Spaces:
Runtime error
Runtime error
File size: 3,990 Bytes
5425743 36961e5 5425743 36961e5 3da13dc 0e92ec6 36961e5 80fa73a 5425743 d8f08b3 5425743 7e48556 5425743 d8f08b3 ddac078 0c0ea5a d8f08b3 5425743 c226973 5425743 27ad720 5425743 27ad720 5425743 27ad720 5425743 c226973 5425743 59cc0d3 5425743 d8f08b3 5425743 0c0ea5a 3da13dc 5425743 843d100 3da13dc 5425743 b8d1d45 5425743 |
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 |
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.util import save_videos_grid
import torch
import gradio as gr
from bs4 import BeautifulSoup
import requests
def model_url_list():
url_list = []
for i in range(0, 7):
url_list.append(f"https://huggingface.co/models?p={i}&sort=downloads&search=sd-dreambooth-library")
return url_list
def data_scraping(url_list):
model_list = []
for url in url_list:
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
div_class = 'grid gap-5 grid-cols-1 2xl:grid-cols-2'
div = soup.find('div', {'class': div_class})
for a in div.find_all('a', href=True):
model_list.append(a['href'])
return model_list
model_list = data_scraping(model_url_list())
for i in range(len(model_list)):
model_list[i] = model_list[i][1:]
def tune_video_predict(
pipe_id: str,
prompt: str,
video_length: int,
height: int,
width: int,
num_inference_steps: int,
guidance_scale: float,
):
unet = UNet3DConditionModel.from_pretrained("Tune-A-Video-library/a-man-is-surfing", subfolder='unet', torch_dtype=torch.float16).to('cuda')
pipe = TuneAVideoPipeline.from_pretrained(pipe_id, unet=unet, torch_dtype=torch.float16).to("cuda")
video = pipe(prompt, video_length=video_length, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).videos
output_path = save_videos_grid(video, save_path='output', path=f"{prompt}.gif")
return output_path
demo_inputs = [
gr.inputs.Dropdown(
label="Model",
choices=model_list,
value="Tune-A-Video-library/a-man-is-surfing",
),
gr.inputs.Textbox(
label="Prompt",
default='a flower blooming'
),
gr.inputs.Slider(
label="Video Length",
minimum=1,
maximum=50,
default=8,
step=1,
),
gr.inputs.Slider(
label="Height",
minimum=128,
maximum=1280,
default=416,
step=32,
),
gr.inputs.Slider(
label="Width",
minimum=128,
maximum=1280,
default=416,
step=32,
),
gr.inputs.Slider(
label="Num Inference Steps",
minimum=1,
maximum=100,
default=50,
step=1,
),
gr.inputs.Slider(
label="Guidance Scale",
minimum=0.0,
maximum=100,
default=7.5,
step=0.5,
)
]
demo_outputs = gr.outputs.Video(type="gif", label="Output")
examples = [
["Tune-A-Video-library/a-man-is-surfing", "a panda is surfing", 5, 416, 416, 50, 7.5],
["Tune-A-Video-library/a-man-is-surfing", "a flower blooming", 5, 416, 416, 50, 7.5],
["sd-dreambooth-library/mr-potato-head", "sks mr potato head, wearing a pink hat, is surfing.", 5, 416, 416, 50, 7.5],
["sd-dreambooth-library/mr-potato-head", "sks mr potato head is surfing in the forest.", 5, 416, 416, 50, 7.5],
]
description = "This is an application that generates video based on a text prompt. To get started, simply input text. The default model in the dropdown is a generic model that you can generate anything. Alternatively, for more photorealistic generations, you can use other models in the dropdown. These models are Dreambooth models, and they're trained with a specific object name, so make sure you know what the object is called. You can find an example prompt for a dreambooth model in Examples section right below the interface."
title = "Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation"
demo_app = gr.Interface(
fn=tune_video_predict,
inputs=demo_inputs,
outputs=demo_outputs,
examples=examples,
cache_examples=False,
title=title,
theme="huggingface",
description=description
)
demo_app.launch(debug=True, enable_queue=True)
|