| import gradio as gr | |
| from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| import torch | |
| import accelerate | |
| from PIL import Image | |
| from diffusers.utils import export_to_video | |
| model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_model_cpu_offload() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| pipe = pipe.to(device) | |
| max_length = 16 | |
| num_beams = 4 | |
| gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
| def image_to_text(image_paths): | |
| images=[image_paths] | |
| pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values | |
| pixel_values = pixel_values.to(device) | |
| output_ids = model.generate(pixel_values, **gen_kwargs) | |
| preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
| preds = [pred.strip() for pred in preds] | |
| return preds[0] | |
| def text_to_video(image_paths): | |
| prompt = image_to_text(image_paths) | |
| video_frames = pipe(prompt, num_inference_steps=25).frames | |
| video_path = export_to_video(video_frames) | |
| return video_frames | |
| title = "" | |
| description = "" | |
| interface = gr.Interface( | |
| fn=text_to_video, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Video(), | |
| title=title, | |
| description=description, | |
| ) | |
| interface.launch(debug=True) |