Question 1

Kini àwọn àwòrán AI?

Accepted Answer

Ṣàfikún àwòrán ìṣàfihàn tí a bá fi pamọ́ fún ẹnikẹ́ni tàbí àwòrán nínú àwọn ààyè tí a fẹ́ kọ́. AI náà ń yọ àwòrán àwọn ààyè (àwọ̀n àwọn ẹ̀yàn, àwọn ààyè ọwọ́, àwọn ìṣàfihàn àwọn ẹ̀yàn) láti mú àwòrán tuntun fún gbogbo nínú àwòrán náà, nínú àwọn ìṣàfihàn àti àwọn ìṣàfihàn tí o sọ̀rọ̀ nínú àgbèwọlé.

Question 2

Bawo ní ìṣàfihàn àwọn ààyè-iṣẹ́ ṣe n ṣiṣẹ́?

Accepted Answer

A pose detector (MediaPipe Pose, Apache 2.0) finds body keypoints - nose, eyes, ears, shoulders, elbows, wrists, hips, knees and ankles - for up to four people in your reference image. Those keypoints are drawn as an OpenPose-format skeleton that the pose ControlNet on SDXL uses as a structural constraint while the diffusion model fills in everything else. Hand and finger positions are not detected.

Question 3

Njẹ́ mò yẹ́ ki n lo yìí láti lo /image/sketch-to-image/?

Accepted Answer

Àwọn àwọn àwòrán-si-àwòrán tọjú àwọn ààyè GBÀ. Àwọn àwọn àwòrán-si-àwòrán tọjú àwọn àwọn ààyè IWỌN/àwọn ààyè. Lo àwọn àwòrán tí o bà fẹ́ àwọn àwọn ààyè gangan lọ́wọ́lọ́wọ́, àwọn àwòrán/àṣọ̀/àwọn ààyè mìíràn. Lò àwọn àwòrán tí o bà kọ́ àwọn ààyè náà láti fi pamọ́ bí a tí wa.

Question 4

Irúfẹ́ àwọn àwòrán ìṣàfihàn wo nínú iṣẹ́ tí o dara jù lọ?

Accepted Answer

Àwọn àwòrán ìsàlẹ̀-ilà ìṣàmúlò-ètò àti àwọn àwòrán àwọn ààyè-iṣẹ́ àti àwọn ààyè-iṣẹ́ àwọn à

Question 5

O ti kọ̀ọ̀kan?

Accepted Answer

Ya. Ìṣàfihàn kan nínú àwọn

Question 6

Ń lè kọ́ àwòrán láti inú àwòrán àìdálẹ̀?

Accepted Answer

Àkóónú àtòjọ-ẹ̀yàn àwọn ààyè-iṣẹ́ ìṣàmúlò-ètò - àwọn ààyè-iṣẹ́ ìṣàmúlò-ètò nínú àwọn ààyè-iṣẹ́ - kò lè gba àwọn ààyè-iṣẹ́ nínú àwọn ìṣàmúlò-ètò àwọn ìṣàmúlò-ètò. Àwọn àwọn

Question 7

Ń ṣé iṣẹ́ fun àwọn ẹrankò tàbí àwọn nǹkan tí kò jẹ́ eniyan?

Accepted Answer

Àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè àwọn ààyè

Question 8

Ń lè fi àwọn ìṣàfarawé àwọn ìṣàmúlò-ètò àwọn ìṣàfihàn pamọ́?

Accepted Answer

Yà — "ControlNet strength" àwọn ìṣàmúlò-ètò (òdúrò 0.7) ń ṣakoso ìgbà tí AI á ń tẹle àwọn ààyè. 1.0 = ààyè tòjú, 0.4 = ìṣàfihàn tí a kò lè fi hàn. Fì pàtó tí àwọn ààyè ìjánu-ìjánú náà bá jẹ́ àìdá; fìpàtó tí àwọn ààyè náà bá jẹ́ àìdá.

Question 9

Ìwọ̀n àwọn ààtòjútó wo ní a fọwọ́sì?

Accepted Answer

512×512 ìpéwọ̀n. Àwọn ìṣàfarawé àwọn ìṣàfarawé àwọn àwọn ìṣàfarawé SDXL (768×1024, 1024×768, 1024×1024) gbogbo wọn ló. Àwọn ìwọ̀n tí o jú lọ náà ló VRAM àti tókè̀ diẹ́ sii; H200 náà ń gbọ́dọ̀ sí 1024×1024 nípa ìrànwọ́.

Question 10

Bawo ni o ṣe ṣe iyatọ si ComfyUI / Automatic1111 pẹlu OpenPose?

Accepted Answer

Same SDXL + pose ControlNet approach. ComfyUI / A1111 require local GPU + setup; we run it on shared infrastructure free up to the daily pool, paid beyond that. One difference: the skeleton comes from MediaPipe Pose, which is licensed for commercial use, instead of the CMU OpenPose detector, which is not, so hands and faces are not part of the skeleton.

Question 11

Àwọn àwòrán ìṣàfihàn tí a fi pamọ́?

Accepted Answer

Àwọn àwòrán ìṣàmúlò-ètò ní pàtó ní àìpẹ̀, a lò fún ìṣàmúlò-ètò ìṣàmúlò-ètò, àti àwọn àwòrán tí a pàtó láti inú àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn àà

Question 12

Àwọn Ìṣàmúlò-ètò

Accepted Answer

Ya — POST multipart to /v1/image/generate/ with model=sdxl, prompt, control_image (file), control_type=pose. Returns the image URL + share token. Bearer auth, 10K tokens/month free. Curl example at /api/.

Pose → Image (ControlNet)

Àwọn Àtòjọ-ẹ̀yàn

Pose → Image (ControlNet)

Àwọn Àtòjọ-ẹ̀yàn

30,000 Free Tokens, Gbogbo ọjọ

Wá: 30K tí a fi pamọ́/ọ̀jọ́!

Kò lè fi pamọ́