Spaces:
Running
Running
File size: 7,134 Bytes
06a6e91 eacc26a 6123d43 e7f513f 06a6e91 6123d43 eacc26a 6123d43 eacc26a 836daad 6123d43 eacc26a 836daad eacc26a 6123d43 30994c1 eacc26a 30994c1 6123d43 836daad 6123d43 836daad eacc26a 6123d43 eacc26a 6123d43 eacc26a a361f34 6123d43 eacc26a 836daad a361f34 2cde650 6123d43 30994c1 2cde650 30994c1 2cde650 e7f513f eacc26a 836daad a361f34 e7f513f 2cde650 6123d43 e7f513f 6123d43 eacc26a e7f513f 6123d43 eacc26a 836daad eacc26a 6123d43 30994c1 eacc26a 30994c1 6123d43 836daad e7f513f 836daad 6123d43 836daad eacc26a 6123d43 2cde650 e7f513f 6123d43 30994c1 2cde650 30994c1 a361f34 e7f513f 06a6e91 eacc26a 06a6e91 eacc26a 06a6e91 eacc26a 836daad 2cde650 eacc26a 836daad eacc26a 2cde650 eacc26a e7f513f eacc26a 836daad eacc26a e7f513f 836daad eacc26a 57e4050 30994c1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
import gradio as gr
import os
from lumaai import AsyncLumaAI
import asyncio
import aiohttp
import tempfile
async def generate_video(api_key, prompt, loop=False, aspect_ratio="16:9", progress=gr.Progress()):
client = AsyncLumaAI(auth_token=api_key)
progress(0, desc="Initiating video generation...")
generation = await client.generations.create(
prompt=prompt,
loop=loop,
aspect_ratio=aspect_ratio
)
progress(0.1, desc="Video generation started. Waiting for completion...")
# Poll for completion
start_time = asyncio.get_event_loop().time()
while True:
status = await client.generations.get(id=generation.id)
if status.state == "completed":
break
elif status.state == "failed":
raise Exception("Video generation failed")
# Update progress based on time elapsed (assuming 60 seconds total)
elapsed_time = asyncio.get_event_loop().time() - start_time
progress_value = min(0.1 + (elapsed_time / 60) * 0.8, 0.9)
progress(progress_value, desc="Generating video...")
await asyncio.sleep(5)
progress(0.9, desc="Downloading generated video...")
# Download the video
video_url = status.assets.video
async with aiohttp.ClientSession() as session:
async with session.get(video_url) as resp:
if resp.status == 200:
file_name = f"luma_ai_generated_{generation.id}.mp4"
with open(file_name, 'wb') as fd:
while True:
chunk = await resp.content.read(1024)
if not chunk:
break
fd.write(chunk)
progress(1.0, desc="Video generation complete!")
return file_name
async def text_to_video(api_key, prompt, loop, aspect_ratio, progress=gr.Progress()):
if not api_key:
raise gr.Error("Please enter your Luma AI API key.")
try:
video_path = await generate_video(api_key, prompt, loop, aspect_ratio, progress)
return video_path, ""
except Exception as e:
return None, f"An error occurred: {str(e)}"
async def image_to_video(api_key, prompt, image, loop, aspect_ratio, progress=gr.Progress()):
if not api_key:
raise gr.Error("Please enter your Luma AI API key.")
if image is None:
raise gr.Error("Please upload an image.")
try:
client = AsyncLumaAI(auth_token=api_key)
progress(0, desc="Uploading image...")
# Create a temporary file to store the uploaded image
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
temp_file.write(image)
temp_file_path = temp_file.name
# Upload the image to Luma AI (you might need to implement this function)
image_url = await upload_image_to_luma(client, temp_file_path)
progress(0.1, desc="Initiating video generation from image...")
generation = await client.generations.create(
prompt=prompt,
loop=loop,
aspect_ratio=aspect_ratio,
keyframes={
"frame0": {
"type": "image",
"url": image_url
}
}
)
progress(0.2, desc="Video generation started. Waiting for completion...")
# Poll for completion
start_time = asyncio.get_event_loop().time()
while True:
status = await client.generations.get(id=generation.id)
if status.state == "completed":
break
elif status.state == "failed":
raise Exception("Video generation failed")
# Update progress based on time elapsed (assuming 60 seconds total)
elapsed_time = asyncio.get_event_loop().time() - start_time
progress_value = min(0.2 + (elapsed_time / 60) * 0.7, 0.9)
progress(progress_value, desc="Generating video...")
await asyncio.sleep(5)
progress(0.9, desc="Downloading generated video...")
# Download the video
video_url = status.assets.video
async with aiohttp.ClientSession() as session:
async with session.get(video_url) as resp:
if resp.status == 200:
file_name = f"luma_ai_generated_{generation.id}.mp4"
with open(file_name, 'wb') as fd:
while True:
chunk = await resp.content.read(1024)
if not chunk:
break
fd.write(chunk)
# Clean up the temporary file
os.unlink(temp_file_path)
progress(1.0, desc="Video generation complete!")
return file_name, ""
except Exception as e:
return None, f"An error occurred: {str(e)}"
# You need to implement this function based on Luma AI's API for image uploading
async def upload_image_to_luma(client, image_path):
# This is a placeholder. You need to implement the actual image upload logic
# using the Luma AI API. The function should return the URL of the uploaded image.
raise NotImplementedError("Image upload to Luma AI is not implemented yet.")
with gr.Blocks() as demo:
gr.Markdown("# Luma AI Text-to-Video Demo")
api_key = gr.Textbox(label="Luma AI API Key", type="password")
with gr.Tab("Text to Video"):
prompt = gr.Textbox(label="Prompt")
generate_btn = gr.Button("Generate Video")
video_output = gr.Video(label="Generated Video")
error_output = gr.Textbox(label="Error Messages", visible=True)
with gr.Accordion("Advanced Options", open=False):
loop = gr.Checkbox(label="Loop", value=False)
aspect_ratio = gr.Dropdown(label="Aspect Ratio", choices=["16:9", "1:1", "9:16", "4:3", "3:4"], value="16:9")
generate_btn.click(
text_to_video,
inputs=[api_key, prompt, loop, aspect_ratio],
outputs=[video_output, error_output]
)
with gr.Tab("Image to Video"):
img_prompt = gr.Textbox(label="Prompt")
img_input = gr.Image(label="Upload Image", type="numpy")
img_generate_btn = gr.Button("Generate Video from Image")
img_video_output = gr.Video(label="Generated Video")
img_error_output = gr.Textbox(label="Error Messages", visible=True)
with gr.Accordion("Advanced Options", open=False):
img_loop = gr.Checkbox(label="Loop", value=False)
img_aspect_ratio = gr.Dropdown(label="Aspect Ratio", choices=["16:9", "1:1", "9:16", "4:3", "3:4"], value="16:9")
img_generate_btn.click(
image_to_video,
inputs=[api_key, img_prompt, img_input, img_loop, img_aspect_ratio],
outputs=[img_video_output, img_error_output]
)
demo.queue().launch(share=True) |