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24
README.md
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@@ -100,21 +100,6 @@ python -m scripts.animate --config configs/prompts/7-FilmVelvia.yaml
python -m scripts.animate --config configs/prompts/8-GhibliBackground.yaml
```
### Optionally Run with WebUI
Configure yaml options, model settings & generate animations as desired.
```
python webui.py
python webui.py --server_port 7860 --share
python webui.py --server_port 7860 --share --username NAME --password PASS
```
<table class="center">
<tr>
<td><img src="__assets__/ui/first-tab.png"></td>
<td><img src="__assets__/ui/second-tab.png"></td>
</tr>
</table>
To generate animations with a new DreamBooth/LoRA model, you may create a new config `.yaml` file in the following format:
```
NewModel:
@@ -139,6 +124,15 @@ Then run the following commands:
python -m scripts.animate --config [path to the config file]
```
## Gradio Demo
We have created a Gradio demo to make AnimateDiff easier to use. To launch the demo, please run the following commands:
```
conda activate animatediff
python app.py
```
By default, the demo will run at `localhost:7860`.
<br><img src="__assets__/figs/gradio.jpg" style="width: 50em; margin-top: 1em">
## Gallery
Here we demonstrate several best results we found in our experiments.

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app.py Normal file
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@@ -0,0 +1,333 @@
import gradio as gr
import os
from glob import glob
import random
from diffusers import AutoencoderKL
from datetime import datetime
import os
from omegaconf import OmegaConf
import json
import torch
from diffusers import AutoencoderKL
from diffusers import DDIMScheduler, EulerDiscreteScheduler, PNDMScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationPipeline
from animatediff.utils.util import save_videos_grid
from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora
from diffusers.utils.import_utils import is_xformers_available
from safetensors import safe_open
sample_idx = 0
scheduler_dict = {
"Euler": EulerDiscreteScheduler,
"PNDM": PNDMScheduler,
"DDIM": DDIMScheduler,
}
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")
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/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):
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,
stable_diffusion_dropdown,
motion_module_dropdown,
base_model_dropdown,
lora_alpha_slider,
prompt_textbox,
negative_prompt_textbox,
sampler_dropdown,
sample_step_slider,
width_slider,
length_slider,
height_slider,
cfg_scale_slider,
seed_textbox
):
if self.unet is None:
raise gr.Error(f"Please select a pretrained model path.")
if motion_module_dropdown == "":
raise gr.Error(f"Please select a motion module.")
if base_model_dropdown == "":
raise gr.Error(f"Please select a base DreamBooth model.")
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")
if self.lora_model_state_dict != {}:
pipeline = convert_lora(pipeline, self.lora_model_state_dict, alpha=lora_alpha_slider)
pipeline.to("cuda")
if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
else: torch.seed()
seed = torch.initial_seed()
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
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()
def ui():
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)
Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>
[Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)
"""
)
with gr.Column(variant="panel"):
gr.Markdown(
"""
### 1. Model checkpoints (select pretrained model path first).
"""
)
with gr.Row():
stable_diffusion_dropdown = gr.Dropdown(
label="Pretrained Model Path",
choices=controller.stable_diffusion_list,
interactive=True,
)
stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown])
stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
def update_stable_diffusion():
controller.refresh_stable_diffusion()
return gr.Dropdown.update(choices=controller.stable_diffusion_list)
stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown])
with gr.Row():
motion_module_dropdown = gr.Dropdown(
label="Select motion module",
choices=controller.motion_module_list,
interactive=True,
)
motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
def update_motion_module():
controller.refresh_motion_module()
return gr.Dropdown.update(choices=controller.motion_module_list)
motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown])
base_model_dropdown = gr.Dropdown(
label="Select base Dreambooth model (required)",
choices=controller.personalized_model_list,
interactive=True,
)
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"] + controller.personalized_model_list,
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)
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 AnimateDiff.
"""
)
prompt_textbox = gr.Textbox(label="Prompt", lines=2)
negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2)
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=25, minimum=10, maximum=100, 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=8, maximum=24, step=1)
cfg_scale_slider = gr.Slider(label="CFG Scale", value=7.5, minimum=0, maximum=20)
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=[
stable_diffusion_dropdown,
motion_module_dropdown,
base_model_dropdown,
lora_alpha_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]
)
return demo
if __name__ == "__main__":
demo = ui()
demo.launch(share=True)

3
configs/inference/inference.yaml
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@@ -21,9 +21,6 @@ unet_additional_kwargs:
temporal_attention_dim_div: 1
noise_scheduler_kwargs:
num_train_timesteps: 1000
beta_start: 0.00085
beta_end: 0.012
beta_schedule: "linear"
steps_offset: 1
clip_sample: false

2
environment.yaml
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@@ -18,5 +18,5 @@ dependencies:
- einops
- omegaconf
- safetensors
- gradio
- pyyaml
- gradio=3.36.1