Up tókànItọsọna atẹle
Iṣatunṣe Aworan ibaraẹnisọrọ pẹlu Awọn awoṣe Multimodal
AI wiwo
Visual AI Itọsọna
InstructPix2Pix Ilana Ṣiṣatunṣe
InstructPix2Pix jẹ ki o ṣatunkọ fọto kan nipa titẹ aṣẹ lasan bi 'ṣe ni igba otutu' tabi 'yi ologbo naa pada si aja', ko si awọn iboju iparada tabi awọn irinṣẹ yiyan.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
It taught a diffusion model to follow editing instructions directly.
Jin Dive
InstructPix2Pix is a diffusion model conditioned on an input image and an editing instruction. Its authors constructed synthetic training pairs by using GPT-3 to generate instructions and Prompt-to-Prompt with Stable Diffusion to create edited image examples. They report that the trained model can apply user-written instructions to real images without per-example fine-tuning or inversion. This does not mean inference is a single network evaluation: diffusion sampling still uses iterative denoising. Two guidance scales trade instruction adherence against similarity to the source image, and the model can alter details beyond the requested edit, so review remains necessary.
Real-World imuse
Awọn oriṣi bulọọgi kan 'fi awọn foliage isubu kun' lati tun awọ aworan ala-ilẹ ooru kan fun ifiweranṣẹ igba kan.
Olutaja e-commerce n kọni 'yi awọ seeti pada si ọgagun' lati ṣe agbejade awọn iyatọ awọ ọja lati ibọn kan.
Olukọ kan ṣatunkọ fọto itan kan pẹlu 'ṣe awọ eleyi' lati jẹ ki aworan ibi ipamọ dudu ati funfun han kedere fun ẹkọ kan.
Ẹlẹda meme kan paṣẹ 'fi awọn gilaasi jigi sori aja' laisi ọwọ boju oju aja naa.
Ọjọ iwaju ti Ṣiṣatunṣe Ilana Ilana InstructPix2Pix
Instruction-based editing can make image changes easier to request, but generative outputs still need review for unintended alterations, missing details, and unsupported historical claims.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Iyara ati iwọn. Visual AI le ṣe adaṣe adaṣe, wiwa, ati awọn iṣẹ ṣiṣe taagi ni iwọn.
- Kọ awọn yiyan. Awọn ẹgbẹ ẹda le ṣe apẹrẹ awọn imọran yiyara pẹlu awọn atunyẹwo afọwọṣe diẹ.
- Ẹgbẹ ati ṣiṣan iṣẹ. Awọn iṣẹ ṣiṣe le lo aworan ati awọn ifihan agbara fidio ti o nira tẹlẹ lati ṣiṣẹ.
Ṣọra fun
- Awọn ẹtọ aworan ati igbanilaaye le di awọn eewu labẹ ofin ti o ba jẹ afihan.
- Iṣe awoṣe le yatọ kọja ina, awọn ẹda eniyan, ati awọn agbegbe.
- Awọn idaniloju eke le ma ṣe akiyesi ayafi ti a ba ṣe abojuto awọn ala igbẹkẹle.
Next awọn igbesẹ
- Ṣetumo awọn ibeere gbigba fun pipe, iranti, ati awọn idiyele aṣiṣe.
- Ṣe idanwo pẹlu data ti o baamu awọn ipo iṣelọpọ gidi.
- Ṣafikun atunyẹwo eniyan fun igbẹkẹle kekere tabi awọn asọtẹlẹ ipa-giga.
- Tọpinpin awoṣe ki o ṣe tunṣe lẹhin kamẹra tabi awọn ayipada datasetto.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the InstructPix2Pix Instruction Editing quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
What is InstructPix2Pix Instruction Editing?
InstructPix2Pix jẹ ki o ṣatunkọ fọto kan nipa titẹ aṣẹ lasan bi 'ṣe ni igba otutu' tabi 'yi ologbo naa pada si aja', ko si awọn iboju iparada tabi awọn irinṣẹ yiyan. O kọ awoṣe tan kaakiri lati tẹle awọn itọnisọna ṣiṣatunṣe taara.
Bawo ni o ṣe sọ fun InstructPix2Pix kini lati yipada?
O kan tẹ itọnisọna bi 'ṣe igba otutu'; ko si awọn iboju iparada tabi yiyan agbegbe ti a nilo.
Bawo ni a ṣe ṣẹda data ikẹkọ InstructPix2Pix?
Awọn onkọwe ni idapo GPT-3-ipilẹṣẹ ifori awọn orisii pẹlu Iṣagbepọ aworan Tọ-si-Tara lati kọ awọn ilọpo mẹta laifọwọyi.
Kini iṣakoso awọn iwọn itoni meji ti InstructPix2Pix?
Iwọn kan n ṣe akoso bi o ṣe lagbara ti satunkọ naa tẹle ilana naa; ekeji n ṣe akoso iye ti aworan orisun ti wa ni ipamọ.
Kini idi ti awoṣe naa ṣe lati fi awọn ẹya ti a ko darukọ ti aworan naa silẹ nikan?
Ilana kan fojusi iyipada kan pato, nitorinaa awoṣe ṣe itọju awọn agbegbe ti itọnisọna ko mẹnuba.
Does InstructPix2Pix require separate per-image fine-tuning or inversion before applying an edit?
The paper describes applying the trained model to an image and instruction without per-example fine-tuning or inversion. The diffusion model still performs iterative denoising during inference.
Tesiwaju kikọ
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