Janus Pro 7B

Görüntü OluşturmaMevcut
DeepSeek tarafındanModel Kimliği: janus-pro-7b

DeepSeek's unified multimodal model. Decouples vision encoding for both understanding and generation tasks.

Fiyat
≈ $0,0169/çalıştırma
Giriş → Çıkış
Metin → Görüntü
Geliştirici
DeepSeek
Güncellendi
23 Eylül 2026
01

Playground

Janus Pro 7B'ı deneyin

Giriş formu yok

≈ $0,0169/çalıştırma

Bu model için henüz giriş formu yok

Girdileri henüz belgelenmemiştir. Yanlış girdide hiçbir çalıştırmanın başarısız olmaması için burada form sunmuyoruz. Bunun yerine karşılaştırılabilir bir model seçin.

02

Examples

Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
  • InputInput

    Prompt

    Convert the formula into latex code.

    Response

    Here is the formula in LaTeX code: \[ A_n = a_0 \left[ 1 + \frac{3}{4} \sum_{k=1}^{n} \left( \frac{4}{9} \right)^k \right] \]

  • InputInput

    Prompt

    explain this meme

    Response

    This image is a humorous meme that plays on the similarity in appearance between two individuals, one labeled "OpenAI" and the other "DeepSeek." Here's a detailed breakdown: 1. **Top Image (OpenAI)**: - The person is holding a firearm and appears to be in a shooting stance. - The label "OpenAI" is placed over the individual, suggesting that they are being humorously compared to the AI company OpenAI. 2. **Bottom Image (DeepSeek)**: - The person is also holding a firearm and in a similar shooting stance. - The label "DeepSeek" is placed over the individual, implying a humorous comparison to DeepSeek, a company known for its work in artificial intelligence and machine learning. 3. **Humor and Meaning**: - The meme humorously compares the two individuals based on their similar appearance and actions. - It plays on the idea that both OpenAI and DeepSeek are involved in AI research and development, and the meme creator is using the visual similarity to create a joke. 4. **Context**: - The image is likely taken from a sporting event, as indicated by the background and the seating. - The meme is a lighthearted way to highlight the similarities between the two entities, with no serious intent. In summary, the meme is a playful comparison between two individuals, using their visual similarity and the context of holding firearms to create a humorous joke.

  • InputInput

    Prompt

    explain this meme

    Response

    This image is a humorous meme comparing two concepts: "Decoupling Visual Encoding" and "Single Visual Encoder." 1. **Left Side: Decoupling Visual Encoding** - The image shows a muscular, strong-looking dog, representing a robust and powerful concept. - This symbolizes "Decoupling Visual Encoding," which implies a more efficient or effective way of encoding visual information. The muscular dog suggests strength, efficiency, and robustness in handling visual data. 2. **Right Side: Single Visual Encoder** - The image shows a smaller, less muscular dog, looking tired or less confident. - This represents "Single Visual Encoder," which suggests a simpler or less powerful approach to encoding visual information. The smaller dog implies that this method might not be as effective or robust as the decoupled approach. ### Overall Meaning: The meme humorously contrasts two concepts in the field of computer vision or machine learning, where "Decoupling Visual Encoding" is depicted as a more powerful and efficient method, while "Single Visual Encoder" is portrayed as less effective or less robust. The use of the Doge meme characters adds a layer of humor and relatability to the comparison.

03

Janus Pro 7B Hakkında

Özet23 Eylül 2026 itibariyle

Janus Pro 7B, DeepSeek tarafından Görüntü Oluşturma kategorisinde geliştirilen bir modeldir. Railwail üzerinde Janus Pro 7B maliyeti ≈ $0,0169 çalıştırma başına.

Arka plan

DeepSeek hakkında

Kuruluş yılı 2023 · Hangzhou, China

DeepSeek (深度求索) is a Chinese AI research lab founded in May 2023 in Hangzhou by Liang Wenfeng, the founder of the quant hedge-fund High-Flyer (which funds the lab). DeepSeek became globally prominent in late 2024 and early 2025 with the release of DeepSeek-V3 (Dec 2024), DeepSeek-R1 (Jan 2025) and the Janus multimodal family. The Janus series is DeepSeek's unified understanding-and-generation model: Janus (Oct 2024), JanusFlow (Nov 2024) and Janus Pro (Jan 2025) extend a single Transformer to both interpret images and generate them with a decoupled visual encoder design. All Janus models are open-sourced under a permissive license on Hugging Face and GitHub.

DeepSeek ziyaret edin

Mimari

Unified autoregressive multimodal Transformer (understanding + image generation)

Janus-Pro-7B (January 2025) is the largest member of the Janus family from DeepSeek. The model unifies multimodal understanding and image generation in a single autoregressive Transformer, but uniquely decouples the visual encoders for the two tasks: a SigLIP-style ViT encoder is used for image understanding (so visual features are semantic), while a VQ tokeniser based on LlamaGen is used for image generation (so visual features are reconstruction-oriented). Both feature paths are projected to the same 7B LLM backbone, which generates either text or image tokens depending on the task. For image generation Janus-Pro produces 384x384 images by autoregressive sampling of VQ tokens which are then decoded by the LlamaGen decoder. Janus-Pro improves over Janus by scaling data to ~90M image-text pairs and adding a second stage of supervised fine-tuning. DeepSeek reports that Janus-Pro-7B beats DALL-E 3, SD3-Medium and SDXL on GenEval and DPG-Bench, despite being a much smaller and unified model.

Parametreler
7B parameters (Janus-Pro-7B)
Bağlam
4.096 token

Yetenekler

  • Unified image understanding + image generation in one 7B model
  • Open weights under permissive DeepSeek license
  • Outperforms DALL-E 3 and SD3-Medium on GenEval (per DeepSeek paper)
  • 384x384 native generation resolution
  • Compatible with Hugging Face Transformers and vLLM
  • Useful for multimodal agents that both see and draw
  • Strong instruction-following thanks to LLM-style backbone
  • Best for: research, multimodal agents, prototyping unified pipelines, fine-tuning.

Eğitim ve lisans

Pretrained on a mix of ~90M image-text pairs, text-only data and image-only data. Janus-Pro-7B adds extra supervised fine-tuning stages and a larger unified dataset compared to Janus 1B.

Lisans: DeepSeek Janus License — open weights, free for research and commercial use with attribution and standard restrictions.

Güvenlik testleri: DeepSeek applies content filters and reports safety evaluations in the Janus-Pro technical report, but the open weights ship without integrated safety classifier.

Bilinen sınırlamalar

  • Only 384x384 native resolution — needs upscaler for production
  • Image quality below dedicated diffusion models like FLUX 1.1 [pro]
  • Open weights have no built-in safety filter
  • Autoregressive sampling is slower per pixel than diffusion at high res
04

Fiyatlandırma

Fiyatlar ABD doları cinsinden. Kullanım, ön ödemeli kredilerden tahsil edilir.
Tipik çalıştırma (≈ 14 s, L40S üzerinde)$0,0169 çalıştırma başına
GPU Süresi (L40S)$0,00117 GPU saniyesi başına
  • Çalıştırmanın gerçekten aldığı GPU süresi için faturalandırılır. Çalıştırma başladığında, bakiyenizden tipik fiyatın 3 katı ayrılır ve daha sonra kapatılır.
  • 1 kredi = $0,01

Maliyet Hesaplayıcı

Fiyat hesaplayıcı

s

Sağlayıcıya göre tipik: yaklaşık 14,4 s

Toplam

$1,69

169 kredi

Çalışma başına

$0,0169 · 1,69 kredi

Gerçek GPU süresi faturalandırılır; bu bir tahmindir.

05

API

Janus Pro 7B öğesini Railwail API anahtarınızla çağırın. İstekte bu model kimliğini kullanın:

Doğrulanmış API örneği yok

Bu modelin girdileri henüz belgelenmedi.

06

Özellikler

Model Kimliği
janus-pro-7b
Geliştirici
DeepSeek
Giriş
Metin
Çıkış
Görüntü
Faturalandırma
Kullanıma göre (token veya GPU süresi)
Model boyutu
7B parameters (Janus-Pro-7B)
Lisans
DeepSeek Janus License — open weights, free for research and commercial use with attribution and standard restrictions.
Katalog girişi güncellendi
23 Eylül 2026

Etiketler

  • deepseek
  • janus
  • open-weights
  • unified-multimodal
  • pricing-tbd
07

Kullanım Alanları

Nerelerde Kullanılır

  • Multimodal research and prototyping
  • Unified vision-language agents that draw + describe
  • Interactive image dialogue systems
  • Fine-tuning base for specialised generators
  • Educational demos of unified multimodal models
  • Visual question answering with generation
08

Sık Sorulan Sorular

Janus Pro 7B nedir?

Janus Pro 7B, DeepSeek tarafından Görüntü Oluşturma kategorisinde oluşturulan bir modeldir.

Janus Pro 7B Railwail'de ne kadar maliyetlidir?

Railwail üzerinde Janus Pro 7B maliyeti ≈ $0,0169 çalıştırma başına. Her istek gerçekten ne kadar kullanırsa o kadar ücretlendirilirsiniz. Kullanım ön ödemeli kredilerden ödenir; 1 kredi $0,01 değerindedir.

Janus Pro 7B ne kadar hızlıdır?

Railwail'de Janus Pro 7B için henüz çalıştırma süresi belirtmek için yeterli ölçülen çalıştırma yoktur. Bu, giriş, ayarlar ve sağlayıcıdaki yüke bağlıdır.

Janus Pro 7B, FLUX 1.1 Pro'dan daha iyi midir?

Bu göreve bağlıdır. Janus Pro 7B (DeepSeek) ve FLUX 1.1 Pro (Black Forest Labs) her ikisi de Görüntü Oluşturma kategorisinde modellerdir. Karşılaştırma sayfası fiyatlarını ve özelliklerini yan yana gösterir.

Janus Pro 7B ve FLUX 1.1 Pro'ı karşılaştır
09

Karşılaştırılabilir modeller

Bu kategorideki tümü
  • SDXL fine-tune by galleri5 for slick flat icons and pop constructivist graphics with thick edges. Trained on Bing generations, it produces clean single-subject icon art that suits app icons, badges and UI glyphs. Raster output, not true vector.

    ≈ $0,0094/çalıştırma

    Birim başına 44 % daha ucuz

    Janus Pro 7B ve Icons (SDXL Flat Pop) karşılaştır
  • InstantIDCommunity

    InstantID makes realistic portraits of a real person from a single reference photo without per-user training. Combines a face encoder with an IdentityNet adapter on SDXL to keep identity and pose while following a text prompt, so it is fast and tuning-free.

    ≈ $0,084/çalıştırma

    Birim başına 397 % daha pahalı

    Janus Pro 7B ve InstantID karşılaştır
  • Sticker MakerCommunity

    fofr's sticker generator that outputs graphics with transparent backgrounds, so the result drops straight into chat apps or print sheets. Runs an SDXL-based pipeline at high speed (default 17 steps) and returns die-cut style art without manual background removal.

    ≈ $0,0055/çalıştırma

    Birim başına 67 % daha ucuz

    Janus Pro 7B ve Sticker Maker karşılaştır

Tüm Modeller Tek Bir API Aracılığıyla

Railwail'deki her model için bir API anahtarı. Kullanım, ön ödemeli kredilerden tahsil edilir, 1 kredi = $0,01.