Kuis Generasi Gerak AnimateDiff

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Apa ide inti di balik AnimateDiff?
   • Melatih model video baru dari awal pada jutaan klip
   • Interpolasi antara dua gambar hanya menggunakan aliran optik
   • Merekam video nyata dan menata ulang bingkai demi bingkai
   • Memasukkan modul gerak terlatih ke dalam model difusi gambar beku
2. What checkpoint compatibility condition matters when reusing an AnimateDiff motion module?
   • Every image generator accepts every motion module regardless of architecture
   • The personalized image checkpoint must be compatible with the supported base architecture and module
   • Freezing any arbitrary model makes it automatically compatible
   • The image checkpoint's filename alone establishes compatibility
3. Bagaimana modul gerak menjaga frame tetap konsisten satu sama lain?
   • Melalui lapisan perhatian temporal yang memungkinkan bingkai hadir sepanjang waktu
   • Dengan memburamkan setiap frame secara merata
   • Dengan hanya menghasilkan satu frame dan menyalinnya
   • Dengan menggunakan animasi tetap yang telah direkam sebelumnya
4. Keluarga model manakah yang paling banyak dikaitkan dengan perluasan AnimateDiff?
   • Model bahasa GPT
   • Model pengenalan ucapan
   • Model gambar Difusi Stabil
   • Model regresi tabel
5. Apa yang biasanya dikontrol oleh gerakan LoRA di ekosistem AnimateDiff?
   • Kosakata encoder teks
   • Gerakan kamera tertentu seperti menggeser, memperbesar, atau memutar
   • Trek audio dari video
   • Format file keluaran
6. How should a sixteen-frame AnimateDiff result be interpreted relative to the method's possible configurations?
   • As a guarantee that every deployment must output exactly sixteen frames
   • As proof that the method generates a feature-length film in one step
   • As a rule that the generated video contains only a single still image
   • As a short-clip configuration, rather than a universal fixed duration for every version
7. What does SparseCtrl add to an AnimateDiff-style generation workflow?
   • A requirement to provide a manually labeled image for every frame
   • An audio transcription model that replaces temporal attention
   • Conditioning from a sparse set of reference images at selected frame positions
   • A guarantee that any unrelated image architecture is compatible
8. How does a motion LoRA's camera control differ from SparseCtrl's reference-image guidance?
   • A motion LoRA steers a learned movement pattern, while SparseCtrl supplies visual content at selected frames
   • They are identical names for a text tokenizer
   • SparseCtrl controls only the video filename
   • Motion LoRAs require a complete reference frame at every timestamp