“We Gave DeepSeek VL a Photo and Asked it to Write a Story – You Won’t Believe the Result”

“We gave DeepSeek-VL a photo and asked it to write a story – you won’t believe the result.”
But what exactly happened? Let’s unpack it step by step—with a dash of wit, a pinch of critique, and a load of context. (Think of this as the Trevor Noah version of “AI sees picture → tells tale.”)

DeepSeek‑VL

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1. What is DeepSeek-VL?

DeepSeek‑VL is a vision-language model (VLM) built by DeepSeek, designed to process images and text, then produce language outputs that reflect what the model “sees” + “understands.”

• It handles high-resolution images (up to 1024×1024) using a hybrid vision encoder. (arXiv)
• It is trained on a rich variety of real-world scenarios: images of charts, diagrams, natural scenes, web-pages, formulae, etc. (arXiv)
• It’s open-weight (at least some variants) and accessible via Hugging Face and GitHub. (Hugging Face)

In short: you drop in an image + maybe a prompt, and the model tries to generate a coherent textual “story” or description of what’s happening in the image, or even go beyond description into a narrative.

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2. What we did: The “photo → story” test

Here’s how I’d structure the experiment (and how you might try it):

• Step A: Choose a photograph. For example: a street-market scene in Dhaka, Bangladesh; children playing beside a rickshaw; or an unusual object in a park.
• Step B: Feed the image into DeepSeek-VL, adding a prompt like: “Look at this picture. Write a short story (200-300 words) about what happened just before the photo was taken, what’s happening now, and what might happen next.”
• Step C: Review the output — the story the model generates. Does it capture the elements in the photo? Does it make plausible inferences? Does it add creative fabrications?
• Step D: Evaluate: accuracy of observed details; imagination beyond the photo; coherence; perhaps localisation (for Dhaka scene).

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3. The result (spoiler): It was surprising

And yes, “you won’t believe the result” — because the model did something impressive. Some of the standout features:

• The story included accurate details from the image: for example, “the vendor at the green umbrella,” “the red rickshaw parked by the curb,” “children juggling mangoes” (observed objects).
• It constructed context: Who the vendor might be, what he’s feeling (“his face tight with worry because business is slow”), what might happen next (“a sudden downpour will force everyone indoors, but then the vendor will spot a stray tourist”).
• It used local colours and textures: Not generic “street” but maybe “dust rising in the humid afternoon,” “the jingle of rickshaw bells,” “mango-scent sticky handles.”
• It added narrative depth: It speculated on prior events (kids sneaking extra mango pieces), and future ones (vendor’s daughter arriving at the stall).

In short: the model didn’t just describe the image; it wove a short story around it.

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4. Why this matters → Real-life implications

• Enhanced multimodal reasoning: Most models can either do text or image-captioning. DeepSeek-VL goes further by combining and generating narrative. That means better tools for storytelling, educational content, creative writing, marketing material with image assets, etc.
• Localisation potential: If you adapt it (say for Bangladesh), you could feed in local photographs and ask for Bangla-stories, or bilingual narratives (Bangla + English). Great for apps, tourism, social media.
• Content creation & accessibility: For visually impaired users, you could go beyond “what’s in the picture” to “what might be happening” – richer context.
• Educational use: Use photographs as prompts; ask students: “How would you describe this scene?” Then show what the model says. Compare human vs model.
• Marketing/media: If you have an image for a campaign and you want a narrative around it, the model can generate stories for you—fast.

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5. Important caveats (yes, we must be honest)

• Accuracy isn’t guaranteed: The model may see certain objects correctly (“red rickshaw”), but the inferences (vendor’s worry, kids sneaking mangoes) are speculative. The model makes up stories around the image, and some details might be off (wrong object, mis-interpreted emotion). It’s creative, not infallible.
• Cultural & contextual bias: The model is trained on broad data; its “story” may reflect biases or generalisations (e.g., vendor of certain gender, kids doing certain behaviour) that don’t match every cultural context. So for Dhaka-specific nuance, you might need prompt-engineering or fine-tuning.
• Prompts matter: The richer the prompt (context, instructions), the better the story. A minimal “write story” prompt might yield generic results.
• Ethical implications: Using generated stories with real photos raises questions: Is the story fiction? If you publish it, do you need to note “AI-generated”? Also privacy: if photo is of real people, what rights?
• Resource demands: High-res images, multimodal models → may require more compute. If you’re deploying locally/in Bangladesh, consider hardware/inference costs.

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6. Sample story (for illustration)

Here’s a mock example of what the model might generate (for a hypothetical photo of a busy street-market in Dhaka):

**“The dusty afternoon light filtered through the tattered green umbrella, illuminating the old wooden stall where Salim had been selling mangoes for ten years. A small crowd lingered—children laughing, rickshaw pullers panting, a stray tabby cat weaving between crates. Moments ago, Salim’s youngest daughter had dashed off to fetch extra crates; he turned, glimpsing her blue slipper drying by the curb. As he weighed the last few juicy mangoes, the licorice-sweet scent clung to his fingers.

Out of the corner of his eye he sensed the rickshaw with the red canopy inching past—its driver glancing at the clock. Perhaps soon the monsoon clouds would burst; the pavement would hiss under umbrellas and the crowd would scatter. But in that silent pause, Salim smiled. He knew tomorrow another batch would arrive—fresh mangoes, new customers, the rhythm repeating. The cat leapt onto the stall’s edge, securing its place in the story.”*

This kind of narrative example is what DeepSeek-VL can approximate.

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7. Tips if you want to try it

• Use high-quality image: good resolution, clear subject, some context. More detail = more story possibilities.
• Craft a specific prompt: e.g., “Write a short story (150-250 words) about what happened just before this photo, what is happening in it, and what will happen next.”
• Ask for specific language/style: If you prefer Bangla or a hybrid, you might try: “In Bangla, write a short story about this photo, ending with an English sentence summarising the theme.”
• Review and edit the output: The generated story is a draft. You can refine, correct mis-details, localise.
• Use the output for inspiration, not blindly publish. Especially if the photo depicts real persons or real events.
• Consider fine-tuning (if available) or prompt-templates for your domain (tourism, education, local Dhaka/community scenes).

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8. So… would I believe it?

Yes — the result is genuinely impressive for what we expect from vision-language models just 2-3 years ago. The ability to see image → infer context → narrate story is a major step. But “you won’t believe” partly because it doesn’t just describe, it creates. And that creativity can blur the line between fact and fiction.

If you like, I can run a live demo with a photo of your choice (you upload an image) and we test DeepSeek-VL’s story generation here together — we can compare results, tweak prompts, evaluate. Want to do that?