app.py

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/sd15_t2v_beta_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.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)
            del motion_module_state_dict
            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)
            del converted_unet_checkpoint
            del converted_vae_checkpoint
            del base_model_state_dict

            # 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()
    @torch.no_grad()
    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
    ):

        if is_xformers_available():
            self.unet.enable_xformers_memory_efficient_attention()

        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")

        original_state_dict = {k: v.cpu().clone() for k, v in pipeline.unet.state_dict().items() if "motion_modules." not in k}
        pipeline.unet = convert_lcm_lora(pipeline.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"):
            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,
            ).videos

        pipeline.unet.load_state_dict(original_state_dict,strict=False)
        del original_state_dict

        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("sd15_t2v_beta_motion.ckpt")
controller.update_base_model("realistic2.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)

            Related Models:
            [AnimateLCM-t2v](https://huggingface.co/wangfuyun/AnimateLCM): Personalized Text-to-Video Generation
            [AnimateLCM-SVD-xt](https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt): General Image-to-Video Generation
            [AnimateLCM-i2v](https://huggingface.co/wangfuyun/AnimateLCM-I2V): Personalized Image-to-Video Generation
            """
            
            '''
            Important Notes: 
            1. The generation speed is around few seconds. There is delay in the space.
            2. Increase the sampling step and cfg and set proper negative prompt if you want more fancy videos.
            '''
        )
        with gr.Column(variant="panel"):
            with gr.Row():

                base_model_dropdown = gr.Dropdown(
                    label="Select base Dreambooth model (required)",
                    choices=controller.personalized_model_list,
                    interactive=True,
                    value="realistic2.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="a boy holding a rabbit")
            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=6, minimum=1, maximum=25, step=1)

                    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=1.5, 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,
                ],
                outputs=[result_video]
            )
            
            examples = [
                [0.8, 0.8, "a boy is holding a rabbit", "bad quality", "LCM", 8, 512, 16, 512, 1.5, 123],
                [0.8, 0.8, "1girl smiling", "bad quality", "LCM", 4, 512, 16, 512, 1.5, 1233],
                [0.8, 0.8, "1girl,face,white background,", "bad quality", "LCM", 6, 512, 16, 512, 1.5, 1234],
                [0.8, 0.8, "clouds in the sky, best quality", "bad quality", "LCM", 4, 512, 16, 512, 1.5, 1234],
                
                
            ]
            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,
                ],
                outputs=[result_video],
                fn=controller.animate,
                cache_examples=True,
            )

    return demo



if __name__ == "__main__":
    demo = ui()
    # gr.close_all()
    # restart
    demo.queue(api_open=False)
    demo.launch()