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设计与创意 · SKILL.md

text-to-image

text-to-image

【设计与创意】Generate images from text descriptions using each::sense AI. Create photorealistic scenes, illustrations, concept art, digital paintings, and any visual from a text prompt. Supports all styles from hyperrealism to anime, abstract to architectural. Use for: concept art, illustrations, marketing visuals, social media images, creative exploration, story…

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技能说明

GitHub

name: text-to-image description: "Generate images from text descriptions using each::sense AI. Create photorealistic scenes, illustrations, concept art, digital paintings, and any visual from a text prompt. Supports all styles from hyperrealism to anime, abstract to architectural. Use for: concept art, illustrations, marketing visuals, social media images, creative exploration, storyboarding. Triggers: text to image, generate image, create image, ai image, image generation, txt2img, prompt to image, ai art, generate picture, create visual, image from text, ai illustration" allowed-tools: Bash(curl *), WebFetch

Text to Image

Generate images from text descriptions using each::sense — the intelligent AI agent that automatically selects the best model for your request.

Quick Start

Requires an each::labs API key. Get one at eachlabs.ai.

Using curl

curl -X POST https://eachsense-agent.core.eachlabs.run/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "A lone astronaut standing on a red desert planet, looking up at a sky filled with three massive moons, cinematic lighting, photorealistic, wide shot"
      }
    ],
    "stream": false
  }'

Using Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_EACHLABS_API_KEY",
    base_url="https://eachsense-agent.core.eachlabs.run/v1"
)

response = client.chat.completions.create(
    model="eachsense/beta",
    messages=[{
        "role": "user",
        "content": "A lone astronaut standing on a red desert planet, looking up at a sky filled with three massive moons, cinematic lighting, photorealistic, wide shot"
    }]
)

print(response.choices[0].message.content)

Streaming

Set "stream": true for real-time Server-Sent Events (SSE) responses, or "stream": false to receive the complete result in a single response. Streaming is useful for showing progress in UIs; non-streaming is simpler for scripts and automation.

Prompt Engineering Guide

Prompt Structure

[subject] + [action/pose] + [environment] + [style] + [lighting] + [camera/composition]

Style Keywords

StyleKeywords
Photorealisticphotorealistic, ultra-realistic, DSLR photo, 8K, raw photo
Illustrationdigital illustration, vector art, hand-drawn, watercolor
Cinematiccinematic, film still, movie scene, dramatic lighting, anamorphic
Animeanime style, manga, cel shading, Studio Ghibli
3D Render3D render, octane render, unreal engine, isometric
Oil Paintingoil painting, impasto, classical art, Renaissance
Pixel Artpixel art, 16-bit, retro game, 8-bit

Quality Boosters

masterpiece, highly detailed, sharp focus, professional,
studio quality, award-winning, 8K resolution, HDR

Examples

Photorealistic Landscape

curl -X POST https://eachsense-agent.core.eachlabs.run/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "A misty fjord in Norway at sunrise, mirror-like water reflecting snow-capped mountains, a small red fishing cabin on the shore, photorealistic, 8K, golden hour lighting"
      }
    ],
    "stream": false
  }'

Digital Illustration

curl -X POST https://eachsense-agent.core.eachlabs.run/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "A cozy bookstore interior with a cat sleeping on a stack of old books, warm lamp light, plants hanging from the ceiling, digital illustration, Studio Ghibli style, soft colors"
      }
    ],
    "stream": false
  }'

Concept Art

curl -X POST https://eachsense-agent.core.eachlabs.run/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "A floating cyberpunk city above the clouds, neon lights reflecting off chrome towers, flying vehicles streaming between buildings, concept art, matte painting, epic scale, dramatic perspective"
      }
    ],
    "stream": false
  }'

Product Visualization

curl -X POST https://eachsense-agent.core.eachlabs.run/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "A sleek wireless earbud case on a marble surface, soft studio lighting, product photography, shallow depth of field, clean minimalist background, commercial quality"
      }
    ],
    "stream": false
  }'

Common Pitfalls

  • Vague prompts produce generic results. Be specific about subject, style, and mood.
  • Too many subjects in one prompt cause confusion. Focus on one main subject.
  • Text rendering is unreliable in AI-generated images. Avoid asking for specific words.
  • Hands and fingers can be distorted. Crop or hide hands if they are not the focus.
  • Contradictory styles (e.g., "photorealistic watercolor") confuse the model.

Batch Generation Workflow

# Generate multiple variations by iterating prompts
PROMPTS=(
  "A medieval castle on a cliff at sunset, oil painting style"
  "A medieval castle on a cliff at sunset, photorealistic"
  "A medieval castle on a cliff at sunset, anime style"
)

for PROMPT in "${PROMPTS[@]}"; do
  curl -X POST https://eachsense-agent.core.eachlabs.run/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "X-API-Key: $EACHLABS_API_KEY" \
    -d "{
      \"messages\": [{\"role\": \"user\", \"content\": \"$PROMPT\"}],
      \"stream\": false
    }"
  echo "---"
done

Related Skills

Related Models

Documentation

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