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# this is the huggingface handler file
from animatediff.pipelines.pipeline_animation import AnimationPipeline
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from omegaconf import OmegaConf
from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationPipeline
from animatediff.utils.util import save_videos_grid
from animatediff.utils.util import load_weights
from diffusers.utils.import_utils import is_xformers_available
from typing import Any
import torch
from einops import rearrange
import torchvision
import numpy as np
class EndpointHandler():
def __init__(self, model_path: str = "models/StableDiffusion/", inference_config_path: str = "configs/inference/inference-v3.yaml", motion_module: str = "models/Motion_Module/mm_sd_v15.ckpt"):
inference_config = OmegaConf.load(inference_config_path)
### >>> create validation pipeline >>> ###
tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(model_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(model_path, subfolder="vae")
unet = UNet3DConditionModel.from_pretrained_2d(model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
if is_xformers_available(): unet.enable_xformers_memory_efficient_attention()
else: assert False
self.pipeline = AnimationPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
).to("cuda")
self.pipeline = load_weights(
self.pipeline,
# motion module
motion_module_path = motion_module,
motion_module_lora_configs = [],
# image layers
dreambooth_model_path = "",
lora_model_path = "",
lora_alpha = 0.8,
).to("cuda")
def initialize(self, context):
"""
Initialize model. This will be called during model loading time
"""
def preprocess(self, data):
"""
preprocess will be called once for each request.
"""
def __call__(self, prompt, negative_prompt, steps, guidance_scale):
"""
__call__ method will be called once per request. This can be used to
run inference.
"""
vids = self.pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=steps,
guidance_scale=guidance_scale,
width= 256,
height= 256,
video_length= 5,
).videos
videos = rearrange(vids, "b c t h w -> t b c h w")
n_rows=6
fps=1
loop = True
rescale=False
outputs = []
for x in videos:
x = torchvision.utils.make_grid(x, nrow=n_rows)
x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
if rescale:
x = (x + 1.0) / 2.0 # -1,1 -> 0,1
x = (x * 255).numpy().astype(np.uint8)
outputs.append(x)
# imageio.mimsave(path, outputs, fps=fps)
# return a gif file as bytes
return outputs |