import spaces import gradio as gr import torch from PIL import Image from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor, pipeline from transformers import AutoProcessor, AutoModelForCausalLM from diffusers import AuraFlowPipeline import re import random import numpy as np import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) # Initialize models device = "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.float16 # AuraFlow model pipe = AuraFlowPipeline.from_pretrained( "fal/AuraFlow-v0.3", torch_dtype=torch.float16 ).to(device) # VLM Captioner vlm_model = PaliGemmaForConditionalGeneration.from_pretrained("gokaygokay/sd3-long-captioner-v2").to(device).eval() vlm_processor = PaliGemmaProcessor.from_pretrained("gokaygokay/sd3-long-captioner-v2") # Initialize Florence model 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) # Prompt Enhancer enhancer_medium = pipeline("summarization", model="gokaygokay/Lamini-fal-prompt-enchance", device=device) enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 # Florence caption function def florence_caption(image): # Convert image to PIL if it's not already if not isinstance(image, Image.Image): image = Image.fromarray(image) inputs = florence_processor(text="", 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="", image_size=(image.width, image.height) ) return parsed_answer[""] # VLM Captioner function def create_captions_rich(image): prompt = "caption en" model_inputs = vlm_processor(text=prompt, images=image, return_tensors="pt").to(device) input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = vlm_model.generate(**model_inputs, repetition_penalty=1.10, max_new_tokens=256, do_sample=False) generation = generation[0][input_len:] decoded = vlm_processor.decode(generation, skip_special_tokens=True) return modify_caption(decoded) # Helper function for caption modification def modify_caption(caption: str) -> str: prefix_substrings = [ ('captured from ', ''), ('captured at ', '') ] pattern = '|'.join([re.escape(opening) for opening, _ in prefix_substrings]) replacers = {opening: replacer for opening, replacer in prefix_substrings} def replace_fn(match): return replacers[match.group(0)] return re.sub(pattern, replace_fn, caption, count=1, flags=re.IGNORECASE) # Prompt Enhancer function def enhance_prompt(input_prompt, model_choice): if model_choice == "Medium": result = enhancer_medium("Enhance the description: " + input_prompt) enhanced_text = result[0]['summary_text'] else: # Long result = enhancer_long("Enhance the description: " + input_prompt) enhanced_text = result[0]['summary_text'] return enhanced_text def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device=device).manual_seed(seed) image = pipe( prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, generator=generator ).images[0] return image, seed @spaces.GPU(duration=100) def process_workflow(image, text_prompt, vlm_model_choice, use_enhancer, model_choice, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): if image is not None: # Convert image to PIL if it's not already if not isinstance(image, Image.Image): image = Image.fromarray(image) if vlm_model_choice == "Long Captioner": prompt = create_captions_rich(image) else: # Florence prompt = florence_caption(image) else: prompt = text_prompt if use_enhancer: prompt = enhance_prompt(prompt, model_choice) generated_image, used_seed = generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps) return generated_image, prompt, used_seed custom_css = """ .input-group, .output-group { border: 1px solid #e0e0e0; border-radius: 10px; padding: 20px; margin-bottom: 20px; background-color: #f9f9f9; } .submit-btn { background-color: #2980b9 !important; color: white !important; } .submit-btn:hover { background-color: #3498db !important; } """ title = """

AuraFlow with VLM Captioner and Prompt Enhancer

[AuraFlow Model] [Original Space] [Florence-2 Model] [Long Captioner Model] [Prompt Enhancer Long] [Prompt Enhancer Medium]

Create long prompts from images or enhance your short prompts with prompt enhancer

""" with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo: gr.HTML(title) with gr.Row(): with gr.Column(scale=1): with gr.Group(elem_classes="input-group"): input_image = gr.Image(label="Input Image (VLM Captioner)") vlm_model_choice = gr.Radio(["Florence-2", "Long Captioner"], label="VLM Model", value="Florence-2") with gr.Accordion("Advanced Settings", open=False): text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)") use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False) model_choice = gr.Radio(["Medium", "Long"], label="Enhancer Model", value="Medium") negative_prompt = gr.Textbox(label="Negative Prompt") seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) randomize_seed = gr.Checkbox(label="Randomize Seed", value=True) width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0) num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=28) generate_btn = gr.Button("Generate Image", elem_classes="submit-btn") with gr.Column(scale=1): with gr.Group(elem_classes="output-group"): output_image = gr.Image(label="Result", elem_id="gallery", show_label=False) final_prompt = gr.Textbox(label="Final Prompt Used") used_seed = gr.Number(label="Seed Used") generate_btn.click( fn=process_workflow, inputs=[ input_image, text_prompt, vlm_model_choice, use_enhancer, model_choice, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps ], outputs=[output_image, final_prompt, used_seed] ) demo.launch(debug=True)