1284 lines
43 KiB
Python
1284 lines
43 KiB
Python
from pathlib import Path
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import hashlib
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import math
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import os
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import time
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import shutil
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import cv2
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import ox
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import requests
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import fal_client
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from byteplussdkarkruntime import Ark
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from django.conf import settings
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from item.models import Item
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from document.models import Document
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from archive.models import File, Stream
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import itemlist.models
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os.environ["FAL_KEY"] = settings.FAL_KEY
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MAX_DURATION = 12
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headers = {
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"Authorization": "Bearer " + settings.BYTEPLUSE_TOKEN,
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"Content-Type": "application/json",
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}
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def public_url(path):
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return path.replace("/srv/pandora/static/", settings.PUBLIC_URL + "static/")
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def public_document_url(document):
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url = "%sdocuments/%s/source.%s?token=%s" % (
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settings.PUBLIC_URL,
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document.get_id(),
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document.extension,
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settings.PUBLIC_TOKEN,
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)
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return url
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def public_video_url(item):
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url = "%s%s/download/source/?token=%s" % (
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settings.PUBLIC_URL,
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item.public_id,
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settings.PUBLIC_TOKEN,
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)
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return url
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def public_frame_url(item, position):
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image = "%s%s/source%s.png?token=%s" % (
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settings.PUBLIC_URL,
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item.public_id,
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position,
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settings.PUBLIC_TOKEN,
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)
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return image
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def trim_video(src, dst, frames, start0=False):
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cap = cv2.VideoCapture(src)
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fps = cap.get(cv2.CAP_PROP_FPS)
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frames_src = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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frame_count = 0
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offset = int((frames_src - frames) / 2)
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if start0:
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offset = 0
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print(frames_src, frames, offset)
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fourcc = cv2.VideoWriter_fourcc(*"avc1")
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out = cv2.VideoWriter(dst, fourcc, fps, (width, height))
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written = 0
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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if frame_count < offset:
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continue
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if frame_count >= (frames + offset):
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continue
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out.write(frame)
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written += 1
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out.release()
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cap.release()
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def make_single_character_image(character):
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character = get_character_document(character, type='Character')
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character_url = public_document_url(character)
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data = {
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"model": "seedream-4-5-251128",
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"size": "2K",
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"watermark": False,
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'image': character_url,
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"prompt": "character from image 1 is standing straight up, full body portrait from head to toe. face, clothing, skin are photo realistic, camera facing straight at character. background is white"
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}
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url = bytedance_image_generation(data)
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extension = url.split(".")[-1].split("?")[0]
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if extension == "jpeg":
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extension = "jpg"
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file = Document(user=character.user)
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file.data["title"] = character.data['title'].replace('Character', 'Single Character')
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file.extension = extension
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file.width = -1
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file.pages = -1
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file.uploading = True
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file.save()
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file.uploading = True
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name = "data.%s" % file.extension
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file.file.name = file.path(name)
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ox.net.save_url(url, file.file.path, overwrite=True)
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file.get_info()
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file.get_ratio()
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file.oshash = ox.oshash(file.file.path)
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file.save()
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file.update_sort()
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return file
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def make_single_character_image_flux(character):
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character = get_character_document(character, type='Character')
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character_url = public_document_url(character)
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prompt = 'character from @image 1 is standing straight up, full body portrait from head to toe. face, clothing, skin are photo realistic, camera facing straight at character. background is white'
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url = flux_edit_image([character_url], prompt)
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extension = url.split(".")[-1].split("?")[0]
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if extension == "jpeg":
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extension = "jpg"
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file = Document(user=character.user)
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file.data["title"] = character.data['title'].replace('Character', 'FLUX Single Character')
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file.extension = extension
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file.width = -1
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file.pages = -1
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file.uploading = True
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file.save()
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file.uploading = True
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name = "data.%s" % file.extension
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file.file.name = file.path(name)
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ox.net.save_url(url, file.file.path, overwrite=True)
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file.get_info()
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file.get_ratio()
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file.oshash = ox.oshash(file.file.path)
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file.save()
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file.update_sort()
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return file
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def bytedance_task(data):
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url = "https://ark.ap-southeast.bytepluses.com/api/v3/contents/generations/tasks"
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model = "seedance-1-5-pro-251215"
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resolution = "720p"
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defaults = {
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"model": model,
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"generate_audio": False,
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"ratio": "16:9",
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"watermark": False,
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"resolution": resolution,
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"camera_fixed": True,
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"return_last_frame": True,
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}
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for key, value in defaults.items():
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if key not in data:
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data[key] = value
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if data["model"] in EP:
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data["model"] = EP[data["model"]]
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print(data)
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r = requests.post(url, headers=headers, json=data).json()
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print(r)
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task_id = r["id"]
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status = requests.get(url + "/" + task_id, headers=headers).json()
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while status["status"] in ("queued", "running", "cancelled"):
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time.sleep(10)
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status = requests.get(url + "/" + task_id, headers=headers).json()
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print(status)
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return status
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def bytedance_response(data):
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url = "https://ark.ap-southeast.bytepluses.com/api/v3/responses"
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defaults = {"model": "seed-1-8-251228"}
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for key, value in defaults.items():
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if key not in data:
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data[key] = value
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print(data)
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if data["model"] in EP:
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data["model"] = EP[data["model"]]
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response = requests.post(url, headers=headers, json=data).json()
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print(response)
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return response
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def t2v_bytedance(prompt, duration, output):
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nduration = max(4, int(math.ceil(duration)))
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data = {
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"duration": nduration,
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"content": [{"type": "text", "text": prompt}],
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}
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status = bytedance_task(data)
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output_url = status["content"]["video_url"]
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ox.net.save_url(output_url, output, overwrite=True)
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if "last_frame_url" in status["content"]:
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ox.net.save_url(
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status["content"]["last_frame_url"],
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output + ".last_frame.png",
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overwrite=True,
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)
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return status
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def i2v_bytedance(first_frame, prompt, duration, output, last_frame=None, seed=None):
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nduration = max(4, int(math.ceil(duration)))
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data = {
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"duration": nduration,
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"content": [
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{
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"type": "text",
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"text": prompt,
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},
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{
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"type": "image_url",
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"role": "first_frame",
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"image_url": {"url": first_frame},
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},
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],
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}
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if last_frame:
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data["content"].append(
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{
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"type": "image_url",
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"role": "last_frame",
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"image_url": {"url": last_frame},
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}
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)
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if seed:
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data["seed"] = seed
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status = bytedance_task(data)
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output_url = status["content"]["video_url"]
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ox.net.save_url(output_url, output, overwrite=True)
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if "last_frame_url" in status["content"]:
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ox.net.save_url(
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status["content"]["last_frame_url"],
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output + ".last_frame.png",
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overwrite=True,
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)
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return status
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def first_last(first_frame, last_frame, prompt, duration, output):
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nduration = max(4, int(math.ceil(duration)))
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data = {
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"duration": nduration,
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"content": [
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{
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"type": "text",
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"text": prompt,
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},
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{
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"type": "image_url",
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"role": "first_frame",
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"image_url": {"url": first_frame},
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},
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{
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"type": "image_url",
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"role": "last_frame",
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"image_url": {"url": last_frame},
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},
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],
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}
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status = bytedance_task(data)
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output_url = status["content"]["video_url"]
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ox.net.save_url(output_url, output, overwrite=True)
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if "last_frame_url" in status["content"]:
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ox.net.save_url(
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status["content"]["last_frame_url"],
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output + ".last_frame.png",
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overwrite=True,
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)
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return status
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def get_item_segments(item, max_duration=MAX_DURATION):
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cuts = item.get("cuts")
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filename = item.files.all()[0].data.path
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input_info = ox.avinfo(filename)
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p = 0
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nc = []
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for c in cuts:
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d = c - p
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if d < 0.5:
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continue
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p = c
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nc.append(c)
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nc = nc + [input_info["duration"]]
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if len(nc) > 3:
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if nc[-1] - nc[-2] < 0.5:
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nc = nc[:-2] + nc[-1:]
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segments = []
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position = 0
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for out in nc:
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duration = out - position
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while duration > max_duration:
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position += max_duration
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if len(segments):
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segments.append(["c", position])
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else:
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segments.append(position)
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duration = out - position
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else:
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segments.append(out)
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position = out
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return segments
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def join_segments(processed, joined_output):
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out = None
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for filename in processed:
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cap = cv2.VideoCapture(filename)
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if out is None:
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fps = cap.get(cv2.CAP_PROP_FPS)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fourcc = cv2.VideoWriter_fourcc(*"avc1")
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out = cv2.VideoWriter(joined_output, fourcc, fps, (width, height))
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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out.write(frame)
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cap.release()
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if out is not None:
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out.release()
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def remake_video(item_id, prompt):
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item = Item.objects.get(public_id=item_id)
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segments = get_item_segments(item)
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print(segments)
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prompt_hash = hashlib.sha1(prompt.encode()).hexdigest()
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position = n = 0
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processed = []
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prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
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for segment in segments:
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if isinstance(segment, list):
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stype, segment = segment
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else:
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stype = "n"
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duration = segment - position
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if stype == "c":
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first_frame_path = "%s/%06d.mp4.last_frame.png" % (prefix, n - 1)
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first_frame = public_url(first_frame_path)
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else:
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first_frame = "%s%s/source%s.png?token=%s" % (
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settings.PUBLIC_URL,
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item.public_id,
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position,
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settings.PUBLIC_TOKEN,
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)
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last_frame_position = segment - 2 / 24
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last_frame = "%s%s/source%0.3f.png?token=%s" % (
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settings.PUBLIC_URL,
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item.public_id,
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last_frame_position,
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settings.PUBLIC_TOKEN,
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)
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output = "%s/%06d.mp4" % (prefix, n)
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if not os.path.exists(output):
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first_last(first_frame, last_frame, prompt, duration, output)
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trimmed = "%s/%06d_trimmed.mp4" % (prefix, n)
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frames = int(duration * 24)
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if not os.path.exists(trimmed):
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trim_video(output, trimmed, frames, stype == "c")
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processed.append(trimmed)
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position = segment
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n += 1
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joined_output = "%s/joined.mp4" % (prefix,)
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join_segments(processed, joined_output)
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return joined_output
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EP = {
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"seedream-4-5-251128": "ep-20260122071519-pbf7l",
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"seed-1-8-251228": "ep-20260122071243-8qfrk",
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"seedance-1-5-pro-251215": "ep-20260122071613-blmsd",
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}
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def bytedance_image_generation(data):
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model = "seedream-4-5-251128"
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url = "https://ark.ap-southeast.bytepluses.com/api/v3/images/generations"
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defaults = {
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"model": model,
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"size": "2560x1440",
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"watermark": False,
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}
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for key in defaults:
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if key not in data:
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data[key] = defaults[key]
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if data["model"] in EP:
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data["model"] = EP[data["model"]]
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print("prepare_image", data)
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response = requests.post(url, headers=headers, json=data).json()
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print(response)
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return response["data"][0]["url"]
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def prepare_image(image, prompt, out=None):
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if not image.startswith("http:"):
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image = public_url(image)
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data = {
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"prompt": prompt,
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"image": image,
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"size": "2560x1440",
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}
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output_url = bytedance_image_generation(data)
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if out is None:
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out = image + ".ai.png"
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ox.net.save_url(output_url, out, overwrite=True)
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return r
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def process_frame(item, prompt, character=None, position=0, seed=None):
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model = "seedream-4-5-251128"
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if isinstance(item, str):
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item = Item.objects.get(public_id=item)
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if isinstance(character, Document):
|
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character = public_document_url(character)
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image = public_frame_url(item, position)
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if character is not None:
|
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image = [image, character]
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data = {
|
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"model": model,
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"prompt": prompt,
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"image": image,
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"size": "2560x1440",
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}
|
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if seed:
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data["seed"] = seed
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url = bytedance_image_generation(data)
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img = add_ai_image(item, position, url)
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img.refresh_from_db()
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img.data["model"] = model
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img.data["prompt"] = prompt
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img.data["source"] = item.public_id
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if character:
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img.data["source"] += " " + character.split("?")[0]
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print(img, img.data)
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img.save()
|
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img.update_sort()
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img.update_find()
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return img
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|
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def get_character_document(character, type="Single Character"):
|
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if character in ("P1", "P2", "P3", "P4", "P5"):
|
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return Document.objects.get(data__title=type + " " + character)
|
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return character
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"""
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REPLACE_CHARACTER_PROMPT = "Replace the foreground character in image 1 with the character in image 2, keep the posture, clothing, background, light, atmosphere from image 1, but take the facial features and personality from image 2. Make sure the size of the character is adjusted since the new character is a child and make sure the size of the head matches the body. The quality of the image should be the same between foreground and background, adjust the quality of the character to match the background. Use the style of image 1 for the character: if image 1 is a photo make the character a real person, if image 1 is a drawing make the character a drawn character, if image 1 is a comic use a comic character and so on"
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"""
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REPLACE_CHARACTER_PROMPT = "Replace the foreground character in image 1 with the character in image 2, keep the posture, clothing, background, light, atmosphere from image 1, but take the facial features and personality from image 2. Make sure the size of the character is adjusted since the new character is a child and make sure the size of the head matches the body. The quality of the image should be the same between foreground and background, adjust the quality of the character to match the background. Use the style of image 1 for the character: if image 1 is a photo make the character a real person, if image 1 is a drawing make the character a drawn character, if image 1 is a comic use a comic character"
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|
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def fal_replace_character(item, character, position=0):
|
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if isinstance(item, str):
|
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item = Item.objects.get(public_id=item)
|
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prompt = REPLACE_CHARACTER_PROMPT.replace("image 1", "@image 1").replace(
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"image 2", "@image 2"
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)
|
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if character == "P5":
|
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prompt = prompt.replace("child", "teenager")
|
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|
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character = get_character_document(character)
|
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if isinstance(character, Document):
|
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character = public_document_url(character)
|
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image = public_frame_url(item, position)
|
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image = [image, character]
|
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url = flux_edit_image(image, prompt)
|
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img = add_ai_image(item, position, url)
|
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img.refresh_from_db()
|
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img.data["model"] = "flux-2-pro"
|
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img.data["prompt"] = prompt
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img.data["source"] = item.public_id
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img.data["source"] += " " + character.split("?")[0]
|
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if isinstance(character, Document):
|
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img.data["character"] = character.get_id()
|
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else:
|
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img.data["character"] = character
|
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img.data["position"] = position
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print(img, img.data)
|
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img.save()
|
|
img.update_sort()
|
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img.update_find()
|
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return img
|
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|
|
|
|
def replace_character(item, character, position=0, seed=None):
|
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prompt = REPLACE_CHARACTER_PROMPT
|
|
if character == "P5":
|
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prompt = prompt.replace("child", "teenager")
|
|
character = get_character_document(character)
|
|
if isinstance(character, Document):
|
|
character_url = public_document_url(character)
|
|
else:
|
|
character_url = character
|
|
frame = process_frame(item, prompt, character_url, position, seed=seed)
|
|
if isinstance(character, Document):
|
|
frame.data["character"] = character.get_id()
|
|
else:
|
|
frame.data["character"] = character
|
|
frame.data["position"] = position
|
|
frame.save()
|
|
return frame
|
|
|
|
|
|
def kling_lipsync(audio_item, video_item):
|
|
video_url = public_video_url(video_item)
|
|
audio_url = public_video_url(audio_item)
|
|
model = "fal-ai/kling-video/lipsync/audio-to-video"
|
|
data = {
|
|
"video_url": video_url,
|
|
"audio_url": audio_url
|
|
}
|
|
print(data)
|
|
handler = fal_client.submit(model, arguments=data)
|
|
request_id = handler.request_id
|
|
print(request_id)
|
|
result = fal_wait_for(model, request_id)
|
|
print(result)
|
|
output_url = result["video"]["url"]
|
|
ox.net.save_url(output_url, output, overwrite=True)
|
|
ai = add_ai_variant(item, output, "ai:lipsync")
|
|
ai.data["model"] = model
|
|
ai.save()
|
|
if not keep:
|
|
shutil.rmtree(os.path.dirname(output))
|
|
img.add(ai)
|
|
return ai
|
|
|
|
def kling_v2v_reference(item, character, keep=False):
|
|
# https://fal.ai/models/fal-ai/kling-video/o1/video-to-video/reference/api
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
|
|
if character in ("P1", "P2", "P3", "P4", "P5"):
|
|
character = public_document_url(
|
|
Document.objects.get(data__title="Character " + character)
|
|
)
|
|
character = get_character_document(character)
|
|
if isinstance(character, Document):
|
|
character_url = public_document_url(character)
|
|
else:
|
|
character_url = character
|
|
video_url = public_video_url(item)
|
|
prompt = "Replace the main character in @Video1 with the character from the reference images, adjust the style of the character to match the style of the video"
|
|
model = "fal-ai/kling-video/o1/video-to-video/reference"
|
|
prompt_hash = hashlib.sha1((prompt + character_url).encode()).hexdigest()
|
|
output = "/srv/pandora/static/power/cache/%s_%s/ai.mp4" % (
|
|
item.public_id,
|
|
prompt_hash,
|
|
)
|
|
for d in [3, 4, 5, 6, 7, 8, 9, 10]:
|
|
if d > item.sort.duration:
|
|
break
|
|
duration = d
|
|
|
|
data = {
|
|
"prompt": prompt,
|
|
"image_url": image_url,
|
|
"video_url": video_url,
|
|
"keep_original_sound": False,
|
|
"character_orientation": "video",
|
|
}
|
|
data = {
|
|
"prompt": prompt,
|
|
"keep_audio": False,
|
|
"aspect_ratio": "16:9",
|
|
"video_url": video_url,
|
|
"image_urls": [character_url],
|
|
"duration": str(duration)
|
|
}
|
|
'''
|
|
data["elements"] = [
|
|
{
|
|
"reference_image_urls": [
|
|
image_url,
|
|
image_url,
|
|
],
|
|
"frontal_image_url": image_url
|
|
}
|
|
]
|
|
'''
|
|
print(data)
|
|
handler = fal_client.submit(model, arguments=data)
|
|
request_id = handler.request_id
|
|
print(request_id)
|
|
result = fal_wait_for(model, request_id)
|
|
print(result)
|
|
output_url = result["video"]["url"]
|
|
ox.net.save_url(output_url, output, overwrite=True)
|
|
ai = add_ai_variant(item, output, "ai:v2v-replace")
|
|
ai.data["prompt"] = ox.escape_html(prompt)
|
|
ai.data["firstframe"] = image_url.split("?")[0]
|
|
ai.data["model"] = model
|
|
ai.save()
|
|
if not keep:
|
|
shutil.rmtree(os.path.dirname(output))
|
|
img.add(ai)
|
|
return ai
|
|
|
|
def replace_character_motion_control(item, character, keep=False):
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
|
|
# FIXME get character from documents
|
|
if isinstance(character, str):
|
|
img = replace_character(item, character, 0)
|
|
else:
|
|
img = character
|
|
image_url = public_document_url(img)
|
|
video_url = public_video_url(item)
|
|
prompt = ""
|
|
model = "fal-ai/kling-video/v2.6/pro/motion-control"
|
|
prompt_hash = hashlib.sha1((prompt + image_url).encode()).hexdigest()
|
|
output = "/srv/pandora/static/power/cache/%s_%s/ai.mp4" % (
|
|
item.public_id,
|
|
prompt_hash,
|
|
)
|
|
data = {
|
|
"prompt": prompt,
|
|
"image_url": image_url,
|
|
"video_url": video_url,
|
|
"keep_original_sound": False,
|
|
"character_orientation": "video",
|
|
}
|
|
print(data)
|
|
handler = fal_client.submit(model, arguments=data)
|
|
request_id = handler.request_id
|
|
print(request_id)
|
|
result = fal_wait_for(model, request_id)
|
|
print(result)
|
|
output_url = result["video"]["url"]
|
|
ox.net.save_url(output_url, output, overwrite=True)
|
|
ai = add_ai_variant(item, output, "ai:replace:p1:motion-control")
|
|
ai.data["prompt"] = ox.escape_html(prompt)
|
|
ai.data["firstframe"] = image_url.split("?")[0]
|
|
ai.data["model"] = model
|
|
ai.save()
|
|
if not keep:
|
|
shutil.rmtree(os.path.dirname(output))
|
|
img.add(ai)
|
|
return ai
|
|
|
|
|
|
def describe_video(url, neutral=False, face=False):
|
|
if face:
|
|
extra = 'describe the facial expression to allow an actor to recreate the scene, '
|
|
else:
|
|
extra = ''
|
|
if neutral:
|
|
prompt = (
|
|
"Detect cuts or scene changes and describe each scene, use as much details as you can. "
|
|
f"Describe each person incudling detalied apreance, haircut in a gender neutral way, {extra}"
|
|
"describe each objects, animal or plant, describe foreground and backgroud, "
|
|
"describe from what angle the scene is filmed, incude details about camera model, lense, depth of field used to film this scene. "
|
|
"Use the format: <description of scene 1>. CAMERA CUT TO <description of scene 2>. CAMERA CUT TO <description of scene 3>. "
|
|
"Don't mention it if you don't find a cut."
|
|
)
|
|
else:
|
|
prompt = (
|
|
"Detect cuts or scene changes and describe each scene, use as much details as you can. "
|
|
"Describe each person incudling detalied apreance, ethnicity, haircolor, haircut, "
|
|
"describe each objects, animal or plant, describe foreground and backgroud, "
|
|
"describe from what angle the scene is filmed, incude details about camera model, lense, depth of field used to film this scene. "
|
|
"Use the format: <description of scene 1>. CAMERA CUT TO <description of scene 2>. CAMERA CUT TO <description of scene 3>. "
|
|
"Don't mention it if you don't find a cut."
|
|
)
|
|
data = {
|
|
"input": [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "input_video", "video_url": url, "fps": 1},
|
|
{"type": "input_text", "text": prompt},
|
|
],
|
|
}
|
|
],
|
|
}
|
|
response = bytedance_response(data)
|
|
return response["output"][1]["content"][0]["text"]
|
|
|
|
|
|
def describe_item(item, neutral=False):
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
video_url = public_video_url(item)
|
|
return describe_video(video_url, neutral)
|
|
|
|
|
|
def reshoot_item(item, extra_prompt=None, first_frame=None, keep=False, prompt=None):
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
if isinstance(first_frame, Document):
|
|
first_frame_url = public_document_url(first_frame)
|
|
else:
|
|
first_frame_url = first_frame
|
|
duration = item.sort.duration
|
|
frames = int(duration * 24)
|
|
neutral = first_frame is not None
|
|
if prompt is None:
|
|
prompt = describe_item(item, neutral)
|
|
if extra_prompt:
|
|
prompt += " " + extra_prompt
|
|
prompt_hash = hashlib.sha1((prompt).encode()).hexdigest()
|
|
output = "/srv/pandora/static/power/cache/%s_%s/ai.mp4" % (
|
|
item.public_id,
|
|
prompt_hash,
|
|
)
|
|
if first_frame:
|
|
status = i2v_bytedance(first_frame_url, prompt, duration, output)
|
|
else:
|
|
status = t2v_bytedance(prompt, duration, output)
|
|
|
|
trimmed = "/srv/pandora/static/power/cache/%s_%s/trimmed.mp4" % (
|
|
item.public_id,
|
|
prompt_hash,
|
|
)
|
|
trim_video(output, trimmed, frames)
|
|
variant = "ai:0:reshoot"
|
|
if first_frame:
|
|
variant = "ai:0:reshoot-firstframe"
|
|
ai = add_ai_variant(item, trimmed, variant)
|
|
ai.data["prompt"] = ox.escape_html(prompt)
|
|
ai.data["model"] = status["model"]
|
|
ai.data["seed"] = status["seed"]
|
|
if first_frame:
|
|
if isinstance(first_frame, Document):
|
|
first_frame.add(ai)
|
|
ai.data["firstframe"] = first_frame.get_id()
|
|
else:
|
|
ai.data["firstframe"] = first_frame.split("?")[0]
|
|
ai.save()
|
|
if not keep:
|
|
shutil.rmtree(os.path.dirname(output))
|
|
return ai
|
|
|
|
|
|
def reshoot_item_segments(item, character, keep=False):
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
max_duration = 12
|
|
source = item.files.all()[0].data.path
|
|
info = ox.avinfo(source)
|
|
duration = info["duration"]
|
|
if duration < max_duration:
|
|
segments = [duration]
|
|
else:
|
|
segments = get_item_segments(item, max_duration=max_duration)
|
|
print(segments)
|
|
prompt_hash = hashlib.sha1("reshoot_segment".encode()).hexdigest()
|
|
processed = []
|
|
prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
|
|
video_segments = fragment_video(source, prefix, segments)
|
|
prompts = []
|
|
first_frames = []
|
|
seed = None
|
|
n = 0
|
|
position = 0
|
|
for segment in segments:
|
|
if isinstance(segment, list):
|
|
stype, segment = segment
|
|
else:
|
|
stype = "n"
|
|
output = "%s/%06d.mp4" % (prefix, n)
|
|
output_ai = "%s/%06d_ai.mp4" % (prefix, n)
|
|
segment_duration = segment - position
|
|
if os.path.exists(output):
|
|
segment_video_url = public_url(output)
|
|
prompt = describe_video(segment_video_url, neutral=True)
|
|
prompts.append("Segment %s: " % (n + 1) + prompt)
|
|
segment_character = character
|
|
if position:
|
|
segment_character = segment_first_frame_url
|
|
segment_first_frame = replace_character(
|
|
item, segment_character, position, seed=seed
|
|
)
|
|
segment_first_frame_url = public_document_url(segment_first_frame)
|
|
status = i2v_bytedance(
|
|
segment_first_frame_url, prompt, segment_duration, output_ai, seed=seed
|
|
)
|
|
seeed = status["seed"]
|
|
trimmed = "%s/%06d_ai_trimmed.mp4" % (prefix, n)
|
|
frames = int(segment_duration * 24)
|
|
trim_video(output_ai, trimmed, frames, stype == "c")
|
|
processed.append(trimmed)
|
|
first_frames.append(segment_first_frame)
|
|
n += 1
|
|
position = segment
|
|
joined_output = "%s/joined.mp4" % (prefix)
|
|
join_segments(processed, joined_output)
|
|
ai = add_ai_variant(item, joined_output, "ai:0:reshoot-firstframe")
|
|
prompt = "\n\n".join(prompts)
|
|
ai.data["prompt"] = ox.escape_html(prompt)
|
|
ai.data["firstframe"] = " ".join([ff.get_id() for ff in first_frames])
|
|
ai.data["model"] = status["model"]
|
|
ai.data["seed"] = seed
|
|
ai.save()
|
|
for first_frame in first_frames:
|
|
first_frame.add(ai)
|
|
if not keep:
|
|
shutil.rmtree(prefix)
|
|
return ai
|
|
|
|
|
|
def describe_image(url):
|
|
system_prompt = ""
|
|
system_prompt = "You are an image analyst describing different aspects of an image. You are focused on the form, composition, and task shown in the image."
|
|
prompt = "Please analyze this image according to the specified structure."
|
|
data = {
|
|
"input": [
|
|
{"role": "system", "content": system_prompt},
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "input_image", "image_url": url},
|
|
{"type": "input_text", "text": prompt},
|
|
],
|
|
},
|
|
],
|
|
}
|
|
response = bytedance_response(data)
|
|
return response["output"][-1]["content"][0]["text"]
|
|
|
|
|
|
def transform_remake_video(item_id, image_prompt, video_prompt):
|
|
item = Item.objects.get(public_id=item_id)
|
|
segments = get_item_segments(item)
|
|
print(segments)
|
|
prompt_hash = hashlib.sha1((image_prompt + video_prompt).encode()).hexdigest()
|
|
position = n = 0
|
|
processed = []
|
|
prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
|
|
for segment in segments:
|
|
if isinstance(segment, list):
|
|
stype, segment = segment
|
|
else:
|
|
stype = "n"
|
|
duration = segment - position
|
|
if stype == "c":
|
|
first_frame_path = "%s/%06d.mp4.last_frame.png" % (prefix, n - 1)
|
|
first_frame = public_url(first_frame_path)
|
|
else:
|
|
first_frame = "%s%s/source%s.png?token=%s" % (
|
|
settings.PUBLIC_URL,
|
|
item.public_id,
|
|
position,
|
|
settings.PUBLIC_TOKEN,
|
|
)
|
|
first_frame_path = "%s/%06d.first_frame.png" % (prefix, n)
|
|
if not os.path.exists(first_frame_path):
|
|
prepare_image(first_frame, image_prompt, first_frame_path)
|
|
first_frame = public_url(first_frame_path)
|
|
last_frame_position = segment - 2 / 24
|
|
last_frame = "%s%s/source%0.3f.png?token=%s" % (
|
|
settings.PUBLIC_URL,
|
|
item.public_id,
|
|
last_frame_position,
|
|
settings.PUBLIC_TOKEN,
|
|
)
|
|
last_frame_path = "%s/%06d.last_frame.png" % (prefix, n)
|
|
if not os.path.exists(last_frame_path):
|
|
prepare_image(last_frame, image_prompt, last_frame_path)
|
|
last_frame = public_url(last_frame_path)
|
|
|
|
output = "%s/%06d.mp4" % (prefix, n)
|
|
if not os.path.exists(output):
|
|
first_last(first_frame, last_frame, video_prompt, duration, output)
|
|
trimmed = "%s/%06d_trimmed.mp4" % (prefix, n)
|
|
frames = int(duration * 24)
|
|
if not os.path.exists(trimmed):
|
|
trim_video(output, trimmed, frames, stype == "c")
|
|
processed.append(trimmed)
|
|
position = segment
|
|
n += 1
|
|
|
|
joined_output = "%s/joined.mp4" % (prefix,)
|
|
join_segments(processed, joined_output)
|
|
return joined_output
|
|
|
|
|
|
def restyle_video(item_id, prompt):
|
|
item = Item.objects.get(public_id=item_id)
|
|
video_url = public_video_url(item)
|
|
model = "decart/lucy-restyle"
|
|
handler = fal_client.submit(
|
|
model,
|
|
arguments={
|
|
"prompt": prompt,
|
|
"video_url": video_url,
|
|
"resolution": "720p",
|
|
"enhance_prompt": True,
|
|
},
|
|
)
|
|
request_id = handler.request_id
|
|
print(request_id)
|
|
status = fal_client.status(model, request_id, with_logs=True)
|
|
while isinstance(status, fal_client.InProgress):
|
|
time.sleep(10)
|
|
status = fal_client.status(model, request_id, with_logs=True)
|
|
result = fal_client.result(model, request_id)
|
|
print(result)
|
|
output_url = result["video"]["url"]
|
|
prompt_hash = hashlib.sha1((prompt).encode()).hexdigest()
|
|
output_path = "/srv/pandora/static/power/cache/%s_%s.mp4" % (
|
|
item.public_id,
|
|
prompt_hash,
|
|
)
|
|
ox.net.save_url(output_url, output_path, overwrite=True)
|
|
return output_path
|
|
|
|
|
|
def fal_wait_for(model, request_id):
|
|
status = fal_client.status(model, request_id, with_logs=True)
|
|
while isinstance(status, fal_client.InProgress):
|
|
time.sleep(10)
|
|
status = fal_client.status(model, request_id, with_logs=True)
|
|
result = fal_client.result(model, request_id)
|
|
return result
|
|
|
|
|
|
def motion_control_preprocess_image(item_id, image_prompt, video_prompt):
|
|
item = Item.objects.get(public_id=item_id)
|
|
video_url = public_video_url(item)
|
|
model = "fal-ai/kling-video/v2.6/pro/motion-control"
|
|
prompt_hash = hashlib.sha1((image_prompt + video_prompt).encode()).hexdigest()
|
|
output = "/srv/pandora/static/power/cache/%s_%s.mp4" % (item.public_id, prompt_hash)
|
|
first_frame = "%s%s/source%s.png?token=%s" % (
|
|
settings.PUBLIC_URL,
|
|
item.public_id,
|
|
0,
|
|
settings.PUBLIC_TOKEN,
|
|
)
|
|
first_frame_path = "/srv/pandora/static/power/cache/%s_%s/%06d.first_frame.png" % (
|
|
item.public_id,
|
|
prompt_hash,
|
|
0,
|
|
)
|
|
if not os.path.exists(first_frame_path):
|
|
os.makedirs(os.path.dirname(first_frame_path), exist_ok=True)
|
|
prepare_image(first_frame, image_prompt, first_frame_path)
|
|
image_url = public_url(first_frame_path)
|
|
data = {
|
|
"prompt": video_prompt,
|
|
"image_url": image_url,
|
|
"video_url": video_url,
|
|
"keep_original_sound": False,
|
|
"character_orientation": "video",
|
|
}
|
|
print(data)
|
|
handler = fal_client.submit(model, arguments=data)
|
|
request_id = handler.request_id
|
|
print(request_id)
|
|
result = fal_wait_for(model, request_id)
|
|
print(result)
|
|
output_url = result["video"]["url"]
|
|
ox.net.save_url(output_url, output, overwrite=True)
|
|
return output
|
|
|
|
|
|
def luma_wait_for(id):
|
|
url = "https://api.lumalabs.ai/dream-machine/v1/generations/%s" % id
|
|
headers = {
|
|
"accept": "application/json",
|
|
"content-type": "application/json",
|
|
"authorization": "Bearer " + settings.LUMA_TOKEN,
|
|
}
|
|
status = requests.get(url, headers=headers).json()
|
|
while status["state"] in ("queued", "dreaming"):
|
|
time.sleep(10)
|
|
status = requests.get(url, headers=headers).json()
|
|
return status
|
|
|
|
|
|
def luma_modify_segment(video_url, prompt, first_frame=None, mode="flex_2"):
|
|
# also got that at fal-ai/luma-dream-machine/ray-2/modify
|
|
url = "https://api.lumalabs.ai/dream-machine/v1/generations/video/modify"
|
|
payload = {
|
|
"generation_type": "modify_video",
|
|
"model": "ray-2",
|
|
"mode": mode,
|
|
"prompt": prompt,
|
|
"media": {"url": video_url},
|
|
}
|
|
if first_frame:
|
|
payload["first_frame"] = {"url": first_frame}
|
|
headers = {
|
|
"accept": "application/json",
|
|
"content-type": "application/json",
|
|
"authorization": "Bearer " + settings.LUMA_TOKEN,
|
|
}
|
|
response = requests.post(url, json=payload, headers=headers).json()
|
|
print(response)
|
|
status = luma_wait_for(response["id"])
|
|
return status["assets"]["video"]
|
|
|
|
|
|
def fragment_video(filename, segmentdir, segments):
|
|
filename = str(filename)
|
|
input_info = ox.avinfo(filename)
|
|
|
|
segments_ = []
|
|
for segment in segments:
|
|
if isinstance(segment, list):
|
|
stype, segment = segment
|
|
else:
|
|
stype = "n"
|
|
segments_.append(segment)
|
|
segments = segments_
|
|
|
|
position = 0
|
|
segment = 0
|
|
|
|
cap = cv2.VideoCapture(filename)
|
|
fps = cap.get(cv2.CAP_PROP_FPS)
|
|
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
|
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
|
|
|
fourcc = cv2.VideoWriter_fourcc(*"avc1")
|
|
frame_count = 0
|
|
next_cut = int(segments.pop(0) * fps)
|
|
|
|
last = None
|
|
os.makedirs(segmentdir, exist_ok=True)
|
|
|
|
while cap.isOpened():
|
|
if frame_count == 0:
|
|
output_path = segmentdir + "/%06d.mp4" % segment
|
|
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
|
|
elif next_cut and frame_count >= next_cut and segments:
|
|
print(frame_count, output_path)
|
|
cv2.imwrite(output_path.replace(".mp4", "_last.jpg"), frame)
|
|
out.release()
|
|
segment += 1
|
|
output_path = segmentdir + "/%06d.mp4" % segment
|
|
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
|
|
if segments:
|
|
next_cut = int(segments.pop(0) * fps)
|
|
else:
|
|
next_cut = None
|
|
last = None
|
|
ret, frame = cap.read()
|
|
if not ret:
|
|
break
|
|
out.write(frame)
|
|
if last is None:
|
|
cv2.imwrite(output_path.replace(".mp4", "_first.jpg"), frame)
|
|
last = frame
|
|
frame_count += 1
|
|
|
|
out.release()
|
|
cap.release()
|
|
|
|
if last is None:
|
|
os.unlink(output_path)
|
|
else:
|
|
cv2.imwrite(output_path.replace(".mp4", "_last.jpg"), last)
|
|
|
|
|
|
def flux_edit_image(image, prompt):
|
|
if isinstance(image, str):
|
|
image = [image]
|
|
data = {
|
|
"prompt": prompt,
|
|
"safety_tolerance": "5",
|
|
"enable_safety_checker": False,
|
|
"output_format": "jpeg",
|
|
"image_urls": image,
|
|
}
|
|
print(data)
|
|
result = fal_client.subscribe("fal-ai/flux-2-pro/edit", arguments=data)
|
|
print(result)
|
|
return result["images"][0]["url"]
|
|
|
|
|
|
def in_the_style_of_fal(image, style):
|
|
prompt = "apply style from @image 2 to @image 1 keep the position of the person in @image 1 but take light, colors, clothing from @image 2"
|
|
return flux_edit_image([image, style], prompt)
|
|
|
|
|
|
def in_the_style_of_byte(image, style):
|
|
prompt = "apply style from image 2 to image 1 keep the position of the person in image 1 but take light, colors, clothing from image 2"
|
|
image_model_name = "seedream-4-5-251128"
|
|
ark_client = Ark(
|
|
base_url="https://ark.ap-southeast.bytepluses.com/api/v3",
|
|
api_key=settings.BYTEPLUSE_TOKEN,
|
|
)
|
|
create_result = ark_client.images.generate(
|
|
model=image_model_name,
|
|
image=[image, style],
|
|
prompt=prompt,
|
|
sequential_image_generation="disabled",
|
|
response_format="url",
|
|
size="2560x1440",
|
|
stream=False,
|
|
watermark=False,
|
|
)
|
|
print(create_result)
|
|
return create_result.data[0].url
|
|
|
|
|
|
def luma_modify_item(item, prompt="", image_prompt=None, first_frame=None, keep=False):
|
|
mode = "flex_2"
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
source = item.files.all()[0].data.path
|
|
info = ox.avinfo(source)
|
|
duration = info["duration"]
|
|
max_duration = 10
|
|
if duration < max_duration:
|
|
segments = [duration]
|
|
else:
|
|
segments = get_item_segments(item, max_duration=max_duration)
|
|
print(segments)
|
|
prompt_hash = hashlib.sha1((prompt + (image_prompt or "")).encode()).hexdigest()
|
|
processed = []
|
|
prefix = "/srv/pandora/static/power/cache/%s_%s" % (item.public_id, prompt_hash)
|
|
video_segments = fragment_video(source, prefix, segments)
|
|
n = 0
|
|
if isinstance(first_frame, Document):
|
|
first_frame_url = public_document_url(first_frame)
|
|
else:
|
|
first_frame_url = first_frame
|
|
for segment in segments:
|
|
if isinstance(segment, list):
|
|
stype, segment = segment
|
|
else:
|
|
stype = "n"
|
|
output = "%s/%06d.mp4" % (prefix, n)
|
|
output_ai = "%s/%06d_ai.mp4" % (prefix, n)
|
|
if os.path.exists(output):
|
|
video_url = luma_modify_segment(
|
|
public_url(output), prompt, first_frame=first_frame_url, mode=mode
|
|
)
|
|
ox.net.save_url(video_url, output_ai, overwrite=True)
|
|
processed.append(output_ai)
|
|
n += 1
|
|
joined_output = "%s/joined.mp4" % (prefix,)
|
|
join_segments(processed, joined_output)
|
|
ai = add_ai_variant(item, joined_output, "ai:replace:p1:luma")
|
|
ai.data["prompt"] = ox.escape_html(prompt)
|
|
if first_frame:
|
|
ai.data["firstframe"] = first_frame_url.split("?")[0]
|
|
ai.data["model"] = "ray-2:%s" % mode
|
|
ai.save()
|
|
if not keep:
|
|
shutil.rmtree(os.path.dirname(joined_output))
|
|
if isinstance(first_frame, Document):
|
|
first_frame.add(ai)
|
|
return ai
|
|
|
|
|
|
def add_ai_variant(item, video_path, type):
|
|
if isinstance(item, str):
|
|
item = Item.objects.get(public_id=item)
|
|
|
|
ai = Item()
|
|
ai.user = item.user
|
|
ai.data["type"] = [type]
|
|
ai.data["title"] = item.data["title"]
|
|
ai.data["chapter"] = item.data.get("chapter", "")
|
|
ai.save()
|
|
file = File()
|
|
file.oshash = ox.oshash(video_path)
|
|
file.item = ai
|
|
file.path = "%s.mp4" % type
|
|
file.info = ox.avinfo(video_path)
|
|
del file.info["path"]
|
|
file.parse_info()
|
|
file.data.name = file.get_path("data." + video_path.split(".")[-1])
|
|
os.makedirs(os.path.dirname(file.data.path), exist_ok=True)
|
|
shutil.copy(video_path, file.data.path)
|
|
file.available = True
|
|
file.selected = True
|
|
file.queued = True
|
|
file.wanted = False
|
|
file.save()
|
|
file.extract_stream()
|
|
return ai
|
|
|
|
|
|
def add_ai_image(item, position, url, extension=None):
|
|
if extension is None:
|
|
extension = url.split(".")[-1].split("?")[0]
|
|
if extension == "jpeg":
|
|
extension = "jpg"
|
|
file = Document(user=item.user)
|
|
file.data["title"] = "%s at %s" % (item.get("title"), position)
|
|
file.data["position"] = position
|
|
file.extension = extension
|
|
file.width = -1
|
|
file.pages = -1
|
|
file.uploading = True
|
|
file.save()
|
|
file.uploading = True
|
|
name = "data.%s" % file.extension
|
|
file.file.name = file.path(name)
|
|
ox.net.save_url(url, file.file.path, overwrite=True)
|
|
file.get_info()
|
|
file.get_ratio()
|
|
file.oshash = ox.oshash(file.file.path)
|
|
file.save()
|
|
file.update_sort()
|
|
file.add(item)
|
|
return file
|
|
|
|
def add_tag(item, tag):
|
|
if 'tags' not in item.data:
|
|
item.data['tags'] = []
|
|
item.data['tags'].append(tag)
|
|
item.save()
|
|
|
|
def extract_firstframe(character='P1'):
|
|
for item in Item.objects.filter(data__type__icontains="source"):
|
|
if 'ai-failed' in item.data.get('tags', []):
|
|
continue
|
|
if not item.documents.all().exists():
|
|
print(item)
|
|
try:
|
|
first_frame = replace_character(item, character, 0)
|
|
except:
|
|
item.refresh_from_db()
|
|
add_tag(item, 'ai-failed')
|
|
|
|
def process_reshoot_firstframe(character='P1'):
|
|
position = 0
|
|
l = itemlist.models.List.objects.get(name='Reshoot-Firstframe')
|
|
for item in l.items.all():
|
|
if 'ai-failed' in item.data.get('tags', []):
|
|
print('>> skip', item)
|
|
continue
|
|
if item.sort.duration > 30:
|
|
pass
|
|
#reshoot_item_segments(item, character)
|
|
else:
|
|
cid = get_character_document(character).get_id()
|
|
first_frame = item.documents.filter(
|
|
data__character=cid, data__position=position
|
|
).order_by('-created').first()
|
|
if not first_frame:
|
|
try:
|
|
first_frame = replace_character(item, character, position)
|
|
except:
|
|
item.refresh_from_db()
|
|
add_tag(item, 'ai-failed')
|
|
print('>> failed', item)
|
|
if first_frame.items.filter(data__type__icontains='ai:').exists():
|
|
continue
|
|
print(item, first_frame)
|
|
try:
|
|
reshoot_item(item, first_frame=first_frame)
|
|
except:
|
|
item.refresh_from_db()
|
|
add_tag(item, 'ai-failed')
|
|
print('>> failed', item)
|
|
|
|
def process_motion_firstframe(character="P1", keep=False):
|
|
l = itemlist.models.List.objects.get(name='Motion-Firstframe')
|
|
for item in l.items.all():
|
|
ai = Item.objects.filter(data__type__icontains='ai').filter(data__title=item.data['title'])
|
|
if ai.exists() or 'ai-failed' in item.data.get('tags', []):
|
|
print('>> skip', item)
|
|
continue
|
|
print(i)
|
|
try:
|
|
replace_character_motion_control(item, character, keep=keep)
|
|
except:
|
|
item.refresh_from_db()
|
|
add_tag(item, 'ai-failed')
|
|
print('>> failed', item)
|
|
|