Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -26,11 +26,10 @@ qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
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).to(device).eval()
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qwen_processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux", trust_remote_code=True)
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# Prompt Enhancer
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enhancer_long = pipeline("summarization", model="prithivMLmods/t5-Flan-Prompt-Enhance", device=device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE =
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# Qwen2VL caption function – updated to request plain text caption instead of JSON
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@spaces.GPU
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@@ -44,7 +43,6 @@ def qwen_caption(image):
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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# Removed "in the form of JSON data {}" to get plain text caption
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{"type": "text", "text": "Generate a detailed and optimized caption for the given image."},
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],
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}
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@@ -161,7 +159,7 @@ with gr.Blocks(css=custom_css) as demo:
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width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
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height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
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guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
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num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=
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generate_btn = gr.Button("Generate Image Prompt", elem_classes="submit-btn")
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).to(device).eval()
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qwen_processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux", trust_remote_code=True)
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enhancer_long = pipeline("summarization", model="prithivMLmods/t5-Flan-Prompt-Enhance", device=device)
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MAX_SEED = np.iinfo(np.int32).max
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+
MAX_IMAGE_SIZE = 2048
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# Qwen2VL caption function – updated to request plain text caption instead of JSON
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@spaces.GPU
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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{"type": "text", "text": "Generate a detailed and optimized caption for the given image."},
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],
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}
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width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
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height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
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guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
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num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=32)
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generate_btn = gr.Button("Generate Image Prompt", elem_classes="submit-btn")
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