Spaces:
Runtime error
Runtime error
File size: 6,059 Bytes
2a37fe9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
import gradio as gr
import torch
from video_diffusion.tuneavideo.models.unet import UNet3DConditionModel
from video_diffusion.tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from video_diffusion.tuneavideo.util import save_videos_grid
from video_diffusion.utils.model_list import stable_model_list
video_diffusion_model_list = [
"Tune-A-Video-library/a-man-is-surfing",
"Tune-A-Video-library/mo-di-bear-guitar",
"Tune-A-Video-library/redshift-man-skiing",
]
class TunaVideoText2VideoGenerator:
def __init__(self):
self.pipe = None
self.unet = None
def load_model(self, video_diffusion_model_list, stable_model_list):
if self.pipe is None:
if self.unet is None:
self.unet = UNet3DConditionModel.from_pretrained(
video_diffusion_model_list, subfolder="unet", torch_dtype=torch.float16
).to("cuda")
self.pipe = TuneAVideoPipeline.from_pretrained(
stable_model_list, unet=self.unet, torch_dtype=torch.float16
)
self.pipe.to("cuda")
self.pipe.enable_xformers_memory_efficient_attention()
return self.pipe
def generate_video(
self,
video_diffusion_model: str,
stable_model_list: str,
prompt: str,
negative_prompt: str,
video_length: int,
height: int,
width: int,
num_inference_steps: int,
guidance_scale: int,
fps: int,
):
pipe = self.load_model(video_diffusion_model, stable_model_list)
video = pipe(
prompt,
negative_prompt=negative_prompt,
video_length=video_length,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
).videos
save_videos_grid(videos=video, path="output.gif", fps=fps)
return "output.gif"
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
tunevideo_video_diffusion_model_list = gr.Dropdown(
choices=video_diffusion_model_list,
label="Video Diffusion Model",
value=video_diffusion_model_list[0],
)
tunevideo_stable_model_list = gr.Dropdown(
choices=stable_model_list,
label="Stable Model List",
value=stable_model_list[0],
)
with gr.Row():
with gr.Column():
tunevideo_prompt = gr.Textbox(
lines=1,
placeholder="Prompt",
show_label=False,
)
tunevideo_video_length = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=10,
label="Video Length",
)
tunevideo_num_inference_steps = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Steps",
)
tunevideo_fps = gr.Slider(
minimum=1,
maximum=60,
step=1,
value=5,
label="Fps",
)
with gr.Row():
with gr.Column():
tunevideo_negative_prompt = gr.Textbox(
lines=1,
placeholder="Negative Prompt",
show_label=False,
)
tunevideo_guidance_scale = gr.Slider(
minimum=1,
maximum=15,
step=1,
value=7.5,
label="Guidance Scale",
)
tunevideo_height = gr.Slider(
minimum=1,
maximum=1280,
step=32,
value=512,
label="Height",
)
tunevideo_width = gr.Slider(
minimum=1,
maximum=1280,
step=32,
value=512,
label="Width",
)
tunevideo_generate = gr.Button(value="Generator")
with gr.Column():
tunevideo_output = gr.Video(label="Output")
tunevideo_generate.click(
fn=TunaVideoText2VideoGenerator().generate_video,
inputs=[
tunevideo_video_diffusion_model_list,
tunevideo_stable_model_list,
tunevideo_prompt,
tunevideo_negative_prompt,
tunevideo_video_length,
tunevideo_height,
tunevideo_width,
tunevideo_num_inference_steps,
tunevideo_guidance_scale,
tunevideo_fps,
],
outputs=tunevideo_output,
)
|