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from ..models import ModelManager
from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder
from ..models.stepvideo_text_encoder import STEP1TextEncoder
from ..models.stepvideo_dit import StepVideoModel
from ..models.stepvideo_vae import StepVideoVAE
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
from ..prompters import StepVideoPrompter
import torch
from einops import rearrange
import numpy as np
from PIL import Image
from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
from transformers.models.bert.modeling_bert import BertEmbeddings
from ..models.stepvideo_dit import RMSNorm
from ..models.stepvideo_vae import CausalConv, CausalConvAfterNorm, Upsample2D, BaseGroupNorm
class StepVideoPipeline(BasePipeline):
def __init__(self, device="cuda", torch_dtype=torch.float16):
super().__init__(device=device, torch_dtype=torch_dtype)
self.scheduler = FlowMatchScheduler(sigma_min=0.0, extra_one_step=True, shift=13.0, reverse_sigmas=True, num_train_timesteps=1)
self.prompter = StepVideoPrompter()
self.text_encoder_1: HunyuanDiTCLIPTextEncoder = None
self.text_encoder_2: STEP1TextEncoder = None
self.dit: StepVideoModel = None
self.vae: StepVideoVAE = None
self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae']
def enable_vram_management(self, num_persistent_param_in_dit=None):
dtype = next(iter(self.text_encoder_1.parameters())).dtype
enable_vram_management(
self.text_encoder_1,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
BertEmbeddings: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
},
module_config = dict(
offload_dtype=dtype,
offload_device="cpu",
onload_dtype=dtype,
onload_device="cpu",
computation_dtype=torch.float32,
computation_device=self.device,
),
)
dtype = next(iter(self.text_encoder_2.parameters())).dtype
enable_vram_management(
self.text_encoder_2,
module_map = {
torch.nn.Linear: AutoWrappedLinear,
RMSNorm: AutoWrappedModule,
torch.nn.Embedding: 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.Conv2d: AutoWrappedModule,
torch.nn.LayerNorm: AutoWrappedModule,
RMSNorm: 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.Conv3d: AutoWrappedModule,
CausalConv: AutoWrappedModule,
CausalConvAfterNorm: AutoWrappedModule,
Upsample2D: AutoWrappedModule,
BaseGroupNorm: 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):
self.text_encoder_1 = model_manager.fetch_model("hunyuan_dit_clip_text_encoder")
self.text_encoder_2 = model_manager.fetch_model("stepvideo_text_encoder_2")
self.dit = model_manager.fetch_model("stepvideo_dit")
self.vae = model_manager.fetch_model("stepvideo_vae")
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):
if device is None: device = model_manager.device
if torch_dtype is None: torch_dtype = model_manager.torch_dtype
pipe = StepVideoPipeline(device=device, torch_dtype=torch_dtype)
pipe.fetch_models(model_manager)
return pipe
def encode_prompt(self, prompt, positive=True):
clip_embeds, llm_embeds, llm_mask = self.prompter.encode_prompt(prompt, device=self.device, positive=positive)
clip_embeds = clip_embeds.to(dtype=self.torch_dtype, device=self.device)
llm_embeds = llm_embeds.to(dtype=self.torch_dtype, device=self.device)
llm_mask = llm_mask.to(dtype=self.torch_dtype, device=self.device)
return {"encoder_hidden_states_2": clip_embeds, "encoder_hidden_states": llm_embeds, "encoder_attention_mask": llm_mask}
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
@torch.no_grad()
def __call__(
self,
prompt,
negative_prompt="",
input_video=None,
denoising_strength=1.0,
seed=None,
rand_device="cpu",
height=544,
width=992,
num_frames=204,
cfg_scale=9.0,
num_inference_steps=30,
tiled=True,
tile_size=(34, 34),
tile_stride=(16, 16),
smooth_scale=0.6,
progress_bar_cmd=lambda x: x,
progress_bar_st=None,
):
# Tiler parameters
tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
# Scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
# Initialize noise
latents = self.generate_noise((1, max(num_frames//17*3, 1), 64, height//16, width//16), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device)
# Encode prompts
self.load_models_to_device(["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)
# Denoise
self.load_models_to_device(["dit"])
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}")
# Inference
noise_pred_posi = self.dit(latents, timestep=timestep, **prompt_emb_posi)
if cfg_scale != 1.0:
noise_pred_nega = self.dit(latents, timestep=timestep, **prompt_emb_nega)
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.vae.decode(latents, device=self.device, smooth_scale=smooth_scale, **tiler_kwargs)
self.load_models_to_device([])
frames = self.tensor2video(frames[0])
return frames
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