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app.py
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app.py
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import os
import json
import torch
import random
import requests
from PIL import Image
import numpy as np
import gradio as gr
from datetime import datetime
import torchvision.transforms as T
from diffusers import DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from consisti2v.pipelines.pipeline_conditional_animation import ConditionalAnimationPipeline
from consisti2v.utils.util import save_videos_grid
from omegaconf import OmegaConf
sample_idx = 0
scheduler_dict = {
"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.savedir = os.path.join(self.basedir, "samples/Gradio", datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
self.savedir_sample = os.path.join(self.savedir, "sample")
os.makedirs(self.savedir, exist_ok=True)
self.image_resolution = (256, 256)
# config models
self.pipeline = ConditionalAnimationPipeline.from_pretrained("TIGER-Lab/ConsistI2V", torch_dtype=torch.float16,)
self.pipeline.to("cuda")
def update_textbox_and_save_image(self, input_image, height_slider, width_slider, center_crop):
pil_image = Image.fromarray(input_image.astype(np.uint8)).convert("RGB")
img_path = os.path.join(self.savedir, "input_image.png")
pil_image.save(img_path)
self.image_resolution = pil_image.size
original_width, original_height = pil_image.size
if center_crop:
crop_aspect_ratio = width_slider / height_slider
aspect_ratio = original_width / original_height
if aspect_ratio > crop_aspect_ratio:
new_width = int(crop_aspect_ratio * original_height)
left = (original_width - new_width) / 2
top = 0
right = left + new_width
bottom = original_height
pil_image = pil_image.crop((left, top, right, bottom))
elif aspect_ratio < crop_aspect_ratio:
new_height = int(original_width / crop_aspect_ratio)
top = (original_height - new_height) / 2
left = 0
right = original_width
bottom = top + new_height
pil_image = pil_image.crop((left, top, right, bottom))
pil_image = pil_image.resize((width_slider, height_slider))
return gr.Textbox.update(value=img_path), gr.Image.update(value=np.array(pil_image))
def animate(
self,
prompt_textbox,
negative_prompt_textbox,
input_image_path,
sampler_dropdown,
sample_step_slider,
width_slider,
height_slider,
txt_cfg_scale_slider,
img_cfg_scale_slider,
center_crop,
frame_stride,
use_frameinit,
frame_init_noise_level,
seed_textbox
):
if self.pipeline is None:
raise gr.Error(f"Please select a pretrained pipeline path.")
if input_image_path == "":
raise gr.Error(f"Please upload an input image.")
if (not center_crop) and (width_slider % 8 != 0 or height_slider % 8 != 0):
raise gr.Error(f"`height` and `width` have to be divisible by 8 but are {height_slider} and {width_slider}.")
if center_crop and (width_slider % 8 != 0 or height_slider % 8 != 0):
raise gr.Error(f"`height` and `width` (after cropping) have to be divisible by 8 but are {height_slider} and {width_slider}.")
if is_xformers_available() and int(torch.__version__.split(".")[0]) < 2: self.pipeline.unet.enable_xformers_memory_efficient_attention()
if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
else: torch.seed()
seed = torch.initial_seed()
if input_image_path.startswith("http://") or input_image_path.startswith("https://"):
first_frame = Image.open(requests.get(input_image_path, stream=True).raw).convert('RGB')
else:
first_frame = Image.open(input_image_path).convert('RGB')
original_width, original_height = first_frame.size
if not center_crop:
img_transform = T.Compose([
T.ToTensor(),
T.Resize((height_slider, width_slider), antialias=None),
T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
else:
aspect_ratio = original_width / original_height
crop_aspect_ratio = width_slider / height_slider
if aspect_ratio > crop_aspect_ratio:
center_crop_width = int(crop_aspect_ratio * original_height)
center_crop_height = original_height
elif aspect_ratio < crop_aspect_ratio:
center_crop_width = original_width
center_crop_height = int(original_width / crop_aspect_ratio)
else:
center_crop_width = original_width
center_crop_height = original_height
img_transform = T.Compose([
T.ToTensor(),
T.CenterCrop((center_crop_height, center_crop_width)),
T.Resize((height_slider, width_slider), antialias=None),
T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
])
first_frame = img_transform(first_frame).unsqueeze(0)
first_frame = first_frame.to("cuda")
if use_frameinit:
self.pipeline.init_filter(
width = width_slider,
height = height_slider,
video_length = 16,
filter_params = OmegaConf.create({'method': 'gaussian', 'd_s': 0.25, 'd_t': 0.25,})
)
sample = self.pipeline(
prompt_textbox,
negative_prompt = negative_prompt_textbox,
first_frames = first_frame,
num_inference_steps = sample_step_slider,
guidance_scale_txt = txt_cfg_scale_slider,
guidance_scale_img = img_cfg_scale_slider,
width = width_slider,
height = height_slider,
video_length = 16,
noise_sampling_method = "pyoco_mixed",
noise_alpha = 1.0,
frame_stride = frame_stride,
use_frameinit = use_frameinit,
frameinit_noise_level = frame_init_noise_level,
camera_motion = None,
).videos
global sample_idx
sample_idx += 1
save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4")
save_videos_grid(sample, save_sample_path, format="mp4")
sample_config = {
"prompt": prompt_textbox,
"n_prompt": negative_prompt_textbox,
"first_frame_path": input_image_path,
"sampler": sampler_dropdown,
"num_inference_steps": sample_step_slider,
"guidance_scale_text": txt_cfg_scale_slider,
"guidance_scale_image": img_cfg_scale_slider,
"width": width_slider,
"height": height_slider,
"video_length": 8,
"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(
"""
# ConsistI2V Text+Image to Video Generation
Input image will be used as the first frame of the video. Text prompts will be used to control the output video content.
"""
)
with gr.Column(variant="panel"):
gr.Markdown(
"""
- Input image can be specified using the "Input Image Path/URL" text box (this can be either a local image path or an image URL) or uploaded by clicking or dragging the image to the "Input Image" box. The uploaded image will be temporarily stored in the "samples/Gradio" folder under the project root folder.
- Input image can be resized and/or center cropped to a given resolution by adjusting the "Width" and "Height" sliders. It is recommended to use the same resolution as the training resolution (256x256).
- After setting the input image path or changed the width/height of the input image, press the "Preview" button to visualize the resized input image.
"""
)
with gr.Row():
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=50, minimum=10, maximum=250, step=1)
with gr.Row():
center_crop = gr.Checkbox(label="Center Crop the Image", value=True)
width_slider = gr.Slider(label="Width", value=256, minimum=0, maximum=512, step=64)
height_slider = gr.Slider(label="Height", value=256, minimum=0, maximum=512, step=64)
with gr.Row():
txt_cfg_scale_slider = gr.Slider(label="Text CFG Scale", value=7.5, minimum=1.0, maximum=20.0, step=0.5)
img_cfg_scale_slider = gr.Slider(label="Image CFG Scale", value=1.0, minimum=1.0, maximum=20.0, step=0.5)
frame_stride = gr.Slider(label="Frame Stride", value=3, minimum=1, maximum=5, step=1)
with gr.Row():
use_frameinit = gr.Checkbox(label="Enable FrameInit", value=True)
frameinit_noise_level = gr.Slider(label="FrameInit Noise Level", value=850, minimum=1, maximum=999, step=1)
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')
with gr.Column():
with gr.Row():
input_image_path = gr.Textbox(label="Input Image Path/URL", lines=1, scale=10, info="Press Enter or the Preview button to confirm the input image.")
preview_button = gr.Button(value="Preview")
with gr.Row():
input_image = gr.Image(label="Input Image", interactive=True)
input_image.upload(fn=controller.update_textbox_and_save_image, inputs=[input_image, height_slider, width_slider, center_crop], outputs=[input_image_path, input_image])
result_video = gr.Video(label="Generated Animation", interactive=False, autoplay=True)
def update_and_resize_image(input_image_path, height_slider, width_slider, center_crop):
if input_image_path.startswith("http://") or input_image_path.startswith("https://"):
pil_image = Image.open(requests.get(input_image_path, stream=True).raw).convert('RGB')
else:
pil_image = Image.open(input_image_path).convert('RGB')
controller.image_resolution = pil_image.size
original_width, original_height = pil_image.size
if center_crop:
crop_aspect_ratio = width_slider / height_slider
aspect_ratio = original_width / original_height
if aspect_ratio > crop_aspect_ratio:
new_width = int(crop_aspect_ratio * original_height)
left = (original_width - new_width) / 2
top = 0
right = left + new_width
bottom = original_height
pil_image = pil_image.crop((left, top, right, bottom))
elif aspect_ratio < crop_aspect_ratio:
new_height = int(original_width / crop_aspect_ratio)
top = (original_height - new_height) / 2
left = 0
right = original_width
bottom = top + new_height
pil_image = pil_image.crop((left, top, right, bottom))
pil_image = pil_image.resize((width_slider, height_slider))
return gr.Image.update(value=np.array(pil_image))
preview_button.click(fn=update_and_resize_image, inputs=[input_image_path, height_slider, width_slider, center_crop], outputs=[input_image])
input_image_path.submit(fn=update_and_resize_image, inputs=[input_image_path, height_slider, width_slider, center_crop], outputs=[input_image])
generate_button.click(
fn=controller.animate,
inputs=[
prompt_textbox,
negative_prompt_textbox,
input_image_path,
sampler_dropdown,
sample_step_slider,
width_slider,
height_slider,
txt_cfg_scale_slider,
img_cfg_scale_slider,
center_crop,
frame_stride,
use_frameinit,
frameinit_noise_level,
seed_textbox,
],
outputs=[result_video]
)
return demo
if __name__ == "__main__":
demo = ui()
demo.launch(share=True)