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[Community] Experimental AnimateDiff Image to Video (open to improvements) (#6509)
* add animatediff img2vid * fix * Update examples/community/README.md Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * fix code snippet between ip adapter face id and animatediff img2vid --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Aryan V S
GitHub
@@ -58,6 +58,8 @@ prompt-to-prompt | change parts of a prompt and retain image structure (see [pap
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| Null-Text Inversion Pipeline | Implement [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://arxiv.org/abs/2211.09794) as a pipeline. | [Null-Text Inversion](https://github.com/google/prompt-to-prompt/) | - | [Junsheng Luan](https://github.com/Junsheng121) |
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| Rerender A Video Pipeline | Implementation of [[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation](https://arxiv.org/abs/2306.07954) | [Rerender A Video Pipeline](#Rerender_A_Video) | - | [Yifan Zhou](https://github.com/SingleZombie) |
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| StyleAligned Pipeline | Implementation of [Style Aligned Image Generation via Shared Attention](https://arxiv.org/abs/2312.02133) | [StyleAligned Pipeline](#stylealigned-pipeline) | [](https://drive.google.com/file/d/15X2E0jFPTajUIjS0FzX50OaHsCbP2lQ0/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
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| AnimateDiff Image-To-Video Pipeline | Experimental Image-To-Video support for AnimateDiff (open to improvements) | [AnimateDiff Image To Video Pipeline](#animatediff-image-to-video-pipeline) | [](https://drive.google.com/file/d/1TvzCDPHhfFtdcJZe4RLloAwyoLKuttWK/view?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
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| IP Adapter FaceID Stable Diffusion | Stable Diffusion Pipeline that supports IP Adapter Face ID | [IP Adapter Face ID](#ip-adapter-face-id) | - | [Fabio Rigano](https://github.com/fabiorigano) |
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To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
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@@ -2990,7 +2992,7 @@ pipe = DiffusionPipeline.from_pretrained(
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custom_pipeline="pipeline_animatediff_controlnet",
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).to(device="cuda", dtype=torch.float16)
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pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained(
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model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1, beta_schedule="linear",
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model_id, subfolder="scheduler", beta_schedule="linear", clip_sample=False, timestep_spacing="linspace", steps_offset=1
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)
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pipe.enable_vae_slicing()
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@@ -3409,7 +3411,32 @@ images = pipe(
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pipe.disable_style_aligned()
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```
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### AnimateDiff Image-To-Video Pipeline
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This pipeline adds experimental support for the image-to-video task using AnimateDiff. Refer to [this](https://github.com/huggingface/diffusers/pull/6328) PR for more examples and results.
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```py
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import torch
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from diffusers import MotionAdapter, DiffusionPipeline, DDIMScheduler
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from diffusers.utils import export_to_gif, load_image
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adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
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pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter, custom_pipeline="pipeline_animatediff_img2video").to("cuda")
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pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace")
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image = load_image("snail.png")
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output = pipe(
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image=image,
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prompt="A snail moving on the ground",
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strength=0.8,
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latent_interpolation_method="slerp", # can be lerp, slerp, or your own callback
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)
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frames = output.frames[0]
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export_to_gif(frames, "animation.gif")
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```
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### IP Adapter Face ID
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IP Adapter FaceID is an experimental IP Adapter model that uses image embeddings generated by `insightface`, so no image encoder needs to be loaded.
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You need to install `insightface` and all its requirements to use this model.
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You must pass the image embedding tensor as `image_embeds` to the StableDiffusionPipeline instead of `ip_adapter_image`.
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989
examples/community/pipeline_animatediff_img2video.py
Normal file
989
examples/community/pipeline_animatediff_img2video.py
Normal file
@@ -0,0 +1,989 @@
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# Copyright 2023 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 types import FunctionType
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import torch
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
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from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
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from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
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from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
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from diffusers.models.lora import adjust_lora_scale_text_encoder
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from diffusers.models.unet_motion_model import MotionAdapter
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from diffusers.pipelines.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 USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
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from diffusers.utils.torch_utils import randn_tensor
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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 import MotionAdapter, DiffusionPipeline, DDIMScheduler
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>>> from diffusers.utils import export_to_gif, load_image
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>>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
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>>> pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter, custom_pipeline="pipeline_animatediff_img2video").to("cuda")
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>>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace")
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>>> image = load_image("snail.png")
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>>> output = pipe(image=image, prompt="A snail moving on the ground", strength=0.8, latent_interpolation_method="slerp")
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>>> frames = output.frames[0]
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>>> export_to_gif(frames, "animation.gif")
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```
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"""
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def lerp(
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v0: torch.Tensor,
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v1: torch.Tensor,
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t: Union[float, torch.Tensor],
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) -> torch.Tensor:
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r"""
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Linear Interpolation between two tensors.
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Args:
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v0 (`torch.Tensor`): First tensor.
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v1 (`torch.Tensor`): Second tensor.
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t: (`float` or `torch.Tensor`): Interpolation factor.
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"""
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t_is_float = False
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input_device = v0.device
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v0 = v0.cpu().numpy()
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v1 = v1.cpu().numpy()
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if isinstance(t, torch.Tensor):
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t = t.cpu().numpy()
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else:
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t_is_float = True
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t = np.array([t], dtype=v0.dtype)
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t = t[..., None]
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v0 = v0[None, ...]
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v1 = v1[None, ...]
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v2 = (1 - t) * v0 + t * v1
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if t_is_float and v0.ndim > 1:
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assert v2.shape[0] == 1
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v2 = np.squeeze(v2, axis=0)
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v2 = torch.from_numpy(v2).to(input_device)
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return v2
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def slerp(
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v0: torch.Tensor,
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v1: torch.Tensor,
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t: Union[float, torch.Tensor],
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DOT_THRESHOLD: float = 0.9995,
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) -> torch.Tensor:
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r"""
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Spherical Linear Interpolation between two tensors.
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Args:
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v0 (`torch.Tensor`): First tensor.
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v1 (`torch.Tensor`): Second tensor.
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t: (`float` or `torch.Tensor`): Interpolation factor.
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DOT_THRESHOLD (`float`):
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Dot product threshold exceeding which linear interpolation will be used
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because input tensors are close to parallel.
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"""
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t_is_float = False
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input_device = v0.device
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v0 = v0.cpu().numpy()
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v1 = v1.cpu().numpy()
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if isinstance(t, torch.Tensor):
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t = t.cpu().numpy()
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else:
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t_is_float = True
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t = np.array([t], dtype=v0.dtype)
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dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
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if np.abs(dot) > DOT_THRESHOLD:
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# v0 and v1 are close to parallel, so use linear interpolation instead
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v2 = lerp(v0, v1, t)
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else:
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theta_0 = np.arccos(dot)
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sin_theta_0 = np.sin(theta_0)
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theta_t = theta_0 * t
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sin_theta_t = np.sin(theta_t)
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s0 = np.sin(theta_0 - theta_t) / sin_theta_0
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s1 = sin_theta_t / sin_theta_0
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s0 = s0[..., None]
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s1 = s1[..., None]
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v0 = v0[None, ...]
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v1 = v1[None, ...]
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v2 = s0 * v0 + s1 * v1
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if t_is_float and v0.ndim > 1:
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assert v2.shape[0] == 1
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v2 = np.squeeze(v2, axis=0)
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v2 = torch.from_numpy(v2).to(input_device)
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return v2
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def tensor2vid(video: torch.Tensor, processor, output_type="np"):
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# Based on:
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# https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
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batch_size, channels, num_frames, height, width = video.shape
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outputs = []
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for batch_idx in range(batch_size):
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batch_vid = video[batch_idx].permute(1, 0, 2, 3)
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batch_output = processor.postprocess(batch_vid, output_type)
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outputs.append(batch_output)
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return outputs
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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.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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**kwargs,
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):
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"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used,
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`timesteps` must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
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timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
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must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(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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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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@dataclass
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class AnimateDiffImgToVideoPipelineOutput(BaseOutput):
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frames: Union[torch.Tensor, np.ndarray]
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class AnimateDiffImgToVideoPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
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r"""
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Pipeline for text-to-video generation.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer (`CLIPTokenizer`):
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A [`~transformers.CLIPTokenizer`] to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
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motion_adapter ([`MotionAdapter`]):
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A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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"""
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model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
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_optional_components = ["feature_extractor", "image_encoder"]
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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motion_adapter: MotionAdapter,
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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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feature_extractor: CLIPImageProcessor = None,
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image_encoder: CLIPVisionModelWithProjection = None,
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):
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super().__init__()
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unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
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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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motion_adapter=motion_adapter,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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image_encoder=image_encoder,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
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def encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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lora_scale: Optional[float] = None,
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clip_skip: Optional[int] = 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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|
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
|
||||
device: (`torch.device`):
|
||||
torch device
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||||
num_images_per_prompt (`int`):
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||||
number of images that should be generated per prompt
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||||
do_classifier_free_guidance (`bool`):
|
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whether to use classifier free guidance or not
|
||||
negative_prompt (`str` or `List[str]`, *optional*):
|
||||
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
|
||||
less than `1`).
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||||
prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
||||
provided, text embeddings will be generated from `prompt` input argument.
|
||||
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
||||
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
||||
argument.
|
||||
lora_scale (`float`, *optional*):
|
||||
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
||||
clip_skip (`int`, *optional*):
|
||||
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
||||
the output of the pre-final layer will be used for computing the prompt embeddings.
|
||||
"""
|
||||
# set lora scale so that monkey patched LoRA
|
||||
# function of text encoder can correctly access it
|
||||
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
||||
self._lora_scale = lora_scale
|
||||
|
||||
# dynamically adjust the LoRA scale
|
||||
if not USE_PEFT_BACKEND:
|
||||
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
|
||||
else:
|
||||
scale_lora_layers(self.text_encoder, lora_scale)
|
||||
|
||||
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 prompt_embeds is None:
|
||||
# textual inversion: procecss multi-vector tokens if necessary
|
||||
if isinstance(self, TextualInversionLoaderMixin):
|
||||
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
||||
|
||||
text_inputs = self.tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=self.tokenizer.model_max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_input_ids = text_inputs.input_ids
|
||||
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
||||
|
||||
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
||||
text_input_ids, untruncated_ids
|
||||
):
|
||||
removed_text = self.tokenizer.batch_decode(
|
||||
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
||||
)
|
||||
logger.warning(
|
||||
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
||||
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
||||
)
|
||||
|
||||
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
||||
attention_mask = text_inputs.attention_mask.to(device)
|
||||
else:
|
||||
attention_mask = None
|
||||
|
||||
if clip_skip is None:
|
||||
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
|
||||
prompt_embeds = prompt_embeds[0]
|
||||
else:
|
||||
prompt_embeds = self.text_encoder(
|
||||
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
|
||||
)
|
||||
# Access the `hidden_states` first, that contains a tuple of
|
||||
# all the hidden states from the encoder layers. Then index into
|
||||
# the tuple to access the hidden states from the desired layer.
|
||||
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
||||
# We also need to apply the final LayerNorm here to not mess with the
|
||||
# representations. The `last_hidden_states` that we typically use for
|
||||
# obtaining the final prompt representations passes through the LayerNorm
|
||||
# layer.
|
||||
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
|
||||
|
||||
if self.text_encoder is not None:
|
||||
prompt_embeds_dtype = self.text_encoder.dtype
|
||||
elif self.unet is not None:
|
||||
prompt_embeds_dtype = self.unet.dtype
|
||||
else:
|
||||
prompt_embeds_dtype = prompt_embeds.dtype
|
||||
|
||||
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
||||
|
||||
bs_embed, seq_len, _ = prompt_embeds.shape
|
||||
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
||||
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
||||
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
# get unconditional embeddings for classifier free guidance
|
||||
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
||||
uncond_tokens: List[str]
|
||||
if negative_prompt is None:
|
||||
uncond_tokens = [""] * batch_size
|
||||
elif prompt is not None and type(prompt) is not type(negative_prompt):
|
||||
raise TypeError(
|
||||
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
||||
f" {type(prompt)}."
|
||||
)
|
||||
elif isinstance(negative_prompt, str):
|
||||
uncond_tokens = [negative_prompt]
|
||||
elif batch_size != len(negative_prompt):
|
||||
raise ValueError(
|
||||
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
||||
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
||||
" the batch size of `prompt`."
|
||||
)
|
||||
else:
|
||||
uncond_tokens = negative_prompt
|
||||
|
||||
# textual inversion: procecss multi-vector tokens if necessary
|
||||
if isinstance(self, TextualInversionLoaderMixin):
|
||||
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
||||
|
||||
max_length = prompt_embeds.shape[1]
|
||||
uncond_input = self.tokenizer(
|
||||
uncond_tokens,
|
||||
padding="max_length",
|
||||
max_length=max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
||||
attention_mask = uncond_input.attention_mask.to(device)
|
||||
else:
|
||||
attention_mask = None
|
||||
|
||||
negative_prompt_embeds = self.text_encoder(
|
||||
uncond_input.input_ids.to(device),
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
negative_prompt_embeds = negative_prompt_embeds[0]
|
||||
|
||||
if do_classifier_free_guidance:
|
||||
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
||||
seq_len = negative_prompt_embeds.shape[1]
|
||||
|
||||
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
||||
|
||||
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
||||
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
|
||||
# Retrieve the original scale by scaling back the LoRA layers
|
||||
unscale_lora_layers(self.text_encoder, lora_scale)
|
||||
|
||||
return prompt_embeds, negative_prompt_embeds
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
|
||||
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
|
||||
dtype = next(self.image_encoder.parameters()).dtype
|
||||
|
||||
if not isinstance(image, torch.Tensor):
|
||||
image = self.feature_extractor(image, return_tensors="pt").pixel_values
|
||||
|
||||
image = image.to(device=device, dtype=dtype)
|
||||
if output_hidden_states:
|
||||
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
|
||||
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
||||
uncond_image_enc_hidden_states = self.image_encoder(
|
||||
torch.zeros_like(image), output_hidden_states=True
|
||||
).hidden_states[-2]
|
||||
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
|
||||
num_images_per_prompt, dim=0
|
||||
)
|
||||
return image_enc_hidden_states, uncond_image_enc_hidden_states
|
||||
else:
|
||||
image_embeds = self.image_encoder(image).image_embeds
|
||||
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
||||
uncond_image_embeds = torch.zeros_like(image_embeds)
|
||||
|
||||
return image_embeds, uncond_image_embeds
|
||||
|
||||
# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
|
||||
def decode_latents(self, latents):
|
||||
latents = 1 / self.vae.config.scaling_factor * latents
|
||||
|
||||
batch_size, channels, num_frames, height, width = latents.shape
|
||||
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
|
||||
|
||||
image = self.vae.decode(latents).sample
|
||||
video = (
|
||||
image[None, :]
|
||||
.reshape(
|
||||
(
|
||||
batch_size,
|
||||
num_frames,
|
||||
-1,
|
||||
)
|
||||
+ image.shape[2:]
|
||||
)
|
||||
.permute(0, 2, 1, 3, 4)
|
||||
)
|
||||
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
||||
video = video.float()
|
||||
return video
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
|
||||
def enable_vae_slicing(self):
|
||||
r"""
|
||||
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
||||
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
||||
"""
|
||||
self.vae.enable_slicing()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
|
||||
def disable_vae_slicing(self):
|
||||
r"""
|
||||
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
||||
computing decoding in one step.
|
||||
"""
|
||||
self.vae.disable_slicing()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
|
||||
def enable_vae_tiling(self):
|
||||
r"""
|
||||
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
|
||||
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
||||
processing larger images.
|
||||
"""
|
||||
self.vae.enable_tiling()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
|
||||
def disable_vae_tiling(self):
|
||||
r"""
|
||||
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
||||
computing decoding in one step.
|
||||
"""
|
||||
self.vae.disable_tiling()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
|
||||
def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
|
||||
r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
|
||||
|
||||
The suffixes after the scaling factors represent the stages where they are being applied.
|
||||
|
||||
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
|
||||
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
|
||||
|
||||
Args:
|
||||
s1 (`float`):
|
||||
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
|
||||
mitigate "oversmoothing effect" in the enhanced denoising process.
|
||||
s2 (`float`):
|
||||
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
|
||||
mitigate "oversmoothing effect" in the enhanced denoising process.
|
||||
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
|
||||
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
|
||||
"""
|
||||
if not hasattr(self, "unet"):
|
||||
raise ValueError("The pipeline must have `unet` for using FreeU.")
|
||||
self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
|
||||
def disable_freeu(self):
|
||||
"""Disables the FreeU mechanism if enabled."""
|
||||
self.unet.disable_freeu()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
||||
def prepare_extra_step_kwargs(self, generator, eta):
|
||||
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
||||
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
||||
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
||||
# and should be between [0, 1]
|
||||
|
||||
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
extra_step_kwargs = {}
|
||||
if accepts_eta:
|
||||
extra_step_kwargs["eta"] = eta
|
||||
|
||||
# check if the scheduler accepts generator
|
||||
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
if accepts_generator:
|
||||
extra_step_kwargs["generator"] = generator
|
||||
return extra_step_kwargs
|
||||
|
||||
def check_inputs(
|
||||
self,
|
||||
prompt,
|
||||
height,
|
||||
width,
|
||||
callback_steps,
|
||||
negative_prompt=None,
|
||||
prompt_embeds=None,
|
||||
negative_prompt_embeds=None,
|
||||
callback_on_step_end_tensor_inputs=None,
|
||||
latent_interpolation_method=None,
|
||||
):
|
||||
if height % 8 != 0 or width % 8 != 0:
|
||||
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
||||
|
||||
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
|
||||
raise ValueError(
|
||||
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
||||
f" {type(callback_steps)}."
|
||||
)
|
||||
if callback_on_step_end_tensor_inputs is not None and not all(
|
||||
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
||||
):
|
||||
raise ValueError(
|
||||
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
||||
)
|
||||
|
||||
if prompt is not None and prompt_embeds is not None:
|
||||
raise ValueError(
|
||||
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
||||
" only forward one of the two."
|
||||
)
|
||||
elif prompt is None and prompt_embeds is None:
|
||||
raise ValueError(
|
||||
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
||||
)
|
||||
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
||||
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
||||
|
||||
if negative_prompt is not None and negative_prompt_embeds is not None:
|
||||
raise ValueError(
|
||||
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
||||
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
||||
)
|
||||
|
||||
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
||||
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
||||
raise ValueError(
|
||||
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
||||
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
||||
f" {negative_prompt_embeds.shape}."
|
||||
)
|
||||
|
||||
if latent_interpolation_method is not None:
|
||||
if latent_interpolation_method not in ["lerp", "slerp"] and not isinstance(
|
||||
latent_interpolation_method, FunctionType
|
||||
):
|
||||
raise ValueError(
|
||||
"`latent_interpolation_method` must be one of `lerp`, `slerp` or a Callable[[torch.Tensor, torch.Tensor, int], torch.Tensor]"
|
||||
)
|
||||
|
||||
def prepare_latents(
|
||||
self,
|
||||
image,
|
||||
strength,
|
||||
batch_size,
|
||||
num_channels_latents,
|
||||
num_frames,
|
||||
height,
|
||||
width,
|
||||
dtype,
|
||||
device,
|
||||
generator,
|
||||
latents=None,
|
||||
latent_interpolation_method="slerp",
|
||||
):
|
||||
shape = (
|
||||
batch_size,
|
||||
num_channels_latents,
|
||||
num_frames,
|
||||
height // self.vae_scale_factor,
|
||||
width // self.vae_scale_factor,
|
||||
)
|
||||
|
||||
if latents is None:
|
||||
image = image.to(device=device, dtype=dtype)
|
||||
|
||||
if image.shape[1] == 4:
|
||||
latents = image
|
||||
else:
|
||||
# make sure the VAE is in float32 mode, as it overflows in float16
|
||||
if self.vae.config.force_upcast:
|
||||
image = image.float()
|
||||
self.vae.to(dtype=torch.float32)
|
||||
|
||||
if isinstance(generator, list):
|
||||
if len(generator) != batch_size:
|
||||
raise ValueError(
|
||||
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
||||
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
||||
)
|
||||
|
||||
init_latents = [
|
||||
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
|
||||
for i in range(batch_size)
|
||||
]
|
||||
init_latents = torch.cat(init_latents, dim=0)
|
||||
else:
|
||||
init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
|
||||
|
||||
if self.vae.config.force_upcast:
|
||||
self.vae.to(dtype)
|
||||
|
||||
init_latents = init_latents.to(dtype)
|
||||
init_latents = self.vae.config.scaling_factor * init_latents
|
||||
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
||||
latents = latents * self.scheduler.init_noise_sigma
|
||||
|
||||
if latent_interpolation_method == "lerp":
|
||||
|
||||
def latent_cls(v0, v1, index):
|
||||
return lerp(v0, v1, index / num_frames * (1 - strength))
|
||||
elif latent_interpolation_method == "slerp":
|
||||
|
||||
def latent_cls(v0, v1, index):
|
||||
return slerp(v0, v1, index / num_frames * (1 - strength))
|
||||
else:
|
||||
latent_cls = latent_interpolation_method
|
||||
|
||||
for i in range(num_frames):
|
||||
latents[:, :, i, :, :] = latent_cls(latents[:, :, i, :, :], init_latents, i)
|
||||
else:
|
||||
if shape != latents.shape:
|
||||
# [B, C, F, H, W]
|
||||
raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}")
|
||||
latents = latents.to(device, dtype=dtype)
|
||||
|
||||
return latents
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(
|
||||
self,
|
||||
image: PipelineImageInput,
|
||||
prompt: Optional[Union[str, List[str]]] = None,
|
||||
height: Optional[int] = None,
|
||||
width: Optional[int] = None,
|
||||
num_frames: int = 16,
|
||||
num_inference_steps: int = 50,
|
||||
timesteps: Optional[List[int]] = None,
|
||||
guidance_scale: float = 7.5,
|
||||
strength: float = 0.8,
|
||||
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||||
num_videos_per_prompt: Optional[int] = 1,
|
||||
eta: float = 0.0,
|
||||
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
||||
latents: Optional[torch.FloatTensor] = None,
|
||||
prompt_embeds: Optional[torch.FloatTensor] = None,
|
||||
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||||
ip_adapter_image: Optional[PipelineImageInput] = None,
|
||||
output_type: Optional[str] = "pil",
|
||||
return_dict: bool = True,
|
||||
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
||||
callback_steps: Optional[int] = 1,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
clip_skip: Optional[int] = None,
|
||||
latent_interpolation_method: Union[str, Callable[[torch.Tensor, torch.Tensor, int], torch.Tensor]] = "slerp",
|
||||
):
|
||||
r"""
|
||||
The call function to the pipeline for generation.
|
||||
|
||||
Args:
|
||||
image (`PipelineImageInput`):
|
||||
The input image to condition the generation on.
|
||||
prompt (`str` or `List[str]`, *optional*):
|
||||
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
||||
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
||||
The height in pixels of the generated video.
|
||||
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
||||
The width in pixels of the generated video.
|
||||
num_frames (`int`, *optional*, defaults to 16):
|
||||
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
|
||||
amounts to 2 seconds of video.
|
||||
num_inference_steps (`int`, *optional*, defaults to 50):
|
||||
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
|
||||
expense of slower inference.
|
||||
strength (`float`, *optional*, defaults to 0.8):
|
||||
Higher strength leads to more differences between original image and generated video.
|
||||
guidance_scale (`float`, *optional*, defaults to 7.5):
|
||||
A higher guidance scale value encourages the model to generate images closely linked to the text
|
||||
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
||||
negative_prompt (`str` or `List[str]`, *optional*):
|
||||
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
||||
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
||||
eta (`float`, *optional*, defaults to 0.0):
|
||||
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
||||
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
||||
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
||||
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
||||
generation deterministic.
|
||||
latents (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
|
||||
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
||||
tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
|
||||
`(batch_size, num_channel, num_frames, height, width)`.
|
||||
prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
||||
provided, text embeddings are generated from the `prompt` input argument.
|
||||
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
||||
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
||||
ip_adapter_image: (`PipelineImageInput`, *optional*):
|
||||
Optional image input to work with IP Adapters.
|
||||
output_type (`str`, *optional*, defaults to `"pil"`):
|
||||
The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
|
||||
`np.array`.
|
||||
return_dict (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to return a [`AnimateDiffImgToVideoPipelineOutput`] instead
|
||||
of a plain tuple.
|
||||
callback (`Callable`, *optional*):
|
||||
A function that calls every `callback_steps` steps during inference. The function is called with the
|
||||
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
||||
callback_steps (`int`, *optional*, defaults to 1):
|
||||
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
||||
every step.
|
||||
cross_attention_kwargs (`dict`, *optional*):
|
||||
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
||||
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
||||
clip_skip (`int`, *optional*):
|
||||
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
||||
the output of the pre-final layer will be used for computing the prompt embeddings.
|
||||
latent_interpolation_method (`str` or `Callable[[torch.Tensor, torch.Tensor, int], torch.Tensor]]`, *optional*):
|
||||
Must be one of "lerp", "slerp" or a callable that takes in a random noisy latent, image latent and a frame index
|
||||
as input and returns an initial latent for sampling.
|
||||
Examples:
|
||||
|
||||
Returns:
|
||||
[`AnimateDiffImgToVideoPipelineOutput`] or `tuple`:
|
||||
If `return_dict` is `True`, [`AnimateDiffImgToVideoPipelineOutput`] is
|
||||
returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
|
||||
"""
|
||||
# 0. Default height and width to unet
|
||||
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
||||
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
||||
|
||||
num_videos_per_prompt = 1
|
||||
|
||||
# 1. Check inputs. Raise error if not correct
|
||||
self.check_inputs(
|
||||
prompt=prompt,
|
||||
height=height,
|
||||
width=width,
|
||||
callback_steps=callback_steps,
|
||||
negative_prompt=negative_prompt,
|
||||
prompt_embeds=prompt_embeds,
|
||||
negative_prompt_embeds=negative_prompt_embeds,
|
||||
latent_interpolation_method=latent_interpolation_method,
|
||||
)
|
||||
|
||||
# 2. Define call parameters
|
||||
if prompt is not None and isinstance(prompt, str):
|
||||
batch_size = 1
|
||||
elif prompt is not None and isinstance(prompt, list):
|
||||
batch_size = len(prompt)
|
||||
else:
|
||||
batch_size = prompt_embeds.shape[0]
|
||||
|
||||
device = self._execution_device
|
||||
|
||||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||||
# corresponds to doing no classifier free guidance.
|
||||
do_classifier_free_guidance = guidance_scale > 1.0
|
||||
|
||||
# 3. Encode input prompt
|
||||
text_encoder_lora_scale = (
|
||||
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
||||
)
|
||||
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
||||
prompt,
|
||||
device,
|
||||
num_videos_per_prompt,
|
||||
do_classifier_free_guidance,
|
||||
negative_prompt,
|
||||
prompt_embeds=prompt_embeds,
|
||||
negative_prompt_embeds=negative_prompt_embeds,
|
||||
lora_scale=text_encoder_lora_scale,
|
||||
clip_skip=clip_skip,
|
||||
)
|
||||
|
||||
# For classifier free guidance, we need to do two forward passes.
|
||||
# Here we concatenate the unconditional and text embeddings into a single batch
|
||||
# to avoid doing two forward passes
|
||||
if do_classifier_free_guidance:
|
||||
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
||||
|
||||
if ip_adapter_image is not None:
|
||||
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
|
||||
image_embeds, negative_image_embeds = self.encode_image(
|
||||
ip_adapter_image, device, num_videos_per_prompt, output_hidden_state
|
||||
)
|
||||
if do_classifier_free_guidance:
|
||||
image_embeds = torch.cat([negative_image_embeds, image_embeds])
|
||||
|
||||
# 4. Preprocess image
|
||||
image = self.image_processor.preprocess(image, height=height, width=width)
|
||||
|
||||
# 5. Prepare timesteps
|
||||
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
||||
|
||||
# 6. Prepare latent variables
|
||||
num_channels_latents = self.unet.config.in_channels
|
||||
latents = self.prepare_latents(
|
||||
image=image,
|
||||
strength=strength,
|
||||
batch_size=batch_size * num_videos_per_prompt,
|
||||
num_channels_latents=num_channels_latents,
|
||||
num_frames=num_frames,
|
||||
height=height,
|
||||
width=width,
|
||||
dtype=prompt_embeds.dtype,
|
||||
device=device,
|
||||
generator=generator,
|
||||
latents=latents,
|
||||
latent_interpolation_method=latent_interpolation_method,
|
||||
)
|
||||
|
||||
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
||||
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
||||
|
||||
# 8. Add image embeds for IP-Adapter
|
||||
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
|
||||
|
||||
# 9. Denoising loop
|
||||
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
||||
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
||||
for i, t in enumerate(timesteps):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
noise_pred = self.unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
).sample
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
||||
|
||||
# call the callback, if provided
|
||||
if 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)
|
||||
|
||||
if output_type == "latent":
|
||||
return AnimateDiffImgToVideoPipelineOutput(frames=latents)
|
||||
|
||||
# 10. Post-processing
|
||||
video_tensor = self.decode_latents(latents)
|
||||
|
||||
if output_type == "pt":
|
||||
video = video_tensor
|
||||
else:
|
||||
video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
|
||||
|
||||
# 11. Offload all models
|
||||
self.maybe_free_model_hooks()
|
||||
|
||||
if not return_dict:
|
||||
return (video,)
|
||||
|
||||
return AnimateDiffImgToVideoPipelineOutput(frames=video)
|
||||
Reference in New Issue
Block a user