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.
ว่าง

$0.025ต่อครั้ง·~40 / $1

A young woman hanging laundry on a sunny balcony, soft shadows, fluttering clothes, warm afternoon light, urban neighborhood, photorealistic, 35mm lens

A couple reading books in a tiny café during rainy afternoon, raindrops on window, warm tones, vintage decor, cozy mood, photorealistic

A kid playing with a dog in a sunlit park, fallen leaves, trees swaying in wind, golden hour lighting, spontaneous joyful expression

A family eating dinner at a small round table, homemade dishes, ambient kitchen light, lively conversation, slightly cluttered background

B0x13ng Boxing Video The boxer throws a quick series of jabs and then a right cross.

B0x13ng Boxing video two boxers are in the ring, but one of them is significantly shorter than the other. The shorter female fighter in black shorts is aggressively throwing punches, trying to reach his taller opponent. The taller fighter, wearing black shorts, simply holds out his glove on the shorter fighter’s forehead, keeping her at a frustrating distance.

A fierce female fighter walking into the ring, hooded robe, determined expression, spotlight on her, crowd in shadows, LoRA: B0x13ng Boxing Video, dramatic sports energy

Boxer resting in the corner of the ring between rounds, exhausted posture, trainer speaking to him, water splashing, towel in hand, grungy gym mood, cinematic realism

cinematic wide shot of a massive bio-mechanical city on a desert planet at dusk. Towers are fused with organic, plant-like structures. A lone wanderer in a weathered cloak walks towards the city gates. The twin suns are setting, casting long shadows and a deep orange glow across the landscape. Matte painting, epic scale, concept art in the style of Syd Mead and John Harris.

a steaming bowl of authentic Japanese tonkotsu ramen, rich and creamy broth, perfectly cooked chashu pork with a seared surface, a soft-boiled egg with a gooey yolk, glistening noodles, garnished with fresh green onions and nori seaweed. Professional studio lighting, dramatic side light creating deep shadows, shallow depth of field, background is a dark, rustic ramen shop.
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.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image you want to generate. |
| image | No | Optional source image for image-to-image transformation. |
| strength | No | How much to transform the source image (0.0-1.0). Default: 0.8. |
| loras | No | Custom LoRA adapters to apply for style control. |
| width | No | Output width in pixels (e.g., 1024). |
| height | No | Output height in pixels (e.g., 1024). |
| seed | No | Random seed for reproducibility. Use -1 for random. |
| output_format | No | File format: jpeg or png. Default: jpeg. |
Flat rate per image.
| Output | Cost |
|---|---|
| Per image | $0.025 |
| Strength | Effect | Best For |
|---|---|---|
| 0.3-0.4 | Subtle changes, preserves original | Minor style adjustments |
| 0.5-0.6 | Moderate transformation | Balanced edits |
| 0.7-0.8 | Significant changes | Style transfer, major edits |
| 0.9-1.0 | Near-complete transformation | Heavy stylization |
For detailed guides on using and training custom LoRAs:
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/text-to-image-lora with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Wan 2.1 Text To Image Lora below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/text-to-image-lora" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"strength": 0.6,
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg",
"enable_base64_output": false,
"enable_sync_mode": false
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/wan-2.1/text-to-image-lora", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"strength": 0.6,
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg",
"enable_base64_output": false,
"enable_sync_mode": false
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.1/text-to-image-lora",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"strength": 0.6,
"size": "1024*1024",
"seed": -1,
"output_format": "jpeg",
"enable_base64_output": false,
"enable_sync_mode": false
}
)
print(output["outputs"][0]) # → URL of the generated outputWan 2.1 Text To Image Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. 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. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-text-to-image-lora.
Wan 2.1 Text To Image Lora starts at $0.025 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `image`, `size`, `seed`, `enable_base64_output`, `enable_sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-text-to-image-lora.
Average end-to-end generation time on WaveSpeedAI is around 8 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.