VideoX-Fun/examples/hunyuanvideo/predict_i2v.py

import os
import sys

import numpy as np
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from diffusers.utils import export_to_video
from omegaconf import OmegaConf
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 diffusers.schedulers.scheduling_unipc_multistep import \
    UniPCMultistepScheduler

from videox_fun.dist import set_multi_gpus_devices, shard_model
from videox_fun.models import (AutoencoderKLHunyuanVideo, CLIPTextModel, CLIPImageProcessor,
                               CLIPTokenizer, HunyuanVideoTransformer3DModel,
                               LlavaForConditionalGeneration, LlamaTokenizerFast)
from videox_fun.models.cache_utils import get_teacache_coefficients
from videox_fun.pipeline import HunyuanVideoPipeline, HunyuanVideoI2VPipeline
from videox_fun.utils.fm_solvers import FlowDPMSolverMultistepScheduler
from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8,
                                               convert_weight_dtype_wrapper,
                                               replace_parameters_by_name)
from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
from videox_fun.utils.utils import (filter_kwargs, get_image_to_video_latent,
                                    save_videos_grid)
from videox_fun.utils.utils import get_image

# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, 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. 
# 
# 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 the fsdp_dit and sequential_cpu_offload.
compile_dit         = False

# model path
model_name          = "models/Diffusion_Transformer/HunyuanVideo-I2V"

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

# Load pretrained model if need
transformer_path    = None
vae_path            = None
lora_path           = None

# Other params
sample_size         = [480, 832]
video_length        = 81
fps                 = 16

# Use torch.float16 if GPU does not support torch.bfloat16
# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
weight_dtype        = torch.bfloat16
# If you want to generate from text, please set the validation_image_start = None and validation_image_end = None
validation_image_start  = "asset/1.png"

# prompts
prompt              = "The dog is shaking head. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic."
negative_prompt     = "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. "
guidance_scale      = 1.0
seed                = 43
num_inference_steps = 40
lora_weight         = 0.55
save_path           = "samples/hunyuanvideo-videos-i2v"

device = set_multi_gpus_devices(ulysses_degree, ring_degree)

transformer = HunyuanVideoTransformer3DModel.from_pretrained(
    os.path.join(model_name, 'transformer'),
    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, safe_open
        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)}")

# Get Vae
vae = AutoencoderKLHunyuanVideo.from_pretrained(
    os.path.join(model_name, 'vae')
).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, safe_open
        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)}")

# Get Tokenizer
tokenizer = LlamaTokenizerFast.from_pretrained(
    os.path.join(model_name, 'tokenizer'),
)

# Get Text encoder
text_encoder = LlavaForConditionalGeneration.from_pretrained(
    os.path.join(model_name, 'text_encoder'),
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)

# Get Tokenizer 2
tokenizer_2 = CLIPTokenizer.from_pretrained(
    os.path.join(model_name, 'tokenizer_2'),
)

# Get Text encoder 2
text_encoder_2 = CLIPTextModel.from_pretrained(
    os.path.join(model_name, 'text_encoder_2'),
    low_cpu_mem_usage=True,
    torch_dtype=weight_dtype,
)

# Get Image Processor
image_processor = CLIPImageProcessor.from_pretrained(
    os.path.join(model_name, 'image_processor'),
)

# Get Scheduler
Chosen_Scheduler = scheduler_dict = {
    "Flow": FlowMatchEulerDiscreteScheduler,
    "Flow_Unipc": FlowUniPCMultistepScheduler,
    "Flow_DPM++": FlowDPMSolverMultistepScheduler,
}[sampler_name]
scheduler = Chosen_Scheduler.from_pretrained(
    os.path.join(model_name, 'scheduler'),
)

# Get Pipeline
pipeline = HunyuanVideoI2VPipeline(
    transformer=transformer,
    vae=vae,
    tokenizer=tokenizer,
    text_encoder=text_encoder,
    tokenizer_2=tokenizer_2,
    text_encoder_2=text_encoder_2,
    scheduler=scheduler,
    image_processor=image_processor,
)
if ulysses_degree > 1 or ring_degree > 1:
    from functools import partial
    transformer.enable_multi_gpus_inference()
    if fsdp_dit:
        shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype, module_to_wrapper=list(transformer.transformer_blocks) + list(transformer.single_transformer_blocks))
        pipeline.transformer = shard_fn(pipeline.transformer)
        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.language_model.layers)
        pipeline.text_encoder = shard_fn(pipeline.text_encoder)
        print("Add FSDP TEXT ENCODER")

if compile_dit:
    for i in range(len(pipeline.transformer.blocks)):
        pipeline.transformer.blocks[i] = torch.compile(pipeline.transformer.blocks[i])
    print("Add Compile")

if GPU_memory_mode == "sequential_cpu_offload":
    pipeline.enable_sequential_cpu_offload(device=device)
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
    convert_model_weight_to_float8(transformer, exclude_module_name=["x_embedder", "context_embedder", "time_text_embed", "rope", "proj_out"], device=device)
    convert_weight_dtype_wrapper(transformer, weight_dtype)
    pipeline.enable_model_cpu_offload(device=device)
elif GPU_memory_mode == "model_cpu_offload":
    pipeline.enable_model_cpu_offload(device=device)
elif GPU_memory_mode == "model_full_load_and_qfloat8":
    convert_model_weight_to_float8(transformer, exclude_module_name=["x_embedder", "context_embedder", "time_text_embed", "rope", "proj_out"], device=device)
    convert_weight_dtype_wrapper(transformer, weight_dtype)
    pipeline.to(device=device)
else:
    pipeline.to(device=device)

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)

with torch.no_grad():
    video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
    latent_frames = (video_length - 1) // vae.config.temporal_compression_ratio + 1

    # open
    image = get_image(validation_image_start)
    
    sample = pipeline(
        prompt, 
        image           = image,
        num_frames      = video_length,
        negative_prompt = negative_prompt,
        height          = sample_size[0],
        width           = sample_size[1],
        generator       = generator,
        true_cfg_scale  = guidance_scale,
        num_inference_steps = num_inference_steps,
    ).videos

if lora_path is not None:
    pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)

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)
    else:
        video_path = os.path.join(save_path, prefix + ".mp4")
        save_videos_grid(sample, video_path, fps=fps)

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