aigc-apps-EasyAnimate/predict_i2v_multi_gpus.py

import os

import numpy as np
import torch
import torch.distributed as dist
from diffusers import (DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from omegaconf import OmegaConf
from PIL import Image
from transformers import (BertModel, BertTokenizer, CLIPImageProcessor,
                          CLIPVisionModelWithProjection,
                          T5EncoderModel, T5Tokenizer)

from easyanimate.models import (name_to_autoencoder_magvit,
                                name_to_transformer3d)
from easyanimate.pipeline.pipeline_easyanimate_inpaint import \
    EasyAnimateInpaintPipeline
from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import \
    EasyAnimatePipeline_Multi_Text_Encoder_Inpaint
from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
from easyanimate.utils.fp8_optimization import convert_weight_dtype_wrapper


try:
    import xfuser
    from xfuser.core.distributed import (
        get_sequence_parallel_world_size,
        get_sequence_parallel_rank,
        get_sp_group,
        initialize_model_parallel,
        init_distributed_environment
    )
except:
    xfuser = None
    get_sequence_parallel_world_size = None
    get_sequence_parallel_rank = None
    get_sp_group = None
    initialize_model_parallel = None
    init_distributed_environment = None

ulysses_degree  = 2
ring_degree = 2

if ulysses_degree > 1 or ring_degree > 1:
    dist.init_process_group("nccl")
    print('parallel inference enabled: ulysses_degree=%d ring_degree=%d rank=%d world_size=%d' % (
        ulysses_degree, ring_degree, dist.get_rank(),
        dist.get_world_size()))
    assert dist.get_world_size() == ring_degree * ulysses_degree, \
                "number of GPUs(%d) should be equal to ring_degree * ulysses_degree." % dist.get_world_size()
    init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size())
    initialize_model_parallel(sequence_parallel_degree=dist.get_world_size(),
            ring_degree=ring_degree,
            ulysses_degree=ulysses_degree)
    device = torch.device("cuda:%d" % dist.get_rank())
    print('rank=%d device=%s' % (dist.get_rank(), str(device)))
else:
    device = "cuda"

# GPU memory mode, which can be choosen in [model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
# 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     = "model_cpu_offload"

# Config and model path
config_path         = "config/easyanimate_video_v5_magvit_multi_text_encoder.yaml"
model_name          = "models/Diffusion_Transformer/EasyAnimateV5-12b-zh-InP"

# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
# EasyAnimateV1, V2 and V3 cannot use DDIM.
# EasyAnimateV4 and V5 support DDIM.
sampler_name        = "DDIM"

# Load pretrained model if need
transformer_path    = None
# Only V1 does need a motion module
motion_module_path  = None
vae_path            = None
lora_path           = None

# Other params
# sample_size         = [384, 672]
# sample_size         = [576, 1008]
sample_size         = [720, 1280]
# In EasyAnimateV1, the video_length of video is 40 ~ 80.
# In EasyAnimateV2, V3, V4, the video_length of video is 1 ~ 144. 
# In EasyAnimateV5, the video_length of video is 1 ~ 49.
# If u want to generate a image, please set the video_length = 1.
video_length        = 49
fps                 = 8

# If you want to generate ultra long videos, please set partial_video_length as the length of each sub video segment
partial_video_length = None
overlap_video_length = 4

# 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"
validation_image_end    = None

# EasyAnimateV1, V2 and V3 support English.
# EasyAnimateV4 and V5 support English and Chinese.
# 使用更长的neg prompt如"模糊，突变，变形，失真，画面暗，文本字幕，画面固定，连环画，漫画，线稿，没有主体。"，可以增加稳定性
# 在neg prompt中添加"安静，固定"等词语可以增加动态性。
prompt                  = "一条狗正在摇头。质量高、杰作、最佳品质、高分辨率、超精细、梦幻般。"
negative_prompt         = "扭曲的身体，肢体残缺，文本字幕，漫画，静止，丑陋，错误，乱码。"
# 
# Using longer neg prompt such as "Blurring, mutation, deformation, distortion, dark and solid, comics, text subtitles, line art." can increase stability
# Adding words such as "quiet, solid" to the neg prompt can increase dynamism.
# 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         = "Twisted body, limb deformities, text captions, comic, static, ugly, error, messy code."
guidance_scale          = 6.0
seed                    = 43
num_inference_steps     = 50
lora_weight             = 0.60
save_path               = "samples/easyanimate-videos_i2v"

config = OmegaConf.load(config_path)

# Get Transformer
Choosen_Transformer3DModel = name_to_transformer3d[
    config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel')
]

transformer_additional_kwargs = OmegaConf.to_container(config['transformer_additional_kwargs'])
if weight_dtype == torch.float16:
    transformer_additional_kwargs["upcast_attention"] = True

transformer = Choosen_Transformer3DModel.from_pretrained_2d(
    model_name, 
    subfolder="transformer",
    transformer_additional_kwargs=transformer_additional_kwargs,
    torch_dtype=torch.float8_e4m3fn if GPU_memory_mode == "model_cpu_offload_and_qfloat8" else weight_dtype,
    low_cpu_mem_usage=True,
)

transformer = transformer.to(device)

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)}")

if motion_module_path is not None:
    print(f"From Motion Module: {motion_module_path}")
    if motion_module_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(motion_module_path)
    else:
        state_dict = torch.load(motion_module_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)}, {u}")

# Get Vae
Choosen_AutoencoderKL = name_to_autoencoder_magvit[
    config['vae_kwargs'].get('vae_type', 'AutoencoderKL')
]
vae = Choosen_AutoencoderKL.from_pretrained(
    model_name, 
    subfolder="vae",
    vae_additional_kwargs=OmegaConf.to_container(config['vae_kwargs'])
).to(weight_dtype).to(device)
if config['vae_kwargs'].get('vae_type', 'AutoencoderKL') == 'AutoencoderKLMagvit' and weight_dtype == torch.float16:
    vae.upcast_vae = True

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)}")

if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
    tokenizer = BertTokenizer.from_pretrained(
        model_name, subfolder="tokenizer"
    )
    tokenizer_2 = T5Tokenizer.from_pretrained(
        model_name, subfolder="tokenizer_2"
    )
else:
    tokenizer = T5Tokenizer.from_pretrained(
        model_name, subfolder="tokenizer"
    )
    tokenizer_2 = None

if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
    text_encoder = BertModel.from_pretrained(
        model_name, subfolder="text_encoder", torch_dtype=weight_dtype
    ).to(device)
    text_encoder_2 = T5EncoderModel.from_pretrained(
        model_name, subfolder="text_encoder_2", torch_dtype=weight_dtype
    ).to(device)
else:
    text_encoder = T5EncoderModel.from_pretrained(
        model_name, subfolder="text_encoder", torch_dtype=weight_dtype
    ).to(device)
    text_encoder_2 = None

if transformer.config.in_channels != vae.config.latent_channels and config['transformer_additional_kwargs'].get('enable_clip_in_inpaint', True):
    clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
        model_name, subfolder="image_encoder"
    ).to(device, weight_dtype)
    clip_image_processor = CLIPImageProcessor.from_pretrained(
        model_name, subfolder="image_encoder"
    )
else:
    clip_image_encoder = None
    clip_image_processor = None

# Get Scheduler
Choosen_Scheduler = scheduler_dict = {
    "Euler": EulerDiscreteScheduler,
    "Euler A": EulerAncestralDiscreteScheduler,
    "DPM++": DPMSolverMultistepScheduler, 
    "PNDM": PNDMScheduler,
    "DDIM": DDIMScheduler,
}[sampler_name]

scheduler = Choosen_Scheduler.from_pretrained(
    model_name, 
    subfolder="scheduler"
)
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
    pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.from_pretrained(
        model_name,
        text_encoder=text_encoder,
        text_encoder_2=text_encoder_2,
        tokenizer=tokenizer,
        tokenizer_2=tokenizer_2,
        vae=vae,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype,
        clip_image_encoder=clip_image_encoder,
        clip_image_processor=clip_image_processor,
    )
else:
    pipeline = EasyAnimateInpaintPipeline.from_pretrained(
        model_name,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        vae=vae,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype,
        clip_image_encoder=clip_image_encoder,
        clip_image_processor=clip_image_processor,
    )

if GPU_memory_mode == "sequential_cpu_offload":
    pipeline.enable_sequential_cpu_offload(device=device)
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
    pipeline.enable_model_cpu_offload(device=device)
    convert_weight_dtype_wrapper(transformer, weight_dtype)
else:
    pipeline.enable_model_cpu_offload(device=device)

# print('pipeline to device=%s' % str(device))
# pipeline.to(device)
# print('pipeline.device=%s' % str(pipeline.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)

if partial_video_length is not None:
    init_frames = 0
    last_frames = init_frames + partial_video_length
    while init_frames < video_length:
        if last_frames >= video_length:
            if pipeline.vae.quant_conv.weight.ndim==5:
                mini_batch_encoder = pipeline.vae.mini_batch_encoder
                _partial_video_length = video_length - init_frames
                if vae.cache_mag_vae:
                    _partial_video_length = int((_partial_video_length - 1) // vae.mini_batch_encoder * vae.mini_batch_encoder) + 1
                else:
                    _partial_video_length = int(_partial_video_length // vae.mini_batch_encoder * vae.mini_batch_encoder)
            else:
                _partial_video_length = video_length - init_frames
            
            if _partial_video_length <= 0:
                break
        else:
            _partial_video_length = partial_video_length

        input_video, input_video_mask, clip_image = get_image_to_video_latent(validation_image, None, video_length=_partial_video_length, sample_size=sample_size)
        
        with torch.no_grad():
            sample = pipeline(
                prompt, 
                video_length = _partial_video_length,
                negative_prompt = negative_prompt,
                height      = sample_size[0],
                width       = sample_size[1],
                generator   = generator,
                guidance_scale = guidance_scale,
                num_inference_steps = num_inference_steps,

                video        = input_video,
                mask_video   = input_video_mask,
                clip_image   = clip_image, 
            ).videos
        
        if init_frames != 0:
            mix_ratio = torch.from_numpy(
                np.array([float(_index) / float(overlap_video_length) for _index in range(overlap_video_length)], np.float32)
            ).unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
            
            new_sample[:, :, -overlap_video_length:] = new_sample[:, :, -overlap_video_length:] * (1 - mix_ratio) + \
                sample[:, :, :overlap_video_length] * mix_ratio
            new_sample = torch.cat([new_sample, sample[:, :, overlap_video_length:]], dim = 2)

            sample = new_sample
        else:
            new_sample = sample

        if last_frames >= video_length:
            break

        validation_image = [
            Image.fromarray(
                (sample[0, :, _index].transpose(0, 1).transpose(1, 2) * 255).numpy().astype(np.uint8)
            ) for _index in range(-overlap_video_length, 0)
        ]

        init_frames = init_frames + _partial_video_length - overlap_video_length
        last_frames = init_frames + _partial_video_length
else:
    if vae.cache_mag_vae:
        video_length = int((video_length - 1) // vae.mini_batch_encoder * vae.mini_batch_encoder) + 1 if video_length != 1 else 1
    else:
        video_length = int(video_length // vae.mini_batch_encoder * vae.mini_batch_encoder) if video_length != 1 else 1
    input_video, input_video_mask, clip_image = get_image_to_video_latent(validation_image_start, validation_image_end, video_length=video_length, sample_size=sample_size)

    with torch.no_grad():
        sample = pipeline(
            prompt, 
            video_length = video_length,
            negative_prompt = negative_prompt,
            height      = sample_size[0],
            width       = sample_size[1],
            generator   = generator,
            guidance_scale = guidance_scale,
            num_inference_steps = num_inference_steps,

            video        = input_video,
            mask_video   = input_video_mask,
            clip_image   = clip_image, 
        ).videos

if lora_path is not None:
    pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
    
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:
    save_sample_path = os.path.join(save_path, prefix + f".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(save_sample_path)
else:
    if ulysses_degree * ring_degree > 1:
       if dist.get_rank() == 0:
            video_path = os.path.join(save_path, prefix + ".mp4")
            save_videos_grid(sample, video_path, fps=fps)
            print('save video to %s' % video_path)
    else:
       video_path = os.path.join(save_path, prefix + ".mp4")
       save_videos_grid(sample, video_path, fps=fps)
       print('save video to %s' % video_path)