import os
import torch
import random
import gradio as gr
from glob import glob
from omegaconf import OmegaConf
from safetensors import safe_open
from diffusers import AutoencoderKL
from diffusers import EulerDiscreteScheduler, DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationFreeInitPipeline
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 diffusers.training_utils import set_seed
from animatediff.utils.freeinit_utils import get_freq_filter
from collections import namedtuple
pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5"
inference_config_path = "configs/inference/inference-v1.yaml"
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
examples = [
# 1-ToonYou
[
"toonyou_beta3.safetensors",
"mm_sd_v14.ckpt",
"(best quality, masterpiece), close up, 1girl, red clothes, sitting, elf, pond, in water, deep forest, waterfall, looking away, blurry background",
"worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 2-Lyriel
[
"lyriel_v16.safetensors",
"mm_sd_v14.ckpt",
"hypercars cyberpunk moving, muted colors, swirling color smokes, legend, cityscape, space",
"3d, cartoon, anime, sketches, worst quality, low quality, nsfw, logo",
512, 512, "4954488479039740",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 3-RCNZ
[
"rcnzCartoon3d_v10.safetensors",
"mm_sd_v14.ckpt",
"A cute raccoon playing guitar in a boat on the ocean",
"worst quality, low quality, nsfw, logo",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 4-MajicMix
[
"majicmixRealistic_v5Preview.safetensors",
"mm_sd_v14.ckpt",
"1girl, reading book",
"bad hand, worst quality, low quality, normal quality, lowres, bad anatomy, bad hands, watermark, moles",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# # 5-RealisticVision
# [
# "realisticVisionV51_v20Novae.safetensors",
# "mm_sd_v14.ckpt",
# "A panda standing on a surfboard in the ocean in sunset.",
# "worst quality, low quality, nsfw, logo",
# 512, 512, "2005563494988190",
# "butterworth", 0.25, 0.25, 3,
# ["use_fp16"]
# ]
# 5-RealisticVision
[
"realisticVisionV51_v20Novae.safetensors",
"mm_sd_v14.ckpt",
"b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3",
"(semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
]
]
# clean unrelated ckpts
# ckpts = [
# "realisticVisionV40_v20Novae.safetensors",
# "majicmixRealistic_v5Preview.safetensors",
# "rcnzCartoon3d_v10.safetensors",
# "lyriel_v16.safetensors",
# "toonyou_beta3.safetensors"
# ]
# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
# for ckpt in ckpts:
# if path.endswith(ckpt): break
# else:
# print(f"### Cleaning {path} ...")
# os.system(f"rm -rf {path}")
# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")
# os.system(f"bash download_bashscripts/1-ToonYou.sh")
# os.system(f"bash download_bashscripts/2-Lyriel.sh")
# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
# os.system(f"bash download_bashscripts/4-MajicMix.sh")
# os.system(f"bash download_bashscripts/5-RealisticVision.sh")
# clean Gradio cache
print(f"### Cleaning cached examples ...")
os.system(f"rm -rf gradio_cached_examples/")
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")
os.makedirs(self.savedir, exist_ok=True)
self.base_model_list = []
self.motion_module_list = []
self.filter_type_list = [
"butterworth",
"gaussian",
"box",
"ideal"
]
self.selected_base_model = None
self.selected_motion_module = None
self.selected_filter_type = None
self.set_width = None
self.set_height = None
self.set_d_s = None
self.set_d_t = None
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.inference_config = OmegaConf.load(inference_config_path)
self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()
self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
self.freq_filter = None
self.update_base_model(self.base_model_list[-2])
self.update_motion_module(self.motion_module_list[0])
self.update_filter(512, 512, self.filter_type_list[0], 0.25, 0.25)
def refresh_motion_module(self):
motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
self.motion_module_list = sorted([os.path.basename(p) for p in motion_module_list])
def refresh_personalized_model(self):
base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
self.base_model_list = sorted([os.path.basename(p) for p in base_model_list])
def update_base_model(self, base_model_dropdown):
self.selected_base_model = base_model_dropdown
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_motion_module(self, motion_module_dropdown):
self.selected_motion_module = motion_module_dropdown
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")
_, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
assert len(unexpected) == 0
return gr.Dropdown.update()
# def update_filter(self, shape, method, n, d_s, d_t):
def update_filter(self, width_slider, height_slider, filter_type_dropdown, d_s_slider, d_t_slider):
self.set_width = width_slider
self.set_height = height_slider
self.selected_filter_type = filter_type_dropdown
self.set_d_s = d_s_slider
self.set_d_t = d_t_slider
vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
shape = [1, 4, 16, self.set_width//vae_scale_factor, self.set_height//vae_scale_factor]
self.freq_filter = get_freq_filter(
shape,
device="cuda",
filter_type=self.selected_filter_type,
n=4,
d_s=self.set_d_s,
d_t=self.set_d_t
)
def animate(
self,
base_model_dropdown,
motion_module_dropdown,
prompt_textbox,
negative_prompt_textbox,
width_slider,
height_slider,
seed_textbox,
# freeinit params
filter_type_dropdown,
d_s_slider,
d_t_slider,
num_iters_slider,
# speed up
speed_up_options
):
# set global seed
set_seed(42)
d_s = float(d_s_slider)
d_t = float(d_t_slider)
num_iters = int(num_iters_slider)
if self.selected_base_model != base_model_dropdown: self.update_base_model(base_model_dropdown)
if self.selected_motion_module != motion_module_dropdown: self.update_motion_module(motion_module_dropdown)
self.set_width = width_slider
self.set_height = height_slider
self.selected_filter_type = filter_type_dropdown
self.set_d_s = d_s
self.set_d_t = d_t
if self.set_width != width_slider or self.set_height != height_slider or self.selected_filter_type != filter_type_dropdown or self.set_d_s != d_s or self.set_d_t != d_t:
self.update_filter(width_slider, height_slider, filter_type_dropdown, d_s, d_t)
if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
pipeline = AnimationFreeInitPipeline(
vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
scheduler=DDIMScheduler(**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
).to("cuda")
# (freeinit) initialize frequency filter for noise reinitialization -------------
pipeline.freq_filter = self.freq_filter
# -------------------------------------------------------------------------------
if int(seed_textbox) > 0: seed = int(seed_textbox)
else: seed = random.randint(1, 1e16)
torch.manual_seed(int(seed))
assert seed == torch.initial_seed()
print(f"### seed: {seed}")
generator = torch.Generator(device="cuda")
generator.manual_seed(seed)
sample_output = pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
num_inference_steps = 25,
guidance_scale = 7.5,
width = width_slider,
height = height_slider,
video_length = 16,
num_iters = num_iters,
use_fast_sampling = True if "use_coarse_to_fine_sampling" in speed_up_options else False,
save_intermediate = False,
return_orig = True,
use_fp16 = True if "use_fp16" in speed_up_options else False
)
orig_sample = sample_output.orig_videos
sample = sample_output.videos
save_sample_path = os.path.join(self.savedir, f"sample.mp4")
save_videos_grid(sample, save_sample_path)
save_orig_sample_path = os.path.join(self.savedir, f"sample_orig.mp4")
save_videos_grid(orig_sample, save_orig_sample_path)
# save_compare_path = os.path.join(self.savedir, f"compare.mp4")
# save_videos_grid(torch.concat([orig_sample, sample]), save_compare_path)
json_config = {
"prompt": prompt_textbox,
"n_prompt": negative_prompt_textbox,
"width": width_slider,
"height": height_slider,
"seed": seed,
"base_model": base_model_dropdown,
"motion_module": motion_module_dropdown,
"filter_type": filter_type_dropdown,
"d_s": d_s,
"d_t": d_t,
"num_iters": num_iters,
"use_fp16": True if "use_fp16" in speed_up_options else False,
"use_coarse_to_fine_sampling": True if "use_coarse_to_fine_sampling" in speed_up_options else False
}
# return gr.Video.update(value=save_compare_path), gr.Json.update(value=json_config)
# return gr.Video.update(value=save_orig_sample_path), gr.Video.update(value=save_sample_path), gr.Video.update(value=save_compare_path), gr.Json.update(value=json_config)
return gr.Video.update(value=save_orig_sample_path), gr.Video.update(value=save_sample_path), gr.Json.update(value=json_config)
controller = AnimateController()
def ui():
with gr.Blocks(css=css) as demo:
# gr.Markdown('# FreeInit')
gr.Markdown(
"""
FreeInit
"""
)
gr.Markdown(
"""
"""
#
#
#
)
gr.Markdown(
"""
Official Gradio Demo for ***FreeInit: Bridging Initialization Gap in Video Diffusion Models***.
FreeInit improves time consistency of diffusion-based video generation at inference time. In this demo, we apply FreeInit on [AnimateDiff v1](https://github.com/guoyww/AnimateDiff) as an example.
"""
)
with gr.Row():
with gr.Column():
# gr.Markdown(
# """
# ### Usage
# 1. Select customized model and motion module in `Model Settings`.
# 3. Set `FreeInit Settings`.
# 3. Provide `Prompt` and `Negative Prompt` for your selected model. You can refer to each model's webpage on CivitAI to learn how to write prompts for them:
# - [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775)
# - [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel)
# - [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009)
# - [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068)
# - [`realisticVisionV20_v20.safetensors`](https://civitai.com/models/4201?modelVersionId=29460)
# 4. Click `Generate`.
# """
# )
prompt_textbox = gr.Textbox( label="Prompt", lines=3, placeholder="Enter your prompt here")
negative_prompt_textbox = gr.Textbox( label="Negative Prompt", lines=3, value="worst quality, low quality, nsfw, logo")
gr.Markdown(
"""
*Prompt Tips:*
For each personalized model in `Model Settings`, you can refer to their webpage on CivitAI to learn how to write good prompts for them:
- [`realisticVisionV51_v20Novae.safetensors`](https://civitai.com/models/4201?modelVersionId=29460)
- [`toonyou_beta3.safetensors`](https://civitai.com/models/30240?modelVersionId=78775)
- [`lyriel_v16.safetensors`](https://civitai.com/models/22922/lyriel)
- [`rcnzCartoon3d_v10.safetensors`](https://civitai.com/models/66347?modelVersionId=71009)
- [`majicmixRealistic_v5Preview.safetensors`](https://civitai.com/models/43331?modelVersionId=79068)
"""
)
with gr.Accordion("Model Settings", open=False):
gr.Markdown(
"""
Select personalized model and motion module for AnimateDiff.
"""
)
base_model_dropdown = gr.Dropdown( label="Base DreamBooth Model", choices=controller.base_model_list, value=controller.base_model_list[-2], interactive=True,
info="Select personalized text-to-image model from community")
motion_module_dropdown = gr.Dropdown( label="Motion Module", choices=controller.motion_module_list, value=controller.motion_module_list[0], interactive=True,
info="Select motion module. Recommend mm_sd_v14.ckpt for larger movements.")
base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])
motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
with gr.Accordion("FreeInit Params", open=False):
gr.Markdown(
"""
Adjust to control the smoothness.
"""
)
filter_type_dropdown = gr.Dropdown( label="Filter Type", choices=controller.filter_type_list, value=controller.filter_type_list[0], interactive=True,
info="Default as Butterworth. To fix large inconsistencies, consider using Gaussian.")
d_s_slider = gr.Slider( label="d_s", value=0.25, minimum=0, maximum=1, step=0.125,
info="Stop frequency for spatial dimensions (0.0-1.0)")
d_t_slider = gr.Slider( label="d_t", value=0.25, minimum=0, maximum=1, step=0.125,
info="Stop frequency for temporal dimension (0.0-1.0)")
# num_iters_textbox = gr.Textbox( label="FreeInit Iterations", value=3, info="Sould be integer >1, larger value leads to smoother results)")
num_iters_slider = gr.Slider( label="FreeInit Iterations", value=3, minimum=2, maximum=5, step=1,
info="Larger value leads to smoother results & longer inference time.")
with gr.Accordion("Advance", open=False):
with gr.Row():
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 )
with gr.Row():
seed_textbox = gr.Textbox( label="Seed", value=1566149281915957)
seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e16)), inputs=[], outputs=[seed_textbox])
with gr.Row():
speed_up_options = gr.CheckboxGroup(
["use_fp16", "use_coarse_to_fine_sampling"],
label="Speed-Up Options",
value=["use_fp16"]
)
generate_button = gr.Button( value="Generate", variant='primary' )
# with gr.Column():
# result_video = gr.Video( label="Generated Animation", interactive=False )
# json_config = gr.Json( label="Config", value=None )
with gr.Column():
with gr.Row():
orig_video = gr.Video( label="AnimateDiff", interactive=False )
freeinit_video = gr.Video( label="AnimateDiff + FreeInit", interactive=False )
# with gr.Row():
# compare_video = gr.Video( label="Compare", interactive=False )
with gr.Row():
json_config = gr.Json( label="Config", value=None )
inputs = [base_model_dropdown, motion_module_dropdown,
prompt_textbox, negative_prompt_textbox, width_slider, height_slider, seed_textbox,
filter_type_dropdown, d_s_slider, d_t_slider, num_iters_slider,
speed_up_options
]
# outputs = [result_video, json_config]
# outputs = [orig_video, freeinit_video, compare_video, json_config]
outputs = [orig_video, freeinit_video, json_config]
generate_button.click( fn=controller.animate, inputs=inputs, outputs=outputs )
gr.Examples( fn=controller.animate, examples=examples, inputs=inputs, outputs=outputs, cache_examples=True)
return demo
if __name__ == "__main__":
demo = ui()
demo.queue(max_size=20)
demo.launch()