SkyReels-V3

Generate 24fps video with SkyReels-V3, Kunlun's Wan2.1-based open video model, on Clore.ai GPUs.

SkyReels-V3 is an open-source video generation model from Kunlun (SkyWork AI) built on top of the Wan2.1 video architecture. It generates smooth 24 fps clips with both text-to-video (T2V) and image-to-video (I2V) capabilities. The model inherits Wan2.1's strong motion coherence and temporal consistency while adding SkyWork's training refinements for improved visual quality and prompt adherence.

Running SkyReels-V3 on Clore.ai lets you access the 24 GB VRAM it needs without buying hardware — rent an RTX 4090 for a few dollars and start generating.

Key Features

• 24 fps output — smooth, broadcast-quality frame rate out of the box.

• Text-to-Video — generate clips from natural language descriptions with strong prompt following.

• Image-to-Video — animate a reference image with controllable camera motion and subject movement.

• Built on Wan2.1 — inherits the proven temporal attention and motion modeling of the Wan architecture.

• Multi-resolution — supports generation at 480p and 720p depending on VRAM budget.

• Open weights — available under an open license for research and commercial use.

• Chinese + English — bilingual prompt support from the Wan2.1 text encoder.

Requirements

Component	Minimum	Recommended
GPU VRAM	16 GB (480p with offload)	24 GB
System RAM	32 GB	64 GB
Disk	25 GB	50 GB
Python	3.10+	3.11
CUDA	12.1+	12.4

Clore.ai GPU recommendation: An RTX 4090 (24 GB, ~$0.5–2/day) is the sweet spot — enough VRAM for 720p generation at full precision. An RTX 3090 (24 GB, ~$0.3–1/day) works for 480p and offers the best price-per-clip ratio on the marketplace.

Quick Start

# Install core dependencies
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
pip install diffusers transformers accelerate sentencepiece
pip install imageio[ffmpeg]

# Verify GPU
python -c "import torch; print(torch.cuda.get_device_name(0))"

Usage Examples

Text-to-Video

import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

# SkyReels-V3 uses the Wan2.1 pipeline architecture
pipe = WanPipeline.from_pretrained(
    "SkyworkAI/SkyReels-V3-T2V",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

prompt = (
    "A samurai walking through a bamboo forest in morning fog, "
    "sunlight filtering through the tall stalks, cinematic composition, "
    "slow deliberate movement"
)

video_frames = pipe(
    prompt=prompt,
    negative_prompt="blurry, low quality, watermark, static",
    num_frames=97,               # ~4 sec at 24 fps
    width=1280,
    height=720,
    num_inference_steps=30,
    guidance_scale=5.0,
    generator=torch.Generator("cuda").manual_seed(42),
).frames[0]

export_to_video(video_frames, "samurai_forest.mp4", fps=24)
print("Saved samurai_forest.mp4")

Image-to-Video

import torch
from PIL import Image
from diffusers import WanImageToVideoPipeline
from diffusers.utils import export_to_video

pipe = WanImageToVideoPipeline.from_pretrained(
    "SkyworkAI/SkyReels-V3-I2V",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

image = Image.open("landscape.png").resize((1280, 720))

video_frames = pipe(
    prompt="Camera slowly pushes forward into the scene, clouds drift overhead",
    image=image,
    negative_prompt="static, jitter, blurry",
    num_frames=97,
    num_inference_steps=30,
    guidance_scale=5.0,
).frames[0]

export_to_video(video_frames, "landscape_anim.mp4", fps=24)

Lower-Resolution Fast Preview

import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

pipe = WanPipeline.from_pretrained(
    "SkyworkAI/SkyReels-V3-T2V", torch_dtype=torch.bfloat16
).to("cuda")

# 480p for quick iteration
frames = pipe(
    prompt="Ocean waves crashing on rocks, dramatic spray, sunset",
    num_frames=49,
    width=854,
    height=480,
    num_inference_steps=20,
    guidance_scale=5.0,
).frames[0]

export_to_video(frames, "waves_preview.mp4", fps=24)

Tips for Clore.ai Users

1. Use Wan pipeline classes — SkyReels-V3 is architecturally based on Wan2.1, so it uses WanPipeline / WanImageToVideoPipeline from diffusers.

2. Start at 480p — iterate on prompts at lower resolution first, then generate final clips at 720p once you're happy with the composition.

3. CPU offloading — enable_model_cpu_offload() is recommended on 24 GB cards for 720p generation to avoid OOM.

4. Persistent storage — set HF_HOME=/workspace/hf_cache on a Clore.ai persistent volume; the model weighs ~15–20 GB.

5. 24 fps native — do not change the export fps; the model's temporal attention was trained for 24 fps output.

6. Bilingual prompts — the Wan2.1 text encoder handles both English and Chinese; you can mix languages if needed.

7. Guidance scale — 4.0–6.0 works best. Higher values (>8) can cause oversaturation.

8. tmux is mandatory — always run generation in a tmux session on Clore.ai to survive SSH disconnects.

Troubleshooting

Problem	Fix
OutOfMemoryError at 720p	Enable pipe.enable_model_cpu_offload(); reduce to 480p if still OOM
Model not found on HuggingFace	Check exact repo name on SkyworkAI HF page — it may be listed under a variant name
Jittery or flickering motion	Increase num_inference_steps to 40; reduce guidance_scale to 4.0
Slow generation	~1–3 min per 4-sec clip on RTX 4090 is normal for 720p; 480p is roughly 2× faster
Color shift / oversaturation	Lower guidance_scale to 4.0–5.0
ImportError: imageio	pip install imageio[ffmpeg]
Weights re-download on restart	Mount persistent storage and set HF_HOME environment variable