Spaces:
Running on Zero
Running on Zero
| import os | |
| os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True") | |
| import spaces # MUST come before torch / any CUDA-touching import | |
| import torch | |
| import gradio as gr | |
| from pathlib import Path | |
| # Monkey-patch safetensors to load on CPU, bypassing ZeroGPU's CUDA interception | |
| import safetensors.torch as _st | |
| _orig_safe_load_file = _st.load_file | |
| def _patched_safe_load_file(filename, device="cpu", **kwargs): | |
| return _orig_safe_load_file(filename, device="cpu", **kwargs) | |
| _st.load_file = _patched_safe_load_file | |
| # Also patch in peft's save_and_load module so it uses our patched version | |
| import peft.utils.save_and_load as _peft_sl | |
| _peft_sl.safe_load_file = _patched_safe_load_file | |
| from transformers import AutoProcessor, Qwen3VLForConditionalGeneration | |
| from peft import PeftModel | |
| from qwen_vl_utils import process_vision_info | |
| BASE_MODEL_ID = "Qwen/Qwen3-VL-8B-Instruct" | |
| LORA_ADAPTER_ID = "RainChow/PinpointQA-Qwen3-VL-8B-Instruct-LoRA" | |
| print("Loading base model on CPU...") | |
| model = Qwen3VLForConditionalGeneration.from_pretrained( | |
| BASE_MODEL_ID, | |
| dtype=torch.bfloat16, | |
| attn_implementation="sdpa", | |
| ) | |
| print("Loading and merging LoRA adapter on CPU...") | |
| model = PeftModel.from_pretrained(model, LORA_ADAPTER_ID) | |
| model = model.merge_and_unload() | |
| model.eval() | |
| print("Moving merged model to CUDA...") | |
| model = model.to("cuda") | |
| print("Loading processor...") | |
| processor = AutoProcessor.from_pretrained(BASE_MODEL_ID) | |
| print("Model loaded successfully!") | |
| def run_inference(video, question, fps=1.0, max_new_tokens=512): | |
| """Answer a spatial understanding question about an indoor video. | |
| Upload a video (or select an example) and ask a question about where a | |
| small object is located. The model will describe the object's spatial | |
| position relative to nearby reference objects. | |
| Args: | |
| video: Uploaded video file (mp4, etc.). | |
| question: A spatial question, e.g. "Where is the scissors?" | |
| fps: Frames per second to sample from the video. | |
| max_new_tokens: Maximum number of tokens to generate. | |
| """ | |
| if video is None: | |
| return "Please upload a video or select an example." | |
| if not question or not question.strip(): | |
| return "Please enter a question about the spatial location of an object." | |
| video_path = video | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "video", | |
| "video": f"file://{video_path}", | |
| "max_pixels": 360 * 420, | |
| "fps": fps, | |
| }, | |
| {"type": "text", "text": question}, | |
| ], | |
| } | |
| ] | |
| text = processor.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| images, videos = process_vision_info(messages) | |
| inputs = processor( | |
| text=[text], | |
| images=images, | |
| videos=videos, | |
| return_tensors="pt", | |
| padding=True, | |
| ) | |
| inputs = inputs.to(model.device) | |
| with torch.inference_mode(): | |
| generated_ids = model.generate( | |
| **inputs, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=False, | |
| use_cache=True, | |
| ) | |
| generated_ids_trimmed = [ | |
| out_ids[len(in_ids):] | |
| for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| ] | |
| output_text = processor.batch_decode( | |
| generated_ids_trimmed, | |
| skip_special_tokens=True, | |
| clean_up_tokenization_spaces=False, | |
| )[0] | |
| return output_text | |
| CSS = """ | |
| #col-container { max-width: 1100px; margin: 0 auto; } | |
| .dark .gradio-container { color: var(--body-text-color); } | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # PinpointQA: Spatial Grounding in Indoor Videos | |
| Ask questions about the spatial location of small objects in indoor video scenes. | |
| The model identifies objects and describes their position relative to nearby references. | |
| Built on [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) fine-tuned with | |
| [PinpointQA LoRA](https://huggingface.co/RainChow/PinpointQA-Qwen3-VL-8B-Instruct-LoRA) from the | |
| [PinpointQA benchmark](https://huggingface.co/papers/2604.08991). | |
| """ | |
| ) | |
| with gr.Row(elem_id="col-container"): | |
| with gr.Column(scale=2): | |
| video_input = gr.Video(label="Indoor Video") | |
| question_input = gr.Textbox( | |
| label="Spatial Question", | |
| placeholder="e.g. Where is the scissors? Describe its location relative to nearby objects.", | |
| lines=2, | |
| ) | |
| run_btn = gr.Button("Run Spatial Grounding", variant="primary") | |
| with gr.Column(scale=3): | |
| output_text = gr.Textbox( | |
| label="Model Response", | |
| lines=12, | |
| interactive=False, | |
| ) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| fps_slider = gr.Slider( | |
| minimum=0.5, maximum=4.0, value=1.0, step=0.5, | |
| label="Sampling FPS", | |
| info="Frames per second to sample from the video", | |
| ) | |
| max_tokens = gr.Slider( | |
| minimum=128, maximum=1024, value=512, step=64, | |
| label="Max New Tokens", | |
| info="Maximum number of tokens the model can generate", | |
| ) | |
| examples_dir = Path("examples") | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| str(examples_dir / "sample1.mp4"), | |
| "Where is the scissors? Describe its location relative to nearby objects.", | |
| ], | |
| [ | |
| str(examples_dir / "sample2.mp4"), | |
| "Where is the pen holder? Describe its location relative to nearby objects.", | |
| ], | |
| [ | |
| str(examples_dir / "sample3.mp4"), | |
| "Where is the mouse? Describe its location relative to nearby objects.", | |
| ], | |
| ], | |
| inputs=[video_input, question_input], | |
| outputs=output_text, | |
| fn=run_inference, | |
| cache_examples=True, | |
| cache_mode="lazy", | |
| ) | |
| run_btn.click( | |
| fn=run_inference, | |
| inputs=[video_input, question_input, fps_slider, max_tokens], | |
| outputs=output_text, | |
| api_name="predict", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(mcp_server=True, theme=gr.themes.Citrus(), css=CSS) |