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

Àwọn àwọn ààyè-iṣẹ́ OpenPose tí wọ́ àwọn àwọn ààyè-iṣẹ́ ìjánu-ìjánu - oju, àwọn ẹ̀sẹ̀, àwọn ẹ̀sẹ̀, àwọn ẹsẹ̀, àwọn ẹ̀sẹ̀, àwọn ẹsẹ̀, àwọn ọwọ̀ àti ojú tí a bà rí - láti inú àwòrán ìṣàfihàn rẹ̀. Àwọn ààyè-iṣẹ́ yìí di àká-ìjánu-ìjánu tí OpenPose ControlNet lò lori SDXL bí a tí n lò bí a ti n ló nínú àwọn àwọn ìṣàfihàn ìṣàfarawe-ìṣàmúlò-ètò àtí módè́ẹ̀lì ìṣàfihàn tí n pa gbogbo àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn àwọn

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

Full-body or three-quarter shots with clear visibility of joints. Photos work great. Stylized art works if the figure is anatomically clear. Avoid extreme close-ups (no joint info), heavily cropped figures, or images where the pose is occluded.

Question 5

O ti kọ̀ọ̀kan?

Accepted Answer

Yes. Each render costs about 1,200 tokens (1,000 base + 20% ControlNet conditioning surcharge). Signed-in users get 30,000 free tokens daily — covers ~4 poses per day at the free tier.

Question 6

Ń lè kọ́ àwòrán láti inú àwòrán àìdálẹ̀?

Accepted Answer

Pose data alone — joint positions in space — is not copyrightable in most jurisdictions. The output character is your own creation. Avoid copying not just the pose but also the costume / face / specific styling of someone else's character if commercial use is the goal.

Question 7

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

Accepted Answer

OpenPose is trained on human anatomy. Animal photos confuse the detector — limbs map poorly to the human keypoint scheme. For animals, /image/sketch-to-image/ with a hand-drawn pose works better.

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 model, same quality. ComfyUI / A1111 require local GPU + setup; we run it on shared infrastructure free up to the daily pool, paid beyond that. Output is identical — same SDXL + same xinsir/controlnet-openpose-sdxl-1.0 weights.

Question 11

Àwọn àwòrán ìṣàfihàn tí a fi pamọ́?

Accepted Answer

Reference images are processed immediately, used for the pose extraction, and discarded after generation completes. Only your generated output + the prompt are kept on /account/?tab=history. Never used for training. Privacy policy at /privacy/.

Question 12

Àwọn Ìṣàmúlò-ètò

Accepted Answer

Yes — 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/.

Àwòrán (ControlNet)

Àwọn Àtòjọ-ẹ̀yàn

Àwòrán (ControlNet)

Àwọn Àtòjọ-ẹ̀yàn

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