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| import time | |
| from PIL import Image | |
| import gradio as gr | |
| from glob import glob | |
| import torch | |
| from transformers import AutoModel, AutoProcessor | |
| DEFAULT_EXAMPLE_PATH = f'examples/example_0' | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| weight_dtype = torch.bfloat16 if device == "cuda" else torch.float32 | |
| print(f"Using device: {device} ({weight_dtype})") | |
| print("Loading model...") | |
| model_pretrained_name_or_path = "yuvalkirstain/PickScore_v1" | |
| processor = AutoProcessor.from_pretrained(model_pretrained_name_or_path) | |
| model = AutoModel.from_pretrained(model_pretrained_name_or_path, torch_dtype=weight_dtype).eval().to(device) | |
| print("Model loaded.") | |
| def calc_probs(prompt, images): | |
| print("Processing inputs...") | |
| image_inputs = processor( | |
| images=images, | |
| padding=True, | |
| truncation=True, | |
| max_length=77, | |
| return_tensors="pt", | |
| ).to(device) | |
| image_inputs = {k: v.to(weight_dtype) for k, v in image_inputs.items()} | |
| text_inputs = processor( | |
| text=prompt, | |
| padding=True, | |
| truncation=True, | |
| max_length=77, | |
| return_tensors="pt", | |
| ).to(device) | |
| with torch.no_grad(): | |
| print("Embedding images and text...") | |
| image_embs = model.get_image_features(**image_inputs) | |
| image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) | |
| text_embs = model.get_text_features(**text_inputs) | |
| text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True) | |
| print("Calculating scores...") | |
| scores = model.logit_scale.exp() * (text_embs.float() @ image_embs.float().T)[0] | |
| print("Calculating probabilities...") | |
| probs = torch.softmax(scores, dim=-1) | |
| return probs.cpu().tolist() | |
| def predict(prompt, image_1, image_2): | |
| print(f"Starting prediction for prompt: {prompt}") | |
| start_time = time.time() | |
| probs = calc_probs(prompt, [image_1, image_2]) | |
| print(f"Prediction: {probs} ({time.time() - start_time:.2f} seconds, ) ") | |
| if device == "cuda": | |
| print(f"GPU mem used: {round(torch.cuda.max_memory_allocated(device) / 1024 / 1024 / 1024, 2)}/{round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024 / 1024, 2)} GB") | |
| return str(round(probs[0], 3)), str(round(probs[1], 3)) | |
| with gr.Blocks(title="PickScore v1") as demo: | |
| gr.Markdown("# PickScore v1") | |
| gr.Markdown( | |
| "This is a demo for the PickScore model - see [paper](https://arxiv.org/abs/2305.01569), [code](https://github.com/yuvalkirstain/PickScore), [dataset](https://huggingface.co/datasets/pickapic-anonymous/pickapic_v1), and [model](https://huggingface.co/yuvalkirstain/PickScore_v1).") | |
| gr.Markdown("## Instructions") | |
| gr.Markdown("Write a prompt, place two images, and press run to get their PickScore!") | |
| with gr.Row(): | |
| prompt = gr.inputs.Textbox(lines=1, label="Prompt", | |
| default=open(f'{DEFAULT_EXAMPLE_PATH}/prompt.txt').readline()) | |
| with gr.Row(): | |
| image_1 = gr.components.Image(type="pil", label="image 1", | |
| value=Image.open(f'{DEFAULT_EXAMPLE_PATH}/image_1.png')) | |
| image_2 = gr.components.Image(type="pil", label="image 2", | |
| value=Image.open(f'{DEFAULT_EXAMPLE_PATH}/image_2.png')) | |
| with gr.Row(): | |
| pred_1 = gr.outputs.Textbox(label="Probability 1") | |
| pred_2 = gr.outputs.Textbox(label="Probability 2") | |
| btn = gr.Button("Run") | |
| btn.click(fn=predict, inputs=[prompt, image_1, image_2], outputs=[pred_1, pred_2]) | |
| prompt.change(lambda: ("", ""), inputs=[], outputs=[pred_1, pred_2]) | |
| gr.Examples( | |
| [[open(f'{path}/prompt.txt').readline(), f'{path}/image_1.png', f'{path}/image_2.png'] for path in | |
| glob(f'examples/*')], | |
| [prompt, image_1, image_2], | |
| [pred_1, pred_2], | |
| predict | |
| ) | |
| demo.queue(concurrency_count=5).launch() | |