predict_t2v.py

import json
import os

import numpy as np
import torch
from diffusers import (AutoencoderKL, CogVideoXDDIMScheduler, DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from transformers import T5EncoderModel, T5Tokenizer
from omegaconf import OmegaConf
from PIL import Image

from cogvideox.models.transformer3d import CogVideoXTransformer3DModel
from cogvideox.models.autoencoder_magvit import AutoencoderKLCogVideoX
from cogvideox.pipeline.pipeline_cogvideox import CogVideoX_Fun_Pipeline
from cogvideox.pipeline.pipeline_cogvideox_inpaint import CogVideoX_Fun_Pipeline_Inpaint
from cogvideox.utils.lora_utils import merge_lora, unmerge_lora
from cogvideox.utils.utils import get_image_to_video_latent, save_videos_grid

# Low gpu memory mode, this is used when the GPU memory is under 16GB
low_gpu_memory_mode = False

# model path
model_name          = "models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP"

# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
sampler_name        = "DDIM_Origin"

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

# Other params
sample_size         = [384, 672]
# V1.0 and V1.1 support up to 49 frames of video generation,
# while V1.5 supports up to 85 frames.  
video_length        = 49
fps                 = 8

# 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
prompt              = "A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. 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      = 6.0
seed                = 43
num_inference_steps = 50
lora_weight         = 0.55
save_path           = "samples/cogvideox-fun-videos-t2v"

transformer = CogVideoXTransformer3DModel.from_pretrained_2d(
    model_name, 
    subfolder="transformer",
    low_cpu_mem_usage=True,
).to(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 = AutoencoderKLCogVideoX.from_pretrained(
    model_name, 
    subfolder="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)}")

text_encoder = T5EncoderModel.from_pretrained(
    model_name, subfolder="text_encoder", torch_dtype=weight_dtype
)
# Get Scheduler
Choosen_Scheduler = scheduler_dict = {
    "Euler": EulerDiscreteScheduler,
    "Euler A": EulerAncestralDiscreteScheduler,
    "DPM++": DPMSolverMultistepScheduler, 
    "PNDM": PNDMScheduler,
    "DDIM_Cog": CogVideoXDDIMScheduler,
    "DDIM_Origin": DDIMScheduler,
}[sampler_name]
scheduler = Choosen_Scheduler.from_pretrained(
    model_name, 
    subfolder="scheduler"
)

if transformer.config.in_channels != vae.config.latent_channels:
    pipeline = CogVideoX_Fun_Pipeline_Inpaint.from_pretrained(
        model_name,
        vae=vae,
        text_encoder=text_encoder,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype
    )
else:
    pipeline = CogVideoX_Fun_Pipeline.from_pretrained(
        model_name,
        vae=vae,
        text_encoder=text_encoder,
        transformer=transformer,
        scheduler=scheduler,
        torch_dtype=weight_dtype
    )
if low_gpu_memory_mode:
    pipeline.enable_sequential_cpu_offload()
else:
    pipeline.enable_model_cpu_offload()

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

if lora_path is not None:
    pipeline = merge_lora(pipeline, lora_path, lora_weight)

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
    if video_length != 1 and transformer.config.patch_size_t is not None and latent_frames % transformer.config.patch_size_t != 0:
        additional_frames = transformer.config.patch_size_t - latent_frames % transformer.config.patch_size_t
        video_length += additional_frames * vae.config.temporal_compression_ratio

    if transformer.config.in_channels != vae.config.latent_channels:
        input_video, input_video_mask, _ = get_image_to_video_latent(None, None, video_length=video_length, sample_size=sample_size)

        sample = pipeline(
            prompt, 
            num_frames = 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,
        ).videos
    else:
        sample = pipeline(
            prompt, 
            num_frames = 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,
        ).videos

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

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)