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app_old.py
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import math
import os
import random
import threading
import time
import argparse
import cv2
import tempfile
import imageio_ffmpeg
import gradio as gr
import torch
from PIL import Image
from diffusers import (
CogVideoXPipeline,
CogVideoXDPMScheduler,
CogVideoXVideoToVideoPipeline,
CogVideoXImageToVideoPipeline,
CogVideoXTransformer3DModel,
)
from diffusers.utils import export_to_video, load_video, load_image
from datetime import datetime, timedelta
from diffusers.image_processor import VaeImageProcessor
from openai import OpenAI
import moviepy.editor as mp
import utils
from rife_model import load_rife_model, rife_inference_with_latents
from huggingface_hub import hf_hub_download, snapshot_download
import gc
import platform
# Add imports for quantization
from transformers import T5EncoderModel
from diffusers import AutoencoderKLCogVideoX
def is_bf16_supported():
if torch.cuda.is_available():
return torch.cuda.is_bf16_supported()
return False
if is_bf16_supported():
default_dtype = torch.bfloat16
print("Using bfloat16 precision")
else:
default_dtype = torch.float16
print("Using float16 precision")
def open_folder(folder_path):
if platform.system() == "Windows":
os.startfile(folder_path)
elif platform.system() == "Linux":
os.system(f'xdg-open "{folder_path}"')
elif platform.system() == "Darwin": # macOS
os.system(f'open "{folder_path}"')
try:
from torchao.quantization import quantize_, int8_weight_only, int8_dynamic_activation_int8_weight
TORCHAO_AVAILABLE = True
except ImportError:
TORCHAO_AVAILABLE = False
device = "cuda" if torch.cuda.is_available() else "cpu"
hf_hub_download(repo_id="ai-forever/Real-ESRGAN", filename="RealESRGAN_x4.pth", local_dir="model_real_esran")
snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife")
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=default_dtype).to("cpu")
pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
i2v_transformer = CogVideoXTransformer3DModel.from_pretrained(
"THUDM/CogVideoX-5b-I2V", subfolder="transformer", torch_dtype=default_dtype
)
os.makedirs("./outputs", exist_ok=True)
os.makedirs("./gradio_tmp", exist_ok=True)
upscale_model = utils.load_sd_upscale("model_real_esran/RealESRGAN_x4.pth", device)
frame_interpolation_model = load_rife_model("model_rife")
def load_and_quantize_model(quantization_type):
text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="text_encoder", torch_dtype=default_dtype)
transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="transformer", torch_dtype=default_dtype)
vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="vae", torch_dtype=default_dtype)
if quantization_type == "int8" and TORCHAO_AVAILABLE:
quantize_(text_encoder, int8_weight_only())
quantize_(transformer, int8_weight_only())
quantize_(vae, int8_weight_only())
elif quantization_type == "fp8": # Check if GPU supports FP8
text_encoder = text_encoder.to(torch.float8_e4m3fn)
transformer = transformer.to(torch.float8_e4m3fn)
vae = vae.to(torch.float8_e4m3fn)
return text_encoder, transformer, vae
def resize_if_unfit(input_video, progress=gr.Progress(track_tqdm=True)):
width, height = get_video_dimensions(input_video)
if width == 720 and height == 480:
processed_video = input_video
else:
processed_video = center_crop_resize(input_video)
return processed_video
def get_video_dimensions(input_video_path):
reader = imageio_ffmpeg.read_frames(input_video_path)
metadata = next(reader)
return metadata["size"]
def center_crop_resize(input_video_path, target_width=720, target_height=480):
cap = cv2.VideoCapture(input_video_path)
orig_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
orig_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
orig_fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
width_factor = target_width / orig_width
height_factor = target_height / orig_height
resize_factor = max(width_factor, height_factor)
inter_width = int(orig_width * resize_factor)
inter_height = int(orig_height * resize_factor)
target_fps = 8
ideal_skip = max(0, math.ceil(orig_fps / target_fps) - 1)
skip = min(5, ideal_skip) # Cap at 5
while (total_frames / (skip + 1)) < 49 and skip > 0:
skip -= 1
processed_frames = []
frame_count = 0
total_read = 0
while frame_count < 49 and total_read < total_frames:
ret, frame = cap.read()
if not ret:
break
if total_read % (skip + 1) == 0:
resized = cv2.resize(frame, (inter_width, inter_height), interpolation=cv2.INTER_AREA)
start_x = (inter_width - target_width) // 2
start_y = (inter_height - target_height) // 2
cropped = resized[start_y : start_y + target_height, start_x : start_x + target_width]
processed_frames.append(cropped)
frame_count += 1
total_read += 1
cap.release()
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_file:
temp_video_path = temp_file.name
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(temp_video_path, fourcc, target_fps, (target_width, target_height))
for frame in processed_frames:
out.write(frame)
out.release()
return temp_video_path
def infer(
prompt: str,
image_input: str,
video_input: str,
video_strenght: float,
num_inference_steps: int,
guidance_scale: float,
seed: int = -1,
use_cpu_offload: bool = True,
use_slicing: bool = True,
use_tiling: bool = True,
quantization_type: str = "none",
progress=gr.Progress(track_tqdm=True),
):
if seed == -1:
seed = random.randint(0, 2**8 - 1)
text_encoder, transformer, vae = load_and_quantize_model(quantization_type)
if video_input is not None:
video = load_video(video_input)[:49] # Limit to 49 frames
pipe_video = CogVideoXVideoToVideoPipeline.from_pretrained(
"THUDM/CogVideoX-5b",
transformer=transformer,
vae=vae,
scheduler=pipe.scheduler,
tokenizer=pipe.tokenizer,
text_encoder=text_encoder,
torch_dtype=default_dtype,
).to(device)
if use_cpu_offload:
pipe_video.enable_sequential_cpu_offload()
if use_slicing:
pipe_video.vae.enable_slicing()
if use_tiling:
pipe_video.vae.enable_tiling()
video_pt = pipe_video(
video=video,
prompt=prompt,
num_inference_steps=num_inference_steps,
num_videos_per_prompt=1,
strength=video_strenght,
use_dynamic_cfg=True,
output_type="pt",
guidance_scale=guidance_scale,
generator=torch.Generator(device="cpu").manual_seed(seed),
).frames
gc.collect()
torch.cuda.empty_cache()
elif image_input is not None:
pipe_image = CogVideoXImageToVideoPipeline.from_pretrained(
"THUDM/CogVideoX-5b-I2V",
transformer=transformer,
vae=vae,
scheduler=pipe.scheduler,
tokenizer=pipe.tokenizer,
text_encoder=text_encoder,
torch_dtype=default_dtype,
).to(device)
if use_cpu_offload:
pipe_image.enable_sequential_cpu_offload()
if use_slicing:
pipe_image.vae.enable_slicing()
if use_tiling:
pipe_image.vae.enable_tiling()
image_input = Image.fromarray(image_input).resize(size=(720, 480)) # Convert to PIL
image = load_image(image_input)
video_pt = pipe_image(
image=image,
prompt=prompt,
num_inference_steps=num_inference_steps,
num_videos_per_prompt=1,
use_dynamic_cfg=True,
output_type="pt",
guidance_scale=guidance_scale,
generator=torch.Generator(device="cpu").manual_seed(seed),
).frames
gc.collect()
torch.cuda.empty_cache()
else:
pipe.to(device)
pipe.transformer = transformer
pipe.vae = vae
pipe.text_encoder = text_encoder
if use_cpu_offload:
pipe.enable_sequential_cpu_offload()
if use_slicing:
pipe.vae.enable_slicing()
if use_tiling:
pipe.vae.enable_tiling()
video_pt = pipe(
prompt=prompt,
num_videos_per_prompt=1,
num_inference_steps=num_inference_steps,
num_frames=49,
use_dynamic_cfg=True,
output_type="pt",
guidance_scale=guidance_scale,
generator=torch.Generator(device="cpu").manual_seed(seed),
).frames
gc.collect()
return (video_pt, seed)
def get_unique_filename(base_path, extension):
directory = os.path.dirname(base_path)
filename = os.path.basename(base_path)
name, ext = os.path.splitext(filename)
counter = 0
while True:
if counter == 0:
new_filename = f"{name}{extension}"
else:
new_filename = f"{name}_{counter:04d}{extension}"
new_path = os.path.join(directory, new_filename)
if not os.path.exists(new_path):
return new_path
counter += 1
def delete_old_files():
while True:
now = datetime.now()
cutoff = now - timedelta(minutes=10)
directories = ["./outputs", "./gradio_tmp"]
for directory in directories:
for filename in os.listdir(directory):
file_path = os.path.join(directory, filename)
if os.path.isfile(file_path):
file_mtime = datetime.fromtimestamp(os.path.getmtime(file_path))
if file_mtime < cutoff:
os.remove(file_path)
time.sleep(600)
threading.Thread(target=delete_old_files, daemon=True).start()
def generate(
prompt,
image_input,
video_input,
video_strength,
seed_value,
num_inference_steps,
guidance_scale,
scale_status,
rife_status,
use_cpu_offload,
use_slicing,
use_tiling,
quantization_type,
num_generations,
progress=gr.Progress(track_tqdm=True)
):
all_video_paths = []
all_gif_paths = []
all_seeds = []
for i in range(num_generations):
latents, seed = infer(
prompt,
image_input,
video_input,
video_strength,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
seed=seed_value if i == 0 else -1, # Use provided seed only for first generation
use_cpu_offload=use_cpu_offload,
use_slicing=use_slicing,
use_tiling=use_tiling,
quantization_type=quantization_type,
progress=progress,
)
if rife_status:
latents = rife_inference_with_latents(frame_interpolation_model, latents)
if scale_status:
latents = utils.upscale_batch_and_concatenate(upscale_model, latents, device)
batch_size = latents.shape[0]
batch_video_frames = []
for batch_idx in range(batch_size):
pt_image = latents[batch_idx]
pt_image = torch.stack([pt_image[i] for i in range(pt_image.shape[0])])
image_np = VaeImageProcessor.pt_to_numpy(pt_image)
image_pil = VaeImageProcessor.numpy_to_pil(image_np)
batch_video_frames.append(image_pil)
base_filename = "output_" if video_input is None else os.path.splitext(os.path.basename(video_input))[0]
video_path = get_unique_filename(os.path.join("outputs", f"{base_filename}.mp4"), ".mp4")
utils.save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0]) - 1) / 6), output_path=video_path)
gif_path = get_unique_filename(video_path.replace(".mp4", ".gif"), ".gif")
clip = mp.VideoFileClip(video_path)
clip = clip.set_fps(8)
clip = clip.resize(height=240)
clip.write_gif(gif_path, fps=8)
all_video_paths.append(video_path)
all_gif_paths.append(gif_path)
all_seeds.append(seed)
# Return only the last generated video for display
video_update = gr.update(visible=True, value=all_video_paths[-1])
gif_update = gr.update(visible=True, value=all_gif_paths[-1])
seed_update = gr.update(visible=True, value=all_seeds[-1])
return all_video_paths[-1], video_update, gif_update, seed_update
with gr.Blocks() as demo:
gr.Markdown("""
<div style="text-align: center; font-size: 22px; font-weight: bold; margin-bottom: 10px;">
CogVideoX-5B by SECourses V1
<a href="https://www.patreon.com/posts/112848192">www.patreon.com/posts/112836177</a>
</div>
<div style="text-align: center; font-size: 18px; font-weight: bold; margin-bottom: 0px;">
- The followings fixed and perfectly works:<br>
* Works on Windows, Runpod & Massed Compute | Super-Resolution (720 × 480 -> 2880 × 1920)<br>
* Properly saving all generations into outputs folder
</div>
""")
#Frame Interpolation (8fps -> 16fps) |
with gr.Row():
with gr.Column():
with gr.Accordion("I2V: Image Input (cannot be used simultaneously with video input)", open=True):
image_input = gr.Image(label="Input Image (will be cropped to 720 * 480)",height=500)
with gr.Accordion("V2V: Video Input (cannot be used simultaneously with image input)", open=False):
video_input = gr.Video(label="Input Video (will be cropped to 49 frames, 6 seconds at 8fps)",height=500)
strength = gr.Slider(0.1, 1.0, value=0.8, step=0.01, label="Strength")
prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)
with gr.Group():
with gr.Column():
with gr.Row():
seed_param = gr.Number(
label="Inference Seed (Enter a positive number, -1 for random)", value=-1
)
with gr.Row():
num_inference_steps = gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps")
guidance_scale = gr.Slider(1.0, 20.0, value=7.0, step=0.1, label="Guidance Scale")
with gr.Row():
enable_scale = gr.Checkbox(label="Super-Resolution (720 × 480 -> 2880 × 1920)", value=False)
enable_rife = gr.Checkbox(label="Frame Interpolation (8fps -> 16fps)", value=False)
with gr.Row():
use_cpu_offload = gr.Checkbox(label="Use CPU Offload", value=True)
use_slicing = gr.Checkbox(label="Use Slicing", value=False)
use_tiling = gr.Checkbox(label="Use Tiling", value=False)
with gr.Row():
quantization_type = gr.Radio(["none", "int8", "fp8"], label="Quantization Type", value="none")
with gr.Row():
num_generations = gr.Slider(1, 999, value=1, step=1, label="Number of Generations")
gr.Markdown(
"✨In this demo, we use [RIFE](https://github.com/hzwer/ECCV2022-RIFE) for frame interpolation and [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) for upscaling(Super-Resolution).<br> The entire process is based on open-source solutions."
)
generate_button = gr.Button("🎬 Generate Video")
with gr.Column():
video_output = gr.Video(label="CogVideoX Generate Video", width=720, height=480)
open_outputs_button = gr.Button("Open Results Folder")
open_outputs_button.click(fn=lambda: open_folder("outputs"))
gr.Markdown(
"""Currently on Windows we have to use CPU Offloading due to shameless OpenAI who takes 10s of billions from Microsoft not giving any support to Windows<br><br>I am trying to find a solution for this but because of this, it will be super slow<br><br>On Linux or WSL you can extra install torchao and use int8<br><br>Because of the Lazy coding of CogVideo team, FP8 only works on H100 and above GPUs :/ I am still searching a solution for this as well<br><br>If your GPU VRAM is below 16 GB, enable Use Slicing and Use Tiling as well (they are used after all steps done)<br><br>Without CPU offloading and without using FP8 or Int8 it uses 26 GB VRAM thus we have to use CPU offloading
<br> <br>
Text to video, Video to Video not working at all yet I opened an issue for this
<br><br>
Frame Interpolation (8fps -> 16fps) not working properly yet I opened an issue for this
<br><br>
You can use here to generate caption : https://poe.com/Claude-3.5-Sonnet
<br>Upload image and use below prompt
<br>
analyze the attached image and write me a detailed video flow description to animate it in a image to video animation generative ai model<br>
e.g. like<br>
Fireworks display over night city. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic."""
)
with gr.Row():
download_video_button = gr.File(label="📥 Download Video", visible=False)
download_gif_button = gr.File(label="📥 Download GIF", visible=False)
seed_text = gr.Number(label="Seed Used for Video Generation", visible=False)
generate_button.click(
generate,
inputs=[prompt, image_input, video_input, strength, seed_param, num_inference_steps, guidance_scale, enable_scale, enable_rife, use_cpu_offload, use_slicing, use_tiling, quantization_type, num_generations],
outputs=[video_output, download_video_button, download_gif_button, seed_text],
)
video_input.upload(resize_if_unfit, inputs=[video_input], outputs=[video_input])
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the CogVideoX demo")
parser.add_argument("--share", action="store_true", help="Enable sharing of the Gradio interface")
args = parser.parse_args()
demo.queue(max_size=15)
demo.launch(inbrowser=True, share=args.share)