init

imageEncoder.py

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+import requests
+from PIL import Image
+
+from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel
+
+# load a fine-tuned image captioning model and corresponding tokenizer and image processor
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = GPT2TokenizerFast.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+image_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# let's perform inference on an image
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+pixel_values = image_processor(image, return_tensors="pt").pixel_values
+
+# autoregressively generate caption (uses greedy decoding by default)
+generated_ids = model.generate(pixel_values)
+generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(generated_text)

run.py

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+
+from transformers import pipeline
+#pipe = pipeline("text-generation", model="TheBloke/llava-v1.5-13B-AWQ",device_map="cuda:1")
+pipe = pipeline("image_classification", model="TheBloke/llava-v1.5-13B-AWQ",device_map="cuda:1")
+
+images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+
+pipe(images)
+
+
+