pipeline/causal_diffusion_inference.py

from typing import List, Optional

import numpy as np
import torch
from tqdm import tqdm

from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
from wan.utils.fm_solvers import FlowDPMSolverMultistepScheduler, get_sampling_sigmas, retrieve_timesteps
from wan.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler


class CausalDiffusionInferencePipeline(torch.nn.Module):
    def __init__(self, args, device, generator=None, text_encoder=None, vae=None):
        super().__init__()
        # Step 1: Initialize all models
        self.args = args
        self.model_name = args.generator_name
        self.generator = (
            WanDiffusionWrapper(
                **getattr(args, "model_kwargs", {}), model_name=self.model_name, is_causal=True, model_config=args
            )
            if generator is None
            else generator
        )
        self.text_encoder = WanTextEncoder(self.model_name) if text_encoder is None else text_encoder
        self.vae = vae
        if vae is None:
            self.vae = WanVAEWrapper(self.model_name)

        # Step 2: Initialize scheduler
        self.num_train_timesteps = args.num_train_timestep
        self.sampling_steps = 50
        self.sample_solver = "unipc"
        self.shift = args.timestep_shift

        # Wan specific hyperparameters
        self.num_transformer_blocks = self.generator.model.num_layers
        self.num_heads = self.generator.model.num_heads
        self.patch_size = self.generator.model.patch_size
        _, _f, _c, _h, _w = args.image_or_video_shape
        self.gradient_frames = _f
        self.frame_seq_length = _h * _w // np.prod(self.patch_size)
        self.token_seq_length = _f * _h * _w // np.prod(self.patch_size)
        self.kv_cache_size = self.frame_seq_length * args.num_training_frames

        self.kv_cache_pos = None
        self.kv_cache_neg = None
        self.crossattn_cache_pos = None
        self.crossattn_cache_neg = None
        self.num_frame_per_block = args.num_frame_per_block
        self.independent_first_frame = args.independent_first_frame
        self.local_attn_size = self.generator.model.local_attn_size

        print(f"KV inference with {self.num_frame_per_block} frames per block")
        if self.local_attn_size != -1:
            # Use the local attention size to compute the KV cache size
            self.kv_cache_size = self.local_attn_size * self.frame_seq_length
        else:
            # Use the default KV cache size
            self.kv_cache_size = self.token_seq_length

        if self.num_frame_per_block > 1:
            self.generator.model.num_frame_per_block = self.num_frame_per_block

    def inference(
        self,
        noise: torch.Tensor,
        text_prompts: List[str],
        initial_latent: Optional[torch.Tensor] = None,
        return_latents: bool = False,
        start_frame_index: Optional[int] = 0,
        **kwargs,
    ) -> torch.Tensor:
        """
        Perform inference on the given noise and text prompts.
        Inputs:
            noise (torch.Tensor): The input noise tensor of shape
                (batch_size, num_output_frames, num_channels, height, width).
            text_prompts (List[str]): The list of text prompts.
            initial_latent (torch.Tensor): The initial latent tensor of shape
                (batch_size, num_input_frames, num_channels, height, width).
                If num_input_frames is 1, perform image to video.
                If num_input_frames is greater than 1, perform video extension.
            return_latents (bool): Whether to return the latents.
            start_frame_index (int): In long video generation, where does the current window start?
        Outputs:
            video (torch.Tensor): The generated video tensor of shape
                (batch_size, num_frames, num_channels, height, width). It is normalized to be in the range [0, 1].
        """
        batch_size, num_frames, num_channels, height, width = noise.shape
        if not self.independent_first_frame or (self.independent_first_frame and initial_latent is not None):
            # If the first frame is independent and the first frame is provided, then the number of frames in the
            # noise should still be a multiple of num_frame_per_block
            assert num_frames % self.num_frame_per_block == 0
            num_blocks = num_frames // self.num_frame_per_block
        elif self.independent_first_frame and initial_latent is None:
            # Using a [1, 4, 4, 4, 4, 4] model to generate a video without image conditioning
            assert (num_frames - 1) % self.num_frame_per_block == 0
            num_blocks = (num_frames - 1) // self.num_frame_per_block
        num_input_frames = initial_latent.shape[1] if initial_latent is not None else 0
        num_output_frames = num_frames + num_input_frames  # add the initial latent frames
        conditional_dict = self.text_encoder(text_prompts=text_prompts)
        unconditional_dict = self.text_encoder(text_prompts=[self.args.negative_prompt] * len(text_prompts))

        output = torch.zeros(
            [batch_size, num_output_frames, num_channels, height, width], device=noise.device, dtype=noise.dtype
        )

        # Step 1: Initialize KV cache to all zeros
        if self.kv_cache_pos is None:
            self._initialize_kv_cache(batch_size=batch_size, dtype=noise.dtype, device=noise.device)
            self._initialize_crossattn_cache(batch_size=batch_size, dtype=noise.dtype, device=noise.device)
        else:
            # reset cross attn cache
            for block_index in range(self.num_transformer_blocks):
                self.crossattn_cache_pos[block_index]["is_init"] = False
                self.crossattn_cache_neg[block_index]["is_init"] = False
            # reset kv cache
            for block_index in range(len(self.kv_cache_pos)):
                self.kv_cache_pos[block_index]["global_end_index"] = torch.tensor(
                    [0], dtype=torch.long, device=noise.device
                )
                self.kv_cache_pos[block_index]["local_end_index"] = torch.tensor(
                    [0], dtype=torch.long, device=noise.device
                )
                self.kv_cache_neg[block_index]["global_end_index"] = torch.tensor(
                    [0], dtype=torch.long, device=noise.device
                )
                self.kv_cache_neg[block_index]["local_end_index"] = torch.tensor(
                    [0], dtype=torch.long, device=noise.device
                )

        # Step 2: Cache context feature
        current_start_frame = start_frame_index
        cache_start_frame = 0
        if initial_latent is not None:
            timestep = torch.ones([batch_size, 1], device=noise.device, dtype=torch.int64) * 0
            if self.independent_first_frame:
                assert (num_input_frames - 1) % self.num_frame_per_block == 0
                num_input_blocks = (num_input_frames - 1) // self.num_frame_per_block
                output[:, :1] = initial_latent[:, :1]
                self.generator(
                    noisy_image_or_video=initial_latent[:, :1],
                    conditional_dict=conditional_dict,
                    timestep=timestep * 0,
                    kv_cache=self.kv_cache_pos,
                    crossattn_cache=self.crossattn_cache_pos,
                    current_start=current_start_frame * self.frame_seq_length,
                    cache_start=cache_start_frame * self.frame_seq_length,
                )
                self.generator(
                    noisy_image_or_video=initial_latent[:, :1],
                    conditional_dict=unconditional_dict,
                    timestep=timestep * 0,
                    kv_cache=self.kv_cache_neg,
                    crossattn_cache=self.crossattn_cache_neg,
                    current_start=current_start_frame * self.frame_seq_length,
                    cache_start=cache_start_frame * self.frame_seq_length,
                )
                current_start_frame += 1
                cache_start_frame += 1
            else:
                assert num_input_frames % self.num_frame_per_block == 0
                num_input_blocks = num_input_frames // self.num_frame_per_block

            for block_index in range(num_input_blocks):
                current_ref_latents = initial_latent[
                    :, cache_start_frame : cache_start_frame + self.num_frame_per_block
                ]
                output[:, cache_start_frame : cache_start_frame + self.num_frame_per_block] = current_ref_latents
                self.generator(
                    noisy_image_or_video=current_ref_latents,
                    conditional_dict=conditional_dict,
                    timestep=timestep * 0,
                    kv_cache=self.kv_cache_pos,
                    crossattn_cache=self.crossattn_cache_pos,
                    current_start=current_start_frame * self.frame_seq_length,
                    cache_start=cache_start_frame * self.frame_seq_length,
                )
                self.generator(
                    noisy_image_or_video=current_ref_latents,
                    conditional_dict=unconditional_dict,
                    timestep=timestep * 0,
                    kv_cache=self.kv_cache_neg,
                    crossattn_cache=self.crossattn_cache_neg,
                    current_start=current_start_frame * self.frame_seq_length,
                    cache_start=cache_start_frame * self.frame_seq_length,
                )
                current_start_frame += self.num_frame_per_block
                cache_start_frame += self.num_frame_per_block

        # Step 3: Temporal denoising loop
        all_num_frames = [self.num_frame_per_block] * num_blocks
        if self.independent_first_frame and initial_latent is None:
            all_num_frames = [1] + all_num_frames
        for current_num_frames in all_num_frames:
            noisy_input = noise[
                :, cache_start_frame - num_input_frames : cache_start_frame + current_num_frames - num_input_frames
            ]
            latents = noisy_input

            # Step 3.1: Spatial denoising loop
            sample_scheduler = self._initialize_sample_scheduler(noise)
            for _, t in enumerate(tqdm(sample_scheduler.timesteps)):
                latent_model_input = latents
                timestep = t * torch.ones([batch_size, current_num_frames], device=noise.device, dtype=torch.float32)

                flow_pred_cond, _ = self.generator(
                    noisy_image_or_video=latent_model_input,
                    conditional_dict=conditional_dict,
                    timestep=timestep,
                    kv_cache=self.kv_cache_pos,
                    crossattn_cache=self.crossattn_cache_pos,
                    current_start=current_start_frame * self.frame_seq_length,
                    cache_start=cache_start_frame * self.frame_seq_length,
                )
                flow_pred_uncond, _ = self.generator(
                    noisy_image_or_video=latent_model_input,
                    conditional_dict=unconditional_dict,
                    timestep=timestep,
                    kv_cache=self.kv_cache_neg,
                    crossattn_cache=self.crossattn_cache_neg,
                    current_start=current_start_frame * self.frame_seq_length,
                    cache_start=cache_start_frame * self.frame_seq_length,
                )

                flow_pred = flow_pred_uncond + self.args.guidance_scale * (flow_pred_cond - flow_pred_uncond)

                temp_x0 = sample_scheduler.step(flow_pred, t, latents, return_dict=False)[0]
                latents = temp_x0
            # Step 3.2: record the model's output
            output[:, cache_start_frame : cache_start_frame + current_num_frames] = latents

            # Step 3.3: rerun with timestep zero to update KV cache using clean context
            self.generator(
                noisy_image_or_video=latents,
                conditional_dict=conditional_dict,
                timestep=timestep * 0,
                kv_cache=self.kv_cache_pos,
                crossattn_cache=self.crossattn_cache_pos,
                current_start=current_start_frame * self.frame_seq_length,
                cache_start=cache_start_frame * self.frame_seq_length,
            )
            self.generator(
                noisy_image_or_video=latents,
                conditional_dict=unconditional_dict,
                timestep=timestep * 0,
                kv_cache=self.kv_cache_neg,
                crossattn_cache=self.crossattn_cache_neg,
                current_start=current_start_frame * self.frame_seq_length,
                cache_start=cache_start_frame * self.frame_seq_length,
            )

            # Step 3.4: update the start and end frame indices
            current_start_frame += current_num_frames
            cache_start_frame += current_num_frames

        # Step 4: Decode the output
        video = self.vae.decode_to_pixel(output)
        video = (video * 0.5 + 0.5).clamp(0, 1)

        if return_latents:
            return video, output
        else:
            return video

    def _initialize_kv_cache(self, batch_size, dtype, device):
        """
        Initialize a Per-GPU KV cache for the Wan model.
        """
        kv_cache_pos = []
        kv_cache_neg = []
        head_dim = self.generator.model.dim // self.generator.model.num_heads
        if self.local_attn_size != -1:
            # Use the local attention size to compute the KV cache size
            kv_cache_size = self.local_attn_size * self.frame_seq_length
        else:
            # Use the default KV cache size
            kv_cache_size = self.token_seq_length

        for _ in range(self.num_transformer_blocks):
            kv_cache_pos.append(
                {
                    "k": torch.zeros(
                        [batch_size, kv_cache_size, self.num_heads, head_dim], dtype=dtype, device=device
                    ),
                    "v": torch.zeros(
                        [batch_size, kv_cache_size, self.num_heads, head_dim], dtype=dtype, device=device
                    ),
                    "global_end_index": torch.tensor([0], dtype=torch.long, device=device),
                    "local_end_index": torch.tensor([0], dtype=torch.long, device=device),
                }
            )
            kv_cache_neg.append(
                {
                    "k": torch.zeros(
                        [batch_size, kv_cache_size, self.num_heads, head_dim], dtype=dtype, device=device
                    ),
                    "v": torch.zeros(
                        [batch_size, kv_cache_size, self.num_heads, head_dim], dtype=dtype, device=device
                    ),
                    "global_end_index": torch.tensor([0], dtype=torch.long, device=device),
                    "local_end_index": torch.tensor([0], dtype=torch.long, device=device),
                }
            )

        self.kv_cache_pos = kv_cache_pos  # always store the clean cache
        self.kv_cache_neg = kv_cache_neg  # always store the clean cache

    def _initialize_crossattn_cache(self, batch_size, dtype, device):
        """
        Initialize a Per-GPU cross-attention cache for the Wan model.
        """
        crossattn_cache_pos = []
        crossattn_cache_neg = []
        text_len = self.generator.model_config.text_len
        head_dim = self.generator.model.dim // self.generator.model.num_heads
        for _ in range(self.num_transformer_blocks):
            crossattn_cache_pos.append(
                {
                    "k": torch.zeros([batch_size, text_len, self.num_heads, head_dim], dtype=dtype, device=device),
                    "v": torch.zeros([batch_size, text_len, self.num_heads, head_dim], dtype=dtype, device=device),
                    "is_init": False,
                }
            )
            crossattn_cache_neg.append(
                {
                    "k": torch.zeros([batch_size, text_len, self.num_heads, head_dim], dtype=dtype, device=device),
                    "v": torch.zeros([batch_size, text_len, self.num_heads, head_dim], dtype=dtype, device=device),
                    "is_init": False,
                }
            )

        self.crossattn_cache_pos = crossattn_cache_pos  # always store the clean cache
        self.crossattn_cache_neg = crossattn_cache_neg  # always store the clean cache

    def _initialize_sample_scheduler(self, noise):
        if self.sample_solver == "unipc":
            sample_scheduler = FlowUniPCMultistepScheduler(
                num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False
            )
            sample_scheduler.set_timesteps(self.sampling_steps, device=noise.device, shift=self.shift)
            self.timesteps = sample_scheduler.timesteps
        elif self.sample_solver == "dpm++":
            sample_scheduler = FlowDPMSolverMultistepScheduler(
                num_train_timesteps=self.num_train_timesteps, shift=1, use_dynamic_shifting=False
            )
            sampling_sigmas = get_sampling_sigmas(self.sampling_steps, self.shift)
            self.timesteps, _ = retrieve_timesteps(sample_scheduler, device=noise.device, sigmas=sampling_sigmas)
        else:
            raise NotImplementedError("Unsupported solver.")
        return sample_scheduler