mirror of
https://github.com/guoyww/AnimateDiff.git
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466 lines
21 KiB
Python
466 lines
21 KiB
Python
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
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import inspect
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from typing import Callable, List, Optional, Union
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from dataclasses import dataclass
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import numpy as np
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import torch
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from tqdm import tqdm
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from diffusers.utils import is_accelerate_available
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from packaging import version
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from transformers import CLIPTextModel, CLIPTokenizer
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from diffusers.configuration_utils import FrozenDict
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from diffusers.models import AutoencoderKL
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import (
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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)
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from diffusers.utils import deprecate, logging, BaseOutput
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from einops import rearrange
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from ..models.unet import UNet3DConditionModel
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from ..models.sparse_controlnet import SparseControlNetModel
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import pdb
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class AnimationPipelineOutput(BaseOutput):
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videos: Union[torch.Tensor, np.ndarray]
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class AnimationPipeline(DiffusionPipeline):
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_optional_components = []
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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: UNet3DConditionModel,
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scheduler: Union[
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DDIMScheduler,
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PNDMScheduler,
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LMSDiscreteScheduler,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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DPMSolverMultistepScheduler,
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],
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controlnet: Union[SparseControlNetModel, None] = None,
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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 hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
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" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
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" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
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" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
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" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
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)
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deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["clip_sample"] = False
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scheduler._internal_dict = FrozenDict(new_config)
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is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
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version.parse(unet.config._diffusers_version).base_version
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) < version.parse("0.9.0.dev0")
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is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
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if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
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deprecation_message = (
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"The configuration file of the unet has set the default `sample_size` to smaller than"
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" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
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" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
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" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
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" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
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" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
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" in the config might lead to incorrect results in future versions. If you have downloaded this"
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" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
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" the `unet/config.json` file"
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)
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deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(unet.config)
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new_config["sample_size"] = 64
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unet._internal_dict = FrozenDict(new_config)
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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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controlnet=controlnet,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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def enable_vae_slicing(self):
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
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self.vae.disable_slicing()
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def enable_sequential_cpu_offload(self, gpu_id=0):
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if is_accelerate_available():
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from accelerate import cpu_offload
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else:
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raise ImportError("Please install accelerate via `pip install accelerate`")
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device = torch.device(f"cuda:{gpu_id}")
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for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
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if cpu_offloaded_model is not None:
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cpu_offload(cpu_offloaded_model, device)
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@property
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def _execution_device(self):
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if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
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return self.device
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for module in self.unet.modules():
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if (
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hasattr(module, "_hf_hook")
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and hasattr(module._hf_hook, "execution_device")
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and module._hf_hook.execution_device is not None
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):
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return torch.device(module._hf_hook.execution_device)
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return self.device
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def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
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batch_size = len(prompt) if isinstance(prompt, list) else 1
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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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truncation=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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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[:, self.tokenizer.model_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = text_inputs.attention_mask.to(device)
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else:
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attention_mask = None
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text_embeddings = self.text_encoder(
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text_input_ids.to(device),
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attention_mask=attention_mask,
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)
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text_embeddings = text_embeddings[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_videos_per_prompt, 1)
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text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
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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 = [""] * batch_size
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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 = text_input_ids.shape[-1]
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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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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = uncond_input.attention_mask.to(device)
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else:
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attention_mask = None
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uncond_embeddings = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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uncond_embeddings = uncond_embeddings[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(1, num_videos_per_prompt, 1)
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uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_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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return text_embeddings
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def decode_latents(self, latents):
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video_length = latents.shape[2]
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latents = 1 / 0.18215 * latents
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latents = rearrange(latents, "b c f h w -> (b f) c h w")
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# video = self.vae.decode(latents).sample
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video = []
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for frame_idx in tqdm(range(latents.shape[0])):
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video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
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video = torch.cat(video)
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video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
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video = (video / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
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video = video.cpu().float().numpy()
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return video
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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def check_inputs(self, prompt, height, width, callback_steps):
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if not isinstance(prompt, str) and not isinstance(prompt, list):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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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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def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
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shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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rand_device = "cpu" if device.type == "mps" else device
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if isinstance(generator, list):
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shape = shape
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# shape = (1,) + shape[1:]
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latents = [
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torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
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for i in range(batch_size)
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]
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latents = torch.cat(latents, dim=0).to(device)
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else:
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latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
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else:
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if latents.shape != shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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video_length: Optional[int],
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height: Optional[int] = None,
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width: Optional[int] = None,
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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_videos_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "tensor",
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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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# support controlnet
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controlnet_images: torch.FloatTensor = None,
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controlnet_image_index: list = [0],
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controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
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**kwargs,
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):
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# Default height and width to unet
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height = height or self.unet.config.sample_size * self.vae_scale_factor
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width = width or self.unet.config.sample_size * self.vae_scale_factor
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# Check inputs. Raise error if not correct
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self.check_inputs(prompt, height, width, callback_steps)
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# Define call parameters
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# batch_size = 1 if isinstance(prompt, str) else len(prompt)
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batch_size = 1
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if latents is not None:
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batch_size = latents.shape[0]
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if isinstance(prompt, list):
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batch_size = len(prompt)
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device = self._execution_device
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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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# Encode input prompt
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prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
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if negative_prompt is not None:
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negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
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text_embeddings = self._encode_prompt(
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prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
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)
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# Prepare timesteps
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self.scheduler.set_timesteps(num_inference_steps, device=device)
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timesteps = self.scheduler.timesteps
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# Prepare latent variables
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num_channels_latents = self.unet.in_channels
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latents = self.prepare_latents(
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batch_size * num_videos_per_prompt,
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num_channels_latents,
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video_length,
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height,
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width,
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text_embeddings.dtype,
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device,
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generator,
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latents,
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)
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latents_dtype = latents.dtype
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# Prepare extra step kwargs.
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extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
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# Denoising loop
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num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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with self.progress_bar(total=num_inference_steps) as progress_bar:
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for i, t in enumerate(timesteps):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
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down_block_additional_residuals = mid_block_additional_residual = None
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if (getattr(self, "controlnet", None) != None) and (controlnet_images != None):
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assert controlnet_images.dim() == 5
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controlnet_noisy_latents = latent_model_input
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controlnet_prompt_embeds = text_embeddings
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controlnet_images = controlnet_images.to(latents.device)
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controlnet_cond_shape = list(controlnet_images.shape)
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controlnet_cond_shape[2] = video_length
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controlnet_cond = torch.zeros(controlnet_cond_shape).to(latents.device)
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controlnet_conditioning_mask_shape = list(controlnet_cond.shape)
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controlnet_conditioning_mask_shape[1] = 1
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controlnet_conditioning_mask = torch.zeros(controlnet_conditioning_mask_shape).to(latents.device)
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assert controlnet_images.shape[2] >= len(controlnet_image_index)
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controlnet_cond[:,:,controlnet_image_index] = controlnet_images[:,:,:len(controlnet_image_index)]
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controlnet_conditioning_mask[:,:,controlnet_image_index] = 1
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down_block_additional_residuals, mid_block_additional_residual = self.controlnet(
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controlnet_noisy_latents, t,
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encoder_hidden_states=controlnet_prompt_embeds,
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controlnet_cond=controlnet_cond,
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conditioning_mask=controlnet_conditioning_mask,
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conditioning_scale=controlnet_conditioning_scale,
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guess_mode=False, return_dict=False,
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)
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# predict the noise residual
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noise_pred = self.unet(
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latent_model_input, t,
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encoder_hidden_states=text_embeddings,
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down_block_additional_residuals = down_block_additional_residuals,
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mid_block_additional_residual = mid_block_additional_residual,
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|
).sample.to(dtype=latents_dtype)
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|
|
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# perform guidance
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|
if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
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|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
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|
|
|
# Post-processing
|
|
video = self.decode_latents(latents)
|
|
|
|
# Convert to tensor
|
|
if output_type == "tensor":
|
|
video = torch.from_numpy(video)
|
|
|
|
if not return_dict:
|
|
return video
|
|
|
|
return AnimationPipelineOutput(videos=video)
|