Update app.py

app.py

@@ -1,18 +1,22 @@
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-from diffusers.utils import export_to_video
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
 
-# load pipeline
-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)
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
 
-# optimize for GPU memory
-pipe.enable_model_cpu_offload()
-pipe.enable_vae_slicing()
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
 
-# generate
-prompt = "Spiderman is surfing. Darth Vader is also surfing and following Spiderman"
-video_frames = pipe(prompt, num_inference_steps=25, num_frames=200).frames
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt")
 
-# convent to video
-video_path = export_to_video(video_frames)
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))