If you like our project, please give us a star ✨ on Github for the latest update.
+
+
+
+
+
+
+
+
+
+""")
+
+block_css = """
+#buttons button {
+ min-width: min(120px,100%);
+}
+"""
+
+
+examples = [
+ [
+ "A soaring drone footage captures the majestic beauty of a coastal cliff, "
+ "its red and yellow stratified rock faces rich in color and against the vibrant turquoise of the sea. "
+ "Seabirds can be seen taking flight around the cliff's precipices. "
+ "As the drone slowly moves from different angles, "
+ "the changing sunlight casts shifting shadows that highlight the rugged textures of the cliff and the surrounding calm sea. "
+ "The water gently laps at the rock base and the greenery that clings to the top of the cliff, "
+ "and the scene gives a sense of peaceful isolation at the fringes of the ocean. "
+ "The video captures the essence of pristine natural beauty untouched by human structures.",
+ 50, 7.5,
+ ],
+ [
+ "The video captures the majestic beauty of a waterfall cascading down a cliff into a serene lake. "
+ "The waterfall, with its powerful flow, is the central focus of the video. "
+ "The surrounding landscape is lush and green, with trees and foliage adding to the natural beauty of the scene. "
+ "The camera angle provides a bird's eye view of the waterfall, "
+ "allowing viewers to appreciate the full height and grandeur of the waterfall. "
+ "The video is a stunning representation of nature's power and beauty.",
+ 50, 7.5,
+ ],
+ [
+ "A vibrant scene of a snowy mountain landscape. "
+ "The sky is filled with a multitude of colorful hot air balloons, "
+ "each floating at different heights, creating a dynamic and lively atmosphere. "
+ "The balloons are scattered across the sky, some closer to the viewer, "
+ "others further away, adding depth to the scene. "
+ "Below, the mountainous terrain is blanketed in a thick layer of snow, "
+ "with a few patches of bare earth visible here and there. "
+ "The snow-covered mountains provide a stark contrast to the colorful balloons, "
+ "enhancing the visual appeal of the scene. In the foreground, "
+ "a few cars can be seen driving along a winding road that cuts through the mountains. "
+ "The cars are small compared to the vastness of the landscape, emphasizing the grandeur of the surroundings. "
+ "The overall style of the video is a mix of adventure and tranquility, "
+ "with the hot air balloons adding a touch of whimsy to the otherwise serene mountain landscape. "
+ "The video is likely shot during the day, as the lighting is bright and even, "
+ "casting soft shadows on the snow-covered mountains.",
+ 50, 7.5,
+ ]
+]
\ No newline at end of file
diff --git a/opensora/serve/gradio_web_server.py b/opensora/serve/gradio_web_server.py
new file mode 100644
index 000000000..38e266623
--- /dev/null
+++ b/opensora/serve/gradio_web_server.py
@@ -0,0 +1,116 @@
+
+
+import argparse
+import sys
+import os
+import random
+
+import imageio
+import torch
+from diffusers import PNDMScheduler
+from huggingface_hub import hf_hub_download
+from torchvision.utils import save_image
+from diffusers.models import AutoencoderKL
+from datetime import datetime
+from typing import List, Union
+import gradio as gr
+import numpy as np
+from gradio.components import Textbox, Video, Image
+from transformers import T5Tokenizer, T5EncoderModel
+
+from opensora.models.ae import ae_stride_config, getae, getae_wrapper
+from opensora.models.ae.videobase import CausalVQVAEModelWrapper, CausalVAEModelWrapper
+from opensora.models.diffusion.latte.modeling_latte import LatteT2V
+from opensora.sample.pipeline_videogen import VideoGenPipeline
+from opensora.serve.gradio_utils import block_css, title_markdown, randomize_seed_fn, set_env, examples
+
+
+@torch.inference_mode()
+def generate_img(prompt, sample_steps, scale, seed=0, randomize_seed=False, force_images=False):
+ seed = int(randomize_seed_fn(seed, randomize_seed))
+ set_env(seed)
+
+ videos = videogen_pipeline(prompt,
+ video_length=transformer_model.config.video_length,
+ height=int(args.version.split('x')[1]),
+ width=int(args.version.split('x')[2]),
+ num_inference_steps=sample_steps,
+ guidance_scale=scale,
+ enable_temporal_attentions=not force_images,
+ num_images_per_prompt=1,
+ mask_feature=True,
+ ).video
+
+ torch.cuda.empty_cache()
+ videos = videos[0]
+ tmp_save_path = 'tmp.mp4'
+ imageio.mimwrite(tmp_save_path, videos, fps=24, quality=9) # highest quality is 10, lowest is 0
+ display_model_info = f"Image size: {int(args.version.split('x')[1])}, \nSampling Step: {sample_steps}, \nGuidance Scale: {scale}"
+ return tmp_save_path, prompt, display_model_info, seed
+
+if __name__ == '__main__':
+ args = type('args', (), {
+ 'force_images': False,
+ 'model_path': 'LanguageBind/Open-Sora-Plan-65x512x512-v1.0.0',
+ 'text_encoder_name': 'DeepFloyd/t5-v1_1-xxl',
+ 'version': '65x512x512'
+ })
+ device = torch.device('cuda:0')
+ # Load model:
+ transformer_model = LatteT2V.from_pretrained(args.model_path, subfolder=args.version, torch_dtype=torch.float16, cache_dir='cache_dir').to(device)
+
+ vae = CausalVAEModelWrapper(args.model_path, subfolder="vae", cache_dir='cache_dir').to(device, dtype=torch.float16)
+ vae.vae.enable_tiling()
+
+ transformer_model.force_images = args.force_images
+ tokenizer = T5Tokenizer.from_pretrained(args.text_encoder_name, cache_dir="cache_dir")
+ text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_name, cache_dir="cache_dir",
+ torch_dtype=torch.float16).to(device)
+
+ # set eval mode
+ transformer_model.eval()
+ vae.eval()
+ text_encoder.eval()
+
+ scheduler = PNDMScheduler()
+ videogen_pipeline = VideoGenPipeline(vae=vae,
+ text_encoder=text_encoder,
+ tokenizer=tokenizer,
+ scheduler=scheduler,
+ transformer=transformer_model).to(device=device)
+
+ demo = gr.Interface(
+ fn=generate_img,
+ inputs=[Textbox(label="",
+ placeholder="Please enter your prompt. \n"),
+ gr.Slider(
+ label='Sample Steps',
+ minimum=1,
+ maximum=500,
+ value=50,
+ step=10
+ ),
+ gr.Slider(
+ label='Guidance Scale',
+ minimum=0.1,
+ maximum=30.0,
+ value=10.0,
+ step=0.1
+ ),
+ gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=203279,
+ step=1,
+ value=0,
+ ),
+ gr.Checkbox(label="Randomize seed", value=True),
+ ],
+ outputs=[Video(label="Vid", width=512, height=512),
+ Textbox(label="input prompt"),
+ Textbox(label="model info"),
+ gr.Slider(label='seed')],
+ title=title_markdown, theme=gr.themes.Default(), css=block_css,
+ examples=examples,
+ )
+ demo.launch()
\ No newline at end of file
+
+ 
+ 
+