AnimateDiff v1.5.3
Stableby guoyww
Motion adapter module that plugs into any SD 1.5 checkpoint to generate short animated clips. Only 0.4B extra parameters on top of the SD 1.5 base model. Generates 16 frames at 8fps (2-second clips).
- Motion adapter — plugs into any SD 1.5 model
- Only 0.4B extra params
- 16 frames at 8fps
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for AnimateDiff v1.5.3 outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
VRAM by Scenario
VRAM estimates at FP16 and FP8 precision. FP8 uses ~40% less memory with minimal quality loss. Grade shows how well each GPU handles the generation workload.
FP16 (full precision)
| Scenario | VRAM | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 · 25 frames | 12.0 GB | S● | B● | F● | S● |
| 768×512 · 25 frames | 25.6 GB | B● | F● | F● | F● |
| 768×512 · 100 frames | 41.9 GB | F● | F● | F● | F● |
| 1280×720 · 25 frames | 47.5 GB | F● | F● | F● | F● |
FP8 (quantized — ~40% less VRAM)
| Scenario | VRAM | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 · 25 frames | 3.3 GB | S | S | S | S |
| 768×512 · 25 frames | 4.4 GB | S | S | S | S |
| 768×512 · 100 frames | 7.5 GB | S | S | B | S |
| 1280×720 · 25 frames | 8.6 GB | S | S | B | S |
Optimization Tips
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
Run with Python
from diffusers import AnimateDiffPipeline
import torch
pipe = AnimateDiffPipeline.from_pretrained(
"guoyww/animatediff-motion-adapter-v1-5-3",
torch_dtype=torch.float16
)
pipe.to("cuda")
frames = pipe(
prompt="your prompt here",
num_inference_steps=25,
guidance_scale=7.5,
num_frames=16,
).frames[0]
# Save frames or export as videoGet started
Setup instructions for running AnimateDiff v1.5.3 locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyMemory Breakdown
VRAM allocation for 25 frames at 768×512 on RTX 4090 24GB
Estimated Generation Time
25 frames at 768×512, 30 steps, FP16.
Sample Outputs
Available Formats & Downloads
Download AnimateDiff v1.5.3 in different precisions. Lower precision = less VRAM but slight quality loss.
| 格式 | 精度 | 大小 | 提供商 | |
|---|---|---|---|---|
| safetensors推荐 | FP16 | 1.5 GB | official | 下载 |
LoRA Ecosystem
Growing EcosystemAnimateDiff-specific motion LoRAs plus full SD 1.5 LoRA ecosystem for style. Motion LoRAs control camera movement and animation style.
Browse all LoRAs on CivitAIRelated Workflows
You might also like
Frequently asked questions
FAQ — AnimateDiff v1.5.3
How much VRAM does AnimateDiff v1.5.3 need for video?
AnimateDiff v1.5.3 (0.4B parameters) requires approximately 25.6 GB of VRAM at FP16 precision for generating 25 frames at 768×512. Video generation typically requires more VRAM than image generation due to temporal attention layers.
Can I run AnimateDiff v1.5.3 on RTX 4090?
AnimateDiff v1.5.3 can run on the RTX 4090 with sequential offloading, though video generation will be significantly slower than native fit.
How long does it take to generate a video with AnimateDiff v1.5.3?
On a reference GPU (RTX 4090 24GB), AnimateDiff v1.5.3 generates a 25-frame video at 768×512 in approximately ~45s at FP16 with 30 inference steps. Faster GPUs with higher memory bandwidth will reduce generation time.
What resolution and frame count does AnimateDiff v1.5.3 support?
AnimateDiff v1.5.3 supports up to 512×512 resolution and 16 frames per generation at 8 FPS. Higher resolutions and frame counts require proportionally more VRAM.
About AnimateDiff v1.5.3
See also