Spaces:
Runtime error
Runtime error
File size: 6,298 Bytes
e532cd7 53b03cb e532cd7 53b03cb e532cd7 53b03cb e532cd7 f53527d e532cd7 53b03cb e532cd7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import subprocess
from datetime import datetime
import numpy as np
import os
# subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
# models = {
# "Qwen/Qwen2-VL-2B-Instruct": AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()
# }
def array_to_image_path(image_array):
# Convert numpy array to PIL Image
img = Image.fromarray(np.uint8(image_array))
# Generate a unique filename using timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"image_{timestamp}.png"
# Save the image
img.save(filename)
# Get the full path of the saved image
full_path = os.path.abspath(filename)
return full_path
models = {
"mateoluksenberg/Qwen-modelo-image": Qwen2VLForConditionalGeneration.from_pretrained("mateoluksenberg/Qwen-modelo-image", trust_remote_code=True, torch_dtype="auto").cuda().eval()
}
processors = {
"mateoluksenberg/Qwen-modelo-image": AutoProcessor.from_pretrained("mateoluksenberg/Qwen-modelo-image", trust_remote_code=True)
}
DESCRIPTION = "[Qwen2-VL-2B Demo](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)"
kwargs = {}
kwargs['torch_dtype'] = torch.bfloat16
user_prompt = '<|user|>\n'
assistant_prompt = '<|assistant|>\n'
prompt_suffix = "<|end|>\n"
@spaces.GPU
def run_example(image, text_input=None, model_id="mateoluksenberg/Qwen-modelo-image"):
image_path = array_to_image_path(image)
print(image_path)
model = models[model_id]
processor = processors[model_id]
prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
image = Image.fromarray(image).convert("RGB")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": text_input},
],
}
]
# Preparation for inference
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")
# Inference: Generation of the output
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
)
"---------------"
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"mateoluksenberg/Qwen-modelo-image", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2VLForConditionalGeneration.from_pretrained(
# "Qwen/Qwen2-VL-2B-Instruct",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# default processer
processor = AutoProcessor.from_pretrained("mateoluksenberg/Qwen-modelo-image")
# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
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")
# Inference: Generation of the output
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)
"---------------"
return output_text[0]
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="Qwen2-VL-2B Input"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="mateoluksenberg/Qwen-modelo-image")
text_input = gr.Textbox(label="Question")
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text])
demo.queue(api_open=False)
demo.launch(debug=True) |