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 = "facebook/metaclip-h14-fullcc2.5b" 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()