136 lines
4.6 KiB
Python
136 lines
4.6 KiB
Python
from __future__ import annotations
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import datetime
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import shutil
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from typing import Any, Dict
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from fastapi import APIRouter
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from PIL import Image
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from ..deps import OUTPUT_DIR
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from ..schemas import AnimateRequest, CharacterAnimateRequest
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from ..services.animation_frames import (
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align_size_to_input,
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composite_rgba_on_background,
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list_png_frames,
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parse_background_color,
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remove_background_by_color,
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)
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from ..services.image_io import b64_to_pil_image, pil_image_to_png_b64
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from ..services.predictors import get_animation_predictor
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router = APIRouter(tags=["animation"])
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@router.post("/animate")
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def animate(req: AnimateRequest) -> Dict[str, Any]:
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try:
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model_name = req.model_name or "animatediff"
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predictor = get_animation_predictor(model_name)
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result_path = predictor(
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prompt=req.prompt,
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negative_prompt=req.negative_prompt or "",
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num_inference_steps=req.num_inference_steps,
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guidance_scale=req.guidance_scale,
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width=req.width,
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height=req.height,
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video_length=req.video_length,
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seed=req.seed,
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)
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out_dir = OUTPUT_DIR / "animation"
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out_dir.mkdir(parents=True, exist_ok=True)
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ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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out_path = out_dir / f"{model_name}_{ts}.gif"
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shutil.copy2(result_path, out_path)
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return {"success": True, "output_path": str(out_path)}
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except Exception as e:
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return {"success": False, "error": str(e)}
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@router.post("/animate/character_sequence")
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def animate_character_sequence(req: CharacterAnimateRequest) -> Dict[str, Any]:
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"""
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输入透明背景角色 PNG，生成 30fps 角色动画 PNG 序列。
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流程：
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- 将角色合成到指定纯色背景，作为 AnimateDiff 控制图
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- 以与输入相近、且对齐到 8 的分辨率生成 PNG 序列
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- 对每帧按背景色做色键剔除，返回透明背景 PNG 序列给前端
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"""
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try:
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model_name = req.model_name or "animatediff"
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background_rgb = parse_background_color(req.background_color)
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character = b64_to_pil_image(req.image_b64).convert("RGBA")
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orig_w, orig_h = character.size
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run_w, run_h = align_size_to_input(orig_w, orig_h, req.max_side)
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predictor = get_animation_predictor(model_name)
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out_dir = OUTPUT_DIR / "animation" / "character_sequence"
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out_dir.mkdir(parents=True, exist_ok=True)
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ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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seq_dir = out_dir / f"{model_name}_{ts}"
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seq_dir.mkdir(parents=True, exist_ok=True)
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control_path = seq_dir / "control_image.png"
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control_image = composite_rgba_on_background(
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character,
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background_rgb,
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size=(run_w, run_h),
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)
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control_image.save(control_path)
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frame_dir = predictor(
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prompt=req.prompt,
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negative_prompt=req.negative_prompt or "",
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num_inference_steps=req.num_inference_steps,
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guidance_scale=req.guidance_scale,
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width=run_w,
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height=run_h,
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video_length=req.video_length,
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seed=req.seed,
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control_image_path=str(control_path),
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output_format="png_sequence",
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)
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frame_paths = list_png_frames(frame_dir)
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if not frame_paths:
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raise FileNotFoundError(f"未找到 AnimateDiff 输出 PNG 序列: {frame_dir}")
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frames: list[dict[str, Any]] = []
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processed_dir = seq_dir / "frames"
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processed_dir.mkdir(parents=True, exist_ok=True)
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for idx, frame_path in enumerate(frame_paths):
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frame = remove_background_by_color(
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Image.open(frame_path),
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background_rgb,
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tolerance=req.background_tolerance,
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output_size=(orig_w, orig_h),
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)
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out_path = processed_dir / f"{idx:04d}.png"
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frame.save(out_path)
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frames.append(
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{
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"index": idx,
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"image_b64": pil_image_to_png_b64(frame),
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"path": str(out_path),
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}
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)
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return {
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"success": True,
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"fps": 30,
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"frame_count": len(frames),
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"width": orig_w,
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"height": orig_h,
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"background_color": req.background_color,
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"output_dir": str(processed_dir),
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"frames": frames,
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}
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except Exception as e:
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return {"success": False, "error": str(e), "frames": []}
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