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import os
import sys
import numpy as np
import torch
from diffusers import (CogVideoXDDIMScheduler, DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
PNDMScheduler)
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 cogvideox.models import (AutoencoderKLCogVideoX,
CogVideoXTransformer3DModel, T5EncoderModel,
T5Tokenizer)
from cogvideox.pipeline import (CogVideoXFunPipeline,
CogVideoXFunInpaintPipeline)
from cogvideox.utils.lora_utils import merge_lora, unmerge_lora
from cogvideox.utils.fp8_optimization import convert_weight_dtype_wrapper
from cogvideox.utils.utils import get_video_to_video_latent, save_videos_grid
# 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_and_qfloat8"
# model path
model_name = "models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP"
# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" "DDIM_Cog" and "DDIM_Origin"
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
# If you are preparing to redraw the reference video, set validation_video and validation_video_mask.
# If you do not use validation_video_mask, the entire video will be redrawn;
# if you use validation_video_mask, only a portion of the video will be redrawn.
# Please set a larger denoise_strength when using validation_video_mask, such as 1.00 instead of 0.70
validation_video = "asset/1.mp4"
validation_video_mask = None
denoise_strength = 0.70
# prompts
prompt = "A cute cat is playing the guitar. "
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_v2v"
transformer = CogVideoXTransformer3DModel.from_pretrained(
model_name,
subfolder="transformer",
low_cpu_mem_usage=True,
torch_dtype=torch.float8_e4m3fn if GPU_memory_mode == "model_cpu_offload_and_qfloat8" else weight_dtype,
).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)}")
# Get tokenizer and text_encoder
tokenizer = T5Tokenizer.from_pretrained(
model_name, subfolder="tokenizer"
)
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 = CogVideoXFunInpaintPipeline(
vae=vae,
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
scheduler=scheduler,
)
else:
pipeline = CogVideoXFunPipeline(
vae=vae,
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
scheduler=scheduler,
)
if GPU_memory_mode == "sequential_cpu_offload":
pipeline.enable_sequential_cpu_offload()
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
convert_weight_dtype_wrapper(transformer, weight_dtype)
pipeline.enable_model_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)
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
input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, video_length=video_length, sample_size=sample_size, validation_video_mask=validation_video_mask, fps=fps)
with torch.no_grad():
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,
strength = denoise_strength,
).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:
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:
video_path = os.path.join(save_path, prefix + ".mp4")
save_videos_grid(sample, video_path, fps=fps) |