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Happy Horse
Happy Horse by Alibaba Cloud is a next-generation multimodal video generation model designed to bridge the gap between creative intent and production-ready output.
Unlike fragmented toolchains that require multiple models stitched together, Happy Horse operates as a cohesive system. It enables consistent visual quality, temporal coherence, and style control across all generation modes, making it particularly valuable for production environments where reliability matters as much as creativity.
You pick the input that fits your workflow — text, image, reference footage, or an existing clip you want to recut.
Describe your scene in natural language and the model handles everything else — motion, camera movement, lighting, ambient sound, and synchronized audio, all generated in a single forward pass. Works at both 720P and 1080P resolution, with durations from 4 to 10 seconds. Particularly strong at physical realism, multi-shot storytelling, and cinematic framing.
Supply a single start frame and the model animates outward from it, preserving subject identity and visual style while introducing fluid, physically plausible motion. The optional text prompt guides the direction of movement and camera behavior. This mode achieved an Elo score of 1,416 on Artificial Analysis — the highest recorded for any image-to-video model at the time of launch.
Maintain consistent character identity, style, or scene environment across multiple generated clips. Provide one or more reference images — a character portrait, product shot, or branded environment — and the model uses them as anchors while generating entirely new motion sequences. This is the go-to mode for branded content, character series, and product launch campaigns that need visual consistency without manual retouching.
Transform, recut, or stylistically revise existing video footage through natural language instructions. Tell the model what to change — replace the background, alter the lighting, switch the visual style, or modify specific elements within a scene — and it applies those changes while preserving temporal coherence across frames. Unlike traditional editing pipelines, no timeline experience is required.
Happy Horse uses the Transfusion (Unified Multimodal) framework — a 40-layer self-attention Transformer where text, image, video, and audio tokens sit in a single sequence. The first and last four layers handle modality-specific encoding and decoding; the middle 32 layers share parameters across all modalities.
Rather than generating silent video and adding audio in a second step, the model produces both in one forward pass. This means sound effects naturally align with on-screen events — a wave splashing, an engine revving, a door closing without requiring manual synchronization or post-production audio work.
Happy Horse outputs full HD without an upscaling step. At 15B parameters and 40 transformer layers, the model carries enough capacity to generate frame-level detail at 1080P directly, rather than generating at a lower resolution and scaling up, which typically introduces blur and temporal flicker.
16:9 landscape, 9:16 vertical, and 1:1 square aspect ratios supported natively. All modes output at either 720P or 1080P depending on the pricing tier selected.
Artificial Analysis Video Arena runs blind human-preference votes using an Elo rating system — the same math used in competitive chess. Nobody votes knowing which model produced which clip.
Turn a product shot into a 1080P hero video in seconds. The image-to-video and reference-to-video APIs preserve branding while introducing motion — no filming required. Run A/B variations at scale using the same source image.
Generate and test dozens of short-form hooks from the same prompt set. The 720P tier keeps costs low during ideation, with 1080P available for final deliverables. Multilingual audio means the same clip works across markets without re-recording.
Directors and studios use text-to-video to prototype scene blocking, camera angles, and visual pacing before committing to a full shoot. Fast iteration at ~10 seconds per generation makes the feedback loop genuinely fast.
Native lip-sync across seven languages means a single generated clip can be re-voiced for different markets natively, not dubbed after the fact. The model handles audio-visual alignment internally, so dialogue tracks match mouth movements without manual correction.
Reference-to-video lets studios maintain character consistency across generated sequences. Feed in a character design once, generate multiple scenes with consistent visual identity. Open weights mean fine-tuning for specific art styles is feasible.
The video editing API accepts existing footage and rewrites it on instruction — change a background, alter lighting, apply a cinematic style, while preserving temporal coherence frame-to-frame. Particularly useful for last-minute creative pivots after a shoot.
Image-to-Video animates a single start frame — the image becomes the first frame and the model generates forward from it. Reference-to-Video uses one or more images as identity or style anchors across an entirely generated clip, without necessarily starting from that image.
No. Audio-visual generation is built into the same unified model and included in the per-second rate. You can pass audio: false if you want silent output — there's no discount for doing so, but the option is there for downstream production workflows that handle audio separately.
The model natively handles Mandarin, Cantonese, English, Japanese, Korean, German, and French. Lip-sync is generated jointly with video in a single pass — not added post-hoc — which is why audio-visual alignment is tighter than in pipeline-based approaches.
Clips from 4 to 10 seconds. Aspect ratios: 16:9 (landscape), 9:16 (vertical/mobile), and 1:1 (square). Both 720P and 1080P are available on all four API modes and all aspect ratios.
You pick the input that fits your workflow — text, image, reference footage, or an existing clip you want to recut.
Describe your scene in natural language and the model handles everything else — motion, camera movement, lighting, ambient sound, and synchronized audio, all generated in a single forward pass. Works at both 720P and 1080P resolution, with durations from 4 to 10 seconds. Particularly strong at physical realism, multi-shot storytelling, and cinematic framing.
Supply a single start frame and the model animates outward from it, preserving subject identity and visual style while introducing fluid, physically plausible motion. The optional text prompt guides the direction of movement and camera behavior. This mode achieved an Elo score of 1,416 on Artificial Analysis — the highest recorded for any image-to-video model at the time of launch.
Maintain consistent character identity, style, or scene environment across multiple generated clips. Provide one or more reference images — a character portrait, product shot, or branded environment — and the model uses them as anchors while generating entirely new motion sequences. This is the go-to mode for branded content, character series, and product launch campaigns that need visual consistency without manual retouching.
Transform, recut, or stylistically revise existing video footage through natural language instructions. Tell the model what to change — replace the background, alter the lighting, switch the visual style, or modify specific elements within a scene — and it applies those changes while preserving temporal coherence across frames. Unlike traditional editing pipelines, no timeline experience is required.
Happy Horse uses the Transfusion (Unified Multimodal) framework — a 40-layer self-attention Transformer where text, image, video, and audio tokens sit in a single sequence. The first and last four layers handle modality-specific encoding and decoding; the middle 32 layers share parameters across all modalities.
Rather than generating silent video and adding audio in a second step, the model produces both in one forward pass. This means sound effects naturally align with on-screen events — a wave splashing, an engine revving, a door closing without requiring manual synchronization or post-production audio work.
Happy Horse outputs full HD without an upscaling step. At 15B parameters and 40 transformer layers, the model carries enough capacity to generate frame-level detail at 1080P directly, rather than generating at a lower resolution and scaling up, which typically introduces blur and temporal flicker.
16:9 landscape, 9:16 vertical, and 1:1 square aspect ratios supported natively. All modes output at either 720P or 1080P depending on the pricing tier selected.
Artificial Analysis Video Arena runs blind human-preference votes using an Elo rating system — the same math used in competitive chess. Nobody votes knowing which model produced which clip.
Turn a product shot into a 1080P hero video in seconds. The image-to-video and reference-to-video APIs preserve branding while introducing motion — no filming required. Run A/B variations at scale using the same source image.
Generate and test dozens of short-form hooks from the same prompt set. The 720P tier keeps costs low during ideation, with 1080P available for final deliverables. Multilingual audio means the same clip works across markets without re-recording.
Directors and studios use text-to-video to prototype scene blocking, camera angles, and visual pacing before committing to a full shoot. Fast iteration at ~10 seconds per generation makes the feedback loop genuinely fast.
Native lip-sync across seven languages means a single generated clip can be re-voiced for different markets natively, not dubbed after the fact. The model handles audio-visual alignment internally, so dialogue tracks match mouth movements without manual correction.
Reference-to-video lets studios maintain character consistency across generated sequences. Feed in a character design once, generate multiple scenes with consistent visual identity. Open weights mean fine-tuning for specific art styles is feasible.
The video editing API accepts existing footage and rewrites it on instruction — change a background, alter lighting, apply a cinematic style, while preserving temporal coherence frame-to-frame. Particularly useful for last-minute creative pivots after a shoot.
Image-to-Video animates a single start frame — the image becomes the first frame and the model generates forward from it. Reference-to-Video uses one or more images as identity or style anchors across an entirely generated clip, without necessarily starting from that image.
No. Audio-visual generation is built into the same unified model and included in the per-second rate. You can pass audio: false if you want silent output — there's no discount for doing so, but the option is there for downstream production workflows that handle audio separately.
The model natively handles Mandarin, Cantonese, English, Japanese, Korean, German, and French. Lip-sync is generated jointly with video in a single pass — not added post-hoc — which is why audio-visual alignment is tighter than in pipeline-based approaches.
Clips from 4 to 10 seconds. Aspect ratios: 16:9 (landscape), 9:16 (vertical/mobile), and 1:1 (square). Both 720P and 1080P are available on all four API modes and all aspect ratios.
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