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import gradio as gr
from text_to_animation.model import ControlAnimationModel
import os
from utils.hf_utils import get_model_list
huggingspace_name = os.environ.get("SPACE_AUTHOR_NAME")
on_huggingspace = huggingspace_name if huggingspace_name is not None else False
examples = [
["an astronaut waving the arm on the moon"],
["a sloth surfing on a wakeboard"],
["an astronaut walking on a street"],
["a cute cat walking on grass"],
["a horse is galloping on a street"],
["an astronaut is skiing down the hill"],
["a gorilla walking alone down the street"],
["a gorilla dancing on times square"],
["A panda dancing dancing like crazy on Times Square"],
]
images = [] # str path of generated images
initial_frame = None
animation_model = None
def generate_initial_frames(
frames_prompt,
model_link,
is_safetensor,
frames_n_prompt,
width,
height,
cfg_scale,
seed,
):
global images
if not model_link:
model_link = "dreamlike-art/dreamlike-photoreal-2.0"
images = animation_model.generate_initial_frames(
frames_prompt,
model_link,
is_safetensor,
frames_n_prompt,
width,
height,
cfg_scale,
seed,
)
return images
def select_initial_frame(evt: gr.SelectData):
global initial_frame
if evt.index < len(images):
initial_frame = images[evt.index]
print(initial_frame)
def create_demo(model: ControlAnimationModel):
global animation_model
animation_model = model
with gr.Blocks() as demo:
with gr.Column(visible=True) as frame_selection_col:
with gr.Row():
with gr.Column():
frames_prompt = gr.Textbox(
placeholder="Prompt", show_label=False, lines=4
)
frames_n_prompt = gr.Textbox(
placeholder="Negative Prompt (optional)",
show_label=False,
lines=2,
)
with gr.Column():
model_link = gr.Textbox(
label="Model Link",
placeholder="dreamlike-art/dreamlike-photoreal-2.0",
info="Give the hugging face model name or URL link to safetensor.",
)
is_safetensor = gr.Checkbox(label="Safetensors")
gen_frames_button = gr.Button(
value="Generate Initial Frames", variant="primary"
)
with gr.Row():
with gr.Column(scale=2):
width = gr.Slider(32, 2048, value=512, label="Width")
height = gr.Slider(32, 2048, value=512, label="Height")
cfg_scale = gr.Slider(1, 20, value=7.0, step=0.1, label="CFG scale")
seed = gr.Slider(
label="Seed",
info="-1 for random seed on each run. Otherwise, the seed will be fixed.",
minimum=-1,
maximum=65536,
value=0,
step=1,
)
with gr.Column(scale=3):
initial_frames = gr.Gallery(
label="Initial Frames", show_label=False
).style(
columns=[2], rows=[2], object_fit="scale-down", height="auto"
)
initial_frames.select(select_initial_frame)
select_frame_button = gr.Button(
value="Select Initial Frame", variant="secondary"
)
with gr.Column(visible=False) as gen_animation_col:
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt")
gen_animation_button = gr.Button(
value="Generate Animation", variant="primary"
)
with gr.Accordion("Advanced options", open=False):
n_prompt = gr.Textbox(
label="Negative Prompt (optional)", value=""
)
if on_huggingspace:
video_length = gr.Slider(
label="Video length", minimum=8, maximum=16, step=1
)
else:
video_length = gr.Number(
label="Video length", value=8, precision=0
)
seed = gr.Slider(
label="Seed",
info="-1 for random seed on each run. Otherwise, the seed will be fixed.",
minimum=-1,
maximum=65536,
value=0,
step=1,
)
motion_field_strength_x = gr.Slider(
label="Global Translation $\\delta_{x}$",
minimum=-20,
maximum=20,
value=12,
step=1,
)
motion_field_strength_y = gr.Slider(
label="Global Translation $\\delta_{y}$",
minimum=-20,
maximum=20,
value=12,
step=1,
)
t0 = gr.Slider(
label="Timestep t0",
minimum=0,
maximum=47,
value=44,
step=1,
info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1 ",
)
t1 = gr.Slider(
label="Timestep t1",
minimum=1,
info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1",
maximum=48,
value=47,
step=1,
)
chunk_size = gr.Slider(
label="Chunk size",
minimum=2,
maximum=16,
value=8,
step=1,
visible=not on_huggingspace,
info="Number of frames processed at once. Reduce for lower memory usage.",
)
merging_ratio = gr.Slider(
label="Merging ratio",
minimum=0.0,
maximum=0.9,
step=0.1,
value=0.0,
visible=not on_huggingspace,
info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference).",
)
with gr.Column():
result = gr.Video(label="Generated Video")
inputs = [
prompt,
model_link,
is_safetensor,
motion_field_strength_x,
motion_field_strength_y,
t0,
t1,
n_prompt,
chunk_size,
video_length,
merging_ratio,
seed,
]
# gr.Examples(examples=examples,
# inputs=inputs,
# outputs=result,
# fn=None,
# run_on_click=False,
# cache_examples=on_huggingspace,
# )
frame_inputs = [
frames_prompt,
model_link,
is_safetensor,
frames_n_prompt,
width,
height,
cfg_scale,
seed,
]
def submit_select():
show = True
if initial_frame is not None: # More to next step
return {
frame_selection_col: gr.update(visible=not show),
gen_animation_col: gr.update(visible=show),
}
return {
frame_selection_col: gr.update(visible=show),
gen_animation_col: gr.update(visible=not show),
}
gen_frames_button.click(
generate_initial_frames,
inputs=frame_inputs,
outputs=initial_frames,
)
select_frame_button.click(
submit_select, inputs=None, outputs=[frame_selection_col, gen_animation_col]
)
gen_animation_button.click(
fn=model.process_text2video,
inputs=inputs,
outputs=result,
)
return demo
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