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Semak penyaman teks Imagen asal, penjanaan bertingkat, ambang dinamik dan pilihan keteguhan dalam latihan resolusi super.

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In the original Imagen study's scaling comparison, which change produced larger image-quality and text-alignment gains?

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  1. In the original Imagen study's scaling comparison, which change produced larger image-quality and text-alignment gains?
    • Discarding the language encoder entirely
    • Increasing the pretrained text encoder's size rather than the diffusion model's size
    • Replacing diffusion with nearest-neighbor image lookup
    • Reducing every prompt to a single word
  2. Pengekod teks manakah yang Imagen bergantung?
    • Jaring imej berbelit
    • LSTM kecil
    • Satu set peraturan tulisan tangan
    • Model bahasa T5-XXL beku
  3. Bagaimanakah Imagen mencapai resolusi tinggi?
    • Resapan bertingkat dengan peringkat peningkatan resolusi super
    • Satu langkah generasi gergasi
    • Dengan memangkas foto stok yang besar
    • Dengan menginterpolasi dua imej
  4. What problem did original Imagen's dynamic thresholding address under strong guidance?
    • A lack of text tokens in the encoder vocabulary
    • The need to compress the final PNG file
    • Saturation of predicted pixel values during sampling
    • A missing training label for every object
  5. Apakah DrawBench?
    • Tablet lukisan
    • Jenama GPU
    • Set data foto kucing
    • Penanda aras gesaan mencabar untuk menilai model teks ke imej
  6. Mengapakah pengekod T5-XXL disimpan beku dalam Imagen?
    • Ia rosak dan tidak boleh dilatih
    • Pemahaman teks yang kaya daripada pemindahan pralatihan tanpa penalaan lanjut
    • Untuk menjimatkan ruang cakera sahaja
    • Kerana teks tidak relevan
  7. What resolution did original Imagen's base diffusion model generate before its super-resolution stages?
    • 256 by 256 pixels as the first stage
    • 1024 by 1024 pixels directly
    • 64 by 64 pixels
    • A single pixel that was copied across the final image
  8. Why did the Imagen researchers add Gaussian noise to low-resolution conditioning images during super-resolution training?
    • To make the upsamplers more robust to imperfect inputs from earlier stages
    • To remove text conditioning from the entire cascade
    • To guarantee that all training images had the same caption
    • To replace the upsamplers with deterministic resizing