| import gc |
| import threading |
|
|
| import gradio as gr |
| import spaces |
| import torch |
| from PIL import Image, ImageOps |
| from transformers import AutoModelForMultimodalLM, AutoProcessor |
|
|
|
|
| MODEL_ID = "Qwen/Qwen3-VL-4B-Instruct" |
| MAX_IMAGE_EDGE = 2048 |
|
|
| model = None |
| processor = None |
| model_lock = threading.Lock() |
|
|
|
|
| STYLE_GUIDANCE = { |
| "Auto-detect": "Infer the most faithful medium and visual style from the image.", |
| "Photorealistic": "Write for a photorealistic image model; emphasize optics, lighting, materials, and natural detail.", |
| "Cinematic": "Write for a cinematic frame; emphasize shot design, lens language, lighting, atmosphere, and color grade.", |
| "Anime / illustration": "Write for an illustration model; emphasize line work, rendering, palette, stylization, and composition.", |
| "Product photography": "Write for product photography; emphasize object geometry, materials, surface finish, studio light, and backdrop.", |
| "Architecture / interior": "Write for architectural visualization; emphasize space, structure, materials, lighting, perspective, and decor.", |
| } |
|
|
| DETAIL_TOKENS = { |
| "Balanced": 700, |
| "Very detailed": 1100, |
| "Maximum detail": 1500, |
| } |
|
|
|
|
| def load_model(): |
| """Load the model once, only when the first generation request arrives.""" |
| global model, processor |
| if model is not None: |
| return model, processor |
|
|
| with model_lock: |
| if model is not None: |
| return model, processor |
| if not torch.cuda.is_available(): |
| raise gr.Error("This Space needs a CUDA GPU. Select ZeroGPU or a GPU in the Space settings.") |
|
|
| processor = AutoProcessor.from_pretrained(MODEL_ID) |
| model = AutoModelForMultimodalLM.from_pretrained( |
| MODEL_ID, |
| dtype=torch.bfloat16, |
| device_map="auto", |
| low_cpu_mem_usage=True, |
| ).eval() |
| return model, processor |
|
|
|
|
| def prepare_image(image: Image.Image) -> Image.Image: |
| image = ImageOps.exif_transpose(image).convert("RGB") |
| if max(image.size) > MAX_IMAGE_EDGE: |
| image.thumbnail((MAX_IMAGE_EDGE, MAX_IMAGE_EDGE), Image.Resampling.LANCZOS) |
| return image |
|
|
|
|
| def build_instruction(style: str, detail: str, focus: str) -> str: |
| extra_focus = (focus or "").strip() |
| return f""" |
| Act as an expert prompt engineer performing image-to-prompt reconstruction. |
| |
| Study the supplied image carefully and return a faithful, highly descriptive prompt that could recreate it in a modern text-to-image model. Describe only visually supported details; do not invent names, brands, hidden facts, or a backstory. If a detail is uncertain, use neutral visual language. Do not identify a real person. |
| |
| Requested style treatment: {STYLE_GUIDANCE[style]} |
| Requested detail level: {detail}. |
| User's optional focus: {extra_focus if extra_focus else "No extra focus; cover the whole image evenly."} |
| |
| Analyze and incorporate, where visible: |
| - primary subject(s), count, appearance, pose, expression, gaze, and interaction |
| - clothing, accessories, objects, textures, materials, and fine details |
| - environment, foreground, midground, background, and spatial relationships |
| - composition, crop, framing, viewpoint, perspective, symmetry, and depth |
| - lighting direction, quality, intensity, shadows, highlights, time-of-day cues, and atmosphere |
| - colors, contrast, palette, medium, rendering technique, and overall aesthetic |
| - camera/lens cues such as shot type, focal-length feel, aperture/depth of field, focus, and motion |
| - any legible text, reproduced exactly in quotation marks; omit text if it is not clearly readable |
| |
| Output exactly these two sections and nothing else: |
| |
| DETAILED PROMPT |
| A cohesive, generator-ready prompt in natural language. Prefer precise visual terms over vague praise. Do not mention this analysis, the source image, or uncertainty. |
| |
| NEGATIVE PROMPT |
| A concise comma-separated list of defects and unwanted changes that are specifically useful for preserving this image's composition and quality. |
| """.strip() |
|
|
|
|
| def split_response(text: str): |
| cleaned = text.strip() |
| marker = "NEGATIVE PROMPT" |
| if marker in cleaned: |
| positive, negative = cleaned.split(marker, 1) |
| positive = positive.replace("DETAILED PROMPT", "", 1).strip(" \n:#") |
| negative = negative.strip(" \n:#") |
| else: |
| positive = cleaned.replace("DETAILED PROMPT", "", 1).strip(" \n:#") |
| negative = "" |
| return positive, negative |
|
|
|
|
| @spaces.GPU(duration=90) |
| def generate_prompt(image, style, detail, focus, progress=gr.Progress()): |
| if image is None: |
| raise gr.Error("Upload an image first.") |
|
|
| progress(0.05, desc="Loading vision model…") |
| vision_model, vision_processor = load_model() |
| image = prepare_image(image) |
| instruction = build_instruction(style, detail, focus) |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": image}, |
| {"type": "text", "text": instruction}, |
| ], |
| } |
| ] |
|
|
| progress(0.2, desc="Reading image details…") |
| inputs = vision_processor.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_dict=True, |
| return_tensors="pt", |
| ).to(vision_model.device) |
|
|
| try: |
| with model_lock, torch.inference_mode(): |
| generated = vision_model.generate( |
| **inputs, |
| max_new_tokens=DETAIL_TOKENS[detail], |
| do_sample=False, |
| repetition_penalty=1.05, |
| ) |
| generated = generated[:, inputs.input_ids.shape[1] :] |
| response = vision_processor.batch_decode( |
| generated, |
| skip_special_tokens=True, |
| clean_up_tokenization_spaces=False, |
| )[0] |
| except torch.cuda.OutOfMemoryError as exc: |
| gc.collect() |
| torch.cuda.empty_cache() |
| raise gr.Error("The GPU ran out of memory. Try a smaller image or a shorter detail level.") from exc |
|
|
| progress(1, desc="Prompt ready") |
| return split_response(response) |
|
|
|
|
| CSS = """ |
| :root { --paper: #f7f4ed; --ink: #191816; --muted: #6f6a61; --accent: #6750e8; } |
| .gradio-container { max-width: 1180px !important; margin: 0 auto !important; background: var(--paper); } |
| .hero { padding: 2.4rem 0 1.2rem; } |
| .hero .kicker { color: var(--accent); font-weight: 800; letter-spacing: .14em; text-transform: uppercase; font-size: .75rem; } |
| .hero h1 { color: var(--ink); font-size: clamp(2.5rem, 6vw, 5.2rem); line-height: .94; letter-spacing: -.055em; margin: .45rem 0 .85rem; } |
| .hero p { color: var(--muted); max-width: 720px; font-size: 1.05rem; } |
| .panel { border: 1px solid #dcd6ca !important; border-radius: 20px !important; background: rgba(255,255,255,.58) !important; } |
| .run-btn { background: var(--accent) !important; border: 0 !important; color: white !important; font-weight: 800 !important; } |
| .output textarea { font-family: ui-monospace, SFMono-Regular, Consolas, monospace !important; line-height: 1.55 !important; } |
| .note { color: var(--muted); font-size: .82rem; padding: .8rem 0 1.5rem; text-align: center; } |
| """ |
|
|
|
|
| with gr.Blocks() as demo: |
| gr.HTML(""" |
| <section class="hero"> |
| <div class="kicker">Image → Prompt</div> |
| <h1>See every detail.<br>Write the whole scene.</h1> |
| <p>Upload any image and turn it into a precise, generator-ready prompt covering subject, composition, lighting, camera, materials, palette, and style.</p> |
| </section> |
| """) |
|
|
| with gr.Row(equal_height=False): |
| with gr.Column(scale=5, elem_classes="panel"): |
| source = gr.Image( |
| type="pil", |
| image_mode="RGB", |
| label="Source image", |
| sources=["upload", "clipboard"], |
| height=470, |
| ) |
| with gr.Row(): |
| style = gr.Dropdown(list(STYLE_GUIDANCE), value="Auto-detect", label="Prompt style") |
| detail = gr.Radio(list(DETAIL_TOKENS), value="Very detailed", label="Detail level") |
| focus = gr.Textbox( |
| label="Optional focus", |
| placeholder="Example: emphasize the lighting, outfit, exact composition, or product materials", |
| ) |
| run = gr.Button("Generate detailed prompt", variant="primary", size="lg", elem_classes="run-btn") |
|
|
| with gr.Column(scale=6): |
| positive = gr.Textbox( |
| label="Detailed prompt", |
| lines=18, |
| elem_classes="output", |
| ) |
| negative = gr.Textbox( |
| label="Negative prompt", |
| lines=5, |
| elem_classes="output", |
| ) |
|
|
| gr.HTML("<div class='note'>Powered by Qwen3-VL-4B-Instruct · Generated descriptions may need human review for tiny or ambiguous details.</div>") |
|
|
| run.click( |
| fn=generate_prompt, |
| inputs=[source, style, detail, focus], |
| outputs=[positive, negative], |
| api_name="generate_prompt", |
| ) |
| source.upload( |
| fn=generate_prompt, |
| inputs=[source, style, detail, focus], |
| outputs=[positive, negative], |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| demo.queue(default_concurrency_limit=1).launch(css=CSS, show_error=True) |
|
|
