Wan 2.1 Text To Image LoRA

Wan 2.1 Text To Image LoRA

Playground

Wan 2.1 Text-to-Image LoRA repurposes Wan 2.1 to create ultra-realistic images with exceptional detail and LoRA fine-tuning support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Generate photorealistic images with custom style control using Wan 2.1 Text-to-Image LoRA. This versatile model supports both pure text-to-image generation and image-to-image transformation with adjustable strength — plus full LoRA support for unique visual styles.

Why It Looks Great

• Photorealistic output: Optimized for lifelike, natural imagery.
• LoRA support: Apply custom LoRA adapters for unique styles and characters.
• Image-to-image mode: Transform existing images with adjustable strength control.
• Custom dimensions: Precise control over width and height for any aspect ratio.
• Prompt Enhancer: Built-in tool to refine your descriptions automatically.
• Reproducible results: Use the seed parameter to recreate exact outputs.

Parameters

How to Use

Text-to-Image

1. Write your prompt — describe the image in detail.
2. Use Prompt Enhancer (optional) — click to enrich your description.
3. Add LoRAs (optional) — click ”+ Add Item” for custom styles.
4. Set dimensions — adjust width and height as needed.
5. Run — click the button to generate.
6. Download — preview and save your image.

Image-to-Image

1. Upload source image — the image to transform.
2. Write your prompt — describe the desired transformation.
3. Adjust strength — lower values preserve more of the original (0.3-0.5), higher values allow more change (0.7-1.0).
4. Run — click the button to generate.

Pricing

Flat rate per image.

Strength Guide (Image-to-Image)

Best Use Cases

• Photorealistic Portraits — Generate lifelike portraits with natural lighting.
• Style Transfer — Transform images with custom LoRA styles.
• Lifestyle Photography — Create authentic everyday scenes.
• Character Consistency — Use character LoRAs for consistent identity.
• Custom Aesthetics — Apply trained LoRAs for unique visual styles.

Example Prompts

• “A young woman hanging laundry on a sunny balcony, soft shadows, fluttering clothes, warm afternoon light, urban neighborhood, photorealistic, 35mm lens”
• “Professional headshot, soft studio lighting, neutral background, sharp focus”
• “Cozy kitchen scene, morning light through window, steam rising from coffee”
• “Street photography, candid moment, natural expressions, urban environment”
• “Product shot on marble surface, dramatic lighting, clean composition”

How to Use LoRAs

For detailed guides on using and training custom LoRAs:

• Use Your LoRA
• Train Your LoRA

Pro Tips for Best Results

• Include camera/lens details for realistic style: “35mm lens”, “f/1.8”, “bokeh”.
• Describe lighting: “soft shadows”, “warm afternoon light”, “studio lighting”.
• Use image-to-image with lower strength (0.3-0.5) to preserve composition.
• LoRAs can dramatically change output style — experiment with different adapters.
• Square dimensions (1024×1024) work well for portraits; adjust for other compositions.

Notes

• Supports both text-to-image and image-to-image workflows.
• If using a URL for the source image, ensure it is publicly accessible.
• The strength parameter only applies when a source image is provided.
• LoRA effects are cumulative — start with one and add more as needed.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "strength": 0.6,
  "size": "1024*1024",
  "seed": -1,
  "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/text-to-image-lora" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}")
  RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters