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update I2V infer code and draft readme

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zR 2024-09-15 16:39:13 +08:00
parent 70ff60c925
commit 098640337d
6 changed files with 74 additions and 103 deletions
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42
inference/cli_demo.py
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@ -21,11 +21,13 @@ import argparse
from typing import Literal
import torch
from diffusers import (CogVideoXPipeline,
from diffusers import (
CogVideoXPipeline,
CogVideoXDDIMScheduler,
CogVideoXDPMScheduler,
CogVideoXImageToVideoPipeline,
CogVideoXVideoToVideoPipeline)
CogVideoXVideoToVideoPipeline,
)
from diffusers.utils import export_to_video, load_image, load_video
@ -53,7 +55,7 @@ def generate_video(
- guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
- num_videos_per_prompt (int): Number of videos to generate per prompt.
- dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
- generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').
- generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
- seed (int): The seed for reproducibility.
"""
@ -97,7 +99,7 @@ def generate_video(
image=image, # The path of the image to be used as the background of the video
num_videos_per_prompt=num_videos_per_prompt, # Number of videos to generate per prompt
num_inference_steps=num_inference_steps, # Number of inference steps
num_frames=49, # Number of frames to generate，changed to 49 for diffusers version `0.31.0` and after.
num_frames=49, # Number of frames to generate，changed to 49 for diffusers version `0.30.3` and after.
use_dynamic_cfg=True, ## This id used for DPM Sechduler, for DDIM scheduler, it should be False
guidance_scale=guidance_scale,
generator=torch.Generator().manual_seed(seed), # Set the seed for reproducibility
@ -130,19 +132,29 @@ def generate_video(
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
parser.add_argument("--image_or_video_path", type=str, default=None,
help="The path of the image to be used as the background of the video")
parser.add_argument("--model_path", type=str, default="THUDM/CogVideoX-5b",
help="The path of the pre-trained model to be used")
parser.add_argument("--output_path", type=str, default="./output.mp4",
help="The path where the generated video will be saved")
parser.add_argument(
"--image_or_video_path",
type=str,
default=None,
help="The path of the image to be used as the background of the video",
)
parser.add_argument(
"--model_path", type=str, default="THUDM/CogVideoX-5b", help="The path of the pre-trained model to be used"
)
parser.add_argument(
"--output_path", type=str, default="./output.mp4", help="The path where the generated video will be saved"
)
parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
parser.add_argument("--num_inference_steps", type=int, default=50, help="Number of steps for the inference process")
parser.add_argument(
"--num_inference_steps", type=int, default=50, help="Number of steps for the inference process"
)
parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
parser.add_argument("--generate_type", type=str, default="t2v",
help="The type of video generation (e.g., 't2v', 'i2v', 'v2v')")
parser.add_argument("--dtype", type=str, default="bfloat16",
help="The data type for computation (e.g., 'float16' or 'bfloat16')")
parser.add_argument(
"--generate_type", type=str, default="t2v", help="The type of video generation (e.g., 't2v', 'i2v', 'v2v')"
)
parser.add_argument(
"--dtype", type=str, default="bfloat16", help="The data type for computation (e.g., 'float16' or 'bfloat16')"
)
parser.add_argument("--seed", type=int, default=42, help="The seed for reproducibility")
args = parser.parse_args()

69
inference/gradio_composite_demo/rife_model.py
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@ -8,7 +8,7 @@ import numpy as np
import logging
import skvideo.io
from rife.RIFE_HDv3 import Model
from huggingface_hub import hf_hub_download, snapshot_download
logger = logging.getLogger(__name__)
device = "cuda" if torch.cuda.is_available() else "cpu"
@ -19,9 +19,8 @@ def pad_image(img, scale):
tmp = max(32, int(32 / scale))
ph = ((h - 1) // tmp + 1) * tmp
pw = ((w - 1) // tmp + 1) * tmp
padding = (0, pw - w, 0, ph - h)
return F.pad(img, padding), padding
padding = (0, 0, pw - w, ph - h)
return F.pad(img, padding)
def make_inference(model, I0, I1, upscale_amount, n):
@ -45,15 +44,9 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi
for b in range(samples.shape[0]):
frame = samples[b : b + 1]
_, _, h, w = frame.shape
I0 = samples[b : b + 1]
I1 = samples[b + 1 : b + 2] if b + 2 < samples.shape[0] else samples[-1:]
I0, padding = pad_image(I0, upscale_amount)
I0 = I0.to(torch.float)
I1, _ = pad_image(I1, upscale_amount)
I1 = I1.to(torch.float)
I1 = pad_image(I1, upscale_amount)
# [c, h, w]
I0_small = F.interpolate(I0, (32, 32), mode="bilinear", align_corners=False)
I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False)
@ -61,24 +54,14 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
if ssim > 0.996:
I1 = samples[b : b + 1]
# print(f'upscale_amount:{upscale_amount}')
# print(f'ssim:{upscale_amount}')
# print(f'I0 shape:{I0.shape}')
# print(f'I1 shape:{I1.shape}')
I1, padding = pad_image(I1, upscale_amount)
# print(f'I0 shape:{I0.shape}')
# print(f'I1 shape:{I1.shape}')
I1 = I0
I1 = pad_image(I1, upscale_amount)
I1 = make_inference(model, I0, I1, upscale_amount, 1)
# print(f'I0 shape:{I0.shape}')
# print(f'I1[0] shape:{I1[0].shape}')
I1 = I1[0]
# print(f'I1[0] unpadded shape:{I1.shape}')
I1_small = F.interpolate(I1, (32, 32), mode="bilinear", align_corners=False)
I1_small = F.interpolate(I1[0], (32, 32), mode="bilinear", align_corners=False)
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
frame = I1[padding[0]:, padding[2]:, padding[3]:,padding[1]:]
frame = I1[0]
I1 = I1[0]
tmp_output = []
if ssim < 0.2:
@ -88,13 +71,9 @@ def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_devi
else:
tmp_output = make_inference(model, I0, I1, upscale_amount, 2**exp - 1) if exp else []
frame, _ = pad_image(frame, upscale_amount)
print(f'frame shape:{frame.shape}')
print(f'tmp_output[0] shape:{tmp_output[0].shape}')
frame = pad_image(frame, upscale_amount)
tmp_output = [frame] + tmp_output
for i, frame in enumerate(tmp_output):
frame = F.interpolate(frame, size=(h, w))
output.append(frame.to(output_device))
return output
@ -117,26 +96,14 @@ def frame_generator(video_capture):
def rife_inference_with_path(model, video_path):
# Open the video file
video_capture = cv2.VideoCapture(video_path)
fps = video_capture.get(cv2.CAP_PROP_FPS) # Get the frames per second
tot_frame = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT)) # Total frames in the video
tot_frame = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
pt_frame_data = []
pt_frame = skvideo.io.vreader(video_path)
# Cyclic reading of the video frames
while video_capture.isOpened():
ret, frame = video_capture.read()
if not ret:
break
# BGR to RGB
frame_rgb = frame[..., ::-1]
frame_rgb = frame_rgb.copy()
tensor = torch.from_numpy(frame_rgb).float().to("cpu", non_blocking=True).float() / 255.0
for frame in pt_frame:
pt_frame_data.append(
tensor.permute(2, 0, 1)
) # to [c, h, w,]
torch.from_numpy(np.transpose(frame, (2, 0, 1))).to("cpu", non_blocking=True).float() / 255.0
)
pt_frame = torch.from_numpy(np.stack(pt_frame_data))
pt_frame = pt_frame.to(device)
@ -164,11 +131,3 @@ def rife_inference_with_latents(model, latents):
rife_results.append(pt_image)
return torch.stack(rife_results)
if __name__ == "__main__":
snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife")
model = load_rife_model("model_rife")
video_path = rife_inference_with_path(model, "/mnt/ceph/develop/jiawei/CogVideo/sat/configs/outputs/1_In_the_heart_of_a_bustling_city,_a_young_woman_with_long,_flowing_brown_hair_and_a_radiant_smile_stands_out._She's_donne/0/000000.mp4")
print(video_path)

3
inference/gradio_web_demo.py
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@ -19,8 +19,9 @@ from openai import OpenAI
import moviepy.editor as mp
dtype = torch.bfloat16
device = "cuda" # Need to use cuda
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=dtype)
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=dtype).to(device)
pipe.enable_model_cpu_offload()
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()

26
requirements.txt
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@ -1,15 +1,15 @@
diffusers>=0.30.1 #git+https://github.com/huggingface/diffusers.git@main#egg=diffusers is suggested
transformers>=4.44.2 # The development team is working on version 4.44.2
accelerate>=0.33.0 #git+https://github.com/huggingface/accelerate.git@main#egg=accelerate is suggested
sentencepiece>=0.2.0 # T5 used
SwissArmyTransformer>=0.4.12
diffusers>=0.30.3
accelerate>=0.34.2
transformers>=4.44.2
numpy==1.26.0
torch>=2.4.0 # Tested in 2.2 2.3 2.4 and 2.5, The development team is working on version 2.4.0.
torchvision>=0.19.0 # The development team is working on version 0.19.0.
gradio>=4.42.0 # For HF gradio demo
streamlit>=1.38.0 # For streamlit web demo
imageio==2.34.2 # For diffusers inference export video
imageio-ffmpeg==0.5.1 # For diffusers inference export video
openai>=1.42.0 # For prompt refiner
moviepy==1.0.3 # For export video
torch==2.4.0
torchvision==0.19.0
sentencepiece==0.2.0
SwissArmyTransformer>=0.4.12
gradio>=4.44.0
streamlit>=1.38.0
imageio>=2.35.1
imageio-ffmpeg>=0.5.1
openai>=1.45.0
moviepy==1.0.3
pillow==9.5.0

1
sat/diffusion_video.py
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@ -323,7 +323,6 @@ class SATVideoDiffusionEngine(nn.Module):
if isinstance(c[k], torch.Tensor):
c[k], uc[k] = map(lambda y: y[k][:N].to(self.device), (c, uc))
if self.noised_image_input:
image = x[:, :, 0:1]
image = self.add_noise_to_first_frame(image)

4
sat/finetune_multi_gpus.sh
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@ -1,8 +1,8 @@
#! /bin/bash
echo "RUN on $(hostname), CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True"
echo "RUN on $(hostname), CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
run_cmd="torchrun --standalone --nproc_per_node=8 train_video.py --base configs/cogvideox_5b_i2v_lora.yaml configs/sft.yaml --seed $RANDOM"
run_cmd="PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True torchrun --standalone --nproc_per_node=8 train_video.py --base configs/test_cogvideox_5b_i2v_lora.yaml configs/test_sft.yaml --seed $RANDOM"
echo ${run_cmd}
eval ${run_cmd}