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Zero
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from ..models import ModelManager
from ..models.wan_video_dit import WanModel
from ..models.wan_video_text_encoder import WanTextEncoder
from ..models.wan_video_vae import WanVideoVAE
from ..models.wan_video_image_encoder import WanImageEncoder
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
from ..prompters import WanPrompter
import torch, os
from einops import rearrange
import numpy as np
from PIL import Image
from tqdm import tqdm
from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm
from ..models.wan_video_dit import WanLayerNorm, WanRMSNorm
from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample
class WanVideoPipeline(BasePipeline):
def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None):
super().__init__(device=device, torch_dtype=torch_dtype)
self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True)
self.prompter = WanPrompter(tokenizer_path=tokenizer_path)
self.text_encoder: WanTextEncoder = None
self.image_encoder: WanImageEncoder = None
self.dit: WanModel = None
self.vae: WanVideoVAE = None
self.model_names = ['text_encoder', 'dit', 'vae']
self.height_division_factor = 16
self.width_division_factor = 16
def enable_vram_management(self, num_persistent_param_in_dit=None):
dtype = next(iter(self.text_encoder.parameters())).dtype
enable_vram_management(
self.text_encoder,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Embedding: AutoWrappedModule,
T5RelativeEmbedding: AutoWrappedModule,
T5LayerNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device="cpu",
computation_dtype=self.torch_dtype,
computation_device=self.device,
),
)
dtype = next(iter(self.dit.parameters())).dtype
enable_vram_management(
self.dit,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Conv3d: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
WanLayerNorm: AutoWrappedModule,
WanRMSNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device=self.device,
computation_dtype=self.torch_dtype,
computation_device=self.device,
),
max_num_param=num_persistent_param_in_dit,
overflow_module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device="cpu",
computation_dtype=self.torch_dtype,
computation_device=self.device,
),
)
dtype = next(iter(self.vae.parameters())).dtype
enable_vram_management(
self.vae,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Conv2d: AutoWrappedModule,
RMS_norm: AutoWrappedModule,
CausalConv3d: AutoWrappedModule,
Upsample: AutoWrappedModule,
torch.nn.SiLU: AutoWrappedModule,
torch.nn.Dropout: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device=self.device,
computation_dtype=self.torch_dtype,
computation_device=self.device,
),
)
if self.image_encoder is not None:
dtype = next(iter(self.image_encoder.parameters())).dtype
enable_vram_management(
self.image_encoder,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Conv2d: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device="cpu",
computation_dtype=self.torch_dtype,
computation_device=self.device,
),
)
self.enable_cpu_offload()
def fetch_models(self, model_manager: ModelManager):
text_encoder_model_and_path = model_manager.fetch_model("wan_video_text_encoder", require_model_path=True)
if text_encoder_model_and_path is not None:
self.text_encoder, tokenizer_path = text_encoder_model_and_path
self.prompter.fetch_models(self.text_encoder)
self.prompter.fetch_tokenizer(os.path.join(os.path.dirname(tokenizer_path), "google/umt5-xxl"))
self.dit = model_manager.fetch_model("wan_video_dit")
self.vae = model_manager.fetch_model("wan_video_vae")
self.image_encoder = model_manager.fetch_model("wan_video_image_encoder")
@staticmethod
def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None):
if device is None: device = model_manager.device
if torch_dtype is None: torch_dtype = model_manager.torch_dtype
pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
pipe.fetch_models(model_manager)
return pipe
def denoising_model(self):
return self.dit
def encode_prompt(self, prompt, positive=True):
prompt_emb = self.prompter.encode_prompt(prompt, positive=positive)
return {"context": prompt_emb}
def encode_image(self, image, num_frames, height, width):
with torch.amp.autocast(dtype=torch.bfloat16, device_type=torch.device(self.device).type):
image = self.preprocess_image(image.resize((width, height))).to(self.device)
clip_context = self.image_encoder.encode_image([image])
msk = torch.ones(1, num_frames, height//8, width//8, device=self.device)
msk[:, 1:] = 0
msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
msk = msk.transpose(1, 2)[0]
y = self.vae.encode([torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)], device=self.device)[0]
y = torch.concat([msk, y])
return {"clip_fea": clip_context, "y": [y]}
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 prepare_extra_input(self, latents=None):
return {"seq_len": latents.shape[2] * latents.shape[3] * latents.shape[4] // 4}
def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
with torch.amp.autocast(dtype=torch.bfloat16, device_type=torch.device(self.device).type):
latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return latents
def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
with torch.amp.autocast(dtype=torch.bfloat16, device_type=torch.device(self.device).type):
frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
return frames
def set_ip(self, local_path):
pass
@torch.no_grad()
def __call__(
self,
prompt,
negative_prompt="",
input_image=None,
input_video=None,
denoising_strength=1.0,
seed=None,
rand_device="cpu",
height=480,
width=832,
num_frames=81,
cfg_scale=5.0,
audio_cfg_scale=None,
num_inference_steps=50,
sigma_shift=5.0,
tiled=True,
tile_size=(30, 52),
tile_stride=(15, 26),
progress_bar_cmd=tqdm,
progress_bar_st=None,
**kwargs,
):
# Parameter check
height, width = self.check_resize_height_width(height, width)
if num_frames % 4 != 1:
num_frames = (num_frames + 2) // 4 * 4 + 1
print(f"Only `num_frames % 4 != 1` is acceptable. We round it up to {num_frames}.")
# Tiler parameters
tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
# Scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength, shift=sigma_shift)
# Initialize noise
noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32).to(self.device)
if input_video is not None:
self.load_models_to_device(['vae'])
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=noise.dtype, device=noise.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"])
prompt_emb_posi = self.encode_prompt(prompt, positive=True)
if cfg_scale != 1.0:
prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
# Encode image
if input_image is not None and self.image_encoder is not None:
self.load_models_to_device(["image_encoder", "vae"])
image_emb = self.encode_image(input_image, num_frames, height, width)
else:
image_emb = {}
# Extra input
extra_input = self.prepare_extra_input(latents)
# Denoise
self.load_models_to_device(["dit"])
with torch.amp.autocast(dtype=torch.bfloat16, device_type=torch.device(self.device).type):
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = timestep.unsqueeze(0).to(dtype=torch.float32, device=self.device)
# Inference
noise_pred_posi = self.dit(latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **kwargs) # (zt,audio,prompt)
if audio_cfg_scale is not None:
audio_scale = kwargs['audio_scale']
kwargs['audio_scale'] = 0.0
noise_pred_noaudio = self.dit(latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **kwargs) #(zt,0,prompt)
# kwargs['ip_scale'] = ip_scale
if cfg_scale != 1.0: #prompt cfg
noise_pred_no_cond = self.dit(latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **kwargs) # (zt,0,0)
noise_pred = noise_pred_no_cond + cfg_scale * (noise_pred_noaudio - noise_pred_no_cond) + audio_cfg_scale * (noise_pred_posi - noise_pred_noaudio)
else:
noise_pred = noise_pred_noaudio + audio_cfg_scale * (noise_pred_posi - noise_pred_noaudio)
kwargs['audio_scale'] = audio_scale
else:
if cfg_scale != 1.0:
noise_pred_nega = self.dit(latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **kwargs) #(zt,audio,0)
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'])
frames = self.decode_video(latents, **tiler_kwargs)
self.load_models_to_device([])
frames = self.tensor2video(frames[0])
return frames
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