Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -13,8 +13,6 @@ import numpy as np
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from PIL import Image
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import cv2
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from keye_vl_utils import process_vision_info
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from transformers import (
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Qwen2_5_VLForConditionalGeneration,
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AutoModel,
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@@ -24,9 +22,6 @@ from transformers import (
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)
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from transformers.image_utils import load_image
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import subprocess
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
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-
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# Constants for text generation
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MAX_MAX_NEW_TOKENS = 2048
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DEFAULT_MAX_NEW_TOKENS = 1024
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@@ -52,16 +47,6 @@ model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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torch_dtype=torch.float16
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).to(device).eval()
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# Load Keye-VL-8B-Preview
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MODEL_ID_K = "Kwai-Keye/Keye-VL-8B-Preview"
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processor_k = AutoModel.from_pretrained(MODEL_ID_K, trust_remote_code=True)
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model_k = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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MODEL_ID_K,
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attn_implementation="flash_attention_2",
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trust_remote_code=True,
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torch_dtype=torch.float16
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).to(device).eval()
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def downsample_video(video_path):
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"""
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Downsamples the video to evenly spaced frames.
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@@ -99,9 +84,6 @@ def generate_image(model_name: str, text: str, image: Image.Image,
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elif model_name == "Qwen2.5-VL-3B-Instruct":
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processor = processor_x
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model = model_x
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elif model_name == "Keye-VL-8B-Preview":
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processor = processor_k
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model = model_k
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else:
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yield "Invalid model selected."
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return
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@@ -152,9 +134,6 @@ def generate_video(model_name: str, text: str, video_path: str,
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elif model_name == "Qwen2.5-VL-3B-Instruct":
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processor = processor_x
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model = model_x
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elif model_name == "Keye-VL-8B-Preview":
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processor = processor_k
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model = model_k
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else:
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yield "Invalid model selected."
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return
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@@ -253,7 +232,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
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with gr.Column():
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output = gr.Textbox(label="Output", interactive=False, lines=2, scale=2)
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model_choice = gr.Radio(
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choices=["Qwen2.5-VL-7B-Instruct", "Qwen2.5-VL-3B-Instruct"
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label="Select Model",
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value="Qwen2.5-VL-7B-Instruct"
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)
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from PIL import Image
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import cv2
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from transformers import (
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Qwen2_5_VLForConditionalGeneration,
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AutoModel,
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)
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from transformers.image_utils import load_image
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# Constants for text generation
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MAX_MAX_NEW_TOKENS = 2048
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DEFAULT_MAX_NEW_TOKENS = 1024
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torch_dtype=torch.float16
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).to(device).eval()
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def downsample_video(video_path):
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"""
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Downsamples the video to evenly spaced frames.
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elif model_name == "Qwen2.5-VL-3B-Instruct":
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processor = processor_x
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model = model_x
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else:
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yield "Invalid model selected."
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return
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elif model_name == "Qwen2.5-VL-3B-Instruct":
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processor = processor_x
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model = model_x
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else:
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yield "Invalid model selected."
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return
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with gr.Column():
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output = gr.Textbox(label="Output", interactive=False, lines=2, scale=2)
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model_choice = gr.Radio(
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choices=["Qwen2.5-VL-7B-Instruct", "Qwen2.5-VL-3B-Instruct"],
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label="Select Model",
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value="Qwen2.5-VL-7B-Instruct"
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)
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