How to Use RunwayML’s Stable Diffusion XL for Batch Video Background Removal in 2026

Why Batch Background Removal Matters in 2026

Video creators are producing more content than ever. Brands need dozens of product clips, influencers need quick green-screen swaps, and e-learning teams generate hundreds of tutorial videos each month. Doing a manual rotoscope on each clip can take hours.

RunwayML’s Stable Diffusion XL (SDXL) model, released in late 2025, adds a diffusion-based segmentation head that can isolate subjects in under a second per frame. When paired with Runway’s REST API, you can send a list of video URLs and get back masked clips automatically.

In practice, this means a 30-second clip at 1080p can be processed in about 30 seconds of compute time, freeing editors to focus on creative tasks instead of tedious masking.

Step-by-Step Setup (No Code Needed)

1️⃣ Create a Runway account. The free tier lets you process up to 10 minutes of video per month, which is enough for testing.

2️⃣ Upgrade to the Pro plan for batch work. As of March 2026 the Pro plan costs $35 / month and includes 400 credits (≈ 1 hour 40 minutes of 1080p output). Credits are also used for background removal, at 1 credit per minute.

3️⃣ Open the Dashboard → Tools → Remove Background. You’ll see a “Batch Mode” toggle at the top right.

4️⃣ Upload your video list. Drag-and-drop a CSV file that contains a column of public URLs or asset IDs. Runway will queue each clip.

5️⃣ Choose the SDXL model. In the model selector, pick “Stable Diffusion XL – Segmentation”. This model uses a diffusion backbone to produce cleaner edges than the legacy Green Screen tool.

6️⃣ Set parameters:

• Edge Feather: 3 px (default)
• Mask Opacity: 100 %
• Background Output: Transparent (ProRes 4444) or custom image/video

7️⃣ Start the batch job. Runway shows a progress bar for each clip and an overall ETA. When finished, each masked video appears in your Assets folder.

8️⃣ Export. Select the batch, click “Export All”, and choose MP4 (1080p) or ProRes (4K) with alpha channel.

Using the REST API for Full Automation

If you need to integrate background removal into a pipeline (e.g., a CMS that uploads product videos), the API is the cleanest route.

First, obtain an API key from your account settings. Then follow this simplified flow:

# 1. Upload assets (max 5 min per file)
curl -X POST https://runwayml.rest/assets/upload \
  -H "Authorization: Bearer $RUNWAY_API_KEY" \
  -F "file=@/path/to/video.mp4"

# 2. Submit batch background removal request
curl -X POST https://runwayml.rest/video/remove-background \
  -H "Authorization: Bearer $RUNWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "asset_ids": ["asset_123", "asset_456"],
    "model": "sdxl_segmentation",
    "output": "transparent",
    "edge_feather": 3
  }'

# 3. Poll for completion
curl -X GET https://runwayml.rest/generations/{taskId} \
  -H "Authorization: Bearer $RUNWAY_API_KEY"

# 4. Download results
curl -O https://runwayml.rest/assets/{result_asset_id}

Each request processes up to 20 seconds of video per API call. For longer clips, split the file into segments (Runway’s CLI can do this automatically) and re-assemble after export.

Cost Analysis – How Much Does It Really Cost?

Runway charges per minute of output, not per frame. Below is a quick calculation for a typical batch of 50 product videos, each 15 seconds long at 1080p.

• Total minutes = 50 × 0.25 ≈ 12.5 minutes
• Credits needed = 12.5 credits (1 credit = 1 minute)
• Pro plan already includes 400 credits, so the batch costs $0 extra.

Compare that to hiring a junior editor at $20 / hour. The same batch would take roughly 8 hours of manual rotoscoping, costing $160. The AI route saves $148 and frees up creative time.

Comparison Table: RunwayML SDXL vs. Competitors

Practical Tips for Best Results

✅ Use well-lit footage. The model relies on contrast between subject and background. Soft shadows are fine, but heavy backlighting can cause leaks.

✅ Keep camera motion smooth. Sudden cuts or fast pans increase the chance of flickering masks. If you must use fast motion, split the clip at each cut and run separate jobs.

✅ Set Edge Feather to 2-4 px. Too low leaves hard halos; too high blurs fine details like hair strands.

❌ Avoid busy backgrounds. While SDXL handles complexity better than the legacy Green Screen tool, a cluttered scene still adds processing time.

💡 Combine with custom assets. Generate stylized backdrops with Stable Diffusion 3, then feed them into Runway’s compositor for a fully AI-driven pipeline.

Who Should Use This?

Small e-commerce teams that need to showcase products on a uniform backdrop without hiring a studio.

Content creators who publish daily short-form videos and want a one-click green-screen effect.

Agencies managing multiple client campaigns; the batch API lets them automate hundreds of clips per week.

Educators producing lecture recordings; they can replace a plain wall with a branded slide deck in seconds.

Conclusion

RunwayML’s Stable Diffusion XL brings fast, high-quality segmentation to video background removal. By using the built-in batch mode or the REST API, you can process dozens of clips for the price of a few credits. Compared with Adobe Firefly or DeepBrain AI, Runway offers the best speed-to-quality ratio and true automation. If you need to scale video production in 2026, the SDXL workflow is the most cost-effective path.