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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" |
), |
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") |
masterpiece, bestquality, sunset. |
Using Motion LoRAs with PEFT You can also leverage the PEFT backend to combine Motion LoRA’s and create more complex animations. First install PEFT with Copied pip install peft Then you can use the following code to combine Motion LoRAs. 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( |
"diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out", |
) |
pipe.load_lora_weights( |
"diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left", |
) |
pipe.set_adapters(["zoom-out", "pan-left"], adapter_weights=[1.0, 1.0]) |
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 |
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