import spaces from functools import lru_cache import gradio as gr from gradio_toggle import Toggle import torch from huggingface_hub import snapshot_download from transformers import CLIPProcessor, CLIPModel, pipeline import random 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 import gc import csv from datetime import datetime from openai import OpenAI # 한글-영어 번역기 초기화 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False torch.backends.cudnn.allow_tf32 = False torch.backends.cudnn.deterministic = False torch.backends.cuda.preferred_blas_library="cublas" torch.set_float32_matmul_precision("highest") MAX_SEED = np.iinfo(np.int32).max # Load Hugging Face token if needed hf_token = os.getenv("HF_TOKEN") openai_api_key = os.getenv("OPENAI_API_KEY") client = OpenAI(api_key=openai_api_key) system_prompt_t2v_path = "assets/system_prompt_t2v.txt" with open(system_prompt_t2v_path, "r") as f: system_prompt_t2v = f.read() # Set model download directory within Hugging Face Spaces model_path = "asset" commit_hash='c7c8ad4c2ddba847b94e8bfaefbd30bd8669fafc' if not os.path.exists(model_path): snapshot_download("Lightricks/LTX-Video", revision=commit_hash, 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") clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32", cache_dir=model_path).to(torch.device("cuda:0")) clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32", cache_dir=model_path) def process_prompt(prompt): # 한글이 포함되어 있는지 확인 if any(ord('가') <= ord(char) <= ord('힣') for char in prompt): # 한글을 영어로 번역 translated = translator(prompt)[0]['translation_text'] return translated return prompt def compute_clip_embedding(text=None): inputs = clip_processor(text=text, return_tensors="pt", padding=True).to(device) outputs = clip_model.get_text_features(**inputs) embedding = outputs.detach().cpu().numpy().flatten().tolist() return embedding 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.to(device).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).to(torch.bfloat16) 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) # Preset options for resolution and frame configuration preset_options = [ {"label": "1216x704, 41 frames", "width": 1216, "height": 704, "num_frames": 41}, {"label": "1088x704, 49 frames", "width": 1088, "height": 704, "num_frames": 49}, {"label": "1056x640, 57 frames", "width": 1056, "height": 640, "num_frames": 57}, {"label": "448x448, 100 frames", "width": 448, "height": 448, "num_frames": 100}, {"label": "448x448, 200 frames", "width": 448, "height": 448, "num_frames": 200}, {"label": "448x448, 300 frames", "width": 448, "height": 448, "num_frames": 300}, {"label": "640x640, 80 frames", "width": 640, "height": 640, "num_frames": 80}, {"label": "640x640, 120 frames", "width": 640, "height": 640, "num_frames": 120}, {"label": "768x768, 64 frames", "width": 768, "height": 768, "num_frames": 64}, {"label": "768x768, 90 frames", "width": 768, "height": 768, "num_frames": 90}, {"label": "720x720, 64 frames", "width": 768, "height": 768, "num_frames": 64}, {"label": "720x720, 100 frames", "width": 768, "height": 768, "num_frames": 100}, {"label": "768x512, 97 frames", "width": 768, "height": 512, "num_frames": 97}, {"label": "512x512, 160 frames", "width": 512, "height": 512, "num_frames": 160}, {"label": "512x512, 200 frames", "width": 512, "height": 512, "num_frames": 200}, ] 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(torch.device("cuda:0")) 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(torch.device("cuda:0")) def enhance_prompt_if_enabled(prompt, enhance_toggle): if not enhance_toggle: print("Enhance toggle is off, Prompt: ", prompt) return prompt messages = [ {"role": "system", "content": system_prompt_t2v}, {"role": "user", "content": prompt}, ] try: response = client.chat.completions.create( model="gpt-4-mini", messages=messages, max_tokens=200, ) print("Enhanced Prompt: ", response.choices[0].message.content.strip()) return response.choices[0].message.content.strip() except Exception as e: print(f"Error: {e}") return prompt @spaces.GPU(duration=90) def generate_video_from_text_90( prompt="", enhance_prompt_toggle=False, negative_prompt="", frame_rate=25, seed=random.randint(0, MAX_SEED), num_inference_steps=30, guidance_scale=3.2, height=768, width=768, num_frames=60, progress=gr.Progress(), ): # 프롬프트 전처리 (한글 -> 영어) prompt = process_prompt(prompt) negative_prompt = process_prompt(negative_prompt) 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, ) prompt = enhance_prompt_if_enabled(prompt, enhance_prompt_toggle) sample = { "prompt": prompt, "prompt_attention_mask": None, "negative_prompt": negative_prompt, "negative_prompt_attention_mask": None, "media_items": None, } generator = torch.Generator(device="cuda").manual_seed(seed) def gradio_progress_callback(self, step, timestep, kwargs): progress((step + 1) / num_inference_steps) try: with torch.no_grad(): images = pipeline( num_inference_steps=num_inference_steps, num_images_per_prompt=1, 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.UNCONDITIONAL, mixed_precision=True, callback_on_step_end=gradio_progress_callback, ).images except Exception as e: raise gr.Error( f"An error occurred while generating the video. Please try again. Error: {e}", duration=5, ) finally: torch.cuda.empty_cache() gc.collect() 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() del images del video_np torch.cuda.empty_cache() return output_path def create_advanced_options(): with gr.Accordion("Step 4: Advanced Options (Optional)", open=False): seed = gr.Slider(label="4.1 Seed", minimum=0, maximum=1000000, step=1, value=646373) inference_steps = gr.Slider(label="4.2 Inference Steps", minimum=5, maximum=150, step=5, value=40) guidance_scale = gr.Slider(label="4.3 Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=4.2) height_slider = gr.Slider( label="4.4 Height", minimum=256, maximum=1024, step=64, value=768, visible=False, ) width_slider = gr.Slider( label="4.5 Width", minimum=256, maximum=1024, step=64, value=768, visible=False, ) num_frames_slider = gr.Slider( label="4.5 Number of Frames", minimum=1, maximum=500, step=1, value=60, visible=False, ) return [ seed, inference_steps, guidance_scale, height_slider, width_slider, num_frames_slider, ] css = """ footer { visibility: hidden; } /* 비디오 출력 컨테이너 크기 조정 */ .video-output-container { max-width: 50%; margin-left: auto; margin-right: auto; } /* 비디오 플레이어 크기 조정 */ .video-player { width: 100%; max-height: 50vh; object-fit: contain; } """ with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as iface: with gr.Row(): # 입력 섹션 (왼쪽) with gr.Column(scale=1): txt2vid_prompt = gr.Textbox( label="Step 1: Enter Your Prompt (한글 또는 영어)", placeholder="Describe the video you want to create (at least 50 characters)...", value="A sleek vintage classic car is driving along a Hawaiian coastal road, seen from a low-angle front bumper camera view, with the ocean waves and palm trees rolling by in the background.", lines=5, ) txt2vid_enhance_toggle = Toggle( label="Enhance Prompt", value=False, interactive=True, ) txt2vid_negative_prompt = gr.Textbox( label="Step 2: Enter Negative Prompt", placeholder="Describe the elements you do not want in the video...", value="low quality, worst quality, deformed, distorted, damaged, motion blur, motion artifacts, fused fingers, incorrect anatomy, strange hands, ugly", lines=2, ) txt2vid_preset = gr.Dropdown( choices=[p["label"] for p in preset_options], value="512x512, 160 frames", label="Step 3.1: Choose Resolution Preset", ) txt2vid_frame_rate = gr.Slider( label="Step 3.2: Frame Rate", minimum=6, maximum=60, step=1, value=20, ) txt2vid_advanced = create_advanced_options() txt2vid_generate = gr.Button( "Step 5: Generate Video", variant="primary", size="lg", ) # 출력 섹션 (오른쪽) with gr.Column(scale=1): txt2vid_output = gr.Video( label="Generated Output", elem_classes=["video-output-container", "video-player"] ) txt2vid_preset.change( fn=preset_changed, inputs=[txt2vid_preset], outputs=txt2vid_advanced[3:], ) txt2vid_generate.click( fn=generate_video_from_text_90, inputs=[ txt2vid_prompt, txt2vid_enhance_toggle, txt2vid_negative_prompt, txt2vid_frame_rate, *txt2vid_advanced, ], outputs=txt2vid_output, concurrency_limit=1, concurrency_id="generate_video", queue=True, ) iface.queue(max_size=64, default_concurrency_limit=1, api_open=False).launch(share=True, show_api=False)