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app-i2v.py
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app-i2v.py
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import os
import json
import torch
import random
import gradio as gr
from glob import glob
from omegaconf import OmegaConf
from datetime import datetime
from safetensors import safe_open
from diffusers import AutoencoderKL
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from animatelcm.scheduler.lcm_scheduler import LCMScheduler
from animatelcm.models.unet import UNet3DConditionModel
from animatelcm.pipelines.pipeline_animation import AnimationPipeline
from animatelcm.utils.util import save_videos_grid
from animatelcm.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from animatelcm.utils.convert_lora_safetensor_to_diffusers import convert_lora
from animatelcm.utils.lcm_utils import convert_lcm_lora
import copy
sample_idx = 0
scheduler_dict = {
"LCM": LCMScheduler,
}
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
class AnimateController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(
self.basedir, "models", "StableDiffusion")
self.motion_module_dir = os.path.join(
self.basedir, "models", "Motion_Module")
self.personalized_model_dir = os.path.join(
self.basedir, "models", "DreamBooth_LoRA")
self.savedir = os.path.join(
self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
self.savedir_sample = os.path.join(self.savedir, "sample")
self.lcm_lora_path = "models/LCM_LoRA/AnimateLCM_sd15_i2v_lora.safetensors"
os.makedirs(self.savedir, exist_ok=True)
self.stable_diffusion_list = []
self.motion_module_list = []
self.personalized_model_list = []
self.refresh_stable_diffusion()
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.tokenizer = None
self.text_encoder = None
self.vae = None
self.unet = None
self.pipeline = None
self.lora_model_state_dict = {}
self.inference_config = OmegaConf.load("configs/inference-i2v.yaml")
def refresh_stable_diffusion(self):
self.stable_diffusion_list = glob(
os.path.join(self.stable_diffusion_dir, "*/"))
def refresh_motion_module(self):
motion_module_list = glob(os.path.join(
self.motion_module_dir, "*.ckpt"))
self.motion_module_list = [
os.path.basename(p) for p in motion_module_list]
def refresh_personalized_model(self):
personalized_model_list = glob(os.path.join(
self.personalized_model_dir, "*.safetensors"))
self.personalized_model_list = [
os.path.basename(p) for p in personalized_model_list]
def update_stable_diffusion(self, stable_diffusion_dropdown):
stable_diffusion_dropdown = os.path.join(self.stable_diffusion_dir,stable_diffusion_dropdown)
self.tokenizer = CLIPTokenizer.from_pretrained(
stable_diffusion_dropdown, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(
stable_diffusion_dropdown, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(
stable_diffusion_dropdown, subfolder="vae").cuda()
self.unet = UNet3DConditionModel.from_pretrained_2d(
stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
return gr.Dropdown.update()
def update_motion_module(self, motion_module_dropdown):
if self.unet is None:
gr.Info(f"Please select a pretrained model path.")
return gr.Dropdown.update(value=None)
else:
motion_module_dropdown = os.path.join(
self.motion_module_dir, motion_module_dropdown)
motion_module_state_dict = torch.load(
motion_module_dropdown, map_location="cpu")
missing, unexpected = self.unet.load_state_dict(
motion_module_state_dict, strict=False)
assert len(unexpected) == 0
return gr.Dropdown.update()
def update_base_model(self, base_model_dropdown):
if self.unet is None:
gr.Info(f"Please select a pretrained model path.")
return gr.Dropdown.update(value=None)
else:
base_model_dropdown = os.path.join(
self.personalized_model_dir, base_model_dropdown)
base_model_state_dict = {}
with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
for key in f.keys():
base_model_state_dict[key] = f.get_tensor(key)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
base_model_state_dict, self.vae.config)
self.vae.load_state_dict(converted_vae_checkpoint)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
base_model_state_dict, self.unet.config)
self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
return gr.Dropdown.update()
def update_lora_model(self, lora_model_dropdown):
lora_model_dropdown = os.path.join(
self.personalized_model_dir, lora_model_dropdown)
self.lora_model_state_dict = {}
if lora_model_dropdown == "none":
pass
else:
with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f:
for key in f.keys():
self.lora_model_state_dict[key] = f.get_tensor(key)
return gr.Dropdown.update()
def animate(
self,
lora_alpha_slider,
spatial_lora_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
width_slider,
length_slider,
height_slider,
cfg_scale_slider,
seed_textbox,
image_upload,
beta_end_slider,
motion_scale_slider,
):
print(image_upload)
if is_xformers_available():
self.unet.enable_xformers_memory_efficient_attention()
print(self.inference_config.noise_scheduler_kwargs["beta_end"])
self.inference_config.noise_scheduler_kwargs["beta_end"] = beta_end_slider
self.unet.img_encoder.motion_scale = motion_scale_slider
pipeline = AnimationPipeline(
vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
scheduler=scheduler_dict[sampler_dropdown](
**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
).to("cuda")
if self.lora_model_state_dict != {}:
pipeline = convert_lora(
pipeline, self.lora_model_state_dict, alpha=lora_alpha_slider)
pipeline.unet = convert_lcm_lora(copy.deepcopy(
self.unet), self.lcm_lora_path, spatial_lora_slider)
pipeline.to("cuda")
if seed_textbox != -1 and seed_textbox != "":
torch.manual_seed(int(seed_textbox))
else:
torch.seed()
seed = torch.initial_seed()
with torch.autocast("cuda",torch.float16):
sample = pipeline(
prompt_textbox,
negative_prompt=negative_prompt_textbox,
num_inference_steps=sample_step_slider,
guidance_scale=cfg_scale_slider,
width=width_slider,
height=height_slider,
video_length=length_slider,
image_path=image_upload
).videos
save_sample_path = os.path.join(
self.savedir_sample, f"{sample_idx}.mp4")
save_videos_grid(sample, save_sample_path)
sample_config = {
"prompt": prompt_textbox,
"n_prompt": negative_prompt_textbox,
"sampler": sampler_dropdown,
"num_inference_steps": sample_step_slider,
"guidance_scale": cfg_scale_slider,
"width": width_slider,
"height": height_slider,
"video_length": length_slider,
"seed": seed,
}
json_str = json.dumps(sample_config, indent=4)
with open(os.path.join(self.savedir, "logs.json"), "a") as f:
f.write(json_str)
f.write("\n\n")
return gr.Video.update(value=save_sample_path)
controller = AnimateController()
controller.update_stable_diffusion("stable-diffusion-v1-5")
controller.update_motion_module("AnimateLCM_sd15_i2v.ckpt")
controller.update_base_model("realistic1.safetensors")
def ui():
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# [AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning](https://arxiv.org/abs/2402.00769)
Fu-Yun Wang, Zhaoyang Huang (*Corresponding Author), Xiaoyu Shi, Weikang Bian, Guanglu Song, Yu Liu, Hongsheng Li (*Corresponding Author)<br>
[arXiv Report](https://arxiv.org/abs/2402.00769) | [Project Page](https://animatelcm.github.io/) | [Github](https://github.com/G-U-N/AnimateLCM) | [Civitai](https://civitai.com/models/290375/animatelcm-fast-video-generation) | [Replicate](https://replicate.com/camenduru/animate-lcm)
"""
'''
Important Notes:
1. The generation speed is around few seconds.
2. Increase the sampling step for better sample quality.
'''
)
with gr.Column(variant="panel"):
with gr.Row():
image_upload = gr.Image(label="Upload Image", tool="select", type="filepath")
base_model_dropdown = gr.Dropdown(
label="Select base Dreambooth model (required)",
choices=controller.personalized_model_list,
interactive=True,
value="realistic1.safetensors"
)
base_model_dropdown.change(fn=controller.update_base_model, inputs=[
base_model_dropdown], outputs=[base_model_dropdown])
lora_model_dropdown = gr.Dropdown(
label="Select LoRA model (optional)",
choices=["none"],
value="none",
interactive=True,
)
lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[
lora_model_dropdown], outputs=[lora_model_dropdown])
lora_alpha_slider = gr.Slider(
label="LoRA alpha", value=0.8, minimum=0, maximum=2, interactive=True)
spatial_lora_slider = gr.Slider(
label="LCM LoRA alpha", value=0.8, minimum=0.0, maximum=1.0, interactive=True)
personalized_refresh_button = gr.Button(
value="\U0001F503", elem_classes="toolbutton")
def update_personalized_model():
controller.refresh_personalized_model()
return [
gr.Dropdown.update(
choices=controller.personalized_model_list),
gr.Dropdown.update(
choices=["none"] + controller.personalized_model_list)
]
personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[
base_model_dropdown, lora_model_dropdown])
with gr.Column(variant="panel"):
gr.Markdown(
"""
### 2. Configs for AnimateLCM.
"""
)
prompt_textbox = gr.Textbox(label="Prompt", lines=2, value="best quality")
negative_prompt_textbox = gr.Textbox(
label="Negative prompt", lines=2, value="bad quality")
with gr.Row().style(equal_height=False):
with gr.Column():
with gr.Row():
sampler_dropdown = gr.Dropdown(label="Sampling method", choices=list(
scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
sample_step_slider = gr.Slider(
label="Sampling steps", value=4, minimum=1, maximum=25, step=1)
motion_scale_slider = gr.Slider(
label="motion scale (better identity with smaller scale)", value=0.8, minimum=0.0, maximum=1.5, step=0.05
)
beta_end_slider = gr.Slider(
label="beta end (a tricky way for selecting noisy steps)", value=0.014, minimum=0.012, maximum=0.016, step=0.001)
width_slider = gr.Slider(
label="Width", value=512, minimum=256, maximum=1024, step=64)
height_slider = gr.Slider(
label="Height", value=512, minimum=256, maximum=1024, step=64)
length_slider = gr.Slider(
label="Animation length", value=16, minimum=12, maximum=20, step=1)
cfg_scale_slider = gr.Slider(
label="CFG Scale", value=1, minimum=1, maximum=2)
with gr.Row():
seed_textbox = gr.Textbox(label="Seed", value=-1)
seed_button = gr.Button(
value="\U0001F3B2", elem_classes="toolbutton")
seed_button.click(fn=lambda: gr.Textbox.update(
value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox])
generate_button = gr.Button(
value="Generate", variant='primary')
result_video = gr.Video(
label="Generated Animation", interactive=False)
generate_button.click(
fn=controller.animate,
inputs=[
lora_alpha_slider,
spatial_lora_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
width_slider,
length_slider,
height_slider,
cfg_scale_slider,
seed_textbox,
image_upload,
beta_end_slider,
motion_scale_slider,
],
outputs=[result_video]
)
examples = [
[0.8, 0.8, "good quality, cloud", "bad quality", "LCM", 4, 768, 16, 512, 1, 1234, "test_imgs/cloud.jpeg", 0.014, 0.8],
[0.8, 0.8, "good quality, dog", "bad quality", "LCM", 4, 768, 16, 512, 1, 1234, "test_imgs/dog.jpg", 0.014, 0.8],
[0.8, 0.8, "good quality, fire", "bad quality", "LCM", 4, 768, 16, 512, 1, 1234, "test_imgs/fire.jpg", 0.014, 0.8],
[0.8, 0.8, "good quality, fox, snow", "bad quality", "LCM", 4, 768, 16, 512, 1, 1234, "test_imgs/fox.jpg", 0.014, 0.8],
[0.8, 0.8, "good quality, girl, wind, flower", "bad quality", "LCM", 4, 768, 16, 512, 1, 1235, "test_imgs/girl_flower.jpg", 0.014, 0.8],
[0.8, 0.8, "good quality, lighter, fire", "bad quality", "LCM", 4, 768, 16, 512, 1, 1235, "test_imgs/lighter.jpg", 0.014, 0.8],
[0.8, 0.8, "good quality, snowman, fire", "bad quality", "LCM", 4, 768, 16, 512, 1, 1234, "test_imgs/snow_man_fire.jpg", 0.014, 0.8],
]
gr.Examples(
examples = examples,
inputs=[
lora_alpha_slider,
spatial_lora_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
width_slider,
length_slider,
height_slider,
cfg_scale_slider,
seed_textbox,
image_upload,
beta_end_slider,
motion_scale_slider,
],
outputs=[result_video],
fn=controller.animate,
cache_examples=True,
)
return demo
if __name__ == "__main__":
demo = ui()
demo.queue(concurrency_count=3, max_size=20)
demo.launch(share=True, server_name="127.0.0.1")