mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 23:08:13 +00:00
153 lines
6.7 KiB
Python
153 lines
6.7 KiB
Python
from ..models import ModelManager, SD3TextEncoder1, HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder
|
|
from ..models.hunyuan_video_dit import HunyuanVideoDiT
|
|
from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder
|
|
from ..schedulers.flow_match import FlowMatchScheduler
|
|
from .base import BasePipeline
|
|
from ..prompters import HunyuanVideoPrompter
|
|
import torch
|
|
from einops import rearrange
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
|
|
|
|
class HunyuanVideoPipeline(BasePipeline):
|
|
|
|
def __init__(self, device="cuda", torch_dtype=torch.float16):
|
|
super().__init__(device=device, torch_dtype=torch_dtype)
|
|
self.scheduler = FlowMatchScheduler(shift=7.0, sigma_min=0.0, extra_one_step=True)
|
|
self.prompter = HunyuanVideoPrompter()
|
|
self.text_encoder_1: SD3TextEncoder1 = None
|
|
self.text_encoder_2: HunyuanVideoLLMEncoder = None
|
|
self.dit: HunyuanVideoDiT = None
|
|
self.vae_decoder: HunyuanVideoVAEDecoder = None
|
|
self.vae_encoder: HunyuanVideoVAEEncoder = None
|
|
self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder']
|
|
self.vram_management = False
|
|
|
|
|
|
def enable_vram_management(self):
|
|
self.vram_management = True
|
|
self.enable_cpu_offload()
|
|
self.text_encoder_2.enable_auto_offload(dtype=self.torch_dtype, device=self.device)
|
|
self.dit.enable_auto_offload(dtype=self.torch_dtype, device=self.device)
|
|
|
|
|
|
def fetch_models(self, model_manager: ModelManager):
|
|
self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1")
|
|
self.text_encoder_2 = model_manager.fetch_model("hunyuan_video_text_encoder_2")
|
|
self.dit = model_manager.fetch_model("hunyuan_video_dit")
|
|
self.vae_decoder = model_manager.fetch_model("hunyuan_video_vae_decoder")
|
|
self.vae_encoder = model_manager.fetch_model("hunyuan_video_vae_encoder")
|
|
self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2)
|
|
|
|
|
|
@staticmethod
|
|
def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, enable_vram_management=True):
|
|
if device is None: device = model_manager.device
|
|
if torch_dtype is None: torch_dtype = model_manager.torch_dtype
|
|
pipe = HunyuanVideoPipeline(device=device, torch_dtype=torch_dtype)
|
|
pipe.fetch_models(model_manager)
|
|
if enable_vram_management:
|
|
pipe.enable_vram_management()
|
|
return pipe
|
|
|
|
|
|
def encode_prompt(self, prompt, positive=True, clip_sequence_length=77, llm_sequence_length=256):
|
|
prompt_emb, pooled_prompt_emb, text_mask = self.prompter.encode_prompt(
|
|
prompt, device=self.device, positive=positive, clip_sequence_length=clip_sequence_length, llm_sequence_length=llm_sequence_length
|
|
)
|
|
return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_mask": text_mask}
|
|
|
|
|
|
def prepare_extra_input(self, latents=None, guidance=1.0):
|
|
freqs_cos, freqs_sin = self.dit.prepare_freqs(latents)
|
|
guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
|
|
return {"freqs_cos": freqs_cos, "freqs_sin": freqs_sin, "guidance": guidance}
|
|
|
|
|
|
def tensor2video(self, frames):
|
|
frames = rearrange(frames, "C T H W -> T H W C")
|
|
frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)
|
|
frames = [Image.fromarray(frame) for frame in frames]
|
|
return frames
|
|
|
|
|
|
def encode_video(self, frames, tile_size=(17, 30, 30), tile_stride=(12, 20, 20)):
|
|
tile_size = ((tile_size[0] - 1) * 4 + 1, tile_size[1] * 8, tile_size[2] * 8)
|
|
tile_stride = (tile_stride[0] * 4, tile_stride[1] * 8, tile_stride[2] * 8)
|
|
latents = self.vae_encoder.encode_video(frames, tile_size=tile_size, tile_stride=tile_stride)
|
|
return latents
|
|
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt,
|
|
negative_prompt="",
|
|
input_video=None,
|
|
denoising_strength=1.0,
|
|
seed=None,
|
|
height=720,
|
|
width=1280,
|
|
num_frames=129,
|
|
embedded_guidance=6.0,
|
|
cfg_scale=1.0,
|
|
num_inference_steps=30,
|
|
tile_size=(17, 30, 30),
|
|
tile_stride=(12, 20, 20),
|
|
progress_bar_cmd=lambda x: x,
|
|
progress_bar_st=None,
|
|
):
|
|
# Tiler parameters
|
|
tiler_kwargs = {"tile_size": tile_size, "tile_stride": tile_stride}
|
|
|
|
# Scheduler
|
|
self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
|
|
|
|
# Initialize noise
|
|
noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
|
|
if input_video is not None:
|
|
self.load_models_to_device(['vae_encoder'])
|
|
input_video = self.preprocess_images(input_video)
|
|
input_video = torch.stack(input_video, dim=2)
|
|
latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device)
|
|
latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
|
|
else:
|
|
latents = noise
|
|
|
|
# Encode prompts
|
|
self.load_models_to_device(["text_encoder_1"] if self.vram_management else ["text_encoder_1", "text_encoder_2"])
|
|
prompt_emb_posi = self.encode_prompt(prompt, positive=True)
|
|
if cfg_scale != 1.0:
|
|
prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
|
|
|
|
# Extra input
|
|
extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance)
|
|
|
|
# Denoise
|
|
self.load_models_to_device([] if self.vram_management else ["dit"])
|
|
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
|
|
timestep = timestep.unsqueeze(0).to(self.device)
|
|
print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}")
|
|
|
|
# Inference
|
|
with torch.autocast(device_type=self.device, dtype=self.torch_dtype):
|
|
noise_pred_posi = self.dit(latents, timestep, **prompt_emb_posi, **extra_input)
|
|
if cfg_scale != 1.0:
|
|
noise_pred_nega = self.dit(latents, timestep, **prompt_emb_nega, **extra_input)
|
|
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
|
|
else:
|
|
noise_pred = noise_pred_posi
|
|
|
|
# Scheduler
|
|
latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
|
|
|
|
# Decode
|
|
self.load_models_to_device(['vae_decoder'])
|
|
frames = self.vae_decoder.decode_video(latents, **tiler_kwargs)
|
|
self.load_models_to_device([])
|
|
frames = self.tensor2video(frames[0])
|
|
|
|
return frames
|