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lora-support

lora-support

WAN 2.1 T2V 480P Ultra-Fast

wavespeed-ai/wan-2.1/t2v-480p-lora-ultra-fast

WAN 2.1 T2V 480p delivers ultra-fast text-to-video generation with custom LoRA support for unlimited 480p AI videos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Input
Recommend:
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Idle

Sua solicitação custará $0.125 por execução.

Por $10 você pode executar este modelo aproximadamente 80 vezes.

ExemplosVer todos

README

Wan 2.1 T2V 480p LoRA Ultra Fast — wavespeed-ai/wan-2.1/t2v-480p-lora-ultra-fast

Wan 2.1 T2V 480p LoRA Ultra Fast is a low-latency text-to-video model designed for rapid iteration. It generates short 480p clips from a single prompt, and supports adding LoRAs to steer style, characters, or motion patterns while keeping throughput high.

Key capabilities

  • Text-to-video generation at 480p
  • Ultra-fast inference for quick previews and batch exploration
  • LoRA support for style/character control (up to 3 LoRAs per run)
  • Prompt-driven motion, staging, and shot direction
  • Works well for storyboard drafts, social content, and concept tests

Use cases

  • Fast storyboard and pre-vis from a director-style prompt
  • Stylized “template clips” using LoRAs (brand look, character identity, anime/toy/film looks)
  • Social content ideation: generate 10–20 variations quickly, pick the best, upscale later
  • LoRA-driven series: consistent visual language across multiple clips for campaigns

Pricing

OutputDurationPrice per runEffective price per second
480p T2V (LoRA)5s$0.125$0.025/s
480p T2V (LoRA)10s$0.188$0.0188/s

Inputs

  • prompt (required): describe subject, action, scene, camera, and style

  • negative_prompt (optional): reduce blur, jitter, distortions, low-quality artifacts

  • loras (optional): up to 3 LoRAs, each with:

    • path: owner/model-name or a direct .safetensors URL
    • scale: LoRA strength (commonly around 0.6–1.0 to start)

Parameters

  • duration: clip length (commonly 5s or 10s)
  • size: output size preset (e.g., 832×480)
  • num_inference_steps: more steps can improve stability/detail at the cost of speed
  • guidance_scale: higher values follow the prompt more strongly (can reduce natural motion if too high)
  • flow_shift: motion behavior tuning (useful for more/less dynamic motion)
  • seed: set for reproducible results (-1 for random)

Prompting tips (T2V)

  • Write like a shot list: subject + action + environment + camera + style
  • Keep motion explicit: “slow pan left”, “subtle head turn”, “hands gesture while talking”
  • For multi-panel or UI-like scenes (e.g., a video call), specify layout and per-panel actions clearly
  • If using LoRAs, keep the base prompt simpler and let the LoRA carry most of the style signal