mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-14 22:13:41 +03:00
857 lines
38 KiB
Python
857 lines
38 KiB
Python
import inspect
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from typing import Callable, List, Optional, Union
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from pathlib import Path
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from torchvision.transforms.functional import pil_to_tensor
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import librosa
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from PIL import Image
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from torchvision.io import write_video
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import numpy as np
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import time
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import json
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import torch
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from diffusers.configuration_utils import FrozenDict
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from diffusers.utils import deprecate, logging
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from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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from torch import nn
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from sd_utils import RealESRGANModel
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def get_timesteps_arr(audio_filepath, offset, duration, fps=30, margin=1.0, smooth=0.0):
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y, sr = librosa.load(audio_filepath, offset=offset, duration=duration)
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# librosa.stft hardcoded defaults...
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# n_fft defaults to 2048
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# hop length is win_length // 4
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# win_length defaults to n_fft
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D = librosa.stft(y, n_fft=2048, hop_length=2048 // 4, win_length=2048)
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# Extract percussive elements
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D_harmonic, D_percussive = librosa.decompose.hpss(D, margin=margin)
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y_percussive = librosa.istft(D_percussive, length=len(y))
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# Get normalized melspectrogram
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spec_raw = librosa.feature.melspectrogram(y=y_percussive, sr=sr)
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spec_max = np.amax(spec_raw, axis=0)
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spec_norm = (spec_max - np.min(spec_max)) / np.ptp(spec_max)
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# Resize cumsum of spec norm to our desired number of interpolation frames
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x_norm = np.linspace(0, spec_norm.shape[-1], spec_norm.shape[-1])
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y_norm = np.cumsum(spec_norm)
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y_norm /= y_norm[-1]
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x_resize = np.linspace(0, y_norm.shape[-1], int(duration*fps))
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T = np.interp(x_resize, x_norm, y_norm)
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# Apply smoothing
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return T * (1 - smooth) + np.linspace(0.0, 1.0, T.shape[0]) * smooth
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def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
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"""helper function to spherically interpolate two arrays v1 v2"""
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if not isinstance(v0, np.ndarray):
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inputs_are_torch = True
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input_device = v0.device
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v0 = v0.cpu().numpy()
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v1 = v1.cpu().numpy()
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dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
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if np.abs(dot) > DOT_THRESHOLD:
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v2 = (1 - t) * v0 + t * v1
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else:
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theta_0 = np.arccos(dot)
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sin_theta_0 = np.sin(theta_0)
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theta_t = theta_0 * t
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sin_theta_t = np.sin(theta_t)
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s0 = np.sin(theta_0 - theta_t) / sin_theta_0
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s1 = sin_theta_t / sin_theta_0
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v2 = s0 * v0 + s1 * v1
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if inputs_are_torch:
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v2 = torch.from_numpy(v2).to(input_device)
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return v2
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def make_video_pyav(
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frames_or_frame_dir: Union[str, Path, torch.Tensor],
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audio_filepath: Union[str, Path] = None,
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fps: int = 30,
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audio_offset: int = 0,
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audio_duration: int = 2,
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sr: int = 22050,
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output_filepath: Union[str, Path] = "output.mp4",
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glob_pattern: str = "*.png",
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):
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"""
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TODO - docstring here
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frames_or_frame_dir: (Union[str, Path, torch.Tensor]):
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Either a directory of images, or a tensor of shape (T, C, H, W) in range [0, 255].
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"""
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# Torchvision write_video doesn't support pathlib paths
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output_filepath = str(output_filepath)
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if isinstance(frames_or_frame_dir, (str, Path)):
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frames = None
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for img in sorted(Path(frames_or_frame_dir).glob(glob_pattern)):
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frame = pil_to_tensor(Image.open(img)).unsqueeze(0)
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frames = frame if frames is None else torch.cat([frames, frame])
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else:
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frames = frames_or_frame_dir
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# TCHW -> THWC
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frames = frames.permute(0, 2, 3, 1)
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if audio_filepath:
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# Read audio, convert to tensor
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audio, sr = librosa.load(audio_filepath, sr=sr, mono=True, offset=audio_offset, duration=audio_duration)
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audio_tensor = torch.tensor(audio).unsqueeze(0)
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write_video(
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output_filepath,
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frames,
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fps=fps,
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audio_array=audio_tensor,
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audio_fps=sr,
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audio_codec="aac",
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options={"crf": "10", "pix_fmt": "yuv420p"},
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)
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else:
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write_video(output_filepath, frames, fps=fps, options={"crf": "10", "pix_fmt": "yuv420p"})
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return output_filepath
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class StableDiffusionWalkPipeline(DiffusionPipeline):
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r"""
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Pipeline for generating videos by interpolating Stable Diffusion's latent space.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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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 ([`CLIPTextModel`]):
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Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latens. 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/CompVis/stable-diffusion-v1-4) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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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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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if safety_checker is None:
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logger.warn(
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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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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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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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)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `attention slicing`
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self.enable_attention_slicing(None)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Optional[Union[str, List[str]]] = None,
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height: int = 512,
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width: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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text_embeddings: Optional[torch.FloatTensor] = None,
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**kwargs,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*, defaults to `None`):
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The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
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height (`int`, *optional*, defaults to 512):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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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. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
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Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
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`prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
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the supplied `prompt`.
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if (callback_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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if text_embeddings is None:
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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# get prompt text embeddings
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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print(
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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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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
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else:
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batch_size = text_embeddings.shape[0]
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = text_embeddings.shape
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text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
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text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""]
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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max_length = self.tokenizer.model_max_length
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = uncond_embeddings.shape[1]
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uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
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uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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# get the initial random noise unless the user supplied it
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# Unlike in other pipelines, latents need to be generated in the target device
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# for 1-to-1 results reproducibility with the CompVis implementation.
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# However this currently doesn't work in `mps`.
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latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
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latents_dtype = text_embeddings.dtype
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if latents is None:
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if self.device.type == "mps":
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# randn does not exist on mps
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latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
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self.device
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|
)
|
|
else:
|
|
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
|
|
else:
|
|
if latents.shape != latents_shape:
|
|
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
|
|
latents = latents.to(self.device)
|
|
|
|
# set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps)
|
|
|
|
# Some schedulers like PNDM have timesteps as arrays
|
|
# It's more optimized to move all timesteps to correct device beforehand
|
|
timesteps_tensor = self.scheduler.timesteps.to(self.device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
|
|
# 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
|
|
|
|
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + 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).prev_sample
|
|
|
|
# call the callback, if provided
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
latents = 1 / 0.18215 * latents
|
|
image = self.vae.decode(latents).sample
|
|
|
|
image = (image / 2 + 0.5).clamp(0, 1)
|
|
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
|
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
|
|
|
if self.safety_checker is not None:
|
|
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
|
|
self.device
|
|
)
|
|
image, has_nsfw_concept = self.safety_checker(
|
|
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
|
|
)
|
|
else:
|
|
has_nsfw_concept = None
|
|
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
|
|
|
def generate_inputs(self, prompt_a, prompt_b, seed_a, seed_b, noise_shape, T, batch_size):
|
|
embeds_a = self.embed_text(prompt_a)
|
|
embeds_b = self.embed_text(prompt_b)
|
|
|
|
latents_a = self.init_noise(seed_a, noise_shape)
|
|
latents_b = self.init_noise(seed_b, noise_shape)
|
|
|
|
batch_idx = 0
|
|
embeds_batch, noise_batch = None, None
|
|
for i, t in enumerate(T):
|
|
embeds = torch.lerp(embeds_a, embeds_b, t)
|
|
noise = slerp(float(t), latents_a, latents_b)
|
|
|
|
embeds_batch = embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
|
|
noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise])
|
|
batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
|
|
if not batch_is_ready:
|
|
continue
|
|
yield batch_idx, embeds_batch, noise_batch
|
|
batch_idx += 1
|
|
del embeds_batch, noise_batch
|
|
torch.cuda.empty_cache()
|
|
embeds_batch, noise_batch = None, None
|
|
|
|
def make_clip_frames(
|
|
self,
|
|
prompt_a: str,
|
|
prompt_b: str,
|
|
seed_a: int,
|
|
seed_b: int,
|
|
num_interpolation_steps: int = 5,
|
|
save_path: Union[str, Path] = "outputs/",
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
eta: float = 0.0,
|
|
height: int = 512,
|
|
width: int = 512,
|
|
upsample: bool = False,
|
|
batch_size: int = 1,
|
|
image_file_ext: str = ".png",
|
|
T: np.ndarray = None,
|
|
skip: int = 0,
|
|
):
|
|
save_path = Path(save_path)
|
|
save_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
T = T if T is not None else np.linspace(0.0, 1.0, num_interpolation_steps)
|
|
if T.shape[0] != num_interpolation_steps:
|
|
raise ValueError(f"Unexpected T shape, got {T.shape}, expected dim 0 to be {num_interpolation_steps}")
|
|
|
|
if upsample:
|
|
if getattr(self, "upsampler", None) is None:
|
|
self.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
|
|
self.upsampler.to(self.device)
|
|
|
|
batch_generator = self.generate_inputs(
|
|
prompt_a,
|
|
prompt_b,
|
|
seed_a,
|
|
seed_b,
|
|
(1, self.unet.in_channels, height // 8, width // 8),
|
|
T[skip:],
|
|
batch_size,
|
|
)
|
|
|
|
frame_index = skip
|
|
for _, embeds_batch, noise_batch in batch_generator:
|
|
with torch.autocast("cuda"):
|
|
outputs = self(
|
|
latents=noise_batch,
|
|
text_embeddings=embeds_batch,
|
|
height=height,
|
|
width=width,
|
|
guidance_scale=guidance_scale,
|
|
eta=eta,
|
|
num_inference_steps=num_inference_steps,
|
|
output_type="pil" if not upsample else "numpy",
|
|
)["images"]
|
|
|
|
for image in outputs:
|
|
frame_filepath = save_path / (f"frame%06d{image_file_ext}" % frame_index)
|
|
image = image if not upsample else self.upsampler(image)
|
|
image.save(frame_filepath)
|
|
frame_index += 1
|
|
|
|
def walk(
|
|
self,
|
|
prompts: Optional[List[str]] = None,
|
|
seeds: Optional[List[int]] = None,
|
|
num_interpolation_steps: Optional[Union[int, List[int]]] = 5, # int or list of int
|
|
output_dir: Optional[str] = "./dreams",
|
|
name: Optional[str] = None,
|
|
image_file_ext: Optional[str] = ".png",
|
|
fps: Optional[int] = 30,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 7.5,
|
|
eta: Optional[float] = 0.0,
|
|
height: Optional[int] = 512,
|
|
width: Optional[int] = 512,
|
|
upsample: Optional[bool] = False,
|
|
batch_size: Optional[int] = 1,
|
|
resume: Optional[bool] = False,
|
|
audio_filepath: str = None,
|
|
audio_start_sec: Optional[Union[int, float]] = None,
|
|
margin: Optional[float] = 1.0,
|
|
smooth: Optional[float] = 0.0,
|
|
):
|
|
"""Generate a video from a sequence of prompts and seeds. Optionally, add audio to the
|
|
video to interpolate to the intensity of the audio.
|
|
|
|
Args:
|
|
prompts (Optional[List[str]], optional):
|
|
list of text prompts. Defaults to None.
|
|
seeds (Optional[List[int]], optional):
|
|
list of random seeds corresponding to prompts. Defaults to None.
|
|
num_interpolation_steps (Union[int, List[int]], *optional*):
|
|
How many interpolation steps between each prompt. Defaults to None.
|
|
output_dir (Optional[str], optional):
|
|
Where to save the video. Defaults to './dreams'.
|
|
name (Optional[str], optional):
|
|
Name of the subdirectory of output_dir. Defaults to None.
|
|
image_file_ext (Optional[str], *optional*, defaults to '.png'):
|
|
The extension to use when writing video frames.
|
|
fps (Optional[int], *optional*, defaults to 30):
|
|
The frames per second in the resulting output videos.
|
|
num_inference_steps (Optional[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 (Optional[float], *optional*, defaults to 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality.
|
|
eta (Optional[float], *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
height (Optional[int], *optional*, defaults to 512):
|
|
height of the images to generate.
|
|
width (Optional[int], *optional*, defaults to 512):
|
|
width of the images to generate.
|
|
upsample (Optional[bool], *optional*, defaults to False):
|
|
When True, upsamples images with realesrgan.
|
|
batch_size (Optional[int], *optional*, defaults to 1):
|
|
Number of images to generate at once.
|
|
resume (Optional[bool], *optional*, defaults to False):
|
|
When True, resumes from the last frame in the output directory based
|
|
on available prompt config. Requires you to provide the `name` argument.
|
|
audio_filepath (str, *optional*, defaults to None):
|
|
Optional path to an audio file to influence the interpolation rate.
|
|
audio_start_sec (Optional[Union[int, float]], *optional*, defaults to 0):
|
|
Global start time of the provided audio_filepath.
|
|
margin (Optional[float], *optional*, defaults to 1.0):
|
|
Margin from librosa hpss to use for audio interpolation.
|
|
smooth (Optional[float], *optional*, defaults to 0.0):
|
|
Smoothness of the audio interpolation. 1.0 means linear interpolation.
|
|
|
|
This function will create sub directories for each prompt and seed pair.
|
|
|
|
For example, if you provide the following prompts and seeds:
|
|
|
|
```
|
|
prompts = ['a dog', 'a cat', 'a bird']
|
|
seeds = [1, 2, 3]
|
|
num_interpolation_steps = 5
|
|
output_dir = 'output_dir'
|
|
name = 'name'
|
|
fps = 5
|
|
```
|
|
|
|
Then the following directories will be created:
|
|
|
|
```
|
|
output_dir
|
|
├── name
|
|
│ ├── name_000000
|
|
│ │ ├── frame000000.png
|
|
│ │ ├── ...
|
|
│ │ ├── frame000004.png
|
|
│ │ ├── name_000000.mp4
|
|
│ ├── name_000001
|
|
│ │ ├── frame000000.png
|
|
│ │ ├── ...
|
|
│ │ ├── frame000004.png
|
|
│ │ ├── name_000001.mp4
|
|
│ ├── ...
|
|
│ ├── name.mp4
|
|
| |── prompt_config.json
|
|
```
|
|
|
|
Returns:
|
|
str: The resulting video filepath. This video includes all sub directories' video clips.
|
|
"""
|
|
|
|
output_path = Path(output_dir)
|
|
|
|
name = name or time.strftime("%Y%m%d-%H%M%S")
|
|
save_path_root = output_path / name
|
|
save_path_root.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Where the final video of all the clips combined will be saved
|
|
output_filepath = save_path_root / f"{name}.mp4"
|
|
|
|
# If using same number of interpolation steps between, we turn into list
|
|
if not resume and isinstance(num_interpolation_steps, int):
|
|
num_interpolation_steps = [num_interpolation_steps] * (len(prompts) - 1)
|
|
|
|
if not resume:
|
|
audio_start_sec = audio_start_sec or 0
|
|
|
|
# Save/reload prompt config
|
|
prompt_config_path = save_path_root / "prompt_config.json"
|
|
if not resume:
|
|
prompt_config_path.write_text(
|
|
json.dumps(
|
|
dict(
|
|
prompts=prompts,
|
|
seeds=seeds,
|
|
num_interpolation_steps=num_interpolation_steps,
|
|
fps=fps,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
eta=eta,
|
|
upsample=upsample,
|
|
height=height,
|
|
width=width,
|
|
audio_filepath=audio_filepath,
|
|
audio_start_sec=audio_start_sec,
|
|
),
|
|
indent=2,
|
|
sort_keys=False,
|
|
)
|
|
)
|
|
else:
|
|
data = json.load(open(prompt_config_path))
|
|
prompts = data["prompts"]
|
|
seeds = data["seeds"]
|
|
num_interpolation_steps = data["num_interpolation_steps"]
|
|
fps = data["fps"]
|
|
num_inference_steps = data["num_inference_steps"]
|
|
guidance_scale = data["guidance_scale"]
|
|
eta = data["eta"]
|
|
upsample = data["upsample"]
|
|
height = data["height"]
|
|
width = data["width"]
|
|
audio_filepath = data["audio_filepath"]
|
|
audio_start_sec = data["audio_start_sec"]
|
|
|
|
for i, (prompt_a, prompt_b, seed_a, seed_b, num_step) in enumerate(
|
|
zip(prompts, prompts[1:], seeds, seeds[1:], num_interpolation_steps)
|
|
):
|
|
# {name}_000000 / {name}_000001 / ...
|
|
save_path = save_path_root / f"{name}_{i:06d}"
|
|
|
|
# Where the individual clips will be saved
|
|
step_output_filepath = save_path / f"{name}_{i:06d}.mp4"
|
|
|
|
# Determine if we need to resume from a previous run
|
|
skip = 0
|
|
if resume:
|
|
if step_output_filepath.exists():
|
|
print(f"Skipping {save_path} because frames already exist")
|
|
continue
|
|
|
|
existing_frames = sorted(save_path.glob(f"*{image_file_ext}"))
|
|
if existing_frames:
|
|
skip = int(existing_frames[-1].stem[-6:]) + 1
|
|
if skip + 1 >= num_step:
|
|
print(f"Skipping {save_path} because frames already exist")
|
|
continue
|
|
print(f"Resuming {save_path.name} from frame {skip}")
|
|
|
|
audio_offset = audio_start_sec + sum(num_interpolation_steps[:i]) / fps
|
|
audio_duration = num_step / fps
|
|
|
|
self.make_clip_frames(
|
|
prompt_a,
|
|
prompt_b,
|
|
seed_a,
|
|
seed_b,
|
|
num_interpolation_steps=num_step,
|
|
save_path=save_path,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
eta=eta,
|
|
height=height,
|
|
width=width,
|
|
upsample=upsample,
|
|
batch_size=batch_size,
|
|
skip=skip,
|
|
T=get_timesteps_arr(
|
|
audio_filepath,
|
|
offset=audio_offset,
|
|
duration=audio_duration,
|
|
fps=fps,
|
|
margin=margin,
|
|
smooth=smooth,
|
|
)
|
|
if audio_filepath
|
|
else None,
|
|
)
|
|
make_video_pyav(
|
|
save_path,
|
|
audio_filepath=audio_filepath,
|
|
fps=fps,
|
|
output_filepath=step_output_filepath,
|
|
glob_pattern=f"*{image_file_ext}",
|
|
audio_offset=audio_offset,
|
|
audio_duration=audio_duration,
|
|
sr=44100,
|
|
)
|
|
|
|
return make_video_pyav(
|
|
save_path_root,
|
|
audio_filepath=audio_filepath,
|
|
fps=fps,
|
|
audio_offset=audio_start_sec,
|
|
audio_duration=sum(num_interpolation_steps) / fps,
|
|
output_filepath=output_filepath,
|
|
glob_pattern=f"**/*{image_file_ext}",
|
|
sr=44100,
|
|
)
|
|
|
|
def embed_text(self, text):
|
|
"""Helper to embed some text"""
|
|
with torch.autocast("cuda"):
|
|
text_input = self.tokenizer(
|
|
text,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
with torch.no_grad():
|
|
embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
|
|
return embed
|
|
|
|
def init_noise(self, seed, noise_shape):
|
|
"""Helper to initialize noise"""
|
|
# randn does not exist on mps, so we create noise on CPU here and move it to the device after initialization
|
|
if self.device.type == "mps":
|
|
noise = torch.randn(
|
|
noise_shape,
|
|
device='cpu',
|
|
generator=torch.Generator(device='cpu').manual_seed(seed),
|
|
).to(self.device)
|
|
else:
|
|
noise = torch.randn(
|
|
noise_shape,
|
|
device=self.device,
|
|
generator=torch.Generator(device=self.device).manual_seed(seed),
|
|
)
|
|
return noise
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, *args, tiled=False, **kwargs):
|
|
"""Same as diffusers `from_pretrained` but with tiled option, which makes images tilable"""
|
|
if tiled:
|
|
|
|
def patch_conv(**patch):
|
|
cls = nn.Conv2d
|
|
init = cls.__init__
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
return init(self, *args, **kwargs, **patch)
|
|
|
|
cls.__init__ = __init__
|
|
|
|
patch_conv(padding_mode="circular")
|
|
|
|
pipeline = super().from_pretrained(*args, **kwargs)
|
|
pipeline.tiled = tiled
|
|
return pipeline |