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import gradio as gr |
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import torch |
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from transformers import FuyuForCausalLM, AutoTokenizer |
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from transformers.models.fuyu.processing_fuyu import FuyuProcessor |
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from transformers.models.fuyu.image_processing_fuyu import FuyuImageProcessor |
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from PIL import Image |
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model_id = "adept/fuyu-8b" |
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dtype = torch.bfloat16 |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = FuyuForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=dtype) |
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processor = FuyuProcessor(image_processor=FuyuImageProcessor(), tokenizer=tokenizer) |
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caption_prompt = "Generate a coco-style caption.\\n" |
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def resize_to_max(image, max_width=1080, max_height=1080): |
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width, height = image.size |
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if width <= max_width and height <= max_height: |
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return image |
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scale = min(max_width/width, max_height/height) |
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width = int(width*scale) |
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height = int(height*scale) |
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return image.resize((width, height), Image.LANCZOS) |
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def predict(image, prompt): |
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image = resize_to_max(image) |
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model_inputs = processor(text=prompt, images=[image]) |
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model_inputs = {k: v.to(dtype=dtype if torch.is_floating_point(v) else v.dtype, device=device) for k,v in model_inputs.items()} |
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generation_output = model.generate(**model_inputs, max_new_tokens=40) |
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prompt_len = model_inputs["input_ids"].shape[-1] |
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return tokenizer.decode(generation_output[0][prompt_len:], skip_special_tokens=True) |
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def caption(image): |
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return predict(image, caption_prompt) |
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def set_example_image(example: list) -> dict: |
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return gr.Image.update(value=example[0]) |
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css = """ |
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#mkd { |
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height: 500px; |
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overflow: auto; |
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border: 1px solid #ccc; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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gr.HTML( |
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""" |
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<h1 id="title">Fuyu Multimodal Demo</h1> |
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<h3><a href="https://huggingface.co/adept/fuyu-8b">Fuyu-8B</a> is a multimodal model that supports a variety of tasks combining text and image prompts.</h3> |
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For example, you can use it for captioning by asking it to describe an image. You can also ask it questions about an image, a task known as Visual Question Answering, or VQA. This demo lets you explore captioning and VQA, with more tasks coming soon :) |
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Learn more about the model in <a href="https://www.adept.ai/blog/fuyu-8b">our blog post</a>. |
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<br> |
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<br> |
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<strong>Note: This is a raw model release. We have not added further instruction-tuning, postprocessing or sampling strategies to control for undesirable outputs. The model may hallucinate, and you should expect to have to fine-tune the model for your use-case!</strong> |
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<h3>Play with Fuyu-8B in this demo! 💬</h3> |
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""" |
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) |
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with gr.Tab("Visual Question Answering"): |
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with gr.Row(): |
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with gr.Column(): |
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image_input = gr.Image(label="Upload your Image", type="pil") |
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text_input = gr.Textbox(label="Ask a Question") |
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vqa_output = gr.Textbox(label="Output") |
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vqa_btn = gr.Button("Answer Visual Question") |
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gr.Examples( |
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[["assets/vqa_example_1.png", "How is this made?"], ["assets/vqa_example_2.png", "What is this flower and where is it's origin?"], |
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["assets/docvqa_example.png", "How many items are sold?"], ["assets/screen2words_ui_example.png", "What is this app about?"]], |
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inputs = [image_input, text_input], |
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outputs = [vqa_output], |
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fn=predict, |
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cache_examples=True, |
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label='Click on any Examples below to get VQA results quickly 👇' |
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) |
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with gr.Tab("Image Captioning"): |
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with gr.Row(): |
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captioning_input = gr.Image(label="Upload your Image", type="pil") |
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captioning_output = gr.Textbox(label="Output") |
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captioning_btn = gr.Button("Generate Caption") |
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gr.Examples( |
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[["assets/captioning_example_1.png"], ["assets/captioning_example_2.png"]], |
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inputs = [captioning_input], |
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outputs = [captioning_output], |
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fn=caption, |
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cache_examples=True, |
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label='Click on any Examples below to get captioning results quickly 👇' |
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) |
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captioning_btn.click(fn=caption, inputs=captioning_input, outputs=captioning_output) |
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vqa_btn.click(fn=predict, inputs=[image_input, text_input], outputs=vqa_output) |
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demo.launch(server_name="0.0.0.0") |
