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ComfyUI/models/VQA/blip-vqa-base/.gitattributes

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ComfyUI/models/VQA/blip-vqa-base/README.md

@@ -0,0 +1,129 @@
+---
+pipeline_tag: 'visual-question-answering'
+tags:
+- visual-question-answering
+inference: false
+languages:
+- en
+license: bsd-3-clause
+---
+
+# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
+
+Model card for BLIP trained on visual question answering- base architecture (with ViT base backbone).
+
+| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
+|:--:|
+| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
+
+## TL;DR
+
+Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
+
+*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
+
+## 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, BlipForQuestionAnswering
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
+
+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')
+
+question = "how many dogs are in the picture?"
+inputs = processor(raw_image, question, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> 1
+```
+</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, BlipForQuestionAnswering
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").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')
+
+question = "how many dogs are in the picture?"
+inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> 1
+```
+</details>
+
+##### In half precision (`float16`)
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForQuestionAnswering
+
+processor = BlipProcessor.from_pretrained("ybelkada/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained("ybelkada/blip-vqa-base", 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')
+
+question = "how many dogs are in the picture?"
+inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> 1
+```
+</details>
+
+## BibTex and citation info
+
+```
+@misc{https://doi.org/10.48550/arxiv.2201.12086,
+  doi = {10.48550/ARXIV.2201.12086},
+  
+  url = {https://arxiv.org/abs/2201.12086},
+  
+  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
+  
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

ComfyUI/models/VQA/blip-vqa-base/config.json

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+    "return_dict": true,
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+    "temperature": 1.0,
+    "tf_legacy_loss": false,
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+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.26.0.dev0",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  }
+}

ComfyUI/models/VQA/blip-vqa-base/model.safetensors

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ComfyUI/models/VQA/blip-vqa-base/preprocessor_config.json

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+    0.48145466,
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+  ],
+  "processor_class": "BlipProcessor",
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+  "size": {
+    "height": 384,
+    "width": 384
+  },
+  "size_divisor": 32
+}

ComfyUI/models/VQA/blip-vqa-base/pytorch_model.bin

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ComfyUI/models/VQA/blip-vqa-capfilt-large/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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ComfyUI/models/VQA/blip-vqa-capfilt-large/README.md

@@ -0,0 +1,129 @@
+---
+pipeline_tag: visual-question-answering
+tags:
+  - visual-question-answering
+inference: false
+languages:
+  - en
+license: bsd-3-clause
+---
+
+# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
+
+Model card for BLIP trained on visual question answering - large architecture (with ViT large backbone).
+
+| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
+|:--:|
+| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
+
+## TL;DR
+
+Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
+
+*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
+
+## 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, BlipForQuestionAnswering
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large")
+
+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')
+
+question = "how many dogs are in the picture?"
+inputs = processor(raw_image, question, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> 1
+```
+</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, BlipForQuestionAnswering
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large").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')
+
+question = "how many dogs are in the picture?"
+inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> 1
+```
+</details>
+
+##### In half precision (`float16`)
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForQuestionAnswering
+
+processor = BlipProcessor.from_pretrained("ybelkada/blip-vqa-capfilt-large")
+model = BlipForQuestionAnswering.from_pretrained("ybelkada/blip-vqa-capfilt-large", 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')
+
+question = "how many dogs are in the picture?"
+inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> 1
+```
+</details>
+
+## BibTex and citation info
+
+```
+@misc{https://doi.org/10.48550/arxiv.2201.12086,
+  doi = {10.48550/ARXIV.2201.12086},
+  
+  url = {https://arxiv.org/abs/2201.12086},
+  
+  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
+  
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

ComfyUI/models/VQA/blip-vqa-capfilt-large/config.json

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