feat: add batch inference pipeline

.gitignore

@@ -8,6 +8,8 @@ logs/
 .idea
 output*
 test*
+!tests/
+!tests/**
 venv
 **/.swp
 **/*.log

resources/example_batch_i2v.jsonl

@@ -0,0 +1,8 @@
+# Example image-to-video batch file
+# For i2v generation, include image_path pointing to your input images
+# Run with: python tools/batch_inference.py --batch_file resources/example_batch_i2v.jsonl --model_path THUDM/CogVideoX1.5-5B-I2V --generate_type i2v
+
+{"prompt": "The person in the image starts walking forward with a confident stride", "output_name": "person_walking.mp4", "image_path": "./input_images/person.jpg"}
+{"prompt": "The landscape transforms with clouds moving across the sky and grass swaying", "output_name": "landscape_motion.mp4", "image_path": "./input_images/landscape.jpg", "num_frames": 81}
+{"prompt": "Zoom in slowly on the subject while adding subtle motion blur", "output_name": "zoom_effect.mp4", "image_path": "./input_images/portrait.jpg", "guidance_scale": 5.0}
+{"prompt": "The still life scene comes alive with gentle movement and shifting shadows", "output_name": "still_life_animated.mp4", "image_path": "./input_images/still_life.jpg"}

resources/example_batch_t2v.jsonl

@@ -0,0 +1,12 @@
+# Example text-to-video batch file
+# Each line is a JSON object with prompt and output_name (required)
+# Optional: num_frames, guidance_scale, num_inference_steps, seed, width, height
+
+{"prompt": "A majestic eagle soaring through golden sunset clouds, cinematic lighting, 4K quality", "output_name": "eagle_sunset.mp4"}
+{"prompt": "A cozy coffee shop on a rainy day, steam rising from cups, warm ambient lighting", "output_name": "coffee_shop_rain.mp4", "num_frames": 49}
+{"prompt": "An astronaut floating in space with Earth visible in the background, peaceful and serene", "output_name": "astronaut_space.mp4", "seed": 123}
+{"prompt": "A field of sunflowers swaying gently in the summer breeze, bright and cheerful", "output_name": "sunflowers.mp4", "guidance_scale": 7.0}
+{"prompt": "A futuristic city at night with neon lights and flying cars, cyberpunk aesthetic", "output_name": "cyberpunk_city.mp4", "num_inference_steps": 60}
+{"prompt": "A serene Japanese garden with cherry blossoms falling, koi pond, peaceful atmosphere", "output_name": "japanese_garden.mp4"}
+{"prompt": "Waves crashing on a tropical beach at sunset, palm trees silhouetted against orange sky", "output_name": "tropical_sunset.mp4"}
+{"prompt": "A mystical forest with glowing mushrooms and fireflies, fantasy atmosphere", "output_name": "mystical_forest.mp4", "seed": 456}

resources/example_batch_v2v.jsonl

@@ -0,0 +1,8 @@
+# Example video-to-video batch file
+# For v2v generation, include video_path pointing to your input videos
+# Run with: python tools/batch_inference.py --batch_file resources/example_batch_v2v.jsonl --model_path THUDM/CogVideoX1.5-5B --generate_type v2v
+
+{"prompt": "Transform this video into a watercolor painting style with soft brushstrokes", "output_name": "watercolor_style.mp4", "video_path": "./input_videos/original1.mp4"}
+{"prompt": "Convert to anime style with vibrant colors and dramatic lighting", "output_name": "anime_style.mp4", "video_path": "./input_videos/original2.mp4", "guidance_scale": 7.5}
+{"prompt": "Add cinematic color grading with film grain and dramatic contrast", "output_name": "cinematic_grade.mp4", "video_path": "./input_videos/original3.mp4", "num_inference_steps": 40}
+{"prompt": "Transform into a vintage black and white film with classic aesthetics", "output_name": "vintage_bw.mp4", "video_path": "./input_videos/original4.mp4"}