Spaces:
Sleeping
Sleeping
File size: 5,384 Bytes
8677efd bcec177 8677efd b251db3 753c319 bcec177 8677efd bcec177 8677efd bcec177 8677efd b251db3 8677efd b251db3 8677efd bcec177 8677efd b251db3 753c319 b251db3 8677efd 753c319 8677efd 753c319 8677efd bcec177 8462782 8677efd 4959b00 8677efd 4959b00 8677efd b251db3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor, CLIPModel, BlipForConditionalGeneration, CLIPProcessor, BlipProcessor
from qwen_vl_utils import process_vision_info
import torch
import base64
from PIL import Image, ImageDraw
from io import BytesIO
import re
models = {
"Qwen/Qwen2-VL-7B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"),
"Qwen/Qwen2-VL-2B-Instruct": Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"),
"openai/clip-vit-base-patch32": CLIPModel.from_pretrained("openai/clip-vit-base-patch32"),
"Salesforce/blip-image-captioning-base": BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
}
processors = {
"Qwen/Qwen2-VL-7B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct"),
"Qwen/Qwen2-VL-2B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct"),
"openai/clip-vit-base-patch32": CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"),
"Salesforce/blip-image-captioning-base": BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
}
def image_to_base64(image):
buffered = BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
return img_str
def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
draw = ImageDraw.Draw(image)
for box in bounding_boxes:
xmin, ymin, xmax, ymax = box
draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
return image
def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
x_scale = original_width / scaled_width
y_scale = original_height / scaled_height
rescaled_boxes = []
for box in bounding_boxes:
xmin, ymin, xmax, ymax = box
rescaled_box = [
xmin * x_scale,
ymin * y_scale,
xmax * x_scale,
ymax * y_scale
]
rescaled_boxes.append(rescaled_box)
return rescaled_boxes
@spaces.GPU
def run_example(image, text_input, system_prompt, model_id="Qwen/Qwen2-VL-2B-Instruct"):
model = models[model_id].eval()
processor = processors[model_id]
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
{"type": "text", "text": system_prompt},
{"type": "text", "text": text_input},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
pattern = r'\[\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\]'
matches = re.findall(pattern, str(output_text))
parsed_boxes = [[int(num) for num in match] for match in matches]
scaled_boxes = rescale_bounding_boxes(parsed_boxes, image.width, image.height)
return output_text
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Qwen2-VL Demo
""")
with gr.Tab(label="Qwen2-VL Input"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Image", type="pil")
model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="Qwen/Qwen2-VL-2B-Instruct")
text_input = gr.Textbox(label="Prompt")
submit_btn = gr.Button(value="Submit")
with gr.Column():
model_output_text = gr.Textbox(label="Model Output Text")
gr.Examples(
examples=[
["assets/2024_09_10_10_58_23.png", "Solve the question"],
["assets/2024_09_10_10_58_40.png", "Solve the question"],
["assets/2024_09_10_11_07_31.png", "Solve the question"],
["assets/comics.jpeg", "Describe the scene"],
["assets/rescaled_IMG_3644.PNG", "Describe the scene"],
["assets/rescaled_IMG_4028.PNG", "Describe the scene"]
],
inputs=[input_img, text_input],
outputs=[model_output_text],
fn=run_example,
cache_examples=True,
label="Try examples"
)
submit_btn.click(run_example, [input_img, text_input, model_selector], [model_output_text])
demo.launch(debug=True) |