aigc-apps-VideoX-Fun/examples/mova/predict_i2v.py

import os
import sys

import numpy as np
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from PIL import Image

current_file_path = os.path.abspath(__file__)
project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
for project_root in project_roots:
    sys.path.insert(0, project_root) if project_root not in sys.path else None

from videox_fun.dist import set_multi_gpus_devices, shard_model
from videox_fun.models import (AutoencoderKLMOVAAudio, AutoencoderKLWan,
                               AutoTokenizer, MOVADualTowerConditionalBridge,
                               UMT5EncoderModel, WanAudioTransformer3DModel,
                               WanTransformer3DModel)
from videox_fun.pipeline import MOVAPipeline
from videox_fun.utils import (FlowDPMSolverMultistepScheduler,
                              FlowUniPCMultistepScheduler,
                              apply_gpu_memory_mode, merge_lora,
                              save_videos_with_audio_grid, unmerge_lora)

# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
# model_full_load means that the entire model will be moved to the GPU.
# 
# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
# and the transformer model has been quantized to float8, which can save more GPU memory. 
# 
# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
# 
# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use, 
# and the transformer model has been quantized to float8, which can save more GPU memory. 
# 
# model_group_offload transfers internal layer groups between CPU/CUDA, 
# balancing memory efficiency and speed between full-module and leaf-level offloading methods.
# 
# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use, 
# resulting in slower speeds but saving a large amount of GPU memory.
GPU_memory_mode     = "sequential_cpu_offload"
# Multi GPUs config
# Please ensure that the product of ulysses_degree and ring_degree equals the number of GPUs used. 
# For example, if you are using 8 GPUs, you can set ulysses_degree = 2 and ring_degree = 4.
# If you are using 1 GPU, you can set ulysses_degree = 1 and ring_degree = 1.
ulysses_degree      = 1
ring_degree         = 1
# Use FSDP to save more GPU memory in multi gpus.
fsdp_dit            = False
fsdp_text_encoder   = True
# Compile will give a speedup in fixed resolution and need a little GPU memory.
# The compile_dit is not compatible with sequential_cpu_offload.
compile_dit         = False

# model path
model_name          = "models/Diffusion_Transformer/MOVA-360p"

# Choose the sampler in "Flow", "Flow_Unipc", "Flow_DPM++"
sampler_name        = "Flow"
boundary_ratio      = 0.9

# Load pretrained model if need
# The transformer_path is used for low noise model, the transformer_high_path is used for high noise model.
transformer_path        = None
transformer_high_path   = None
transformer_audio_path  = None
bridge_path         = None
vae_path            = None
audio_vae_path      = None
# Load lora model if need
# The lora_path is used for low noise model, the lora_high_path is used for high noise model.
lora_path           = None
lora_high_path      = None

# Other params
sample_size         = [640, 352]
video_length        = 81
fps                 = 24

# Use torch.float16 if GPU does not support torch.bfloat16
# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
weight_dtype        = torch.bfloat16

# Input image for I2V
validation_image    = "asset/8.png"

# prompts
prompt              = "Medium shot of a girl by the ocean. She starts with a bright smile, then gently nods her head while speaking. Her mouth moves naturally to say: \"Hi, nice to meet you.\" She maintains eye contact throughout. The background shows calm waves. Smooth motion, cinematic quality, realistic facial expressions."
negative_prompt     = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指"
guidance_scale      = 5.0
seed                = 43
num_inference_steps = 50
# The lora_weight is used for low noise model, the lora_high_weight is used for high noise model.
lora_weight         = 0.55
lora_high_weight    = 0.55
save_path           = "samples/mova-videos-i2v"

device = set_multi_gpus_devices(ulysses_degree, ring_degree)

# The from_pretrained method automatically converts WanModel config to WanTransformer3DModel config
print("Loading Video DiT (High Noise) with WanTransformer3DModel...")
transformer = WanTransformer3DModel.from_pretrained(
    model_name,
    subfolder="video_dit_2",
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)

if transformer_path is not None:
    print(f"From checkpoint: {transformer_path}")
    if transformer_path.endswith("safetensors"):
        from safetensors.torch import load_file
        state_dict = load_file(transformer_path)
    else:
        state_dict = torch.load(transformer_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# Video DiT 2 (Low Noise) - Using WanTransformer3DModel
print("Loading Video DiT 2 (Low Noise) with WanTransformer3DModel...")
transformer_2 = WanTransformer3DModel.from_pretrained(
    model_name,
    subfolder="video_dit",
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)

if transformer_high_path is not None:
    print(f"From checkpoint: {transformer_high_path}")
    if transformer_high_path.endswith("safetensors"):
        from safetensors.torch import load_file
        state_dict = load_file(transformer_high_path)
    else:
        state_dict = torch.load(transformer_high_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
    m, u = transformer_2.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# Audio DiT - Using WanAudioTransformer3DModel
print("Loading Audio DiT with WanAudioTransformer3DModel...")
transformer_audio = WanAudioTransformer3DModel.from_pretrained(
    model_name,
    subfolder="audio_dit",
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)

if transformer_audio_path is not None:
    print(f"From checkpoint: {transformer_audio_path}")
    if transformer_audio_path.endswith("safetensors"):
        from safetensors.torch import load_file
        state_dict = load_file(transformer_audio_path)
    else:
        state_dict = torch.load(transformer_audio_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
    m, u = transformer_audio.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# Dual Tower Bridge
print("Loading Dual Tower Bridge...")
dual_tower_bridge = MOVADualTowerConditionalBridge.from_pretrained(
    model_name,
    subfolder="dual_tower_bridge",
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)

if bridge_path is not None:
    print(f"From checkpoint: {bridge_path}")
    if bridge_path.endswith("safetensors"):
        from safetensors.torch import load_file
        state_dict = load_file(bridge_path)
    else:
        state_dict = torch.load(bridge_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
    m, u = dual_tower_bridge.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# Video VAE
print("Loading Video VAE...")
vae = AutoencoderKLWan.from_pretrained(
    os.path.join(model_name, "video_vae/diffusion_pytorch_model.safetensors")
).to(weight_dtype)

if vae_path is not None:
    print(f"From checkpoint: {vae_path}")
    if vae_path.endswith("safetensors"):
        from safetensors.torch import load_file
        state_dict = load_file(vae_path)
    else:
        state_dict = torch.load(vae_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
    m, u = vae.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

audio_vae = AutoencoderKLMOVAAudio.from_pretrained(
    model_name,
    subfolder="audio_vae",
    torch_dtype=torch.float32,
)

if audio_vae_path is not None:
    print(f"From checkpoint: {audio_vae_path}")
    if audio_vae_path.endswith("safetensors"):
        from safetensors.torch import load_file
        state_dict = load_file(audio_vae_path)
    else:
        state_dict = torch.load(audio_vae_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
    m, u = audio_vae.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# Get Tokenizer
print("Loading Tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    subfolder="tokenizer",
)

# Get Text Encoder
print("Loading Text Encoder...")
text_encoder = UMT5EncoderModel.from_pretrained(
    model_name,
    subfolder="text_encoder",
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)
text_encoder = text_encoder.eval()

# Get Scheduler
print("Loading Scheduler...")
Chosen_Scheduler = {
    "Flow": FlowMatchEulerDiscreteScheduler,
    "Flow_Unipc": FlowUniPCMultistepScheduler,
    "Flow_DPM++": FlowDPMSolverMultistepScheduler,
}[sampler_name]
scheduler = Chosen_Scheduler.from_pretrained(
    model_name,
    subfolder="scheduler"
)

# Build Pipeline
print("Building MOVAPipeline Pipeline...")
pipeline = MOVAPipeline(
    vae=vae,
    audio_vae=audio_vae,
    text_encoder=text_encoder,
    tokenizer=tokenizer,
    scheduler=scheduler,
    transformer=transformer,
    transformer_2=transformer_2,
    transformer_audio=transformer_audio,
    dual_tower_bridge=dual_tower_bridge,
    audio_vae_type="dac",
)

if ulysses_degree > 1 or ring_degree > 1:
    from functools import partial

    # Enable multi-GPU inference for visual transformers
    transformer.enable_multi_gpus_inference()
    transformer_2.enable_multi_gpus_inference()
    
    if fsdp_dit:
        # Apply FSDP to visual transformer blocks
        shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype)
        pipeline.transformer = shard_fn(pipeline.transformer)
        pipeline.transformer_2 = shard_fn(pipeline.transformer_2)
        print("Add FSDP DIT")
    
    if fsdp_text_encoder:
        shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype, module_to_wrapper=text_encoder.encoder.block)
        pipeline.text_encoder = shard_fn(pipeline.text_encoder)
        print("Add FSDP TEXT ENCODER")

if compile_dit:
    # Compile MOVAModel blocks
    # NOTE: compile_dit is not compatible with fsdp_dit
    if fsdp_dit:
        print("WARNING: compile_dit is not compatible with fsdp_dit. Disabling compile.")
    else:
        for i in range(len(pipeline.transformer.blocks)):
            pipeline.transformer.blocks[i] = torch.compile(pipeline.transformer.blocks[i])
        for i in range(len(pipeline.transformer_2.blocks)):
            pipeline.transformer_2.blocks[i] = torch.compile(pipeline.transformer_2.blocks[i])
        for i in range(len(pipeline.transformer_audio.blocks)):
            pipeline.transformer_audio.blocks[i] = torch.compile(pipeline.transformer_audio.blocks[i])
        print("Add Compile")

# Quantize (when the mode carries an "_and_<quant>" suffix) and then place the pipeline.
# The order lives inside the helper: quantization has to happen before the offload hooks
# are installed, and both transformers of this MoE setup are handled in one call, which
# is exactly the bookkeeping the old 30-line if/elif chain repeated per script.
apply_gpu_memory_mode(pipeline, GPU_memory_mode, device, weight_dtype)

generator = torch.Generator(device=device).manual_seed(seed)

if lora_path is not None:
    pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
    pipeline = merge_lora(pipeline, lora_high_path, lora_high_weight, device=device, dtype=weight_dtype, sub_transformer_name="transformer_2")

# Run inference
print("Running inference...")
with torch.no_grad():
    image = Image.open(validation_image).convert("RGB")
    output = pipeline(
        prompt=prompt,
        image=image,
        negative_prompt=negative_prompt,
        height=sample_size[0],
        width=sample_size[1],
        num_frames=video_length,
        frame_rate=fps,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=generator,
        boundary=boundary_ratio,
    )

if lora_path is not None:
    pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
    pipeline = unmerge_lora(pipeline, lora_high_path, lora_high_weight, device=device, dtype=weight_dtype, sub_transformer_name="transformer_2")

sample = output.videos
audio = output.audio

# Get audio sample rate from pipeline
audio_sample_rate = pipeline.audio_sample_rate

def save_results():
    if not os.path.exists(save_path):
        os.makedirs(save_path, exist_ok=True)

    index = len([path for path in os.listdir(save_path)]) + 1
    prefix = str(index).zfill(8)
    if video_length == 1:
        video_path = os.path.join(save_path, prefix + ".png")

        image = sample[0, :, 0]
        image = image.transpose(0, 1).transpose(1, 2)
        image = (image * 255).numpy().astype(np.uint8)
        image = Image.fromarray(image)
        image.save(video_path)
        print(f"Saved image to: {video_path}")
    else:
        video_path = os.path.join(save_path, prefix + ".mp4")
        sr = getattr(pipeline.audio_vae.config, "output_sampling_rate", audio_sample_rate)
        save_videos_with_audio_grid(sample, audio, video_path, fps=fps, audio_sample_rate=sr)

if ulysses_degree > 1 or ring_degree > 1:
    import torch.distributed as dist
    if dist.get_rank() == 0:
        save_results()
else:
    save_results()