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import os
from typing import List
from typing import Optional
from typing import Union
import numpy as np
import torch
from diffusers.image_processor import PipelineImageInput
from diffusers.video_processor import VideoProcessor
from PIL import Image
from tqdm import tqdm
from ..modules import get_image_encoder
from ..modules import get_text_encoder
from ..modules import get_transformer
from ..modules import get_vae
from ..scheduler.fm_solvers_unipc import FlowUniPCMultistepScheduler
def resizecrop(image: Image.Image, th, tw):
w, h = image.size
if w == tw and h == th:
return image
if h / w > th / tw:
new_w = int(w)
new_h = int(new_w * th / tw)
else:
new_h = int(h)
new_w = int(new_h * tw / th)
left = (w - new_w) / 2
top = (h - new_h) / 2
right = (w + new_w) / 2
bottom = (h + new_h) / 2
image = image.crop((left, top, right, bottom))
return image
class Image2VideoPipeline:
def __init__(
self, model_path, dit_path, device: str = "cuda", weight_dtype=torch.bfloat16, use_usp=False, offload=False
):
load_device = "cpu" if offload else device
self.transformer = get_transformer(dit_path, load_device, weight_dtype)
vae_model_path = os.path.join(model_path, "Wan2.1_VAE.pth")
self.vae = get_vae(vae_model_path, device, weight_dtype=torch.float32)
self.text_encoder = get_text_encoder(model_path, load_device, weight_dtype)
self.clip = get_image_encoder(model_path, load_device, weight_dtype)
self.sp_size = 1
self.device = device
self.offload = offload
self.video_processor = VideoProcessor(vae_scale_factor=16)
if use_usp:
from xfuser.core.distributed import get_sequence_parallel_world_size
from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward
import types
for block in self.transformer.blocks:
block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
self.transformer.forward = types.MethodType(usp_dit_forward, self.transformer)
self.sp_size = get_sequence_parallel_world_size()
self.scheduler = FlowUniPCMultistepScheduler()
self.vae_stride = (4, 8, 8)
self.patch_size = (1, 2, 2)
@torch.no_grad()
def __call__(
self,
image: PipelineImageInput,
prompt: Union[str, List[str]] = None,
negative_prompt: Union[str, List[str]] = None,
height: int = 544,
width: int = 960,
num_frames: int = 97,
num_inference_steps: int = 50,
guidance_scale: float = 5.0,
shift: float = 5.0,
generator: Optional[torch.Generator] = None,
):
F = num_frames
latent_height = height // 8 // 2 * 2
latent_width = width // 8 // 2 * 2
latent_length = (F - 1) // 4 + 1
h = latent_height * 8
w = latent_width * 8
img = self.video_processor.preprocess(image, height=h, width=w)
img = img.to(device=self.device, dtype=self.transformer.dtype)
padding_video = torch.zeros(img.shape[0], 3, F - 1, h, w, device=self.device)
img = img.unsqueeze(2)
img_cond = torch.concat([img, padding_video], dim=2)
img_cond = self.vae.encode(img_cond)
mask = torch.ones_like(img_cond)
mask[:, :, 1:] = 0
y = torch.cat([mask[:, :4], img_cond], dim=1)
self.clip.to(self.device)
clip_context = self.clip.encode_video(img)
if self.offload:
self.clip.cpu()
torch.cuda.empty_cache()
# preprocess
self.text_encoder.to(self.device)
context = self.text_encoder.encode(prompt).to(self.device)
context_null = self.text_encoder.encode(negative_prompt).to(self.device)
if self.offload:
self.text_encoder.cpu()
torch.cuda.empty_cache()
latent = torch.randn(
16, latent_length, latent_height, latent_width, dtype=torch.float32, generator=generator, device=self.device
)
self.transformer.to(self.device)
with torch.amp.autocast("cuda", dtype=self.transformer.dtype), torch.no_grad():
self.scheduler.set_timesteps(num_inference_steps, device=self.device, shift=shift)
timesteps = self.scheduler.timesteps
arg_c = {
"context": context,
"clip_fea": clip_context,
"y": y,
}
arg_null = {
"context": context_null,
"clip_fea": clip_context,
"y": y,
}
self.transformer.to(self.device)
for _, t in enumerate(tqdm(timesteps)):
latent_model_input = torch.stack([latent]).to(self.device)
timestep = torch.stack([t]).to(self.device)
noise_pred_cond = self.transformer(latent_model_input, t=timestep, **arg_c)[0].to(self.device)
noise_pred_uncond = self.transformer(latent_model_input, t=timestep, **arg_null)[0].to(self.device)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
temp_x0 = self.scheduler.step(
noise_pred.unsqueeze(0), t, latent.unsqueeze(0), return_dict=False, generator=generator
)[0]
latent = temp_x0.squeeze(0)
if self.offload:
self.transformer.cpu()
torch.cuda.empty_cache()
videos = self.vae.decode(latent)
videos = (videos / 2 + 0.5).clamp(0, 1)
videos = [video for video in videos]
videos = [video.permute(1, 2, 3, 0) * 255 for video in videos]
videos = [video.cpu().numpy().astype(np.uint8) for video in videos]
return videos
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