Update app.py

app.py

@@ -1,123 +1,87 @@
 import spaces
 import gradio as gr
 import torch
-from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor, pipeline
-from diffusers import StableDiffusion3Pipeline
-import re
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
+from diffusers import DiffusionPipeline
 import random
 import numpy as np
-import os
-from huggingface_hub import snapshot_download
 
 # Initialize models
 device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.float16
+dtype = torch.bfloat16
 
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+# FLUX.1-dev model
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype).to(device)
 
-model_path = snapshot_download(
-    repo_id="stabilityai/stable-diffusion-3-medium", 
-    revision="refs/pr/26",
-    repo_type="model",     
-    ignore_patterns=["*.md", "*..gitattributes"],
-    local_dir="SD3",
-    token=huggingface_token, # type a new token-id.
-    )
-
-# 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-Prompt-Enchance", device=device)
 enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)
 
-# SD3
-sd3_pipe = StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=dtype).to(device)
-
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1344
-
-# 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]
+MAX_IMAGE_SIZE = 2048
 
-    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}
+# 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)
     
-    def replace_fn(match):
-        return replacers[match.group(0)]
-    
-    return re.sub(pattern, replace_fn, caption, count=1, flags=re.IGNORECASE)
+    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)
+    )
+    return parsed_answer["<MORE_DETAILED_CAPTION>"]
 
 # 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']
-        
-        pattern = r'^.*?of\s+(.*?(?:\.|$))'
-        match = re.match(pattern, enhanced_text, re.IGNORECASE | re.DOTALL)
-        
-        if match:
-            remaining_text = enhanced_text[match.end():].strip()
-            modified_sentence = match.group(1).capitalize()
-            enhanced_text = modified_sentence + ' ' + remaining_text
-    else:  # Long
-        result = enhancer_long("Enhance the description: " + input_prompt)
-        enhanced_text = result[0]['summary_text']
-    
+def enhance_prompt(input_prompt):
+    result = enhancer_long("Enhance the description: " + input_prompt)
+    enhanced_text = result[0]['summary_text']
     return enhanced_text
 
-# SD3 Generation function
-def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+@spaces.GPU(duration=190)
+def process_workflow(image, text_prompt, use_enhancer, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+    if image is not None:
+        # Convert image to PIL if it's not already
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        
+        prompt = florence_caption(image)
+    else:
+        prompt = text_prompt
+    
+    if use_enhancer:
+        prompt = enhance_prompt(prompt)
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     
-    generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator(device=device).manual_seed(seed)
     
-    image = sd3_pipe(
-        prompt=prompt, 
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
+    image = pipe(
+        prompt=prompt,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        width=width,
         height=height,
-        generator=generator
+        guidance_scale=guidance_scale
     ).images[0]
     
-    return image, seed
-
-# Gradio Interface
-@spaces.GPU
-def process_workflow(image, text_prompt, use_vlm, use_enhancer, model_choice, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    if use_vlm and image is not None:
-        prompt = create_captions_rich(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
-
+    return image, prompt, seed
 
 custom_css = """
 .input-group, .output-group {
@@ -136,55 +100,46 @@ custom_css = """
 }
 """
 
-title = """<h1 align="center">VLM Captioner + Prompt Enhancer + SD3 Image Generator</h1>
+title = """<h1 align="center">FLUX.1-dev with Florence-2 Captioner and Prompt Enhancer</h1>
 <p><center>
-<a href="https://huggingface.co/spaces/gokaygokay/SD3-Long-Captioner-V2" target="_blank">[VLM Model]</a>
+<a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" target="_blank">[FLUX.1-dev Model]</a>
+<a href="https://huggingface.co/microsoft/Florence-2-base" target="_blank">[Florence-2 Model]</a>
 <a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
-<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance" target="_blank">[Prompt Enhancer Medium]</a>
-<a href="https://github.com/gokayfem" target="_blank">[Github]</a>
-<a href="https://x.com/NONDA30" target="_blank">[X/twitter]</a>
-<p align="center">Dont forget to click <b>Use VLM Captioner</b> or <b>Use Prompt Enhancer</b> Buttons!</p>
+<p align="center">Create long prompts from images or enhance your short prompts with prompt enhancer</p>
 </center></p>
 """
 
-# Gradio Interface
 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 for VLM")
-                use_vlm = gr.Checkbox(label="Use VLM Captioner", value=False)
-            
-            with gr.Group(elem_classes="input-group"):
-                text_prompt = gr.Textbox(label="Text Prompt")
-                use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
-                model_choice = gr.Radio(["Medium", "Long"], label="Enhancer Model", value="Long")
+                input_image = gr.Image(label="Input Image (Florence-2 Captioner)")
             
             with gr.Accordion("Advanced Settings", open=False):
-                negative_prompt = gr.Textbox(label="Negative Prompt")
+                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
+                use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
                 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=64, value=1024)
-                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=1024)
-                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=5.0)
+                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=1, maximum=15, step=0.1, value=3.5)
                 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="Generated Image")
+                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, use_vlm, use_enhancer, model_choice,
-            negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps
+            input_image, text_prompt, use_enhancer, seed, randomize_seed,
+            width, height, guidance_scale, num_inference_steps
         ],
         outputs=[output_image, final_prompt, used_seed]
     )