidance_end, (tuple, list)):
|
|
control_guidance_end = [control_guidance_end]
|
|
|
|
if len(control_guidance_start) != len(control_guidance_end):
|
|
raise ValueError(
|
|
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
|
|
)
|
|
|
|
for start, end in zip(control_guidance_start, control_guidance_end):
|
|
if start >= end:
|
|
raise ValueError(
|
|
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
|
|
)
|
|
if start < 0.0:
|
|
raise ValueError(
|
|
f"control guidance start: {start} can't be smaller than 0."
|
|
)
|
|
if end > 1.0:
|
|
raise ValueError(
|
|
f"control guidance end: {end} can't be larger than 1.0."
|
|
)
|
|
|
|
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
|
|
raise ValueError(
|
|
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
|
|
)
|
|
|
|
if ip_adapter_image_embeds is not None:
|
|
if not isinstance(ip_adapter_image_embeds, list):
|
|
raise ValueError(
|
|
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
|
|
)
|
|
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
|
|
raise ValueError(
|
|
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
|
|
)
|
|
|
|
def check_image(self, image, mask, prompt, prompt_embeds):
|
|
image_is_pil = isinstance(image, PIL.Image.Image)
|
|
image_is_tensor = isinstance(image, torch.Tensor)
|
|
image_is_np = isinstance(image, np.ndarray)
|
|
image_is_pil_list = isinstance(image, list) and isinstance(
|
|
image[0], PIL.Image.Image
|
|
)
|
|
image_is_tensor_list = isinstance(image, list) and isinstance(
|
|
image[0], torch.Tensor
|
|
)
|
|
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
|
|
|
|
if (
|
|
not image_is_pil
|
|
and not image_is_tensor
|
|
and not image_is_np
|
|
and not image_is_pil_list
|
|
and not image_is_tensor_list
|
|
and not image_is_np_list
|
|
):
|
|
raise TypeError(
|
|
f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
|
|
)
|
|
|
|
mask_is_pil = isinstance(mask, PIL.Image.Image)
|
|
mask_is_tensor = isinstance(mask, torch.Tensor)
|
|
mask_is_np = isinstance(mask, np.ndarray)
|
|
mask_is_pil_list = isinstance(mask, list) and isinstance(
|
|
mask[0], PIL.Image.Image
|
|
)
|
|
mask_is_tensor_list = isinstance(mask, list) and isinstance(
|
|
mask[0], torch.Tensor
|
|
)
|
|
mask_is_np_list = isinstance(mask, list) and isinstance(mask[0], np.ndarray)
|
|
|
|
if (
|
|
not mask_is_pil
|
|
and not mask_is_tensor
|
|
and not mask_is_np
|
|
and not mask_is_pil_list
|
|
and not mask_is_tensor_list
|
|
and not mask_is_np_list
|
|
):
|
|
raise TypeError(
|
|
f"mask must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(mask)}"
|
|
)
|
|
|
|
if image_is_pil:
|
|
image_batch_size = 1
|
|
else:
|
|
image_batch_size = len(image)
|
|
|
|
if prompt is not None and isinstance(prompt, str):
|
|
prompt_batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
prompt_batch_size = len(prompt)
|
|
elif prompt_embeds is not None:
|
|
prompt_batch_size = prompt_embeds.shape[0]
|
|
|
|
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
|
|
raise ValueError(
|
|
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
|
|
)
|
|
|
|
def prepare_image(
|
|
self,
|
|
image,
|
|
width,
|
|
height,
|
|
batch_size,
|
|
num_images_per_prompt,
|
|
device,
|
|
dtype,
|
|
do_classifier_free_guidance=False,
|
|
guess_mode=False,
|
|
):
|
|
image = self.image_processor.preprocess(image, height=height, width=width).to(
|
|
dtype=torch.float32
|
|
)
|
|
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.to(device=device, dtype=dtype)
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
|
def prepare_latents(
|
|
self,
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents=None,
|
|
):
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
height // self.vae_scale_factor,
|
|
width // self.vae_scale_factor,
|
|
)
|
|
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."
|
|
)
|
|
|
|
if latents is None:
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
noise = latents.to(device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = noise * self.scheduler.init_noise_sigma
|
|
return latents, noise
|
|
|
|
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
|
|
def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
|
|
"""
|
|
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
|
|
|
|
Args:
|
|
timesteps (`torch.Tensor`):
|
|
generate embedding vectors at these timesteps
|
|
embedding_dim (`int`, *optional*, defaults to 512):
|
|
dimension of the embeddings to generate
|
|
dtype:
|
|
data type of the generated embeddings
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
|
|
"""
|
|
assert len(w.shape) == 1
|
|
w = w * 1000.0
|
|
|
|
half_dim = embedding_dim // 2
|
|
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
|
|
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
|
|
emb = w.to(dtype)[:, None] * emb[None, :]
|
|
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
|
if embedding_dim % 2 == 1: # zero pad
|
|
emb = torch.nn.functional.pad(emb, (0, 1))
|
|
assert emb.shape == (w.shape[0], embedding_dim)
|
|
return emb
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def clip_skip(self):
|
|
return self._clip_skip
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
promptA: Union[str, List[str]] = None,
|
|
promptB: Union[str, List[str]] = None,
|
|
promptU: Union[str, List[str]] = None,
|
|
tradoff: float = 1.0,
|
|
tradoff_nag: float = 1.0,
|
|
image: PipelineImageInput = None,
|
|
mask: PipelineImageInput = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
timesteps: List[int] = None,
|
|
guidance_scale: float = 7.5,
|
|
negative_promptA: Optional[Union[str, List[str]]] = None,
|
|
negative_promptB: Optional[Union[str, List[str]]] = None,
|
|
negative_promptU: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.FloatTensor] = None,
|
|
prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
|
ip_adapter_image: Optional[PipelineImageInput] = None,
|
|
ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
brushnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
|
guess_mode: bool = False,
|
|
control_guidance_start: Union[float, List[float]] = 0.0,
|
|
control_guidance_end: Union[float, List[float]] = 1.0,
|
|
clip_skip: Optional[int] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
|
|
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
|
|
The BrushNet input condition to provide guidance to the `unet` for generation. If the type is
|
|
specified as `torch.FloatTensor`, it is passed to BrushNet as is. `PIL.Image.Image` can also be
|
|
accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
|
|
and/or width are passed, `image` is resized accordingly. If multiple BrushNets are specified in
|
|
`init`, images must be passed as a list such that each element of the list can be correctly batched for
|
|
input to a single BrushNet. When `prompt` is a list, and if a list of images is passed for a single BrushNet,
|
|
each will be paired with each prompt in the `prompt` list. This also applies to multiple BrushNets,
|
|
where a list of image lists can be passed to batch for each prompt and each BrushNet.
|
|
mask (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
|
|
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
|
|
The BrushNet input condition to provide guidance to the `unet` for generation. If the type is
|
|
specified as `torch.FloatTensor`, it is passed to BrushNet as is. `PIL.Image.Image` can also be
|
|
accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
|
|
and/or width are passed, `image` is resized accordingly. If multiple BrushNets are specified in
|
|
`init`, images must be passed as a list such that each element of the list can be correctly batched for
|
|
input to a single BrushNet. When `prompt` is a list, and if a list of images is passed for a single BrushNet,
|
|
each will be paired with each prompt in the `prompt` list. This also applies to multiple BrushNets,
|
|
where a list of image lists can be passed to batch for each prompt and each BrushNet.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated 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.
|
|
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 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
|
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](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 is 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 (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
|
|
ip_adapter_image_embeds (`List[torch.FloatTensor]`, *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)`. It should contain the negative image embedding
|
|
if `do_classifier_free_guidance` is set to `True`.
|
|
If not provided, embeddings are computed from the `ip_adapter_image` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
|
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
|
every step.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
brushnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
|
The outputs of the BrushNet are multiplied by `brushnet_conditioning_scale` before they are added
|
|
to the residual in the original `unet`. If multiple BrushNets are specified in `init`, you can set
|
|
the corresponding scale as a list.
|
|
guess_mode (`bool`, *optional*, defaults to `False`):
|
|
The BrushNet encoder tries to recognize the content of the input image even if you remove all
|
|
prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
|
|
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
|
|
The percentage of total steps at which the BrushNet starts applying.
|
|
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
|
|
The percentage of total steps at which the BrushNet stops applying.
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
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 pipeine class.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
|
|
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 using `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 using `callback_on_step_end`",
|
|
)
|
|
|
|
brushnet = (
|
|
self.brushnet._orig_mod
|
|
if is_compiled_module(self.brushnet)
|
|
else self.brushnet
|
|
)
|
|
|
|
# align format for control guidance
|
|
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
|
|
):
|
|
control_guidance_start, control_guidance_end = (
|
|
[control_guidance_start],
|
|
[control_guidance_end],
|
|
)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
prompt = promptA
|
|
negative_prompt = negative_promptA
|
|
self.check_inputs(
|
|
prompt,
|
|
image,
|
|
mask,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
brushnet_conditioning_scale,
|
|
control_guidance_start,
|
|
control_guidance_end,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
|
|
# 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
|
|
|
|
global_pool_conditions = (
|
|
brushnet.config.global_pool_conditions
|
|
if isinstance(brushnet, BrushNetModel)
|
|
else brushnet.nets[0].config.global_pool_conditions
|
|
)
|
|
guess_mode = guess_mode or global_pool_conditions
|
|
|
|
# 3. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None)
|
|
if self.cross_attention_kwargs is not None
|
|
else None
|
|
)
|
|
|
|
prompt_embeds = self._encode_prompt(
|
|
promptA,
|
|
promptB,
|
|
tradoff,
|
|
device,
|
|
num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_promptA,
|
|
negative_promptB,
|
|
tradoff_nag,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
)
|
|
prompt_embedsU = None
|
|
negative_prompt_embedsU = None
|
|
prompt_embedsU = self.encode_prompt(
|
|
promptU,
|
|
device,
|
|
num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_promptU,
|
|
prompt_embeds=prompt_embedsU,
|
|
negative_prompt_embeds=negative_prompt_embedsU,
|
|
lora_scale=text_encoder_lora_scale,
|
|
)
|
|
|
|
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,
|
|
self.do_classifier_free_guidance,
|
|
)
|
|
|
|
# 4. Prepare image
|
|
if isinstance(brushnet, BrushNetModel):
|
|
image = self.prepare_image(
|
|
image=image,
|
|
width=width,
|
|
height=height,
|
|
batch_size=batch_size * num_images_per_prompt,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
dtype=brushnet.dtype,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
guess_mode=guess_mode,
|
|
)
|
|
original_mask = self.prepare_image(
|
|
image=mask,
|
|
width=width,
|
|
height=height,
|
|
batch_size=batch_size * num_images_per_prompt,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
dtype=brushnet.dtype,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
guess_mode=guess_mode,
|
|
)
|
|
original_mask = (original_mask.sum(1)[:, None, :, :] < 0).to(image.dtype)
|
|
height, width = image.shape[-2:]
|
|
else:
|
|
assert False
|
|
|
|
# 5. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps
|
|
)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 6. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents, noise = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6.1 prepare condition latents
|
|
# mask_i = transforms.ToPILImage()(image[0:1,:,:,:].squeeze(0))
|
|
# mask_i.save('_mask.png')
|
|
# print(brushnet.dtype)
|
|
conditioning_latents = (
|
|
self.vae.encode(
|
|
image.to(device=device, dtype=brushnet.dtype)
|
|
).latent_dist.sample()
|
|
* self.vae.config.scaling_factor
|
|
)
|
|
mask = torch.nn.functional.interpolate(
|
|
original_mask,
|
|
size=(conditioning_latents.shape[-2], conditioning_latents.shape[-1]),
|
|
)
|
|
conditioning_latents = torch.concat([conditioning_latents, mask], 1)
|
|
# image = self.vae.decode(conditioning_latents[:1,:4,:,:] / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0]
|
|
# from torchvision import transforms
|
|
# mask_i = transforms.ToPILImage()(image[0:1,:,:,:].squeeze(0)/2+0.5)
|
|
# mask_i.save(str(timesteps[0]) +'_C.png')
|
|
|
|
# 6.5 Optionally get Guidance Scale Embedding
|
|
timestep_cond = None
|
|
if self.unet.config.time_cond_proj_dim is not None:
|
|
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(
|
|
batch_size * num_images_per_prompt
|
|
)
|
|
timestep_cond = self.get_guidance_scale_embedding(
|
|
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
|
|
).to(device=device, dtype=latents.dtype)
|
|
|
|
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 7.1 Add image embeds for IP-Adapter
|
|
added_cond_kwargs = (
|
|
{"image_embeds": image_embeds}
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
|
|
else None
|
|
)
|
|
|
|
# 7.2 Create tensor stating which brushnets to keep
|
|
brushnet_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)
|
|
]
|
|
brushnet_keep.append(
|
|
keeps[0] if isinstance(brushnet, BrushNetModel) else keeps
|
|
)
|
|
|
|
# 8. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
is_unet_compiled = is_compiled_module(self.unet)
|
|
is_brushnet_compiled = is_compiled_module(self.brushnet)
|
|
is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# Relevant thread:
|
|
# https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
|
|
if (
|
|
is_unet_compiled and is_brushnet_compiled
|
|
) and is_torch_higher_equal_2_1:
|
|
torch._inductor.cudagraph_mark_step_begin()
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = (
|
|
torch.cat([latents] * 2)
|
|
if self.do_classifier_free_guidance
|
|
else latents
|
|
)
|
|
latent_model_input = self.scheduler.scale_model_input(
|
|
latent_model_input, t
|
|
)
|
|
|
|
# brushnet(s) inference
|
|
if guess_mode and self.do_classifier_free_guidance:
|
|
# Infer BrushNet only for the conditional batch.
|
|
control_model_input = latents
|
|
control_model_input = self.scheduler.scale_model_input(
|
|
control_model_input, t
|
|
)
|
|
brushnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
|
else:
|
|
control_model_input = latent_model_input
|
|
brushnet_prompt_embeds = prompt_embeds
|
|
|
|
if isinstance(brushnet_keep[i], list):
|
|
cond_scale = [
|
|
c * s
|
|
for c, s in zip(brushnet_conditioning_scale, brushnet_keep[i])
|
|
]
|
|
else:
|
|
brushnet_cond_scale = brushnet_conditioning_scale
|
|
if isinstance(brushnet_cond_scale, list):
|
|
brushnet_cond_scale = brushnet_cond_scale[0]
|
|
cond_scale = brushnet_cond_scale * brushnet_keep[i]
|
|
|
|
down_block_res_samples, mid_block_res_sample, up_block_res_samples = (
|
|
self.brushnet(
|
|
control_model_input,
|
|
t,
|
|
encoder_hidden_states=brushnet_prompt_embeds,
|
|
brushnet_cond=conditioning_latents,
|
|
conditioning_scale=cond_scale,
|
|
guess_mode=guess_mode,
|
|
return_dict=False,
|
|
)
|
|
)
|
|
|
|
if guess_mode and self.do_classifier_free_guidance:
|
|
# Infered BrushNet only for the conditional batch.
|
|
# To apply the output of BrushNet to both the unconditional and conditional batches,
|
|
# add 0 to the unconditional batch to keep it unchanged.
|
|
down_block_res_samples = [
|
|
torch.cat([torch.zeros_like(d), d])
|
|
for d in down_block_res_samples
|
|
]
|
|
mid_block_res_sample = torch.cat(
|
|
[torch.zeros_like(mid_block_res_sample), mid_block_res_sample]
|
|
)
|
|
up_block_res_samples = [
|
|
torch.cat([torch.zeros_like(d), d])
|
|
for d in up_block_res_samples
|
|
]
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embedsU,
|
|
timestep_cond=timestep_cond,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
down_block_add_samples=down_block_res_samples,
|
|
mid_block_add_sample=mid_block_res_sample,
|
|
up_block_add_samples=up_block_res_samples,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (
|
|
noise_pred_text - noise_pred_uncond
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(
|
|
noise_pred, t, latents, **extra_step_kwargs, return_dict=False
|
|
)[0]
|
|
|
|
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
|
|
)
|
|
|
|
# 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 callback is not None and i % callback_steps == 0:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
# If we do sequential model offloading, let's offload unet and brushnet
|
|
# manually for max memory savings
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.unet.to("cpu")
|
|
self.brushnet.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
if not output_type == "latent":
|
|
image = self.vae.decode(
|
|
latents / self.vae.config.scaling_factor,
|
|
return_dict=False,
|
|
generator=generator,
|
|
)[0]
|
|
image, has_nsfw_concept = self.run_safety_checker(
|
|
image, device, prompt_embeds.dtype
|
|
)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(
|
|
image, output_type=output_type, do_denormalize=do_denormalize
|
|
)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
)
|