Kediska Jiilka Sawirka-Fiidiyowga

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Noocyo badan oo fiidyow fiidyow ah oo dahsoon, sidee buu sawirka wax gelinta loo siiyaa diidaha?
   • As a text caption generated from the image
   • Its latent is concatenated channel-wise with the noisy frame latents at every step
   • It is used only after generation to color-correct frames
   • It replaces the last frame of the clip
2. Waa maxay qiimaha baaldi dhaqdhaqaaqa Fiidiyowga Stable Video Diffusion u taagan yahay?
   • The number of frames
   • The output file size
   • The amount of motion, learned from optical flow in training clips
   • The camera's focal length
3. Maxaa dhacaya markaad kor u qaaddo kordhinta dhawaaqa sawirka qaboojinta?
   • More motion and freedom, but less fidelity to the input
   • The video becomes perfectly still
   • Resolution doubles
   • The first frame matches the input exactly
4. Waa maxay sababta meesha lagu muujiyey gadaasheeda shay dhaqaaqa inta badan u ekaato mid la alifay?
   • The model deletes background pixels on purpose
   • Compression removes background detail
   • The input image is cropped
   • The model has no information about occluded regions and predicts plausible pixels
5. Waa maxay cilladda ugu weyn ee fidinta clip-ka iyadoo la quudinayo jirkiisii ​​u dambeeyay soo gelid cusub?
   • It is not allowed by any model
   • Errors accumulate, so quality degrades with each extension
   • It always reverses the motion
   • It removes the audio
6. What is a good prompting practice for image-to-video?
   • Restate every object visible in the image
   • Leave the prompt empty always
   • Describe the motion and the camera rather than restating the scene
   • Use only a single noun
7. Which description matches the artifact called texture swimming?
   • Fine patterns appear to slide or shimmer across surfaces
   • The whole video turns black and white
   • Audio drifts out of sync
   • The clip plays backward
8. How does Stable Video Diffusion's reference pipeline apply classifier-free guidance across frames?
   • Highest on the first frame, then off
   • Randomly per frame
   • Identical on all frames
   • Ramped linearly, lower near the conditioning frame and higher for later frames