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import spaces
import gradio as gr
import torch
from huggingface_hub import snapshot_download
from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from xora.models.transformers.transformer3d import Transformer3DModel
from xora.models.transformers.symmetric_patchifier import SymmetricPatchifier
from xora.schedulers.rf import RectifiedFlowScheduler
from xora.pipelines.pipeline_xora_video import XoraVideoPipeline
from transformers import T5EncoderModel, T5Tokenizer
from xora.utils.conditioning_method import ConditioningMethod
from pathlib import Path
import safetensors.torch
import json
import numpy as np
import cv2
from PIL import Image
import tempfile
import os
# Load Hugging Face token if needed
hf_token = os.getenv("HF_TOKEN")
# Set model download directory within Hugging Face Spaces
model_path = "asset"
if not os.path.exists(model_path):
snapshot_download("Lightricks/Xora", local_dir=model_path, repo_type='model', token=hf_token)
# Global variables to load components
vae_dir = Path(model_path) / 'vae'
unet_dir = Path(model_path) / 'unet'
scheduler_dir = Path(model_path) / 'scheduler'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def load_vae(vae_dir):
vae_ckpt_path = vae_dir / "vae_diffusion_pytorch_model.safetensors"
vae_config_path = vae_dir / "config.json"
with open(vae_config_path, 'r') as f:
vae_config = json.load(f)
vae = CausalVideoAutoencoder.from_config(vae_config)
vae_state_dict = safetensors.torch.load_file(vae_ckpt_path)
vae.load_state_dict(vae_state_dict)
return vae.cuda().to(torch.bfloat16)
def load_unet(unet_dir):
unet_ckpt_path = unet_dir / "unet_diffusion_pytorch_model.safetensors"
unet_config_path = unet_dir / "config.json"
transformer_config = Transformer3DModel.load_config(unet_config_path)
transformer = Transformer3DModel.from_config(transformer_config)
unet_state_dict = safetensors.torch.load_file(unet_ckpt_path)
transformer.load_state_dict(unet_state_dict, strict=True)
return transformer.to(device)
def load_scheduler(scheduler_dir):
scheduler_config_path = scheduler_dir / "scheduler_config.json"
scheduler_config = RectifiedFlowScheduler.load_config(scheduler_config_path)
return RectifiedFlowScheduler.from_config(scheduler_config)
# Helper function for image processing
def center_crop_and_resize(frame, target_height, target_width):
h, w, _ = frame.shape
aspect_ratio_target = target_width / target_height
aspect_ratio_frame = w / h
if aspect_ratio_frame > aspect_ratio_target:
new_width = int(h * aspect_ratio_target)
x_start = (w - new_width) // 2
frame_cropped = frame[:, x_start:x_start + new_width]
else:
new_height = int(w / aspect_ratio_target)
y_start = (h - new_height) // 2
frame_cropped = frame[y_start:y_start + new_height, :]
frame_resized = cv2.resize(frame_cropped, (target_width, target_height))
return frame_resized
def load_image_to_tensor_with_resize(image_path, target_height=512, target_width=768):
image = Image.open(image_path).convert("RGB")
image_np = np.array(image)
frame_resized = center_crop_and_resize(image_np, target_height, target_width)
frame_tensor = torch.tensor(frame_resized).permute(2, 0, 1).float()
frame_tensor = (frame_tensor / 127.5) - 1.0
return frame_tensor.unsqueeze(0).unsqueeze(2)
# Preset options for resolution and frame configuration
preset_options = [
{"label": "704x1216, 41 frames", "height": 704, "width": 1216, "num_frames": 41},
{"label": "704x1088, 49 frames", "height": 704, "width": 1088, "num_frames": 49},
{"label": "640x1056, 57 frames", "height": 640, "width": 1056, "num_frames": 57},
{"label": "608x992, 65 frames", "height": 608, "width": 992, "num_frames": 65},
{"label": "608x896, 73 frames", "height": 608, "width": 896, "num_frames": 73},
{"label": "544x896, 81 frames", "height": 544, "width": 896, "num_frames": 81},
{"label": "544x832, 89 frames", "height": 544, "width": 832, "num_frames": 89},
{"label": "512x800, 97 frames", "height": 512, "width": 800, "num_frames": 97},
{"label": "512x768, 97 frames", "height": 512, "width": 768, "num_frames": 97},
{"label": "480x800, 105 frames", "height": 480, "width": 800, "num_frames": 105},
{"label": "480x736, 113 frames", "height": 480, "width": 736, "num_frames": 113},
{"label": "480x704, 121 frames", "height": 480, "width": 704, "num_frames": 121},
{"label": "448x704, 129 frames", "height": 448, "width": 704, "num_frames": 129},
{"label": "448x672, 137 frames", "height": 448, "width": 672, "num_frames": 137},
{"label": "416x640, 153 frames", "height": 416, "width": 640, "num_frames": 153},
{"label": "384x672, 161 frames", "height": 384, "width": 672, "num_frames": 161},
{"label": "384x640, 169 frames", "height": 384, "width": 640, "num_frames": 169},
{"label": "384x608, 177 frames", "height": 384, "width": 608, "num_frames": 177},
{"label": "384x576, 185 frames", "height": 384, "width": 576, "num_frames": 185},
{"label": "352x608, 193 frames", "height": 352, "width": 608, "num_frames": 193},
{"label": "352x576, 201 frames", "height": 352, "width": 576, "num_frames": 201},
{"label": "352x544, 209 frames", "height": 352, "width": 544, "num_frames": 209},
{"label": "352x512, 225 frames", "height": 352, "width": 512, "num_frames": 225},
{"label": "352x512, 233 frames", "height": 352, "width": 512, "num_frames": 233},
{"label": "320x544, 241 frames", "height": 320, "width": 544, "num_frames": 241},
{"label": "320x512, 249 frames", "height": 320, "width": 512, "num_frames": 249},
{"label": "320x512, 257 frames", "height": 320, "width": 512, "num_frames": 257},
{"label": "Custom", "height": None, "width": None, "num_frames": None}
]
# Function to toggle visibility of sliders based on preset selection
def preset_changed(preset):
if preset != "Custom":
selected = next(item for item in preset_options if item["label"] == preset)
return (
selected["height"],
selected["width"],
selected["num_frames"],
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False)
)
else:
return None, None, None, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
# Load models
vae = load_vae(vae_dir)
unet = load_unet(unet_dir)
scheduler = load_scheduler(scheduler_dir)
patchifier = SymmetricPatchifier(patch_size=1)
text_encoder = T5EncoderModel.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder").to(device)
tokenizer = T5Tokenizer.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer")
pipeline = XoraVideoPipeline(
transformer=unet,
patchifier=patchifier,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
vae=vae,
).to(device)
# Modified function to include validation with gr.Error
@spaces.GPU(duration=120)
def generate_video(image_path=None, prompt="", negative_prompt="",
seed=171198, num_inference_steps=40, num_images_per_prompt=1,
guidance_scale=3, height=512, width=768, num_frames=121, frame_rate=25, progress=gr.Progress()):
# Check prompt length and raise an error if it's too short
if len(prompt.strip()) < 50:
raise gr.Error("Prompt must be at least 50 characters long. Please provide more details for the best results.", duration=5)
if image_path:
media_items = load_image_to_tensor_with_resize(image_path, height, width).to(device)
else:
raise ValueError("Image path must be provided.")
sample = {
"prompt": prompt,
'prompt_attention_mask': None,
'negative_prompt': negative_prompt,
'negative_prompt_attention_mask': None,
'media_items': media_items,
}
generator = torch.Generator(device="cpu").manual_seed(seed)
def gradio_progress_callback(self, step, timestep, kwargs):
progress((step + 1) / num_inference_steps)
images = pipeline(
num_inference_steps=num_inference_steps,
num_images_per_prompt=num_images_per_prompt,
guidance_scale=guidance_scale,
generator=generator,
output_type="pt",
height=height,
width=width,
num_frames=num_frames,
frame_rate=frame_rate,
**sample,
is_video=True,
vae_per_channel_normalize=True,
conditioning_method=ConditioningMethod.FIRST_FRAME,
mixed_precision=True,
callback_on_step_end=gradio_progress_callback
).images
output_path = tempfile.mktemp(suffix=".mp4")
video_np = images.squeeze(0).permute(1, 2, 3, 0).cpu().float().numpy()
video_np = (video_np * 255).astype(np.uint8)
height, width = video_np.shape[1:3]
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), frame_rate, (width, height))
for frame in video_np[..., ::-1]:
out.write(frame)
out.release()
return output_path
# Define the Gradio interface with presets
with gr.Blocks() as iface:
gr.Markdown("# Video Generation with Xora")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="filepath", label="Image Input")
prompt = gr.Textbox(label="Prompt", value="A man riding a motorcycle down a winding road, surrounded by lush, green scenery and distant mountains. The sky is clear with a few wispy clouds, and the sunlight glistens on the motorcycle as it speeds along. The rider is dressed in a black leather jacket and helmet, leaning slightly forward as the wind rustles through nearby trees. The wheels kick up dust, creating a slight trail behind the motorcycle, adding a sense of speed and excitement to the scene.")
negative_prompt = gr.Textbox(label="Negative Prompt", value="worst quality, inconsistent motion...")
# Preset dropdown for resolution and frame settings
preset_dropdown = gr.Dropdown(
choices=[p["label"] for p in preset_options],
value="704x1216, 41 frames",
label="Resolution Preset"
)
# Advanced options section
with gr.Accordion("Advanced Options", open=False):
seed = gr.Slider(label="Seed", minimum=0, maximum=1000000, step=1, value=171198)
inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, step=1, value=40)
images_per_prompt = gr.Slider(label="Images per Prompt", minimum=1, maximum=10, step=1, value=1)
guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, step=0.1, value=3.0)
# Sliders to appear at the end of the advanced settings
height_slider = gr.Slider(label="Height", minimum=256, maximum=1024, step=64, value=704, visible=False)
width_slider = gr.Slider(label="Width", minimum=256, maximum=1024, step=64, value=1216, visible=False)
num_frames_slider = gr.Slider(label="Number of Frames", minimum=1, maximum=200, step=1, value=41,
visible=False)
frame_rate = gr.Slider(label="Frame Rate", minimum=1, maximum=60, step=1, value=25, visible=False)
generate_button = gr.Button("Generate Video")
with gr.Column():
output_video = gr.Video(label="Generated Video")
# Link dropdown change to update sliders visibility and values
preset_dropdown.change(
fn=preset_changed,
inputs=[preset_dropdown],
outputs=[height_slider, width_slider, num_frames_slider, height_slider, width_slider, frame_rate]
)
generate_button.click(
fn=generate_video,
inputs=[image_input, prompt, negative_prompt, seed, inference_steps, images_per_prompt, guidance_scale,
height_slider, width_slider, num_frames_slider, frame_rate],
outputs=output_video
)
iface.launch(share=True)
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