unography
/

blip-long-cap

@@ -1,199 +1,108 @@
 ---
-library_name: transformers
-tags: []
 ---
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 ---
+pipeline_tag: image-to-text
+tags:
+- image-captioning
+languages:
+- en
+license: bsd-3-clause
+widget:
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
+  example_title: Savanna
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
+  example_title: Football Match
+- src: >-
+    https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
+  example_title: Airport
+datasets:
+- unography/laion-14k-GPT4V-LIVIS-Captions
+inference:
+  parameters:
+    max_length: 300
 ---
+# LongCap: Finetuned [BLIP](https://huggingface.co/Salesforce/blip-image-captioning-large) for generating long captions of images, suitable for prompts for text-to-image generation and captioning text-to-image datasets
+## Usage
+You can use this model for conditional and un-conditional image captioning
+### Using the Pytorch model
+#### Running the model on CPU
+<details>
+<summary> Click to expand </summary>
+```python
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+processor = BlipProcessor.from_pretrained("unography/blip-large-long-cap")
+model = BlipForConditionalGeneration.from_pretrained("unography/blip-large-long-cap")
+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')
+inputs = processor(raw_image, return_tensors="pt")
+pixel_values = inputs.pixel_values
+out = model.generate(pixel_values=pixel_values, max_length=250)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach, wearing a checkered shirt and a dog collar. the woman is interacting with the dog, which is positioned towards the left side of the image. the setting is a beachfront with a calm sea and a golden hue.
+```
+</details>
+#### Running the model on GPU
+##### In full precision
+<details>
+<summary> Click to expand </summary>
+```python
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+processor = BlipProcessor.from_pretrained("unography/blip-large-long-cap")
+model = BlipForConditionalGeneration.from_pretrained("unography/blip-large-long-cap").to("cuda")
+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')
+inputs = processor(raw_image, return_tensors="pt").to("cuda")
+pixel_values = inputs.pixel_values
+out = model.generate(pixel_values=pixel_values, max_length=250)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach, wearing a checkered shirt and a dog collar. the woman is interacting with the dog, which is positioned towards the left side of the image. the setting is a beachfront with a calm sea and a golden hue.
+```
+</details>
+##### In half precision (`float16`)
+<details>
+<summary> Click to expand </summary>
+```python
+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+processor = BlipProcessor.from_pretrained("unography/blip-large-long-cap")
+model = BlipForConditionalGeneration.from_pretrained("unography/blip-large-long-cap", torch_dtype=torch.float16).to("cuda")
+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')
+inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+pixel_values = inputs.pixel_values
+out = model.generate(pixel_values=pixel_values, max_length=250)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach, wearing a checkered shirt and a dog collar. the woman is interacting with the dog, which is positioned towards the left side of the image. the setting is a beachfront with a calm sea and a golden hue.
+```
+</details>