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import gradio as gr
import torch
import os
import spaces
import uuid
from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
from diffusers.utils import export_to_video
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from PIL import Image
# Constants
bases = {
"Cartoon": "frankjoshua/toonyou_beta6",
"Realistic": "emilianJR/epiCRealism",
"3d": "Lykon/DreamShaper",
"Anime": "Yntec/mistoonAnime2"
}
step_loaded = None
base_loaded = "Realistic"
motion_loaded = None
# CPU configuration
device = "cpu"
dtype = torch.float32
# Initialize pipeline for CPU
pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
# Safety checkers
from transformers import CLIPFeatureExtractor
feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
# Function
def generate_image(prompt, base="Realistic", motion="", step=8, progress=gr.Progress()):
global step_loaded
global base_loaded
global motion_loaded
print(prompt, base, step)
try:
if step_loaded != step:
repo = "ByteDance/AnimateDiff-Lightning"
ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
step_loaded = step
if base_loaded != base:
pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
base_loaded = base
if motion_loaded != motion:
pipe.unload_lora_weights()
if motion != "":
pipe.load_lora_weights(motion, adapter_name="motion")
pipe.set_adapters(["motion"], [0.7])
motion_loaded = motion
progress((0, step))
def progress_callback(i, t, z):
progress((i+1, step))
output = pipe(
prompt=prompt,
guidance_scale=1.2,
num_inference_steps=step,
callback=progress_callback,
callback_steps=1
)
name = str(uuid.uuid4()).replace("-", "")
path = f"/tmp/{name}.mp4"
export_to_video(output.frames[0], path, fps=10)
return path
except Exception as e:
print(f"Error during generation: {str(e)}")
return None
# Gradio Interface
with gr.Blocks(css="style.css") as demo:
gr.HTML(
"<h1><center>Textual Imagination : A Text To Video Synthesis</center></h1>"
)
with gr.Group():
with gr.Row():
prompt = gr.Textbox(
label='Prompt'
)
with gr.Row():
select_base = gr.Dropdown(
label='Base model',
choices=[
"Cartoon",
"Realistic",
"3d",
"Anime",
],
value=base_loaded,
interactive=True
)
select_motion = gr.Dropdown(
label='Motion',
choices=[
("Default", ""),
("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"),
("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"),
("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"),
("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"),
("Pan left", "guoyww/animatediff-motion-lora-pan-left"),
("Pan right", "guoyww/animatediff-motion-lora-pan-right"),
("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"),
("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"),
],
value="guoyww/animatediff-motion-lora-zoom-in",
interactive=True
)
select_step = gr.Dropdown(
label='Inference steps',
choices=[
('1-Step', 1),
('2-Step', 2),
('4-Step', 4),
('8-Step', 8),
],
value=4,
interactive=True
)
submit = gr.Button(
scale=1,
variant='primary'
)
video = gr.Video(
label='AnimateDiff-Lightning',
autoplay=True,
height=512,
width=512,
elem_id="video_output"
)
gr.on(triggers=[
submit.click,
prompt.submit
],
fn = generate_image,
inputs = [prompt, select_base, select_motion, select_step],
outputs = [video],
api_name = "instant_video",
queue = False
)
demo.queue().launch() |