text stringlengths 0 5.54k |
|---|
frames = output.frames[0] |
export_to_gif(frames, "animation.gif") |
Here are some sample outputs: masterpiece, bestquality, sunset. |
AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting clip_sample=False in the scheduler as this can also have an adverse effect on generated samples. Additionally, the AnimateDiff checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to linear. AnimateDiffVideoToVideoPipeline AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities. Copied import imageio |
import requests |
import torch |
from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter |
from diffusers.utils import export_to_gif |
from io import BytesIO |
from PIL import Image |
# Load the motion adapter |
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) |
# load SD 1.5 based finetuned model |
model_id = "SG161222/Realistic_Vision_V5.1_noVAE" |
pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda") |
scheduler = DDIMScheduler.from_pretrained( |
model_id, |
subfolder="scheduler", |
clip_sample=False, |
timestep_spacing="linspace", |
beta_schedule="linear", |
steps_offset=1, |
) |
pipe.scheduler = scheduler |
# enable memory savings |
pipe.enable_vae_slicing() |
pipe.enable_model_cpu_offload() |
# helper function to load videos |
def load_video(file_path: str): |
images = [] |
if file_path.startswith(('http://', 'https://')): |
# If the file_path is a URL |
response = requests.get(file_path) |
response.raise_for_status() |
content = BytesIO(response.content) |
vid = imageio.get_reader(content) |
else: |
# Assuming it's a local file path |
vid = imageio.get_reader(file_path) |
for frame in vid: |
pil_image = Image.fromarray(frame) |
images.append(pil_image) |
return images |
video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif") |
output = pipe( |
video = video, |
prompt="panda playing a guitar, on a boat, in the ocean, high quality", |
negative_prompt="bad quality, worse quality", |
guidance_scale=7.5, |
num_inference_steps=25, |
strength=0.5, |
generator=torch.Generator("cpu").manual_seed(42), |
) |
frames = output.frames[0] |
export_to_gif(frames, "animation.gif") Here are some sample outputs: Source Video Output Video raccoon playing a guitar |
panda playing a guitar |
closeup of margot robbie, fireworks in the background, high quality |
closeup of tony stark, robert downey jr, fireworks |
Using Motion LoRAs Motion LoRAs are a collection of LoRAs that work with the guoyww/animatediff-motion-adapter-v1-5-2 checkpoint. These LoRAs are responsible for adding specific types of motion to the animations. Copied import torch |
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter |
from diffusers.utils import export_to_gif |
# Load the motion adapter |
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) |
# load SD 1.5 based finetuned model |
model_id = "SG161222/Realistic_Vision_V5.1_noVAE" |
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16) |
pipe.load_lora_weights( |
"guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out" |
) |
scheduler = DDIMScheduler.from_pretrained( |
model_id, |
subfolder="scheduler", |
clip_sample=False, |
beta_schedule="linear", |
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" |
), |
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