mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 14:58:12 +00:00
257 lines
11 KiB
Python
257 lines
11 KiB
Python
from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDMotionModel
|
|
from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator
|
|
from ..prompts import SDPrompter
|
|
from ..schedulers import EnhancedDDIMScheduler
|
|
from .dancer import lets_dance
|
|
from typing import List
|
|
import torch
|
|
from tqdm import tqdm
|
|
from PIL import Image
|
|
import numpy as np
|
|
|
|
|
|
def lets_dance_with_long_video(
|
|
unet: SDUNet,
|
|
motion_modules: SDMotionModel = None,
|
|
controlnet: MultiControlNetManager = None,
|
|
sample = None,
|
|
timestep = None,
|
|
encoder_hidden_states = None,
|
|
controlnet_frames = None,
|
|
animatediff_batch_size = 16,
|
|
animatediff_stride = 8,
|
|
unet_batch_size = 1,
|
|
controlnet_batch_size = 1,
|
|
cross_frame_attention = False,
|
|
device = "cuda",
|
|
vram_limit_level = 0,
|
|
):
|
|
num_frames = sample.shape[0]
|
|
hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)]
|
|
|
|
for batch_id in range(0, num_frames, animatediff_stride):
|
|
batch_id_ = min(batch_id + animatediff_batch_size, num_frames)
|
|
|
|
# process this batch
|
|
hidden_states_batch = lets_dance(
|
|
unet, motion_modules, controlnet,
|
|
sample[batch_id: batch_id_].to(device),
|
|
timestep,
|
|
encoder_hidden_states[batch_id: batch_id_].to(device),
|
|
controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None,
|
|
unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size,
|
|
cross_frame_attention=cross_frame_attention,
|
|
device=device, vram_limit_level=vram_limit_level
|
|
).cpu()
|
|
|
|
# update hidden_states
|
|
for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch):
|
|
bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2)
|
|
hidden_states, num = hidden_states_output[i]
|
|
hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias))
|
|
hidden_states_output[i] = (hidden_states, num + 1)
|
|
|
|
if batch_id_ == num_frames:
|
|
break
|
|
|
|
# output
|
|
hidden_states = torch.stack([h for h, _ in hidden_states_output])
|
|
return hidden_states
|
|
|
|
|
|
class SDVideoPipeline(torch.nn.Module):
|
|
|
|
def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True):
|
|
super().__init__()
|
|
self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear")
|
|
self.prompter = SDPrompter()
|
|
self.device = device
|
|
self.torch_dtype = torch_dtype
|
|
# models
|
|
self.text_encoder: SDTextEncoder = None
|
|
self.unet: SDUNet = None
|
|
self.vae_decoder: SDVAEDecoder = None
|
|
self.vae_encoder: SDVAEEncoder = None
|
|
self.controlnet: MultiControlNetManager = None
|
|
self.motion_modules: SDMotionModel = None
|
|
|
|
|
|
def fetch_main_models(self, model_manager: ModelManager):
|
|
self.text_encoder = model_manager.text_encoder
|
|
self.unet = model_manager.unet
|
|
self.vae_decoder = model_manager.vae_decoder
|
|
self.vae_encoder = model_manager.vae_encoder
|
|
# load textual inversion
|
|
self.prompter.load_textual_inversion(model_manager.textual_inversion_dict)
|
|
|
|
|
|
def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
|
|
controlnet_units = []
|
|
for config in controlnet_config_units:
|
|
controlnet_unit = ControlNetUnit(
|
|
Annotator(config.processor_id),
|
|
model_manager.get_model_with_model_path(config.model_path),
|
|
config.scale
|
|
)
|
|
controlnet_units.append(controlnet_unit)
|
|
self.controlnet = MultiControlNetManager(controlnet_units)
|
|
|
|
|
|
def fetch_motion_modules(self, model_manager: ModelManager):
|
|
if "motion_modules" in model_manager.model:
|
|
self.motion_modules = model_manager.motion_modules
|
|
|
|
|
|
def fetch_beautiful_prompt(self, model_manager: ModelManager):
|
|
if "beautiful_prompt" in model_manager.model:
|
|
self.prompter.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"])
|
|
|
|
|
|
@staticmethod
|
|
def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
|
|
pipe = SDVideoPipeline(
|
|
device=model_manager.device,
|
|
torch_dtype=model_manager.torch_dtype,
|
|
use_animatediff="motion_modules" in model_manager.model
|
|
)
|
|
pipe.fetch_main_models(model_manager)
|
|
pipe.fetch_motion_modules(model_manager)
|
|
pipe.fetch_beautiful_prompt(model_manager)
|
|
pipe.fetch_controlnet_models(model_manager, controlnet_config_units)
|
|
return pipe
|
|
|
|
|
|
def preprocess_image(self, image):
|
|
image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
|
|
return image
|
|
|
|
|
|
def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
|
|
image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
|
|
image = image.cpu().permute(1, 2, 0).numpy()
|
|
image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
|
|
return image
|
|
|
|
|
|
def decode_images(self, latents, tiled=False, tile_size=64, tile_stride=32):
|
|
images = [
|
|
self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
|
|
for frame_id in range(latents.shape[0])
|
|
]
|
|
return images
|
|
|
|
|
|
def encode_images(self, processed_images, tiled=False, tile_size=64, tile_stride=32):
|
|
latents = []
|
|
for image in processed_images:
|
|
image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
|
|
latent = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu()
|
|
latents.append(latent)
|
|
latents = torch.concat(latents, dim=0)
|
|
return latents
|
|
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt,
|
|
negative_prompt="",
|
|
cfg_scale=7.5,
|
|
clip_skip=1,
|
|
num_frames=None,
|
|
input_frames=None,
|
|
controlnet_frames=None,
|
|
denoising_strength=1.0,
|
|
height=512,
|
|
width=512,
|
|
num_inference_steps=20,
|
|
animatediff_batch_size = 16,
|
|
animatediff_stride = 8,
|
|
unet_batch_size = 1,
|
|
controlnet_batch_size = 1,
|
|
cross_frame_attention = False,
|
|
smoother=None,
|
|
smoother_progress_ids=[],
|
|
vram_limit_level=0,
|
|
progress_bar_cmd=tqdm,
|
|
progress_bar_st=None,
|
|
):
|
|
# Prepare scheduler
|
|
self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
|
|
|
|
# Prepare latent tensors
|
|
if self.motion_modules is None:
|
|
noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
|
|
else:
|
|
noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype)
|
|
if input_frames is None or denoising_strength == 1.0:
|
|
latents = noise
|
|
else:
|
|
latents = self.encode_images(input_frames)
|
|
latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
|
|
|
|
# Encode prompts
|
|
prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True).cpu()
|
|
prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False).cpu()
|
|
prompt_emb_posi = prompt_emb_posi.repeat(num_frames, 1, 1)
|
|
prompt_emb_nega = prompt_emb_nega.repeat(num_frames, 1, 1)
|
|
|
|
# Prepare ControlNets
|
|
if controlnet_frames is not None:
|
|
if isinstance(controlnet_frames[0], list):
|
|
controlnet_frames_ = []
|
|
for processor_id in range(len(controlnet_frames)):
|
|
controlnet_frames_.append(
|
|
torch.stack([
|
|
self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype)
|
|
for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id])
|
|
], dim=1)
|
|
)
|
|
controlnet_frames = torch.concat(controlnet_frames_, dim=0)
|
|
else:
|
|
controlnet_frames = torch.stack([
|
|
self.controlnet.process_image(controlnet_frame).to(self.torch_dtype)
|
|
for controlnet_frame in progress_bar_cmd(controlnet_frames)
|
|
], dim=1)
|
|
|
|
# Denoise
|
|
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
|
|
timestep = torch.IntTensor((timestep,))[0].to(self.device)
|
|
|
|
# Classifier-free guidance
|
|
noise_pred_posi = lets_dance_with_long_video(
|
|
self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet,
|
|
sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames,
|
|
animatediff_batch_size=animatediff_batch_size, animatediff_stride=animatediff_stride,
|
|
unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size,
|
|
cross_frame_attention=cross_frame_attention,
|
|
device=self.device, vram_limit_level=vram_limit_level
|
|
)
|
|
noise_pred_nega = lets_dance_with_long_video(
|
|
self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet,
|
|
sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames,
|
|
animatediff_batch_size=animatediff_batch_size, animatediff_stride=animatediff_stride,
|
|
unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size,
|
|
cross_frame_attention=cross_frame_attention,
|
|
device=self.device, vram_limit_level=vram_limit_level
|
|
)
|
|
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
|
|
|
|
# DDIM and smoother
|
|
if smoother is not None and progress_id in smoother_progress_ids:
|
|
rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True)
|
|
rendered_frames = self.decode_images(rendered_frames)
|
|
rendered_frames = smoother(rendered_frames, original_frames=input_frames)
|
|
target_latents = self.encode_images(rendered_frames)
|
|
noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents)
|
|
latents = self.scheduler.step(noise_pred, timestep, latents)
|
|
|
|
# UI
|
|
if progress_bar_st is not None:
|
|
progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
|
|
|
|
# Decode image
|
|
output_frames = self.decode_images(latents)
|
|
|
|
return output_frames
|