Spaces:
Running
Running
File size: 2,431 Bytes
40a0c27 fe109d0 4ca9103 87e9ce2 411ddb3 6c489b2 411ddb3 643466f 87e9ce2 411ddb3 446e43e 87e9ce2 411ddb3 ff29f9b 411ddb3 87e9ce2 d218a70 411ddb3 029098a 87e9ce2 |
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 |
import gradio as gr
import subprocess
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
import os
import random
from gradio_client import Client
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
# Initialize Florence model
device = "cuda" if torch.cuda.is_available() else "cpu"
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
api_key = os.getenv("HF_READ_TOKEN")
def generate_caption(image):
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
generated_ids = florence_model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = florence_processor.post_process_generation(
generated_text,
task="<MORE_DETAILED_CAPTION>",
image_size=(image.width, image.height)
)
prompt = parsed_answer["<MORE_DETAILED_CAPTION>"]
print("Generation completed!:"+ prompt)
yield prompt, None
image_path = generate_image(prompt,random.randint(0, 4294967296))
yield prompt, image_path
def generate_image(prompt, seed=42, width=1024, height=1024):
try:
result = Client("KingNish/Realtime-FLUX", hf_token=api_key).predict(
prompt=prompt,
seed=seed,
width=width,
height=height,
api_name="/generate_image"
)
# Extract the image path from the result tuple
image_path = result[0]
return image_path
except Exception as e:
raise Exception(f"Error generating image: {str(e)}")
io = gr.Interface(generate_caption,
inputs=[gr.Image(label="Input Image")],
outputs = [gr.Textbox(label="Output Prompt", lines=2, show_copy_button = True),
gr.Image(label="Output Image")]
)
io.launch(debug=True) |