Spaces:
Sleeping
Sleeping
File size: 2,881 Bytes
0bf8729 71ef59e 0bf8729 71ef59e 0bf8729 71ef59e 0bf8729 71ef59e 0bf8729 71ef59e 0bf8729 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
import spaces
import gradio as gr
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# --- Installing Flash Attention for ZeroGPU is special --- #
import subprocess
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
# --- now we got Flash Attention ---#
# The model is trained on 8.0 FPS which we recommend for optimal inference
@spaces.GPU(duration=30)
def load_model(
model_name: str = "chancharikm/qwen2.5-vl-7b-cam-motion-preview",
use_flash_attention: bool = True,
):
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
model = (
Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="cuda",
)
if use_flash_attention
else Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_name,
torch_dtype="auto",
device_map="cuda",
)
)
return model
@spaces.GPU(duration=120)
def inference(video_path: str):
# default processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": video_path,
"fps": 8.0,
},
{"type": "text", "text": "Describe the camera motion in this video."},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs, video_kwargs = process_vision_info(
messages, return_video_kwargs=True
)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
fps=fps,
padding=True,
return_tensors="pt",
**video_kwargs,
)
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
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,
)
return output_text
demo = gr.Interface(
fn=inference,
inputs=[
gr.Video(label="Input Video"),
],
outputs=gr.JSON(label="Output JSON"),
title="",
api_name="video_inference",
)
demo.launch(
mcp_server=True, app_kwargs={"docs_url": "/docs"} # add FastAPI Swagger API Docs
)
|