README.md

---
pipeline_tag: text-to-video
---
AnimateDiff is a method that allows you to create videos using pre-existing Stable Diffusion Text to Image models.

Converted https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_mm.ckpt to Huggingface Diffusers format 
using Diffuser's convetion script (available https://github.com/huggingface/diffusers/blob/main/scripts/convert_animatediff_motion_module_to_diffusers.py)

The following example demonstrates how you can utilize the motion modules with an existing Stable Diffusion text to image model.


```python
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
from diffusers.utils import export_to_gif

# Load the motion adapter
adapter = MotionAdapter.from_pretrained("Warvito/animatediff-motion-adapter-v1-5-3")
# load SD 1.5 based finetuned model
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
scheduler = DDIMScheduler.from_pretrained(
    model_id,
    subfolder="scheduler",
    beta_schedule="linear",
    clip_sample=False,
    timestep_spacing="linspace",
    steps_offset=1
)

pipe.scheduler = scheduler

# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

output = pipe(
    prompt=(
        "masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
        "orange sky, warm lighting, fishing boats, ocean waves seagulls, "
        "rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
        "golden hour, coastal landscape, seaside scenery"
    ),
    negative_prompt="bad quality, worse quality",
    num_frames=16,
    guidance_scale=7.5,
    num_inference_steps=25,
    generator=torch.Generator("cpu").manual_seed(42),
)
frames = output.frames[0]
export_to_gif(frames, "animation.gif")
```