1272 lines
61 KiB
Python
1272 lines
61 KiB
Python
# Copyright 2025 Lightricks and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import PIL.Image
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import torch
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from transformers import T5EncoderModel, T5TokenizerFast
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from ...callbacks import MultiPipelineCallbacks, PipelineCallback
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from ...image_processor import PipelineImageInput
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from ...loaders import FromSingleFileMixin, LTXVideoLoraLoaderMixin
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from ...models.autoencoders import AutoencoderKLLTXVideo
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from ...models.transformers import LTXVideoTransformer3DModel
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from ...schedulers import FlowMatchEulerDiscreteScheduler
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from ...utils import is_torch_xla_available, logging, replace_example_docstring
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from ...utils.torch_utils import randn_tensor
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from ...video_processor import VideoProcessor
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from ..pipeline_utils import DiffusionPipeline
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from .pipeline_output import LTXPipelineOutput
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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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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>>> import torch
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>>> from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXConditionPipeline, LTXVideoCondition
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>>> from diffusers.utils import export_to_video, load_video, load_image
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>>> pipe = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.5", torch_dtype=torch.bfloat16)
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>>> pipe.to("cuda")
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>>> # Load input image and video
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>>> video = load_video(
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... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input-vid.mp4"
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... )
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>>> image = load_image(
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... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input.jpg"
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... )
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>>> # Create conditioning objects
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>>> condition1 = LTXVideoCondition(
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... image=image,
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... frame_index=0,
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... )
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>>> condition2 = LTXVideoCondition(
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... video=video,
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... frame_index=80,
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... )
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>>> prompt = "The video depicts a long, straight highway stretching into the distance, flanked by metal guardrails. The road is divided into multiple lanes, with a few vehicles visible in the far distance. The surrounding landscape features dry, grassy fields on one side and rolling hills on the other. The sky is mostly clear with a few scattered clouds, suggesting a bright, sunny day. And then the camera switch to a winding mountain road covered in snow, with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the solitude and beauty of a winter drive through a mountainous region."
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>>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
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>>> # Generate video
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>>> generator = torch.Generator("cuda").manual_seed(0)
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>>> # Text-only conditioning is also supported without the need to pass `conditions`
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>>> video = pipe(
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... conditions=[condition1, condition2],
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... prompt=prompt,
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... negative_prompt=negative_prompt,
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... width=768,
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... height=512,
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... num_frames=161,
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... num_inference_steps=40,
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... generator=generator,
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... ).frames[0]
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>>> export_to_video(video, "output.mp4", fps=24)
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```
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"""
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@dataclass
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class LTXVideoCondition:
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"""
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Defines a single frame-conditioning item for LTX Video - a single frame or a sequence of frames.
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Attributes:
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image (`PIL.Image.Image`):
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The image to condition the video on.
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video (`List[PIL.Image.Image]`):
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The video to condition the video on.
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frame_index (`int`):
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The frame index at which the image or video will conditionally effect the video generation.
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strength (`float`, defaults to `1.0`):
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The strength of the conditioning effect. A value of `1.0` means the conditioning effect is fully applied.
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"""
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image: Optional[PIL.Image.Image] = None
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video: Optional[List[PIL.Image.Image]] = None
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frame_index: int = 0
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strength: float = 1.0
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# from LTX-Video/ltx_video/schedulers/rf.py
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def linear_quadratic_schedule(num_steps, threshold_noise=0.025, linear_steps=None):
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if linear_steps is None:
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linear_steps = num_steps // 2
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if num_steps < 2:
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return torch.tensor([1.0])
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linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
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threshold_noise_step_diff = linear_steps - threshold_noise * num_steps
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quadratic_steps = num_steps - linear_steps
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quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2)
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linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps**2)
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const = quadratic_coef * (linear_steps**2)
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quadratic_sigma_schedule = [
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quadratic_coef * (i**2) + linear_coef * i + const for i in range(linear_steps, num_steps)
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]
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sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]
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sigma_schedule = [1.0 - x for x in sigma_schedule]
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return torch.tensor(sigma_schedule[:-1])
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# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
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def calculate_shift(
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image_seq_len,
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base_seq_len: int = 256,
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max_seq_len: int = 4096,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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):
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m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
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b = base_shift - m * base_seq_len
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mu = image_seq_len * m + b
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return mu
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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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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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r"""
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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, `timesteps`
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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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`List[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` 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 and sigmas is not None:
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raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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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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elif sigmas is not None:
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accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accept_sigmas:
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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" sigmas schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, 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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
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def retrieve_latents(
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encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
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):
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if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
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return encoder_output.latent_dist.sample(generator)
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elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
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return encoder_output.latent_dist.mode()
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elif hasattr(encoder_output, "latents"):
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return encoder_output.latents
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else:
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raise AttributeError("Could not access latents of provided encoder_output")
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
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def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
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r"""
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Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
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Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
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Flawed](https://huggingface.co/papers/2305.08891).
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Args:
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noise_cfg (`torch.Tensor`):
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The predicted noise tensor for the guided diffusion process.
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noise_pred_text (`torch.Tensor`):
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The predicted noise tensor for the text-guided diffusion process.
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guidance_rescale (`float`, *optional*, defaults to 0.0):
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A rescale factor applied to the noise predictions.
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Returns:
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noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
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"""
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std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
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std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
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# rescale the results from guidance (fixes overexposure)
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noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
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# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
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noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
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return noise_cfg
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class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
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r"""
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Pipeline for text/image/video-to-video generation.
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Reference: https://github.com/Lightricks/LTX-Video
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Args:
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transformer ([`LTXVideoTransformer3DModel`]):
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Conditional Transformer architecture to denoise the encoded video latents.
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scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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vae ([`AutoencoderKLLTXVideo`]):
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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 ([`T5EncoderModel`]):
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[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
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the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
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tokenizer (`T5TokenizerFast`):
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Second Tokenizer of class
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[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
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"""
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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_optional_components = []
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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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scheduler: FlowMatchEulerDiscreteScheduler,
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vae: AutoencoderKLLTXVideo,
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text_encoder: T5EncoderModel,
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tokenizer: T5TokenizerFast,
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transformer: LTXVideoTransformer3DModel,
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):
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super().__init__()
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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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transformer=transformer,
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scheduler=scheduler,
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)
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self.vae_spatial_compression_ratio = (
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self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32
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)
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self.vae_temporal_compression_ratio = (
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self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8
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)
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self.transformer_spatial_patch_size = (
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self.transformer.config.patch_size if getattr(self, "transformer", None) is not None else 1
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)
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self.transformer_temporal_patch_size = (
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self.transformer.config.patch_size_t if getattr(self, "transformer") is not None else 1
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)
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
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self.tokenizer_max_length = (
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self.tokenizer.model_max_length if getattr(self, "tokenizer", None) is not None else 128
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)
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self.default_height = 512
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self.default_width = 704
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self.default_frames = 121
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def _get_t5_prompt_embeds(
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self,
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prompt: Union[str, List[str]] = None,
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num_videos_per_prompt: int = 1,
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max_sequence_length: int = 256,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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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=max_sequence_length,
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truncation=True,
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add_special_tokens=True,
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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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prompt_attention_mask = text_inputs.attention_mask
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prompt_attention_mask = prompt_attention_mask.bool().to(device)
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because `max_sequence_length` is set to "
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f" {max_sequence_length} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0]
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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_, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
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prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
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prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
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return prompt_embeds, prompt_attention_mask
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# Copied from diffusers.pipelines.mochi.pipeline_mochi.MochiPipeline.encode_prompt
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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negative_prompt: Optional[Union[str, List[str]]] = None,
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do_classifier_free_guidance: bool = True,
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num_videos_per_prompt: int = 1,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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prompt_attention_mask: Optional[torch.Tensor] = None,
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negative_prompt_attention_mask: Optional[torch.Tensor] = None,
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max_sequence_length: int = 256,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = 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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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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do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
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Whether to use classifier free guidance or not.
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num_videos_per_prompt (`int`, *optional*, defaults to 1):
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Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
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prompt_embeds (`torch.Tensor`, *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.Tensor`, *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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device: (`torch.device`, *optional*):
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torch device
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dtype: (`torch.dtype`, *optional*):
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torch dtype
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"""
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device = device or self._execution_device
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prompt = [prompt] if isinstance(prompt, str) else prompt
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if prompt is not None:
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batch_size = len(prompt)
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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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prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
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prompt=prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt or ""
|
|
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
|
|
|
|
if prompt is not None and type(prompt) is not type(negative_prompt):
|
|
raise TypeError(
|
|
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
|
f" {type(prompt)}."
|
|
)
|
|
elif batch_size != len(negative_prompt):
|
|
raise ValueError(
|
|
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
|
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
|
" the batch size of `prompt`."
|
|
)
|
|
|
|
negative_prompt_embeds, negative_prompt_attention_mask = self._get_t5_prompt_embeds(
|
|
prompt=negative_prompt,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
max_sequence_length=max_sequence_length,
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
|
|
return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
conditions,
|
|
image,
|
|
video,
|
|
frame_index,
|
|
strength,
|
|
denoise_strength,
|
|
height,
|
|
width,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
prompt_attention_mask=None,
|
|
negative_prompt_attention_mask=None,
|
|
):
|
|
if height % 32 != 0 or width % 32 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
|
|
|
|
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 prompt_embeds is not None and prompt_attention_mask is None:
|
|
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
|
|
|
|
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
|
|
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
|
|
raise ValueError(
|
|
"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
|
|
f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
|
|
f" {negative_prompt_attention_mask.shape}."
|
|
)
|
|
|
|
if conditions is not None and (image is not None or video is not None):
|
|
raise ValueError("If `conditions` is provided, `image` and `video` must not be provided.")
|
|
|
|
if conditions is None:
|
|
if isinstance(image, list) and isinstance(frame_index, list) and len(image) != len(frame_index):
|
|
raise ValueError(
|
|
"If `conditions` is not provided, `image` and `frame_index` must be of the same length."
|
|
)
|
|
elif isinstance(image, list) and isinstance(strength, list) and len(image) != len(strength):
|
|
raise ValueError("If `conditions` is not provided, `image` and `strength` must be of the same length.")
|
|
elif isinstance(video, list) and isinstance(frame_index, list) and len(video) != len(frame_index):
|
|
raise ValueError(
|
|
"If `conditions` is not provided, `video` and `frame_index` must be of the same length."
|
|
)
|
|
elif isinstance(video, list) and isinstance(strength, list) and len(video) != len(strength):
|
|
raise ValueError("If `conditions` is not provided, `video` and `strength` must be of the same length.")
|
|
|
|
if denoise_strength < 0 or denoise_strength > 1:
|
|
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {denoise_strength}")
|
|
|
|
@staticmethod
|
|
def _prepare_video_ids(
|
|
batch_size: int,
|
|
num_frames: int,
|
|
height: int,
|
|
width: int,
|
|
patch_size: int = 1,
|
|
patch_size_t: int = 1,
|
|
device: torch.device = None,
|
|
) -> torch.Tensor:
|
|
latent_sample_coords = torch.meshgrid(
|
|
torch.arange(0, num_frames, patch_size_t, device=device),
|
|
torch.arange(0, height, patch_size, device=device),
|
|
torch.arange(0, width, patch_size, device=device),
|
|
indexing="ij",
|
|
)
|
|
latent_sample_coords = torch.stack(latent_sample_coords, dim=0)
|
|
latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
|
|
latent_coords = latent_coords.reshape(batch_size, -1, num_frames * height * width)
|
|
|
|
return latent_coords
|
|
|
|
@staticmethod
|
|
def _scale_video_ids(
|
|
video_ids: torch.Tensor,
|
|
scale_factor: int = 32,
|
|
scale_factor_t: int = 8,
|
|
frame_index: int = 0,
|
|
device: torch.device = None,
|
|
) -> torch.Tensor:
|
|
scaled_latent_coords = (
|
|
video_ids
|
|
* torch.tensor([scale_factor_t, scale_factor, scale_factor], device=video_ids.device)[None, :, None]
|
|
)
|
|
scaled_latent_coords[:, 0] = (scaled_latent_coords[:, 0] + 1 - scale_factor_t).clamp(min=0)
|
|
scaled_latent_coords[:, 0] += frame_index
|
|
|
|
return scaled_latent_coords
|
|
|
|
@staticmethod
|
|
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._pack_latents
|
|
def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
|
|
# Unpacked latents of shape are [B, C, F, H, W] are patched into tokens of shape [B, C, F // p_t, p_t, H // p, p, W // p, p].
|
|
# The patch dimensions are then permuted and collapsed into the channel dimension of shape:
|
|
# [B, F // p_t * H // p * W // p, C * p_t * p * p] (an ndim=3 tensor).
|
|
# dim=0 is the batch size, dim=1 is the effective video sequence length, dim=2 is the effective number of input features
|
|
batch_size, num_channels, num_frames, height, width = latents.shape
|
|
post_patch_num_frames = num_frames // patch_size_t
|
|
post_patch_height = height // patch_size
|
|
post_patch_width = width // patch_size
|
|
latents = latents.reshape(
|
|
batch_size,
|
|
-1,
|
|
post_patch_num_frames,
|
|
patch_size_t,
|
|
post_patch_height,
|
|
patch_size,
|
|
post_patch_width,
|
|
patch_size,
|
|
)
|
|
latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3)
|
|
return latents
|
|
|
|
@staticmethod
|
|
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._unpack_latents
|
|
def _unpack_latents(
|
|
latents: torch.Tensor, num_frames: int, height: int, width: int, patch_size: int = 1, patch_size_t: int = 1
|
|
) -> torch.Tensor:
|
|
# Packed latents of shape [B, S, D] (S is the effective video sequence length, D is the effective feature dimensions)
|
|
# are unpacked and reshaped into a video tensor of shape [B, C, F, H, W]. This is the inverse operation of
|
|
# what happens in the `_pack_latents` method.
|
|
batch_size = latents.size(0)
|
|
latents = latents.reshape(batch_size, num_frames, height, width, -1, patch_size_t, patch_size, patch_size)
|
|
latents = latents.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(2, 3)
|
|
return latents
|
|
|
|
@staticmethod
|
|
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._normalize_latents
|
|
def _normalize_latents(
|
|
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
|
|
) -> torch.Tensor:
|
|
# Normalize latents across the channel dimension [B, C, F, H, W]
|
|
latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
|
|
latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
|
|
latents = (latents - latents_mean) * scaling_factor / latents_std
|
|
return latents
|
|
|
|
@staticmethod
|
|
# Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._denormalize_latents
|
|
def _denormalize_latents(
|
|
latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
|
|
) -> torch.Tensor:
|
|
# Denormalize latents across the channel dimension [B, C, F, H, W]
|
|
latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
|
|
latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
|
|
latents = latents * latents_std / scaling_factor + latents_mean
|
|
return latents
|
|
|
|
def trim_conditioning_sequence(self, start_frame: int, sequence_num_frames: int, target_num_frames: int):
|
|
"""
|
|
Trim a conditioning sequence to the allowed number of frames.
|
|
|
|
Args:
|
|
start_frame (int): The target frame number of the first frame in the sequence.
|
|
sequence_num_frames (int): The number of frames in the sequence.
|
|
target_num_frames (int): The target number of frames in the generated video.
|
|
Returns:
|
|
int: updated sequence length
|
|
"""
|
|
scale_factor = self.vae_temporal_compression_ratio
|
|
num_frames = min(sequence_num_frames, target_num_frames - start_frame)
|
|
# Trim down to a multiple of temporal_scale_factor frames plus 1
|
|
num_frames = (num_frames - 1) // scale_factor * scale_factor + 1
|
|
return num_frames
|
|
|
|
@staticmethod
|
|
def add_noise_to_image_conditioning_latents(
|
|
t: float,
|
|
init_latents: torch.Tensor,
|
|
latents: torch.Tensor,
|
|
noise_scale: float,
|
|
conditioning_mask: torch.Tensor,
|
|
generator,
|
|
eps=1e-6,
|
|
):
|
|
"""
|
|
Add timestep-dependent noise to the hard-conditioning latents. This helps with motion continuity, especially
|
|
when conditioned on a single frame.
|
|
"""
|
|
noise = randn_tensor(
|
|
latents.shape,
|
|
generator=generator,
|
|
device=latents.device,
|
|
dtype=latents.dtype,
|
|
)
|
|
# Add noise only to hard-conditioning latents (conditioning_mask = 1.0)
|
|
need_to_noise = (conditioning_mask > 1.0 - eps).unsqueeze(-1)
|
|
noised_latents = init_latents + noise_scale * noise * (t**2)
|
|
latents = torch.where(need_to_noise, noised_latents, latents)
|
|
return latents
|
|
|
|
def prepare_latents(
|
|
self,
|
|
conditions: Optional[List[torch.Tensor]] = None,
|
|
condition_strength: Optional[List[float]] = None,
|
|
condition_frame_index: Optional[List[int]] = None,
|
|
batch_size: int = 1,
|
|
num_channels_latents: int = 128,
|
|
height: int = 512,
|
|
width: int = 704,
|
|
num_frames: int = 161,
|
|
num_prefix_latent_frames: int = 2,
|
|
sigma: Optional[torch.Tensor] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
generator: Optional[torch.Generator] = None,
|
|
device: Optional[torch.device] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
|
|
num_latent_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
|
|
latent_height = height // self.vae_spatial_compression_ratio
|
|
latent_width = width // self.vae_spatial_compression_ratio
|
|
|
|
shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
|
|
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
if latents is not None and sigma is not None:
|
|
if latents.shape != shape:
|
|
raise ValueError(
|
|
f"Latents shape {latents.shape} does not match expected shape {shape}. Please check the input."
|
|
)
|
|
latents = latents.to(device=device, dtype=dtype)
|
|
sigma = sigma.to(device=device, dtype=dtype)
|
|
latents = sigma * noise + (1 - sigma) * latents
|
|
else:
|
|
latents = noise
|
|
|
|
if len(conditions) > 0:
|
|
condition_latent_frames_mask = torch.zeros(
|
|
(batch_size, num_latent_frames), device=device, dtype=torch.float32
|
|
)
|
|
|
|
extra_conditioning_latents = []
|
|
extra_conditioning_video_ids = []
|
|
extra_conditioning_mask = []
|
|
extra_conditioning_num_latents = 0
|
|
for data, strength, frame_index in zip(conditions, condition_strength, condition_frame_index):
|
|
condition_latents = retrieve_latents(self.vae.encode(data), generator=generator)
|
|
condition_latents = self._normalize_latents(
|
|
condition_latents, self.vae.latents_mean, self.vae.latents_std
|
|
).to(device, dtype=dtype)
|
|
|
|
num_data_frames = data.size(2)
|
|
num_cond_frames = condition_latents.size(2)
|
|
|
|
if frame_index == 0:
|
|
latents[:, :, :num_cond_frames] = torch.lerp(
|
|
latents[:, :, :num_cond_frames], condition_latents, strength
|
|
)
|
|
condition_latent_frames_mask[:, :num_cond_frames] = strength
|
|
|
|
else:
|
|
if num_data_frames > 1:
|
|
if num_cond_frames < num_prefix_latent_frames:
|
|
raise ValueError(
|
|
f"Number of latent frames must be at least {num_prefix_latent_frames} but got {num_data_frames}."
|
|
)
|
|
|
|
if num_cond_frames > num_prefix_latent_frames:
|
|
start_frame = frame_index // self.vae_temporal_compression_ratio + num_prefix_latent_frames
|
|
end_frame = start_frame + num_cond_frames - num_prefix_latent_frames
|
|
latents[:, :, start_frame:end_frame] = torch.lerp(
|
|
latents[:, :, start_frame:end_frame],
|
|
condition_latents[:, :, num_prefix_latent_frames:],
|
|
strength,
|
|
)
|
|
condition_latent_frames_mask[:, start_frame:end_frame] = strength
|
|
condition_latents = condition_latents[:, :, :num_prefix_latent_frames]
|
|
|
|
noise = randn_tensor(condition_latents.shape, generator=generator, device=device, dtype=dtype)
|
|
condition_latents = torch.lerp(noise, condition_latents, strength)
|
|
|
|
condition_video_ids = self._prepare_video_ids(
|
|
batch_size,
|
|
condition_latents.size(2),
|
|
latent_height,
|
|
latent_width,
|
|
patch_size=self.transformer_spatial_patch_size,
|
|
patch_size_t=self.transformer_temporal_patch_size,
|
|
device=device,
|
|
)
|
|
condition_video_ids = self._scale_video_ids(
|
|
condition_video_ids,
|
|
scale_factor=self.vae_spatial_compression_ratio,
|
|
scale_factor_t=self.vae_temporal_compression_ratio,
|
|
frame_index=frame_index,
|
|
device=device,
|
|
)
|
|
condition_latents = self._pack_latents(
|
|
condition_latents,
|
|
self.transformer_spatial_patch_size,
|
|
self.transformer_temporal_patch_size,
|
|
)
|
|
condition_conditioning_mask = torch.full(
|
|
condition_latents.shape[:2], strength, device=device, dtype=dtype
|
|
)
|
|
|
|
extra_conditioning_latents.append(condition_latents)
|
|
extra_conditioning_video_ids.append(condition_video_ids)
|
|
extra_conditioning_mask.append(condition_conditioning_mask)
|
|
extra_conditioning_num_latents += condition_latents.size(1)
|
|
|
|
video_ids = self._prepare_video_ids(
|
|
batch_size,
|
|
num_latent_frames,
|
|
latent_height,
|
|
latent_width,
|
|
patch_size_t=self.transformer_temporal_patch_size,
|
|
patch_size=self.transformer_spatial_patch_size,
|
|
device=device,
|
|
)
|
|
if len(conditions) > 0:
|
|
conditioning_mask = condition_latent_frames_mask.gather(1, video_ids[:, 0])
|
|
else:
|
|
conditioning_mask, extra_conditioning_num_latents = None, 0
|
|
video_ids = self._scale_video_ids(
|
|
video_ids,
|
|
scale_factor=self.vae_spatial_compression_ratio,
|
|
scale_factor_t=self.vae_temporal_compression_ratio,
|
|
frame_index=0,
|
|
device=device,
|
|
)
|
|
latents = self._pack_latents(
|
|
latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
|
|
)
|
|
|
|
if len(conditions) > 0 and len(extra_conditioning_latents) > 0:
|
|
latents = torch.cat([*extra_conditioning_latents, latents], dim=1)
|
|
video_ids = torch.cat([*extra_conditioning_video_ids, video_ids], dim=2)
|
|
conditioning_mask = torch.cat([*extra_conditioning_mask, conditioning_mask], dim=1)
|
|
|
|
return latents, conditioning_mask, video_ids, extra_conditioning_num_latents
|
|
|
|
def get_timesteps(self, sigmas, timesteps, num_inference_steps, strength):
|
|
num_steps = min(int(num_inference_steps * strength), num_inference_steps)
|
|
start_index = max(num_inference_steps - num_steps, 0)
|
|
sigmas = sigmas[start_index:]
|
|
timesteps = timesteps[start_index:]
|
|
return sigmas, timesteps, num_inference_steps - start_index
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def guidance_rescale(self):
|
|
return self._guidance_rescale
|
|
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1.0
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def current_timestep(self):
|
|
return self._current_timestep
|
|
|
|
@property
|
|
def attention_kwargs(self):
|
|
return self._attention_kwargs
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
conditions: Union[LTXVideoCondition, List[LTXVideoCondition]] = None,
|
|
image: Union[PipelineImageInput, List[PipelineImageInput]] = None,
|
|
video: List[PipelineImageInput] = None,
|
|
frame_index: Union[int, List[int]] = 0,
|
|
strength: Union[float, List[float]] = 1.0,
|
|
denoise_strength: float = 1.0,
|
|
prompt: Union[str, List[str]] = None,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
height: int = 512,
|
|
width: int = 704,
|
|
num_frames: int = 161,
|
|
frame_rate: int = 25,
|
|
num_inference_steps: int = 50,
|
|
timesteps: List[int] = None,
|
|
guidance_scale: float = 3,
|
|
guidance_rescale: float = 0.0,
|
|
image_cond_noise_scale: float = 0.15,
|
|
num_videos_per_prompt: Optional[int] = 1,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
decode_timestep: Union[float, List[float]] = 0.0,
|
|
decode_noise_scale: Optional[Union[float, List[float]]] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
max_sequence_length: int = 256,
|
|
):
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
conditions (`List[LTXVideoCondition], *optional*`):
|
|
The list of frame-conditioning items for the video generation.If not provided, conditions will be
|
|
created using `image`, `video`, `frame_index` and `strength`.
|
|
image (`PipelineImageInput` or `List[PipelineImageInput]`, *optional*):
|
|
The image or images to condition the video generation. If not provided, one has to pass `video` or
|
|
`conditions`.
|
|
video (`List[PipelineImageInput]`, *optional*):
|
|
The video to condition the video generation. If not provided, one has to pass `image` or `conditions`.
|
|
frame_index (`int` or `List[int]`, *optional*):
|
|
The frame index or frame indices at which the image or video will conditionally effect the video
|
|
generation. If not provided, one has to pass `conditions`.
|
|
strength (`float` or `List[float]`, *optional*):
|
|
The strength or strengths of the conditioning effect. If not provided, one has to pass `conditions`.
|
|
denoise_strength (`float`, defaults to `1.0`):
|
|
The strength of the noise added to the latents for editing. Higher strength leads to more noise added
|
|
to the latents, therefore leading to more differences between original video and generated video. This
|
|
is useful for video-to-video editing.
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
height (`int`, defaults to `512`):
|
|
The height in pixels of the generated image. This is set to 480 by default for the best results.
|
|
width (`int`, defaults to `704`):
|
|
The width in pixels of the generated image. This is set to 848 by default for the best results.
|
|
num_frames (`int`, defaults to `161`):
|
|
The number of video frames to generate
|
|
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`, defaults to `3 `):
|
|
Guidance scale as defined in [Classifier-Free Diffusion
|
|
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
|
|
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
|
|
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
|
|
the text `prompt`, usually at the expense of lower image quality.
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
|
|
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
|
|
Guidance rescale factor should fix overexposure when using zero terminal SNR.
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of videos to generate per prompt.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
|
to make generation deterministic.
|
|
latents (`torch.Tensor`, *optional*):
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor will ge generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
|
prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Pre-generated attention mask for text embeddings.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
|
|
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
|
|
negative_prompt_attention_mask (`torch.FloatTensor`, *optional*):
|
|
Pre-generated attention mask for negative text embeddings.
|
|
decode_timestep (`float`, defaults to `0.0`):
|
|
The timestep at which generated video is decoded.
|
|
decode_noise_scale (`float`, defaults to `None`):
|
|
The interpolation factor between random noise and denoised latents at the decode timestep.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.ltx.LTXPipelineOutput`] instead of a plain tuple.
|
|
attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
callback_on_step_end (`Callable`, *optional*):
|
|
A function that calls at the end of each denoising steps during the inference. The function is called
|
|
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
|
|
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
|
|
`callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
max_sequence_length (`int` defaults to `128 `):
|
|
Maximum sequence length to use with the `prompt`.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.ltx.LTXPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.ltx.LTXPipelineOutput`] is returned, otherwise a `tuple` is
|
|
returned where the first element is a list with the generated images.
|
|
"""
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt=prompt,
|
|
conditions=conditions,
|
|
image=image,
|
|
video=video,
|
|
frame_index=frame_index,
|
|
strength=strength,
|
|
denoise_strength=denoise_strength,
|
|
height=height,
|
|
width=width,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._attention_kwargs = attention_kwargs
|
|
self._interrupt = False
|
|
self._current_timestep = None
|
|
|
|
# 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]
|
|
|
|
if conditions is not None:
|
|
if not isinstance(conditions, list):
|
|
conditions = [conditions]
|
|
|
|
strength = [condition.strength for condition in conditions]
|
|
frame_index = [condition.frame_index for condition in conditions]
|
|
image = [condition.image for condition in conditions]
|
|
video = [condition.video for condition in conditions]
|
|
elif image is not None or video is not None:
|
|
if not isinstance(image, list):
|
|
image = [image]
|
|
num_conditions = 1
|
|
elif isinstance(image, list):
|
|
num_conditions = len(image)
|
|
if not isinstance(video, list):
|
|
video = [video]
|
|
num_conditions = 1
|
|
elif isinstance(video, list):
|
|
num_conditions = len(video)
|
|
|
|
if not isinstance(frame_index, list):
|
|
frame_index = [frame_index] * num_conditions
|
|
if not isinstance(strength, list):
|
|
strength = [strength] * num_conditions
|
|
|
|
device = self._execution_device
|
|
vae_dtype = self.vae.dtype
|
|
|
|
# 3. Prepare text embeddings & conditioning image/video
|
|
(
|
|
prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_embeds,
|
|
negative_prompt_attention_mask,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
max_sequence_length=max_sequence_length,
|
|
device=device,
|
|
)
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
|
|
|
|
conditioning_tensors = []
|
|
is_conditioning_image_or_video = image is not None or video is not None
|
|
if is_conditioning_image_or_video:
|
|
for condition_image, condition_video, condition_frame_index, condition_strength in zip(
|
|
image, video, frame_index, strength
|
|
):
|
|
if condition_image is not None:
|
|
condition_tensor = (
|
|
self.video_processor.preprocess(condition_image, height, width)
|
|
.unsqueeze(2)
|
|
.to(device, dtype=vae_dtype)
|
|
)
|
|
elif condition_video is not None:
|
|
condition_tensor = self.video_processor.preprocess_video(condition_video, height, width)
|
|
num_frames_input = condition_tensor.size(2)
|
|
num_frames_output = self.trim_conditioning_sequence(
|
|
condition_frame_index, num_frames_input, num_frames
|
|
)
|
|
condition_tensor = condition_tensor[:, :, :num_frames_output]
|
|
condition_tensor = condition_tensor.to(device, dtype=vae_dtype)
|
|
else:
|
|
raise ValueError("Either `image` or `video` must be provided for conditioning.")
|
|
|
|
if condition_tensor.size(2) % self.vae_temporal_compression_ratio != 1:
|
|
raise ValueError(
|
|
f"Number of frames in the video must be of the form (k * {self.vae_temporal_compression_ratio} + 1) "
|
|
f"but got {condition_tensor.size(2)} frames."
|
|
)
|
|
conditioning_tensors.append(condition_tensor)
|
|
|
|
# 4. Prepare timesteps
|
|
latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
|
|
latent_height = height // self.vae_spatial_compression_ratio
|
|
latent_width = width // self.vae_spatial_compression_ratio
|
|
if timesteps is None:
|
|
sigmas = linear_quadratic_schedule(num_inference_steps)
|
|
timesteps = sigmas * 1000
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
|
sigmas = self.scheduler.sigmas
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
|
|
latent_sigma = None
|
|
if denoise_strength < 1:
|
|
sigmas, timesteps, num_inference_steps = self.get_timesteps(
|
|
sigmas, timesteps, num_inference_steps, denoise_strength
|
|
)
|
|
latent_sigma = sigmas[:1].repeat(batch_size * num_videos_per_prompt)
|
|
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
latents, conditioning_mask, video_coords, extra_conditioning_num_latents = self.prepare_latents(
|
|
conditioning_tensors,
|
|
strength,
|
|
frame_index,
|
|
batch_size=batch_size * num_videos_per_prompt,
|
|
num_channels_latents=num_channels_latents,
|
|
height=height,
|
|
width=width,
|
|
num_frames=num_frames,
|
|
sigma=latent_sigma,
|
|
latents=latents,
|
|
generator=generator,
|
|
device=device,
|
|
dtype=torch.float32,
|
|
)
|
|
|
|
video_coords = video_coords.float()
|
|
video_coords[:, 0] = video_coords[:, 0] * (1.0 / frame_rate)
|
|
|
|
init_latents = latents.clone() if is_conditioning_image_or_video else None
|
|
|
|
if self.do_classifier_free_guidance:
|
|
video_coords = torch.cat([video_coords, video_coords], dim=0)
|
|
|
|
# 6. Denoising loop
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
self._current_timestep = t
|
|
|
|
if image_cond_noise_scale > 0 and init_latents is not None:
|
|
# Add timestep-dependent noise to the hard-conditioning latents
|
|
# This helps with motion continuity, especially when conditioned on a single frame
|
|
latents = self.add_noise_to_image_conditioning_latents(
|
|
t / 1000.0,
|
|
init_latents,
|
|
latents,
|
|
image_cond_noise_scale,
|
|
conditioning_mask,
|
|
generator,
|
|
)
|
|
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
if is_conditioning_image_or_video:
|
|
conditioning_mask_model_input = (
|
|
torch.cat([conditioning_mask, conditioning_mask])
|
|
if self.do_classifier_free_guidance
|
|
else conditioning_mask
|
|
)
|
|
latent_model_input = latent_model_input.to(prompt_embeds.dtype)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latent_model_input.shape[0]).unsqueeze(-1).float()
|
|
if is_conditioning_image_or_video:
|
|
timestep = torch.min(timestep, (1 - conditioning_mask_model_input) * 1000.0)
|
|
|
|
noise_pred = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=timestep,
|
|
encoder_attention_mask=prompt_attention_mask,
|
|
video_coords=video_coords,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
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)
|
|
timestep, _ = timestep.chunk(2)
|
|
|
|
if self.guidance_rescale > 0:
|
|
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
|
noise_pred = rescale_noise_cfg(
|
|
noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale
|
|
)
|
|
|
|
denoised_latents = self.scheduler.step(
|
|
-noise_pred, t, latents, per_token_timesteps=timestep, return_dict=False
|
|
)[0]
|
|
if is_conditioning_image_or_video:
|
|
tokens_to_denoise_mask = (t / 1000 - 1e-6 < (1.0 - conditioning_mask)).unsqueeze(-1)
|
|
latents = torch.where(tokens_to_denoise_mask, denoised_latents, latents)
|
|
else:
|
|
latents = denoised_latents
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
if is_conditioning_image_or_video:
|
|
latents = latents[:, extra_conditioning_num_latents:]
|
|
|
|
latents = self._unpack_latents(
|
|
latents,
|
|
latent_num_frames,
|
|
latent_height,
|
|
latent_width,
|
|
self.transformer_spatial_patch_size,
|
|
self.transformer_temporal_patch_size,
|
|
)
|
|
|
|
if output_type == "latent":
|
|
video = latents
|
|
else:
|
|
latents = self._denormalize_latents(
|
|
latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
|
|
)
|
|
latents = latents.to(prompt_embeds.dtype)
|
|
|
|
if not self.vae.config.timestep_conditioning:
|
|
timestep = None
|
|
else:
|
|
noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
|
|
if not isinstance(decode_timestep, list):
|
|
decode_timestep = [decode_timestep] * batch_size
|
|
if decode_noise_scale is None:
|
|
decode_noise_scale = decode_timestep
|
|
elif not isinstance(decode_noise_scale, list):
|
|
decode_noise_scale = [decode_noise_scale] * batch_size
|
|
|
|
timestep = torch.tensor(decode_timestep, device=device, dtype=latents.dtype)
|
|
decode_noise_scale = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[
|
|
:, None, None, None, None
|
|
]
|
|
latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
|
|
|
|
video = self.vae.decode(latents, timestep, return_dict=False)[0]
|
|
video = self.video_processor.postprocess_video(video, output_type=output_type)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return LTXPipelineOutput(frames=video)
|