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 typing import Optional from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d 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, 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.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=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): 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] vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device)[0] y = torch.concat([msk, y]) y = y.unsqueeze(0) clip_context = clip_context.to(dtype=self.torch_dtype, device=self.device) y = y.to(dtype=self.torch_dtype, device=self.device) return {"clip_feature": 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 {} def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): 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)): frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) return frames @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, num_inference_steps=50, sigma_shift=5.0, tiled=True, tile_size=(30, 52), tile_stride=(15, 26), tea_cache_l1_thresh=None, tea_cache_model_id="", progress_bar_cmd=tqdm, progress_bar_st=None, ): # 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=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) noise = noise.to(dtype=self.torch_dtype, device=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).to(dtype=self.torch_dtype, device=self.device) 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"]) 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) # TeaCache tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} # 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) # Inference noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi) if cfg_scale != 1.0: noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_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.decode_video(latents, **tiler_kwargs) self.load_models_to_device([]) frames = self.tensor2video(frames[0]) return frames class TeaCache: def __init__(self, num_inference_steps, rel_l1_thresh, model_id): self.num_inference_steps = num_inference_steps self.step = 0 self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = None self.rel_l1_thresh = rel_l1_thresh self.previous_residual = None self.previous_hidden_states = None self.coefficients_dict = { "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], } if model_id not in self.coefficients_dict: supported_model_ids = ", ".join([i for i in self.coefficients_dict]) raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") self.coefficients = self.coefficients_dict[model_id] def check(self, dit: WanModel, x, t_mod): modulated_inp = t_mod.clone() if self.step == 0 or self.step == self.num_inference_steps - 1: should_calc = True self.accumulated_rel_l1_distance = 0 else: coefficients = self.coefficients rescale_func = np.poly1d(coefficients) self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) if self.accumulated_rel_l1_distance < self.rel_l1_thresh: should_calc = False else: should_calc = True self.accumulated_rel_l1_distance = 0 self.previous_modulated_input = modulated_inp self.step += 1 if self.step == self.num_inference_steps: self.step = 0 if should_calc: self.previous_hidden_states = x.clone() return not should_calc def store(self, hidden_states): self.previous_residual = hidden_states - self.previous_hidden_states self.previous_hidden_states = None def update(self, hidden_states): hidden_states = hidden_states + self.previous_residual return hidden_states def model_fn_wan_video( dit: WanModel, x: torch.Tensor, timestep: torch.Tensor, context: torch.Tensor, clip_feature: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, tea_cache: TeaCache = None, **kwargs, ): t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) context = dit.text_embedding(context) if dit.has_image_input: x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) clip_embdding = dit.img_emb(clip_feature) context = torch.cat([clip_embdding, context], dim=1) x, (f, h, w) = dit.patchify(x) freqs = torch.cat([ dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) # TeaCache if tea_cache is not None: tea_cache_update = tea_cache.check(dit, x, t_mod) else: tea_cache_update = False if tea_cache_update: x = tea_cache.update(x) else: # blocks for block in dit.blocks: x = block(x, context, t_mod, freqs) if tea_cache is not None: tea_cache.store(x) x = dit.head(x, t) x = dit.unpatchify(x, (f, h, w)) return x