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105
inference/cli_demo_quantization.py
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inference/cli_demo_quantization.py
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"""
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This script demonstrates how to generate a video from a text prompt using CogVideoX with 🤗Huggingface Diffusers Pipeline.
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Note:
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This script requires the `diffusers>=0.30.1` and `torchao>=0.4.0` library to be installed.
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Run the script:
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$ python cli_demo.py --prompt "A girl ridding a bike." --model_path THUDM/CogVideoX-2b
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In this script, we have only provided the script for testing and inference in INT8 for the entire process
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(including T5 Encoder, CogVideoX Transformer, VAE).
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You can use other functionalities provided by torchao to convert to other precisions.
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Please note that INT4 is not supported.
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"""
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import argparse
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import torch
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from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXPipeline
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from diffusers.utils import export_to_video
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from transformers import T5EncoderModel
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# Make sure to install torchao>=0.4.0
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from torchao.quantization import quantize_, int8_weight_only
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def generate_video(
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prompt: str,
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model_path: str,
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output_path: str = "./output.mp4",
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num_inference_steps: int = 50,
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guidance_scale: float = 6.0,
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num_videos_per_prompt: int = 1,
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dtype: torch.dtype = torch.bfloat16,
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):
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"""
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Generates a video based on the given prompt and saves it to the specified path.
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Parameters:
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- prompt (str): The description of the video to be generated.
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- model_path (str): The path of the pre-trained model to be used.
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- output_path (str): The path where the generated video will be saved.
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- num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
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- guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
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- num_videos_per_prompt (int): Number of videos to generate per prompt.
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- dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
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"""
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text_encoder = T5EncoderModel.from_pretrained(model_path, subfolder="text_encoder", torch_dtype=dtype)
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quantize_(text_encoder, int8_weight_only())
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transformer = CogVideoXTransformer3DModel.from_pretrained(model_path, subfolder="transformer",
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torch_dtype=dtype)
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quantize_(transformer, int8_weight_only())
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vae = AutoencoderKLCogVideoX.from_pretrained(model_path, subfolder="vae", torch_dtype=dtype)
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quantize_(vae, int8_weight_only())
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pipe = CogVideoXPipeline.from_pretrained(
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model_path,
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text_encoder=text_encoder,
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transformer=transformer,
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vae=vae,
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torch_dtype=torch.bfloat16,
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)
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pipe.enable_model_cpu_offload()
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pipe.vae.enable_tiling()
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video = pipe(
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prompt=prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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num_inference_steps=num_inference_steps,
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num_frames=49,
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guidance_scale=guidance_scale,
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generator=torch.Generator().manual_seed(42),
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).frames[0]
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export_to_video(video, output_path, fps=8)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
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parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
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parser.add_argument(
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"--model_path", type=str, default="THUDM/CogVideoX-5b", help="The path of the pre-trained model to be used"
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)
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parser.add_argument(
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"--output_path", type=str, default="./output.mp4", help="The path where the generated video will be saved"
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)
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parser.add_argument(
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"--num_inference_steps", type=int, default=50, help="Number of steps for the inference process"
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)
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parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
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parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
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parser.add_argument(
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"--dtype", type=str, default="bfloat16", help="The data type for computation (e.g., 'float16' or 'bfloat16')"
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)
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args = parser.parse_args()
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dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
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generate_video(
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prompt=args.prompt,
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model_path=args.model_path,
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output_path=args.output_path,
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num_inference_steps=args.num_inference_steps,
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guidance_scale=args.guidance_scale,
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num_videos_per_prompt=args.num_videos_per_prompt,
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dtype=dtype,
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)
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45
inference/gradio_composite_demo/README.md
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inference/gradio_composite_demo/README.md
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---
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title: CogVideoX-5B
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emoji: 🎥
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colorFrom: yellow
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colorTo: blue
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sdk: gradio
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sdk_version: 4.42.0
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suggested_hardware: a10g-large
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suggested_storage: large
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app_port: 7860
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app_file: app.py
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models:
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- THUDM/CogVideoX-5b
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tags:
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- cogvideox
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- video-generation
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- thudm
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short_description: Text-to-Video
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disable_embedding: false
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---
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# Gradio Composite Demo
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This Gradio demo integrates the CogVideoX-5B model, allowing you to perform video inference directly in your browser. It
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supports features like UpScale, RIFE, and other functionalities.
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## Environment Setup
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Set the following environment variables in your system:
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+ OPENAI_API_KEY = your_api_key
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+ OPENAI_BASE_URL= your_base_url
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+ GRADIO_TEMP_DIR= gradio_tmp
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## Installation
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```bash
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pip install -r requirements.txt
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```
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## Running the code
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```bash
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python gradio_web_demo.py
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```
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285
inference/gradio_composite_demo/app.py
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inference/gradio_composite_demo/app.py
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import math
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import os
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import random
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import threading
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import time
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import gradio as gr
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import torch
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from diffusers import CogVideoXPipeline, CogVideoXDDIMScheduler
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from datetime import datetime, timedelta
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from diffusers.image_processor import VaeImageProcessor
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from openai import OpenAI
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import moviepy.editor as mp
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import utils
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from rife_model import load_rife_model, rife_inference_with_latents
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from huggingface_hub import hf_hub_download, snapshot_download
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device = "cuda" if torch.cuda.is_available() else "cpu"
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hf_hub_download(repo_id="ai-forever/Real-ESRGAN", filename="RealESRGAN_x4.pth", local_dir="model_real_esran")
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snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife")
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pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to(device)
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pipe.scheduler = CogVideoXDDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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os.makedirs("./output", exist_ok=True)
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os.makedirs("./gradio_tmp", exist_ok=True)
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upscale_model = utils.load_sd_upscale("model_real_esran/RealESRGAN_x4.pth", device)
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frame_interpolation_model = load_rife_model("model_rife")
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sys_prompt = """You are part of a team of bots that creates videos. You work with an assistant bot that will draw anything you say in square brackets.
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For example , outputting " a beautiful morning in the woods with the sun peaking through the trees " will trigger your partner bot to output an video of a forest morning , as described. You will be prompted by people looking to create detailed , amazing videos. The way to accomplish this is to take their short prompts and make them extremely detailed and descriptive.
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There are a few rules to follow:
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You will only ever output a single video description per user request.
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When modifications are requested , you should not simply make the description longer . You should refactor the entire description to integrate the suggestions.
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Other times the user will not want modifications , but instead want a new image . In this case , you should ignore your previous conversation with the user.
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Video descriptions must have the same num of words as examples below. Extra words will be ignored.
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"""
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def convert_prompt(prompt: str, retry_times: int = 3) -> str:
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if not os.environ.get("OPENAI_API_KEY"):
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return prompt
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client = OpenAI()
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text = prompt.strip()
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for i in range(retry_times):
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response = client.chat.completions.create(
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messages=[
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{"role": "system", "content": sys_prompt},
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{
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"role": "user",
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"content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "a girl is on the beach"',
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},
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{
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"role": "assistant",
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"content": "A radiant woman stands on a deserted beach, arms outstretched, wearing a beige trench coat, white blouse, light blue jeans, and chic boots, against a backdrop of soft sky and sea. Moments later, she is seen mid-twirl, arms exuberant, with the lighting suggesting dawn or dusk. Then, she runs along the beach, her attire complemented by an off-white scarf and black ankle boots, the tranquil sea behind her. Finally, she holds a paper airplane, her pose reflecting joy and freedom, with the ocean's gentle waves and the sky's soft pastel hues enhancing the serene ambiance.",
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},
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{
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"role": "user",
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"content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : "A man jogging on a football field"',
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},
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{
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"role": "assistant",
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"content": "A determined man in athletic attire, including a blue long-sleeve shirt, black shorts, and blue socks, jogs around a snow-covered soccer field, showcasing his solitary exercise in a quiet, overcast setting. His long dreadlocks, focused expression, and the serene winter backdrop highlight his dedication to fitness. As he moves, his attire, consisting of a blue sports sweatshirt, black athletic pants, gloves, and sneakers, grips the snowy ground. He is seen running past a chain-link fence enclosing the playground area, with a basketball hoop and children's slide, suggesting a moment of solitary exercise amidst the empty field.",
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},
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{
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"role": "user",
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"content": 'Create an imaginative video descriptive caption or modify an earlier caption for the user input : " A woman is dancing, HD footage, close-up"',
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},
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{
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"role": "assistant",
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"content": "A young woman with her hair in an updo and wearing a teal hoodie stands against a light backdrop, initially looking over her shoulder with a contemplative expression. She then confidently makes a subtle dance move, suggesting rhythm and movement. Next, she appears poised and focused, looking directly at the camera. Her expression shifts to one of introspection as she gazes downward slightly. Finally, she dances with confidence, her left hand over her heart, symbolizing a poignant moment, all while dressed in the same teal hoodie against a plain, light-colored background.",
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},
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{
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"role": "user",
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"content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"',
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},
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],
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model="glm-4-0520",
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temperature=0.01,
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top_p=0.7,
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stream=False,
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max_tokens=200,
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)
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if response.choices:
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return response.choices[0].message.content
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return prompt
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def infer(
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prompt: str,
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num_inference_steps: int,
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guidance_scale: float,
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seed: int = -1,
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progress=gr.Progress(track_tqdm=True),
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):
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if seed == -1:
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seed = random.randint(0, 2 ** 8 - 1)
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video_pt = pipe(
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prompt=prompt,
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num_videos_per_prompt=1,
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num_inference_steps=num_inference_steps,
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num_frames=49,
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output_type="pt",
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guidance_scale=guidance_scale,
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generator=torch.Generator(device=device).manual_seed(seed),
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).frames
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return (video_pt, seed)
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def convert_to_gif(video_path):
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clip = mp.VideoFileClip(video_path)
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clip = clip.set_fps(8)
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clip = clip.resize(height=240)
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gif_path = video_path.replace(".mp4", ".gif")
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clip.write_gif(gif_path, fps=8)
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return gif_path
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def delete_old_files():
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while True:
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now = datetime.now()
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cutoff = now - timedelta(minutes=10)
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directories = ["./output", "./gradio_tmp"]
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for directory in directories:
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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if os.path.isfile(file_path):
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file_mtime = datetime.fromtimestamp(os.path.getmtime(file_path))
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if file_mtime < cutoff:
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os.remove(file_path)
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time.sleep(600)
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threading.Thread(target=delete_old_files, daemon=True).start()
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with gr.Blocks() as demo:
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gr.Markdown("""
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<div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
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CogVideoX-5B Huggingface Space🤗
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</div>
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<div style="text-align: center;">
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<a href="https://huggingface.co/THUDM/CogVideoX-5B">🤗 5B Model Hub</a> |
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<a href="https://github.com/THUDM/CogVideo">🌐 Github</a> |
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<a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
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</div>
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<div style="text-align: center; font-size: 15px; font-weight: bold; color: red; margin-bottom: 20px;">
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⚠️ This demo is for academic research and experiential use only.
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</div>
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""")
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with gr.Row():
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with gr.Column():
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prompt = gr.Textbox(label="Prompt (Less than 200 Words)", placeholder="Enter your prompt here", lines=5)
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with gr.Row():
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gr.Markdown(
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"✨Upon pressing the enhanced prompt button, we will use [GLM-4 Model](https://github.com/THUDM/GLM-4) to polish the prompt and overwrite the original one."
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)
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enhance_button = gr.Button("✨ Enhance Prompt(Optional)")
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gr.Markdown(
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"<span style='color:red; font-weight:bold;'>For the CogVideoX-5B model, 50 steps will take approximately 300 seconds.</span>"
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)
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with gr.Group():
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with gr.Column():
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with gr.Row():
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seed_param = gr.Number(
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label="Inference Seed (Enter a positive number, -1 for random)", value=-1
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)
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with gr.Row():
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enable_scale = gr.Checkbox(label="Super-Resolution (720 × 480 -> 1440 × 960)", value=False)
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enable_rife = gr.Checkbox(label="Frame Interpolation (8fps -> 16fps)", value=False)
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gr.Markdown(
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"✨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."
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)
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generate_button = gr.Button("🎬 Generate Video")
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with gr.Column():
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video_output = gr.Video(label="CogVideoX Generate Video", width=720, height=480)
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with gr.Row():
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download_video_button = gr.File(label="📥 Download Video", visible=False)
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download_gif_button = gr.File(label="📥 Download GIF", visible=False)
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seed_text = gr.Number(label="Seed Used for Video Generation", visible=False)
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gr.Markdown("""
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<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
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<div style="text-align: center; font-size: 32px; font-weight: bold; margin-bottom: 20px;">
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🎥 Video Gallery
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</div>
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<div style="text-align: center; font-size: 18px; font-weight: bold; margin-bottom: 20px;">
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The following videos were generated purely by CogVideoX-5B without using super-resolution or frame interpolation techniques. 🌟
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<br>
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<span style="color: pink;">seed=42, num_inference_steps=50 and guidance_scale=6.0.</span>
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</div>
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<tr>
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<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
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<p>A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting.</p>
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</td>
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<td style="width: 25%; vertical-align: top;">
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<video src="https://github.com/user-attachments/assets/ea3af39a-3160-4999-90ec-2f7863c5b0e9" width="100%" controls autoplay loop></video>
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</td>
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<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
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<p>The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope, dust kicks up from its tires, the sunlight shines on the SUV as it speeds along the dirt road, casting a warm glow over the scene. The dirt road curves gently into the distance, with no other cars or vehicles in sight. The trees on either side of the road are redwoods, with patches of greenery scattered throughout. The car is seen from the rear following the curve with ease, making it seem as if it is on a rugged drive through the rugged terrain. The dirt road itself is surrounded by steep hills and mountains, with a clear blue sky above with wispy clouds.</p>
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</td>
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<td style="width: 25%; vertical-align: top;">
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<video src="https://github.com/user-attachments/assets/9de41efd-d4d1-4095-aeda-246dd834e91d" width="100%" controls autoplay loop></video>
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</td>
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</tr>
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<tr>
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<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
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<p>A street artist, clad in a worn-out denim jacket and a colorful bandana, stands before a vast concrete wall in the heart, holding a can of spray paint, spray-painting a colorful bird on a mottled wall.</p>
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</td>
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<td style="width: 25%; vertical-align: top;">
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<video src="https://github.com/user-attachments/assets/941d6661-6a8d-4a1b-b912-59606f0b2841" width="100%" controls autoplay loop></video>
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</td>
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<td style="width: 25%; vertical-align: top; font-size: 0.8em;">
|
||||
<p>In the haunting backdrop of a war-torn city, where ruins and crumbled walls tell a story of devastation, a poignant close-up frames a young girl. Her face is smudged with ash, a silent testament to the chaos around her. Her eyes glistening with a mix of sorrow and resilience, capturing the raw emotion of a world that has lost its innocence to the ravages of conflict.</p>
|
||||
</td>
|
||||
<td style="width: 25%; vertical-align: top;">
|
||||
<video src="https://github.com/user-attachments/assets/938529c4-91ae-4f60-b96b-3c3947fa63cb" width="100%" controls autoplay loop></video>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
""")
|
||||
|
||||
|
||||
def generate(prompt, seed_value, scale_status, rife_status, progress=gr.Progress(track_tqdm=True)):
|
||||
|
||||
latents, seed = infer(
|
||||
prompt,
|
||||
num_inference_steps=50, # NOT Changed
|
||||
guidance_scale=6, # NOT Changed
|
||||
seed=seed_value,
|
||||
progress=progress,
|
||||
)
|
||||
if scale_status:
|
||||
latents = utils.upscale_batch_and_concatenate(upscale_model, latents, device)
|
||||
if rife_status:
|
||||
latents = rife_inference_with_latents(frame_interpolation_model, latents)
|
||||
|
||||
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)
|
||||
|
||||
video_path = utils.save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0]) - 1) / 6))
|
||||
video_update = gr.update(visible=True, value=video_path)
|
||||
gif_path = convert_to_gif(video_path)
|
||||
gif_update = gr.update(visible=True, value=gif_path)
|
||||
seed_update = gr.update(visible=True, value=seed)
|
||||
|
||||
return video_path, video_update, gif_update, seed_update
|
||||
|
||||
|
||||
def enhance_prompt_func(prompt):
|
||||
return convert_prompt(prompt, retry_times=1)
|
||||
|
||||
|
||||
generate_button.click(
|
||||
generate,
|
||||
inputs=[prompt, seed_param, enable_scale, enable_rife],
|
||||
outputs=[video_output, download_video_button, download_gif_button, seed_text],
|
||||
)
|
||||
|
||||
enhance_button.click(enhance_prompt_func, inputs=[prompt], outputs=[prompt])
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo.launch(server_port=7870)
|
||||
5
inference/gradio_composite_demo/requirements.txt
Normal file
5
inference/gradio_composite_demo/requirements.txt
Normal file
@ -0,0 +1,5 @@
|
||||
safetensors>=0.4.4
|
||||
spandrel>=0.3.4
|
||||
tqdm>=4.66.5
|
||||
opencv-python>=4.10.0.84
|
||||
scikit-video>=1.1.11
|
||||
123
inference/gradio_composite_demo/rife/IFNet.py
Normal file
123
inference/gradio_composite_demo/rife/IFNet.py
Normal file
@ -0,0 +1,123 @@
|
||||
from .refine import *
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=True,
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
class IFBlock(nn.Module):
|
||||
def __init__(self, in_planes, c=64):
|
||||
super(IFBlock, self).__init__()
|
||||
self.conv0 = nn.Sequential(
|
||||
conv(in_planes, c // 2, 3, 2, 1),
|
||||
conv(c // 2, c, 3, 2, 1),
|
||||
)
|
||||
self.convblock = nn.Sequential(
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
)
|
||||
self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
|
||||
|
||||
def forward(self, x, flow, scale):
|
||||
if scale != 1:
|
||||
x = F.interpolate(x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False)
|
||||
if flow != None:
|
||||
flow = F.interpolate(flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False) * 1.0 / scale
|
||||
x = torch.cat((x, flow), 1)
|
||||
x = self.conv0(x)
|
||||
x = self.convblock(x) + x
|
||||
tmp = self.lastconv(x)
|
||||
tmp = F.interpolate(tmp, scale_factor=scale * 2, mode="bilinear", align_corners=False)
|
||||
flow = tmp[:, :4] * scale * 2
|
||||
mask = tmp[:, 4:5]
|
||||
return flow, mask
|
||||
|
||||
|
||||
class IFNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(IFNet, self).__init__()
|
||||
self.block0 = IFBlock(6, c=240)
|
||||
self.block1 = IFBlock(13 + 4, c=150)
|
||||
self.block2 = IFBlock(13 + 4, c=90)
|
||||
self.block_tea = IFBlock(16 + 4, c=90)
|
||||
self.contextnet = Contextnet()
|
||||
self.unet = Unet()
|
||||
|
||||
def forward(self, x, scale=[4, 2, 1], timestep=0.5):
|
||||
img0 = x[:, :3]
|
||||
img1 = x[:, 3:6]
|
||||
gt = x[:, 6:] # In inference time, gt is None
|
||||
flow_list = []
|
||||
merged = []
|
||||
mask_list = []
|
||||
warped_img0 = img0
|
||||
warped_img1 = img1
|
||||
flow = None
|
||||
loss_distill = 0
|
||||
stu = [self.block0, self.block1, self.block2]
|
||||
for i in range(3):
|
||||
if flow != None:
|
||||
flow_d, mask_d = stu[i](
|
||||
torch.cat((img0, img1, warped_img0, warped_img1, mask), 1), flow, scale=scale[i]
|
||||
)
|
||||
flow = flow + flow_d
|
||||
mask = mask + mask_d
|
||||
else:
|
||||
flow, mask = stu[i](torch.cat((img0, img1), 1), None, scale=scale[i])
|
||||
mask_list.append(torch.sigmoid(mask))
|
||||
flow_list.append(flow)
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
merged_student = (warped_img0, warped_img1)
|
||||
merged.append(merged_student)
|
||||
if gt.shape[1] == 3:
|
||||
flow_d, mask_d = self.block_tea(
|
||||
torch.cat((img0, img1, warped_img0, warped_img1, mask, gt), 1), flow, scale=1
|
||||
)
|
||||
flow_teacher = flow + flow_d
|
||||
warped_img0_teacher = warp(img0, flow_teacher[:, :2])
|
||||
warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
|
||||
mask_teacher = torch.sigmoid(mask + mask_d)
|
||||
merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
|
||||
else:
|
||||
flow_teacher = None
|
||||
merged_teacher = None
|
||||
for i in range(3):
|
||||
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
|
||||
if gt.shape[1] == 3:
|
||||
loss_mask = (
|
||||
((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01)
|
||||
.float()
|
||||
.detach()
|
||||
)
|
||||
loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
|
||||
res = tmp[:, :3] * 2 - 1
|
||||
merged[2] = torch.clamp(merged[2] + res, 0, 1)
|
||||
return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill
|
||||
123
inference/gradio_composite_demo/rife/IFNet_2R.py
Normal file
123
inference/gradio_composite_demo/rife/IFNet_2R.py
Normal file
@ -0,0 +1,123 @@
|
||||
from .refine_2R import *
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=True,
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
class IFBlock(nn.Module):
|
||||
def __init__(self, in_planes, c=64):
|
||||
super(IFBlock, self).__init__()
|
||||
self.conv0 = nn.Sequential(
|
||||
conv(in_planes, c // 2, 3, 1, 1),
|
||||
conv(c // 2, c, 3, 2, 1),
|
||||
)
|
||||
self.convblock = nn.Sequential(
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
)
|
||||
self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
|
||||
|
||||
def forward(self, x, flow, scale):
|
||||
if scale != 1:
|
||||
x = F.interpolate(x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False)
|
||||
if flow != None:
|
||||
flow = F.interpolate(flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False) * 1.0 / scale
|
||||
x = torch.cat((x, flow), 1)
|
||||
x = self.conv0(x)
|
||||
x = self.convblock(x) + x
|
||||
tmp = self.lastconv(x)
|
||||
tmp = F.interpolate(tmp, scale_factor=scale, mode="bilinear", align_corners=False)
|
||||
flow = tmp[:, :4] * scale
|
||||
mask = tmp[:, 4:5]
|
||||
return flow, mask
|
||||
|
||||
|
||||
class IFNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(IFNet, self).__init__()
|
||||
self.block0 = IFBlock(6, c=240)
|
||||
self.block1 = IFBlock(13 + 4, c=150)
|
||||
self.block2 = IFBlock(13 + 4, c=90)
|
||||
self.block_tea = IFBlock(16 + 4, c=90)
|
||||
self.contextnet = Contextnet()
|
||||
self.unet = Unet()
|
||||
|
||||
def forward(self, x, scale=[4, 2, 1], timestep=0.5):
|
||||
img0 = x[:, :3]
|
||||
img1 = x[:, 3:6]
|
||||
gt = x[:, 6:] # In inference time, gt is None
|
||||
flow_list = []
|
||||
merged = []
|
||||
mask_list = []
|
||||
warped_img0 = img0
|
||||
warped_img1 = img1
|
||||
flow = None
|
||||
loss_distill = 0
|
||||
stu = [self.block0, self.block1, self.block2]
|
||||
for i in range(3):
|
||||
if flow != None:
|
||||
flow_d, mask_d = stu[i](
|
||||
torch.cat((img0, img1, warped_img0, warped_img1, mask), 1), flow, scale=scale[i]
|
||||
)
|
||||
flow = flow + flow_d
|
||||
mask = mask + mask_d
|
||||
else:
|
||||
flow, mask = stu[i](torch.cat((img0, img1), 1), None, scale=scale[i])
|
||||
mask_list.append(torch.sigmoid(mask))
|
||||
flow_list.append(flow)
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
merged_student = (warped_img0, warped_img1)
|
||||
merged.append(merged_student)
|
||||
if gt.shape[1] == 3:
|
||||
flow_d, mask_d = self.block_tea(
|
||||
torch.cat((img0, img1, warped_img0, warped_img1, mask, gt), 1), flow, scale=1
|
||||
)
|
||||
flow_teacher = flow + flow_d
|
||||
warped_img0_teacher = warp(img0, flow_teacher[:, :2])
|
||||
warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
|
||||
mask_teacher = torch.sigmoid(mask + mask_d)
|
||||
merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
|
||||
else:
|
||||
flow_teacher = None
|
||||
merged_teacher = None
|
||||
for i in range(3):
|
||||
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
|
||||
if gt.shape[1] == 3:
|
||||
loss_mask = (
|
||||
((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01)
|
||||
.float()
|
||||
.detach()
|
||||
)
|
||||
loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
|
||||
res = tmp[:, :3] * 2 - 1
|
||||
merged[2] = torch.clamp(merged[2] + res, 0, 1)
|
||||
return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill
|
||||
138
inference/gradio_composite_demo/rife/IFNet_HDv3.py
Normal file
138
inference/gradio_composite_demo/rife/IFNet_HDv3.py
Normal file
@ -0,0 +1,138 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from .warplayer import warp
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=True,
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(out_planes),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
class IFBlock(nn.Module):
|
||||
def __init__(self, in_planes, c=64):
|
||||
super(IFBlock, self).__init__()
|
||||
self.conv0 = nn.Sequential(
|
||||
conv(in_planes, c // 2, 3, 2, 1),
|
||||
conv(c // 2, c, 3, 2, 1),
|
||||
)
|
||||
self.convblock0 = nn.Sequential(conv(c, c), conv(c, c))
|
||||
self.convblock1 = nn.Sequential(conv(c, c), conv(c, c))
|
||||
self.convblock2 = nn.Sequential(conv(c, c), conv(c, c))
|
||||
self.convblock3 = nn.Sequential(conv(c, c), conv(c, c))
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.ConvTranspose2d(c, c // 2, 4, 2, 1),
|
||||
nn.PReLU(c // 2),
|
||||
nn.ConvTranspose2d(c // 2, 4, 4, 2, 1),
|
||||
)
|
||||
self.conv2 = nn.Sequential(
|
||||
nn.ConvTranspose2d(c, c // 2, 4, 2, 1),
|
||||
nn.PReLU(c // 2),
|
||||
nn.ConvTranspose2d(c // 2, 1, 4, 2, 1),
|
||||
)
|
||||
|
||||
def forward(self, x, flow, scale=1):
|
||||
x = F.interpolate(
|
||||
x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False
|
||||
)
|
||||
flow = (
|
||||
F.interpolate(
|
||||
flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False
|
||||
)
|
||||
* 1.0
|
||||
/ scale
|
||||
)
|
||||
feat = self.conv0(torch.cat((x, flow), 1))
|
||||
feat = self.convblock0(feat) + feat
|
||||
feat = self.convblock1(feat) + feat
|
||||
feat = self.convblock2(feat) + feat
|
||||
feat = self.convblock3(feat) + feat
|
||||
flow = self.conv1(feat)
|
||||
mask = self.conv2(feat)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* scale
|
||||
)
|
||||
mask = F.interpolate(
|
||||
mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False
|
||||
)
|
||||
return flow, mask
|
||||
|
||||
|
||||
class IFNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(IFNet, self).__init__()
|
||||
self.block0 = IFBlock(7 + 4, c=90)
|
||||
self.block1 = IFBlock(7 + 4, c=90)
|
||||
self.block2 = IFBlock(7 + 4, c=90)
|
||||
self.block_tea = IFBlock(10 + 4, c=90)
|
||||
# self.contextnet = Contextnet()
|
||||
# self.unet = Unet()
|
||||
|
||||
def forward(self, x, scale_list=[4, 2, 1], training=False):
|
||||
if training == False:
|
||||
channel = x.shape[1] // 2
|
||||
img0 = x[:, :channel]
|
||||
img1 = x[:, channel:]
|
||||
flow_list = []
|
||||
merged = []
|
||||
mask_list = []
|
||||
warped_img0 = img0
|
||||
warped_img1 = img1
|
||||
flow = (x[:, :4]).detach() * 0
|
||||
mask = (x[:, :1]).detach() * 0
|
||||
loss_cons = 0
|
||||
block = [self.block0, self.block1, self.block2]
|
||||
for i in range(3):
|
||||
f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i])
|
||||
f1, m1 = block[i](
|
||||
torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1),
|
||||
torch.cat((flow[:, 2:4], flow[:, :2]), 1),
|
||||
scale=scale_list[i],
|
||||
)
|
||||
flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
|
||||
mask = mask + (m0 + (-m1)) / 2
|
||||
mask_list.append(mask)
|
||||
flow_list.append(flow)
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
merged.append((warped_img0, warped_img1))
|
||||
"""
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
|
||||
res = tmp[:, 1:4] * 2 - 1
|
||||
"""
|
||||
for i in range(3):
|
||||
mask_list[i] = torch.sigmoid(mask_list[i])
|
||||
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
|
||||
# merged[i] = torch.clamp(merged[i] + res, 0, 1)
|
||||
return flow_list, mask_list[2], merged
|
||||
127
inference/gradio_composite_demo/rife/IFNet_m.py
Normal file
127
inference/gradio_composite_demo/rife/IFNet_m.py
Normal file
@ -0,0 +1,127 @@
|
||||
from .refine import *
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=True,
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
class IFBlock(nn.Module):
|
||||
def __init__(self, in_planes, c=64):
|
||||
super(IFBlock, self).__init__()
|
||||
self.conv0 = nn.Sequential(
|
||||
conv(in_planes, c // 2, 3, 2, 1),
|
||||
conv(c // 2, c, 3, 2, 1),
|
||||
)
|
||||
self.convblock = nn.Sequential(
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
conv(c, c),
|
||||
)
|
||||
self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
|
||||
|
||||
def forward(self, x, flow, scale):
|
||||
if scale != 1:
|
||||
x = F.interpolate(x, scale_factor=1.0 / scale, mode="bilinear", align_corners=False)
|
||||
if flow != None:
|
||||
flow = F.interpolate(flow, scale_factor=1.0 / scale, mode="bilinear", align_corners=False) * 1.0 / scale
|
||||
x = torch.cat((x, flow), 1)
|
||||
x = self.conv0(x)
|
||||
x = self.convblock(x) + x
|
||||
tmp = self.lastconv(x)
|
||||
tmp = F.interpolate(tmp, scale_factor=scale * 2, mode="bilinear", align_corners=False)
|
||||
flow = tmp[:, :4] * scale * 2
|
||||
mask = tmp[:, 4:5]
|
||||
return flow, mask
|
||||
|
||||
|
||||
class IFNet_m(nn.Module):
|
||||
def __init__(self):
|
||||
super(IFNet_m, self).__init__()
|
||||
self.block0 = IFBlock(6 + 1, c=240)
|
||||
self.block1 = IFBlock(13 + 4 + 1, c=150)
|
||||
self.block2 = IFBlock(13 + 4 + 1, c=90)
|
||||
self.block_tea = IFBlock(16 + 4 + 1, c=90)
|
||||
self.contextnet = Contextnet()
|
||||
self.unet = Unet()
|
||||
|
||||
def forward(self, x, scale=[4, 2, 1], timestep=0.5, returnflow=False):
|
||||
timestep = (x[:, :1].clone() * 0 + 1) * timestep
|
||||
img0 = x[:, :3]
|
||||
img1 = x[:, 3:6]
|
||||
gt = x[:, 6:] # In inference time, gt is None
|
||||
flow_list = []
|
||||
merged = []
|
||||
mask_list = []
|
||||
warped_img0 = img0
|
||||
warped_img1 = img1
|
||||
flow = None
|
||||
loss_distill = 0
|
||||
stu = [self.block0, self.block1, self.block2]
|
||||
for i in range(3):
|
||||
if flow != None:
|
||||
flow_d, mask_d = stu[i](
|
||||
torch.cat((img0, img1, timestep, warped_img0, warped_img1, mask), 1), flow, scale=scale[i]
|
||||
)
|
||||
flow = flow + flow_d
|
||||
mask = mask + mask_d
|
||||
else:
|
||||
flow, mask = stu[i](torch.cat((img0, img1, timestep), 1), None, scale=scale[i])
|
||||
mask_list.append(torch.sigmoid(mask))
|
||||
flow_list.append(flow)
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
merged_student = (warped_img0, warped_img1)
|
||||
merged.append(merged_student)
|
||||
if gt.shape[1] == 3:
|
||||
flow_d, mask_d = self.block_tea(
|
||||
torch.cat((img0, img1, timestep, warped_img0, warped_img1, mask, gt), 1), flow, scale=1
|
||||
)
|
||||
flow_teacher = flow + flow_d
|
||||
warped_img0_teacher = warp(img0, flow_teacher[:, :2])
|
||||
warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
|
||||
mask_teacher = torch.sigmoid(mask + mask_d)
|
||||
merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
|
||||
else:
|
||||
flow_teacher = None
|
||||
merged_teacher = None
|
||||
for i in range(3):
|
||||
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
|
||||
if gt.shape[1] == 3:
|
||||
loss_mask = (
|
||||
((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01)
|
||||
.float()
|
||||
.detach()
|
||||
)
|
||||
loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
|
||||
if returnflow:
|
||||
return flow
|
||||
else:
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
|
||||
res = tmp[:, :3] * 2 - 1
|
||||
merged[2] = torch.clamp(merged[2] + res, 0, 1)
|
||||
return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill
|
||||
95
inference/gradio_composite_demo/rife/RIFE.py
Normal file
95
inference/gradio_composite_demo/rife/RIFE.py
Normal file
@ -0,0 +1,95 @@
|
||||
from torch.optim import AdamW
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from .IFNet import *
|
||||
from .IFNet_m import *
|
||||
from .loss import *
|
||||
from .laplacian import *
|
||||
from .refine import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1, arbitrary=False):
|
||||
if arbitrary == True:
|
||||
self.flownet = IFNet_m()
|
||||
else:
|
||||
self.flownet = IFNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(
|
||||
self.flownet.parameters(), lr=1e-6, weight_decay=1e-3
|
||||
) # use large weight decay may avoid NaN loss
|
||||
self.epe = EPE()
|
||||
self.lap = LapLoss()
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
|
||||
def load_model(self, path, rank=0):
|
||||
def convert(param):
|
||||
return {k.replace("module.", ""): v for k, v in param.items() if "module." in k}
|
||||
|
||||
if rank <= 0:
|
||||
self.flownet.load_state_dict(convert(torch.load("{}/flownet.pkl".format(path))))
|
||||
|
||||
def save_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(), "{}/flownet.pkl".format(path))
|
||||
|
||||
def inference(self, img0, img1, scale=1, scale_list=[4, 2, 1], TTA=False, timestep=0.5):
|
||||
for i in range(3):
|
||||
scale_list[i] = scale_list[i] * 1.0 / scale
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(
|
||||
imgs, scale_list, timestep=timestep
|
||||
)
|
||||
if TTA == False:
|
||||
return merged[2]
|
||||
else:
|
||||
flow2, mask2, merged2, flow_teacher2, merged_teacher2, loss_distill2 = self.flownet(
|
||||
imgs.flip(2).flip(3), scale_list, timestep=timestep
|
||||
)
|
||||
return (merged[2] + merged2[2].flip(2).flip(3)) / 2
|
||||
|
||||
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
|
||||
for param_group in self.optimG.param_groups:
|
||||
param_group["lr"] = learning_rate
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(
|
||||
torch.cat((imgs, gt), 1), scale=[4, 2, 1]
|
||||
)
|
||||
loss_l1 = (self.lap(merged[2], gt)).mean()
|
||||
loss_tea = (self.lap(merged_teacher, gt)).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = (
|
||||
loss_l1 + loss_tea + loss_distill * 0.01
|
||||
) # when training RIFEm, the weight of loss_distill should be 0.005 or 0.002
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
else:
|
||||
flow_teacher = flow[2]
|
||||
return merged[2], {
|
||||
"merged_tea": merged_teacher,
|
||||
"mask": mask,
|
||||
"mask_tea": mask,
|
||||
"flow": flow[2][:, :2],
|
||||
"flow_tea": flow_teacher,
|
||||
"loss_l1": loss_l1,
|
||||
"loss_tea": loss_tea,
|
||||
"loss_distill": loss_distill,
|
||||
}
|
||||
86
inference/gradio_composite_demo/rife/RIFE_HDv3.py
Normal file
86
inference/gradio_composite_demo/rife/RIFE_HDv3.py
Normal file
@ -0,0 +1,86 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.optim import AdamW
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from .warplayer import warp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from .IFNet_HDv3 import *
|
||||
import torch.nn.functional as F
|
||||
from .loss import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
|
||||
self.epe = EPE()
|
||||
# self.vgg = VGGPerceptualLoss().to(device)
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
|
||||
def load_model(self, path, rank=0):
|
||||
def convert(param):
|
||||
if rank == -1:
|
||||
return {k.replace("module.", ""): v for k, v in param.items() if "module." in k}
|
||||
else:
|
||||
return param
|
||||
|
||||
if rank <= 0:
|
||||
if torch.cuda.is_available():
|
||||
self.flownet.load_state_dict(convert(torch.load("{}/flownet.pkl".format(path))))
|
||||
else:
|
||||
self.flownet.load_state_dict(convert(torch.load("{}/flownet.pkl".format(path), map_location="cpu")))
|
||||
|
||||
def save_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(), "{}/flownet.pkl".format(path))
|
||||
|
||||
def inference(self, img0, img1, scale=1.0):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
scale_list = [4 / scale, 2 / scale, 1 / scale]
|
||||
flow, mask, merged = self.flownet(imgs, scale_list)
|
||||
return merged[2]
|
||||
|
||||
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
|
||||
for param_group in self.optimG.param_groups:
|
||||
param_group["lr"] = learning_rate
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
scale = [4, 2, 1]
|
||||
flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
|
||||
loss_l1 = (merged[2] - gt).abs().mean()
|
||||
loss_smooth = self.sobel(flow[2], flow[2] * 0).mean()
|
||||
# loss_vgg = self.vgg(merged[2], gt)
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_cons + loss_smooth * 0.1
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
else:
|
||||
flow_teacher = flow[2]
|
||||
return merged[2], {
|
||||
"mask": mask,
|
||||
"flow": flow[2][:, :2],
|
||||
"loss_l1": loss_l1,
|
||||
"loss_cons": loss_cons,
|
||||
"loss_smooth": loss_smooth,
|
||||
}
|
||||
0
inference/gradio_composite_demo/rife/__init__.py
Normal file
0
inference/gradio_composite_demo/rife/__init__.py
Normal file
69
inference/gradio_composite_demo/rife/laplacian.py
Normal file
69
inference/gradio_composite_demo/rife/laplacian.py
Normal file
@ -0,0 +1,69 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def gauss_kernel(size=5, channels=3):
|
||||
kernel = torch.tensor(
|
||||
[
|
||||
[1.0, 4.0, 6.0, 4.0, 1],
|
||||
[4.0, 16.0, 24.0, 16.0, 4.0],
|
||||
[6.0, 24.0, 36.0, 24.0, 6.0],
|
||||
[4.0, 16.0, 24.0, 16.0, 4.0],
|
||||
[1.0, 4.0, 6.0, 4.0, 1.0],
|
||||
]
|
||||
)
|
||||
kernel /= 256.0
|
||||
kernel = kernel.repeat(channels, 1, 1, 1)
|
||||
kernel = kernel.to(device)
|
||||
return kernel
|
||||
|
||||
|
||||
def downsample(x):
|
||||
return x[:, :, ::2, ::2]
|
||||
|
||||
|
||||
def upsample(x):
|
||||
cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3]).to(device)], dim=3)
|
||||
cc = cc.view(x.shape[0], x.shape[1], x.shape[2] * 2, x.shape[3])
|
||||
cc = cc.permute(0, 1, 3, 2)
|
||||
cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[3], x.shape[2] * 2).to(device)], dim=3)
|
||||
cc = cc.view(x.shape[0], x.shape[1], x.shape[3] * 2, x.shape[2] * 2)
|
||||
x_up = cc.permute(0, 1, 3, 2)
|
||||
return conv_gauss(x_up, 4 * gauss_kernel(channels=x.shape[1]))
|
||||
|
||||
|
||||
def conv_gauss(img, kernel):
|
||||
img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode="reflect")
|
||||
out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1])
|
||||
return out
|
||||
|
||||
|
||||
def laplacian_pyramid(img, kernel, max_levels=3):
|
||||
current = img
|
||||
pyr = []
|
||||
for level in range(max_levels):
|
||||
filtered = conv_gauss(current, kernel)
|
||||
down = downsample(filtered)
|
||||
up = upsample(down)
|
||||
diff = current - up
|
||||
pyr.append(diff)
|
||||
current = down
|
||||
return pyr
|
||||
|
||||
|
||||
class LapLoss(torch.nn.Module):
|
||||
def __init__(self, max_levels=5, channels=3):
|
||||
super(LapLoss, self).__init__()
|
||||
self.max_levels = max_levels
|
||||
self.gauss_kernel = gauss_kernel(channels=channels)
|
||||
|
||||
def forward(self, input, target):
|
||||
pyr_input = laplacian_pyramid(img=input, kernel=self.gauss_kernel, max_levels=self.max_levels)
|
||||
pyr_target = laplacian_pyramid(img=target, kernel=self.gauss_kernel, max_levels=self.max_levels)
|
||||
return sum(torch.nn.functional.l1_loss(a, b) for a, b in zip(pyr_input, pyr_target))
|
||||
130
inference/gradio_composite_demo/rife/loss.py
Normal file
130
inference/gradio_composite_demo/rife/loss.py
Normal file
@ -0,0 +1,130 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision.models as models
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
class EPE(nn.Module):
|
||||
def __init__(self):
|
||||
super(EPE, self).__init__()
|
||||
|
||||
def forward(self, flow, gt, loss_mask):
|
||||
loss_map = (flow - gt.detach()) ** 2
|
||||
loss_map = (loss_map.sum(1, True) + 1e-6) ** 0.5
|
||||
return loss_map * loss_mask
|
||||
|
||||
|
||||
class Ternary(nn.Module):
|
||||
def __init__(self):
|
||||
super(Ternary, self).__init__()
|
||||
patch_size = 7
|
||||
out_channels = patch_size * patch_size
|
||||
self.w = np.eye(out_channels).reshape((patch_size, patch_size, 1, out_channels))
|
||||
self.w = np.transpose(self.w, (3, 2, 0, 1))
|
||||
self.w = torch.tensor(self.w).float().to(device)
|
||||
|
||||
def transform(self, img):
|
||||
patches = F.conv2d(img, self.w, padding=3, bias=None)
|
||||
transf = patches - img
|
||||
transf_norm = transf / torch.sqrt(0.81 + transf**2)
|
||||
return transf_norm
|
||||
|
||||
def rgb2gray(self, rgb):
|
||||
r, g, b = rgb[:, 0:1, :, :], rgb[:, 1:2, :, :], rgb[:, 2:3, :, :]
|
||||
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
|
||||
return gray
|
||||
|
||||
def hamming(self, t1, t2):
|
||||
dist = (t1 - t2) ** 2
|
||||
dist_norm = torch.mean(dist / (0.1 + dist), 1, True)
|
||||
return dist_norm
|
||||
|
||||
def valid_mask(self, t, padding):
|
||||
n, _, h, w = t.size()
|
||||
inner = torch.ones(n, 1, h - 2 * padding, w - 2 * padding).type_as(t)
|
||||
mask = F.pad(inner, [padding] * 4)
|
||||
return mask
|
||||
|
||||
def forward(self, img0, img1):
|
||||
img0 = self.transform(self.rgb2gray(img0))
|
||||
img1 = self.transform(self.rgb2gray(img1))
|
||||
return self.hamming(img0, img1) * self.valid_mask(img0, 1)
|
||||
|
||||
|
||||
class SOBEL(nn.Module):
|
||||
def __init__(self):
|
||||
super(SOBEL, self).__init__()
|
||||
self.kernelX = torch.tensor(
|
||||
[
|
||||
[1, 0, -1],
|
||||
[2, 0, -2],
|
||||
[1, 0, -1],
|
||||
]
|
||||
).float()
|
||||
self.kernelY = self.kernelX.clone().T
|
||||
self.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(device)
|
||||
self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(device)
|
||||
|
||||
def forward(self, pred, gt):
|
||||
N, C, H, W = pred.shape[0], pred.shape[1], pred.shape[2], pred.shape[3]
|
||||
img_stack = torch.cat([pred.reshape(N * C, 1, H, W), gt.reshape(N * C, 1, H, W)], 0)
|
||||
sobel_stack_x = F.conv2d(img_stack, self.kernelX, padding=1)
|
||||
sobel_stack_y = F.conv2d(img_stack, self.kernelY, padding=1)
|
||||
pred_X, gt_X = sobel_stack_x[: N * C], sobel_stack_x[N * C :]
|
||||
pred_Y, gt_Y = sobel_stack_y[: N * C], sobel_stack_y[N * C :]
|
||||
|
||||
L1X, L1Y = torch.abs(pred_X - gt_X), torch.abs(pred_Y - gt_Y)
|
||||
loss = L1X + L1Y
|
||||
return loss
|
||||
|
||||
|
||||
class MeanShift(nn.Conv2d):
|
||||
def __init__(self, data_mean, data_std, data_range=1, norm=True):
|
||||
c = len(data_mean)
|
||||
super(MeanShift, self).__init__(c, c, kernel_size=1)
|
||||
std = torch.Tensor(data_std)
|
||||
self.weight.data = torch.eye(c).view(c, c, 1, 1)
|
||||
if norm:
|
||||
self.weight.data.div_(std.view(c, 1, 1, 1))
|
||||
self.bias.data = -1 * data_range * torch.Tensor(data_mean)
|
||||
self.bias.data.div_(std)
|
||||
else:
|
||||
self.weight.data.mul_(std.view(c, 1, 1, 1))
|
||||
self.bias.data = data_range * torch.Tensor(data_mean)
|
||||
self.requires_grad = False
|
||||
|
||||
|
||||
class VGGPerceptualLoss(torch.nn.Module):
|
||||
def __init__(self, rank=0):
|
||||
super(VGGPerceptualLoss, self).__init__()
|
||||
blocks = []
|
||||
pretrained = True
|
||||
self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features
|
||||
self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).cuda()
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, X, Y, indices=None):
|
||||
X = self.normalize(X)
|
||||
Y = self.normalize(Y)
|
||||
indices = [2, 7, 12, 21, 30]
|
||||
weights = [1.0 / 2.6, 1.0 / 4.8, 1.0 / 3.7, 1.0 / 5.6, 10 / 1.5]
|
||||
k = 0
|
||||
loss = 0
|
||||
for i in range(indices[-1]):
|
||||
X = self.vgg_pretrained_features[i](X)
|
||||
Y = self.vgg_pretrained_features[i](Y)
|
||||
if (i + 1) in indices:
|
||||
loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1
|
||||
k += 1
|
||||
return loss
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
img0 = torch.zeros(3, 3, 256, 256).float().to(device)
|
||||
img1 = torch.tensor(np.random.normal(0, 1, (3, 3, 256, 256))).float().to(device)
|
||||
ternary_loss = Ternary()
|
||||
print(ternary_loss(img0, img1).shape)
|
||||
203
inference/gradio_composite_demo/rife/pytorch_msssim/__init__.py
Normal file
203
inference/gradio_composite_demo/rife/pytorch_msssim/__init__.py
Normal file
@ -0,0 +1,203 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from math import exp
|
||||
import numpy as np
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
def gaussian(window_size, sigma):
|
||||
gauss = torch.Tensor([exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) for x in range(window_size)])
|
||||
return gauss / gauss.sum()
|
||||
|
||||
|
||||
def create_window(window_size, channel=1):
|
||||
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
||||
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device)
|
||||
window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
|
||||
return window
|
||||
|
||||
|
||||
def create_window_3d(window_size, channel=1):
|
||||
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
||||
_2D_window = _1D_window.mm(_1D_window.t())
|
||||
_3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t())
|
||||
window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device)
|
||||
return window
|
||||
|
||||
|
||||
def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
|
||||
# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
|
||||
if val_range is None:
|
||||
if torch.max(img1) > 128:
|
||||
max_val = 255
|
||||
else:
|
||||
max_val = 1
|
||||
|
||||
if torch.min(img1) < -0.5:
|
||||
min_val = -1
|
||||
else:
|
||||
min_val = 0
|
||||
L = max_val - min_val
|
||||
else:
|
||||
L = val_range
|
||||
|
||||
padd = 0
|
||||
(_, channel, height, width) = img1.size()
|
||||
if window is None:
|
||||
real_size = min(window_size, height, width)
|
||||
window = create_window(real_size, channel=channel).to(img1.device)
|
||||
|
||||
# mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
|
||||
# mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
|
||||
mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=channel)
|
||||
mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=channel)
|
||||
|
||||
mu1_sq = mu1.pow(2)
|
||||
mu2_sq = mu2.pow(2)
|
||||
mu1_mu2 = mu1 * mu2
|
||||
|
||||
sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu1_sq
|
||||
sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu2_sq
|
||||
sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), "replicate"), window, padding=padd, groups=channel) - mu1_mu2
|
||||
|
||||
C1 = (0.01 * L) ** 2
|
||||
C2 = (0.03 * L) ** 2
|
||||
|
||||
v1 = 2.0 * sigma12 + C2
|
||||
v2 = sigma1_sq + sigma2_sq + C2
|
||||
cs = torch.mean(v1 / v2) # contrast sensitivity
|
||||
|
||||
ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
|
||||
|
||||
if size_average:
|
||||
ret = ssim_map.mean()
|
||||
else:
|
||||
ret = ssim_map.mean(1).mean(1).mean(1)
|
||||
|
||||
if full:
|
||||
return ret, cs
|
||||
return ret
|
||||
|
||||
|
||||
def ssim_matlab(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
|
||||
# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
|
||||
if val_range is None:
|
||||
if torch.max(img1) > 128:
|
||||
max_val = 255
|
||||
else:
|
||||
max_val = 1
|
||||
|
||||
if torch.min(img1) < -0.5:
|
||||
min_val = -1
|
||||
else:
|
||||
min_val = 0
|
||||
L = max_val - min_val
|
||||
else:
|
||||
L = val_range
|
||||
|
||||
padd = 0
|
||||
(_, _, height, width) = img1.size()
|
||||
if window is None:
|
||||
real_size = min(window_size, height, width)
|
||||
window = create_window_3d(real_size, channel=1).to(img1.device, dtype=img1.dtype)
|
||||
# Channel is set to 1 since we consider color images as volumetric images
|
||||
|
||||
img1 = img1.unsqueeze(1)
|
||||
img2 = img2.unsqueeze(1)
|
||||
|
||||
mu1 = F.conv3d(F.pad(img1, (5, 5, 5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=1)
|
||||
mu2 = F.conv3d(F.pad(img2, (5, 5, 5, 5, 5, 5), mode="replicate"), window, padding=padd, groups=1)
|
||||
|
||||
mu1_sq = mu1.pow(2)
|
||||
mu2_sq = mu2.pow(2)
|
||||
mu1_mu2 = mu1 * mu2
|
||||
|
||||
sigma1_sq = F.conv3d(F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu1_sq
|
||||
sigma2_sq = F.conv3d(F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu2_sq
|
||||
sigma12 = F.conv3d(F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), "replicate"), window, padding=padd, groups=1) - mu1_mu2
|
||||
|
||||
C1 = (0.01 * L) ** 2
|
||||
C2 = (0.03 * L) ** 2
|
||||
|
||||
v1 = 2.0 * sigma12 + C2
|
||||
v2 = sigma1_sq + sigma2_sq + C2
|
||||
cs = torch.mean(v1 / v2) # contrast sensitivity
|
||||
|
||||
ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
|
||||
|
||||
if size_average:
|
||||
ret = ssim_map.mean()
|
||||
else:
|
||||
ret = ssim_map.mean(1).mean(1).mean(1)
|
||||
|
||||
if full:
|
||||
return ret, cs
|
||||
return ret
|
||||
|
||||
|
||||
def msssim(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False):
|
||||
device = img1.device
|
||||
weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(device)
|
||||
levels = weights.size()[0]
|
||||
mssim = []
|
||||
mcs = []
|
||||
for _ in range(levels):
|
||||
sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
|
||||
mssim.append(sim)
|
||||
mcs.append(cs)
|
||||
|
||||
img1 = F.avg_pool2d(img1, (2, 2))
|
||||
img2 = F.avg_pool2d(img2, (2, 2))
|
||||
|
||||
mssim = torch.stack(mssim)
|
||||
mcs = torch.stack(mcs)
|
||||
|
||||
# Normalize (to avoid NaNs during training unstable models, not compliant with original definition)
|
||||
if normalize:
|
||||
mssim = (mssim + 1) / 2
|
||||
mcs = (mcs + 1) / 2
|
||||
|
||||
pow1 = mcs**weights
|
||||
pow2 = mssim**weights
|
||||
# From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/
|
||||
output = torch.prod(pow1[:-1] * pow2[-1])
|
||||
return output
|
||||
|
||||
|
||||
# Classes to re-use window
|
||||
class SSIM(torch.nn.Module):
|
||||
def __init__(self, window_size=11, size_average=True, val_range=None):
|
||||
super(SSIM, self).__init__()
|
||||
self.window_size = window_size
|
||||
self.size_average = size_average
|
||||
self.val_range = val_range
|
||||
|
||||
# Assume 3 channel for SSIM
|
||||
self.channel = 3
|
||||
self.window = create_window(window_size, channel=self.channel)
|
||||
|
||||
def forward(self, img1, img2):
|
||||
(_, channel, _, _) = img1.size()
|
||||
|
||||
if channel == self.channel and self.window.dtype == img1.dtype:
|
||||
window = self.window
|
||||
else:
|
||||
window = create_window(self.window_size, channel).to(img1.device).type(img1.dtype)
|
||||
self.window = window
|
||||
self.channel = channel
|
||||
|
||||
_ssim = ssim(img1, img2, window=window, window_size=self.window_size, size_average=self.size_average)
|
||||
dssim = (1 - _ssim) / 2
|
||||
return dssim
|
||||
|
||||
|
||||
class MSSSIM(torch.nn.Module):
|
||||
def __init__(self, window_size=11, size_average=True, channel=3):
|
||||
super(MSSSIM, self).__init__()
|
||||
self.window_size = window_size
|
||||
self.size_average = size_average
|
||||
self.channel = channel
|
||||
|
||||
def forward(self, img1, img2):
|
||||
return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)
|
||||
107
inference/gradio_composite_demo/rife/refine.py
Normal file
107
inference/gradio_composite_demo/rife/refine.py
Normal file
@ -0,0 +1,107 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from .warplayer import warp
|
||||
import torch.nn.functional as F
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=True,
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(
|
||||
in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
|
||||
c = 16
|
||||
|
||||
|
||||
class Contextnet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Contextnet, self).__init__()
|
||||
self.conv1 = Conv2(3, c)
|
||||
self.conv2 = Conv2(c, 2 * c)
|
||||
self.conv3 = Conv2(2 * c, 4 * c)
|
||||
self.conv4 = Conv2(4 * c, 8 * c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f2 = warp(x, flow)
|
||||
x = self.conv3(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
|
||||
class Unet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Unet, self).__init__()
|
||||
self.down0 = Conv2(17, 2 * c)
|
||||
self.down1 = Conv2(4 * c, 4 * c)
|
||||
self.down2 = Conv2(8 * c, 8 * c)
|
||||
self.down3 = Conv2(16 * c, 16 * c)
|
||||
self.up0 = deconv(32 * c, 8 * c)
|
||||
self.up1 = deconv(16 * c, 4 * c)
|
||||
self.up2 = deconv(8 * c, 2 * c)
|
||||
self.up3 = deconv(4 * c, c)
|
||||
self.conv = nn.Conv2d(c, 3, 3, 1, 1)
|
||||
|
||||
def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return torch.sigmoid(x)
|
||||
104
inference/gradio_composite_demo/rife/refine_2R.py
Normal file
104
inference/gradio_composite_demo/rife/refine_2R.py
Normal file
@ -0,0 +1,104 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from .warplayer import warp
|
||||
import torch.nn.functional as F
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
||||
return nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_planes,
|
||||
out_planes,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=True,
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(
|
||||
in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True
|
||||
),
|
||||
nn.PReLU(out_planes),
|
||||
)
|
||||
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
|
||||
c = 16
|
||||
|
||||
|
||||
class Contextnet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Contextnet, self).__init__()
|
||||
self.conv1 = Conv2(3, c, 1)
|
||||
self.conv2 = Conv2(c, 2 * c)
|
||||
self.conv3 = Conv2(2 * c, 4 * c)
|
||||
self.conv4 = Conv2(4 * c, 8 * c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
# flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f2 = warp(x, flow)
|
||||
x = self.conv3(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = (
|
||||
F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False)
|
||||
* 0.5
|
||||
)
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
|
||||
class Unet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Unet, self).__init__()
|
||||
self.down0 = Conv2(17, 2 * c, 1)
|
||||
self.down1 = Conv2(4 * c, 4 * c)
|
||||
self.down2 = Conv2(8 * c, 8 * c)
|
||||
self.down3 = Conv2(16 * c, 16 * c)
|
||||
self.up0 = deconv(32 * c, 8 * c)
|
||||
self.up1 = deconv(16 * c, 4 * c)
|
||||
self.up2 = deconv(8 * c, 2 * c)
|
||||
self.up3 = deconv(4 * c, c)
|
||||
self.conv = nn.Conv2d(c, 3, 3, 2, 1)
|
||||
|
||||
def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return torch.sigmoid(x)
|
||||
34
inference/gradio_composite_demo/rife/warplayer.py
Normal file
34
inference/gradio_composite_demo/rife/warplayer.py
Normal file
@ -0,0 +1,34 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
backwarp_tenGrid = {}
|
||||
|
||||
|
||||
def warp(tenInput, tenFlow):
|
||||
k = (str(tenFlow.device), str(tenFlow.size()))
|
||||
if k not in backwarp_tenGrid:
|
||||
tenHorizontal = (
|
||||
torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device)
|
||||
.view(1, 1, 1, tenFlow.shape[3])
|
||||
.expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
|
||||
)
|
||||
tenVertical = (
|
||||
torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device)
|
||||
.view(1, 1, tenFlow.shape[2], 1)
|
||||
.expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
|
||||
)
|
||||
backwarp_tenGrid[k] = torch.cat([tenHorizontal, tenVertical], 1).to(device)
|
||||
|
||||
tenFlow = torch.cat(
|
||||
[
|
||||
tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
|
||||
tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0),
|
||||
],
|
||||
1,
|
||||
)
|
||||
|
||||
g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
|
||||
return torch.nn.functional.grid_sample(
|
||||
input=tenInput, grid=g, mode="bilinear", padding_mode="border", align_corners=True
|
||||
)
|
||||
141
inference/gradio_composite_demo/rife_model.py
Normal file
141
inference/gradio_composite_demo/rife_model.py
Normal file
@ -0,0 +1,141 @@
|
||||
import torch
|
||||
from diffusers.image_processor import VaeImageProcessor
|
||||
from torch.nn import functional as F
|
||||
import cv2
|
||||
import utils
|
||||
from rife.pytorch_msssim import ssim_matlab
|
||||
import numpy as np
|
||||
import logging
|
||||
import skvideo.io
|
||||
from rife.RIFE_HDv3 import Model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
|
||||
def pad_image(img, scale):
|
||||
_, _, h, w = img.shape
|
||||
tmp = max(32, int(32 / scale))
|
||||
ph = ((h - 1) // tmp + 1) * tmp
|
||||
pw = ((w - 1) // tmp + 1) * tmp
|
||||
padding = (0, 0, pw - w, ph - h)
|
||||
return F.pad(img, padding)
|
||||
|
||||
|
||||
def make_inference(model, I0, I1, upscale_amount, n):
|
||||
middle = model.inference(I0, I1, upscale_amount)
|
||||
if n == 1:
|
||||
return [middle]
|
||||
first_half = make_inference(model, I0, middle, upscale_amount, n=n // 2)
|
||||
second_half = make_inference(model, middle, I1, upscale_amount, n=n // 2)
|
||||
if n % 2:
|
||||
return [*first_half, middle, *second_half]
|
||||
else:
|
||||
return [*first_half, *second_half]
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def ssim_interpolation_rife(model, samples, exp=1, upscale_amount=1, output_device="cpu"):
|
||||
print(f"samples dtype:{samples.dtype}")
|
||||
print(f"samples shape:{samples.shape}")
|
||||
output = []
|
||||
# [f, c, h, w]
|
||||
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:]
|
||||
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)
|
||||
|
||||
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
|
||||
|
||||
if ssim > 0.996:
|
||||
I1 = I0
|
||||
I1 = pad_image(I1, upscale_amount)
|
||||
I1 = make_inference(model, I0, I1, upscale_amount, 1)
|
||||
|
||||
I1_small = F.interpolate(I1[0], (32, 32), mode="bilinear", align_corners=False)
|
||||
ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
|
||||
frame = I1[0]
|
||||
I1 = I1[0]
|
||||
|
||||
tmp_output = []
|
||||
if ssim < 0.2:
|
||||
for i in range((2**exp) - 1):
|
||||
tmp_output.append(I0)
|
||||
|
||||
else:
|
||||
tmp_output = make_inference(model, I0, I1, upscale_amount, 2**exp - 1) if exp else []
|
||||
|
||||
frame = pad_image(frame, upscale_amount)
|
||||
tmp_output = [frame] + tmp_output
|
||||
for i, frame in enumerate(tmp_output):
|
||||
output.append(frame.to(output_device))
|
||||
return output
|
||||
|
||||
|
||||
def load_rife_model(model_path):
|
||||
torch.set_grad_enabled(False)
|
||||
if torch.cuda.is_available():
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.benchmark = True
|
||||
torch.set_default_tensor_type(torch.cuda.FloatTensor)
|
||||
|
||||
model = Model()
|
||||
model.load_model(model_path, -1)
|
||||
model.eval()
|
||||
print("Loaded v3.x HD model.")
|
||||
|
||||
return model
|
||||
|
||||
|
||||
# Create a generator that yields each frame, similar to cv2.VideoCapture
|
||||
def frame_generator(video_capture):
|
||||
while True:
|
||||
ret, frame = video_capture.read()
|
||||
if not ret:
|
||||
break
|
||||
yield frame
|
||||
video_capture.release()
|
||||
|
||||
|
||||
def rife_inference_with_path(model, video_path):
|
||||
video_capture = cv2.VideoCapture(video_path)
|
||||
tot_frame = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
||||
pt_frame_data = []
|
||||
pt_frame = skvideo.io.vreader(video_path)
|
||||
for frame in pt_frame:
|
||||
pt_frame_data.append(
|
||||
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)
|
||||
pbar = utils.ProgressBar(tot_frame, desc="RIFE inference")
|
||||
frames = ssim_interpolation_rife(model, pt_frame)
|
||||
pt_image = torch.stack([frames[i].squeeze(0) for i in range(len(frames))])
|
||||
image_np = VaeImageProcessor.pt_to_numpy(pt_image) # (to [49, 512, 480, 3])
|
||||
image_pil = VaeImageProcessor.numpy_to_pil(image_np)
|
||||
video_path = utils.save_video(image_pil, fps=16)
|
||||
if pbar:
|
||||
pbar.update(1)
|
||||
return video_path
|
||||
|
||||
|
||||
def rife_inference_with_latents(model, latents):
|
||||
pbar = utils.ProgressBar(latents.shape[1], desc="RIFE inference")
|
||||
rife_results = []
|
||||
latents = latents.to(device)
|
||||
for i in range(latents.size(0)):
|
||||
# [f, c, w, h]
|
||||
latent = latents[i]
|
||||
|
||||
frames = ssim_interpolation_rife(model, latent)
|
||||
pt_image = torch.stack([frames[i].squeeze(0) for i in range(len(frames))]) # (to [f, c, w, h])
|
||||
rife_results.append(pt_image)
|
||||
|
||||
return torch.stack(rife_results)
|
||||
221
inference/gradio_composite_demo/utils.py
Normal file
221
inference/gradio_composite_demo/utils.py
Normal file
@ -0,0 +1,221 @@
|
||||
import math
|
||||
from typing import Union, List
|
||||
|
||||
import torch
|
||||
import os
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
import itertools
|
||||
import PIL.Image
|
||||
import safetensors.torch
|
||||
import tqdm
|
||||
import logging
|
||||
from diffusers.utils import export_to_video
|
||||
from spandrel import ModelLoader
|
||||
|
||||
logger = logging.getLogger(__file__)
|
||||
|
||||
|
||||
def load_torch_file(ckpt, device=None, dtype=torch.float16):
|
||||
if device is None:
|
||||
device = torch.device("cpu")
|
||||
if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
|
||||
sd = safetensors.torch.load_file(ckpt, device=device.type)
|
||||
else:
|
||||
if not "weights_only" in torch.load.__code__.co_varnames:
|
||||
logger.warning(
|
||||
"Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely."
|
||||
)
|
||||
|
||||
pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
|
||||
if "global_step" in pl_sd:
|
||||
logger.debug(f"Global Step: {pl_sd['global_step']}")
|
||||
if "state_dict" in pl_sd:
|
||||
sd = pl_sd["state_dict"]
|
||||
elif "params_ema" in pl_sd:
|
||||
sd = pl_sd["params_ema"]
|
||||
else:
|
||||
sd = pl_sd
|
||||
|
||||
sd = {k: v.to(dtype) for k, v in sd.items()}
|
||||
return sd
|
||||
|
||||
|
||||
def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
|
||||
if filter_keys:
|
||||
out = {}
|
||||
else:
|
||||
out = state_dict
|
||||
for rp in replace_prefix:
|
||||
replace = list(
|
||||
map(
|
||||
lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp) :])),
|
||||
filter(lambda a: a.startswith(rp), state_dict.keys()),
|
||||
)
|
||||
)
|
||||
for x in replace:
|
||||
w = state_dict.pop(x[0])
|
||||
out[x[1]] = w
|
||||
return out
|
||||
|
||||
|
||||
def module_size(module):
|
||||
module_mem = 0
|
||||
sd = module.state_dict()
|
||||
for k in sd:
|
||||
t = sd[k]
|
||||
module_mem += t.nelement() * t.element_size()
|
||||
return module_mem
|
||||
|
||||
|
||||
def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
|
||||
return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def tiled_scale_multidim(
|
||||
samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", pbar=None
|
||||
):
|
||||
dims = len(tile)
|
||||
print(f"samples dtype:{samples.dtype}")
|
||||
output = torch.empty(
|
||||
[samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])),
|
||||
device=output_device,
|
||||
)
|
||||
|
||||
for b in range(samples.shape[0]):
|
||||
s = samples[b : b + 1]
|
||||
out = torch.zeros(
|
||||
[s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])),
|
||||
device=output_device,
|
||||
)
|
||||
out_div = torch.zeros(
|
||||
[s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])),
|
||||
device=output_device,
|
||||
)
|
||||
|
||||
for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
|
||||
s_in = s
|
||||
upscaled = []
|
||||
|
||||
for d in range(dims):
|
||||
pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
|
||||
l = min(tile[d], s.shape[d + 2] - pos)
|
||||
s_in = s_in.narrow(d + 2, pos, l)
|
||||
upscaled.append(round(pos * upscale_amount))
|
||||
|
||||
ps = function(s_in).to(output_device)
|
||||
mask = torch.ones_like(ps)
|
||||
feather = round(overlap * upscale_amount)
|
||||
for t in range(feather):
|
||||
for d in range(2, dims + 2):
|
||||
m = mask.narrow(d, t, 1)
|
||||
m *= (1.0 / feather) * (t + 1)
|
||||
m = mask.narrow(d, mask.shape[d] - 1 - t, 1)
|
||||
m *= (1.0 / feather) * (t + 1)
|
||||
|
||||
o = out
|
||||
o_d = out_div
|
||||
for d in range(dims):
|
||||
o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
|
||||
o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
|
||||
|
||||
o += ps * mask
|
||||
o_d += mask
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
|
||||
output[b : b + 1] = out / out_div
|
||||
return output
|
||||
|
||||
|
||||
def tiled_scale(
|
||||
samples,
|
||||
function,
|
||||
tile_x=64,
|
||||
tile_y=64,
|
||||
overlap=8,
|
||||
upscale_amount=4,
|
||||
out_channels=3,
|
||||
output_device="cpu",
|
||||
pbar=None,
|
||||
):
|
||||
return tiled_scale_multidim(
|
||||
samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar
|
||||
)
|
||||
|
||||
|
||||
def load_sd_upscale(ckpt, inf_device):
|
||||
sd = load_torch_file(ckpt, device=inf_device)
|
||||
if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
|
||||
sd = state_dict_prefix_replace(sd, {"module.": ""})
|
||||
out = ModelLoader().load_from_state_dict(sd).half()
|
||||
return out
|
||||
|
||||
|
||||
def upscale(upscale_model, tensor: torch.Tensor, inf_device, output_device="cpu") -> torch.Tensor:
|
||||
memory_required = module_size(upscale_model.model)
|
||||
memory_required += (
|
||||
(512 * 512 * 3) * tensor.element_size() * max(upscale_model.scale, 1.0) * 384.0
|
||||
) # The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate
|
||||
memory_required += tensor.nelement() * tensor.element_size()
|
||||
print(f"UPScaleMemory required: {memory_required / 1024 / 1024 / 1024} GB")
|
||||
|
||||
upscale_model.to(inf_device)
|
||||
tile = 512
|
||||
overlap = 32
|
||||
|
||||
steps = tensor.shape[0] * get_tiled_scale_steps(
|
||||
tensor.shape[3], tensor.shape[2], tile_x=tile, tile_y=tile, overlap=overlap
|
||||
)
|
||||
|
||||
pbar = ProgressBar(steps, desc="Tiling and Upscaling")
|
||||
|
||||
s = tiled_scale(
|
||||
samples=tensor.to(torch.float16),
|
||||
function=lambda a: upscale_model(a),
|
||||
tile_x=tile,
|
||||
tile_y=tile,
|
||||
overlap=overlap,
|
||||
upscale_amount=upscale_model.scale,
|
||||
pbar=pbar,
|
||||
)
|
||||
|
||||
upscale_model.to(output_device)
|
||||
return s
|
||||
|
||||
|
||||
def upscale_batch_and_concatenate(upscale_model, latents, inf_device, output_device="cpu") -> torch.Tensor:
|
||||
upscaled_latents = []
|
||||
for i in range(latents.size(0)):
|
||||
latent = latents[i]
|
||||
upscaled_latent = upscale(upscale_model, latent, inf_device, output_device)
|
||||
upscaled_latents.append(upscaled_latent)
|
||||
return torch.stack(upscaled_latents)
|
||||
|
||||
|
||||
def save_video(tensor: Union[List[np.ndarray], List[PIL.Image.Image]], fps: int = 8):
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
video_path = f"./output/{timestamp}.mp4"
|
||||
os.makedirs(os.path.dirname(video_path), exist_ok=True)
|
||||
export_to_video(tensor, video_path, fps=fps)
|
||||
return video_path
|
||||
|
||||
|
||||
class ProgressBar:
|
||||
def __init__(self, total, desc=None):
|
||||
self.total = total
|
||||
self.current = 0
|
||||
self.b_unit = tqdm.tqdm(total=total, desc="ProgressBar context index: 0" if desc is None else desc)
|
||||
|
||||
def update(self, value):
|
||||
if value > self.total:
|
||||
value = self.total
|
||||
self.current = value
|
||||
if self.b_unit is not None:
|
||||
self.b_unit.set_description("ProgressBar context index: {}".format(self.current))
|
||||
self.b_unit.refresh()
|
||||
|
||||
# 更新进度
|
||||
self.b_unit.update(self.current)
|
||||
Loading…
x
Reference in New Issue
Block a user