Delete app.py

app.py

@@ -1,196 +0,0 @@
-import spaces
-import gradio as gr
-import torch
-from PIL import Image
-from transformers import AutoProcessor, Qwen2VLForConditionalGeneration, pipeline
-from diffusers import DiffusionPipeline
-import random
-import numpy as np
-import os
-from qwen_vl_utils import process_vision_info
-
-# Initialize models
-device = "cuda" if torch.cuda.is_available() else "cpu"
-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, token=huggingface_token
-).to(device)
-
-# Initialize Qwen2VL model
-qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "prithivMLmods/JSONify-Flux", trust_remote_code=True, torch_dtype=torch.float16
-).to(device).eval()
-qwen_processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux", trust_remote_code=True)
-
-# Prompt Enhancer
-enhancer_long = pipeline("summarization", model="prithivMLmods/t5-Flan-Prompt-Enhance", device=device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-# Qwen2VL caption function – updated to request plain text caption instead of JSON
-@spaces.GPU
-def qwen_caption(image):
-    # Convert image to PIL if needed
-    if not isinstance(image, Image.Image):
-        image = Image.fromarray(image)
-    
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": "Generate a detailed and optimized caption for the given image."},
-            ],
-        }
-    ]
-
-    text = qwen_processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = qwen_processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    ).to(device)
-
-    generated_ids = qwen_model.generate(**inputs, max_new_tokens=1024)
-    generated_ids_trimmed = [
-        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
-    output_text = qwen_processor.batch_decode(
-        generated_ids_trimmed,
-        skip_special_tokens=True,
-        clean_up_tokenization_spaces=False,
-    )[0]
-    
-    return output_text
-
-# Prompt Enhancer function (unchanged)
-def enhance_prompt(input_prompt):
-    result = enhancer_long("Enhance the description: " + input_prompt)
-    enhanced_text = result[0]['summary_text']
-    return enhanced_text
-
-@spaces.GPU
-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:
-        if not isinstance(image, Image.Image):
-            image = Image.fromarray(image)
-        prompt = qwen_caption(image)
-        print(prompt)
-    else:
-        prompt = text_prompt
-    
-    if use_enhancer:
-        prompt = enhance_prompt(prompt)
-    
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
-    torch.cuda.empty_cache()
-    
-    try:
-        image = pipe(
-            prompt=prompt,
-            generator=generator,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale
-        ).images[0]
-    except RuntimeError as e:
-        if "CUDA out of memory" in str(e):
-            raise RuntimeError("CUDA out of memory. Try reducing image size or inference steps.")
-        else:
-            raise e
-    
-    return image, prompt, seed
-
-custom_css = """
-.input-group, .output-group {
-
-}
-.submit-btn {
-    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
-    border: none !important;
-    color: white !important;
-}
-.submit-btn:hover {
-    background-color: #3498db !important;
-}
-"""
-
-title = """<h1 align="center">FLUX.1-dev with Qwen2VL Captioner and Prompt Enhancer</h1>
-<p><center>
-<a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" target="_blank">[FLUX.1-dev Model]</a>
-<a href="https://huggingface.co/prithivMLmods/JSONify-Flux" target="_blank">[JSONify Flux Model]</a>
-<a href="https://huggingface.co/prithivMLmods/t5-Flan-Prompt-Enhance" target="_blank">[Prompt Enhancer t5]</a>
-<p align="center">Create long prompts from images or enhance your short prompts with prompt enhancer</p>
-</center></p>
-"""
-
-with gr.Blocks(css=custom_css) as demo:
-    gr.HTML(title)
-    
-    with gr.Sidebar(label="Parameters", open=True):
-        gr.Markdown(
-            """
-            ### About
-            
-            #### Flux.1-Dev
-            FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. FLUX.1 [dev] is an open-weight, guidance-distilled model for non-commercial applications. Directly distilled from FLUX.1 [pro], FLUX.1 [dev] obtains similar quality and prompt adherence capabilities, while being more efficient than a standard model of the same size.  
-            [FLUX.1-dev Model](https://huggingface.co/black-forest-labs/FLUX.1-dev)
-            
-            #### JSONify-Flux
-            JSONify-Flux is a multimodal image-text-text model trained on a dataset of FLUX-generated images with context-rich captions based on the Qwen2VL architecture. The JSON-based instruction has been manually removed to avoid JSON format captions.  
-            [JSONify-Flux Model](https://huggingface.co/prithivMLmods/JSONify-Flux)
-            
-            #### t5-Flan-Prompt-Enhance
-            t5-Flan-Prompt-Enhance is a prompt summarization model that enriches synthetic FLUX prompts with more detailed descriptions.  
-            [t5-Flan-Prompt-Enhance Model](https://huggingface.co/prithivMLmods/t5-Flan-Prompt-Enhance)
-            """
-        )
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            with gr.Group(elem_classes="input-group"):
-                input_image = gr.Image(label="Input Image (Qwen2VL Captioner)")
-            
-            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)
-                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=512)
-                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
-                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=32)
-            
-            generate_btn = gr.Button("Generate Image & Prompt", 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="prompt")
-                used_seed = gr.Number(label="seed")
-    
-    generate_btn.click(
-        fn=process_workflow,
-        inputs=[
-            input_image, text_prompt, use_enhancer, seed, randomize_seed,
-            width, height, guidance_scale, num_inference_steps
-        ],
-        outputs=[output_image, final_prompt, used_seed]
-    )
-
-demo.launch(debug=True)