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Add example space.
Browse files- app.py +57 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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import torch
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from PIL import Image
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from transformers import AutoModelForCausalLM, AutoProcessor
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from starvector.data.util import process_and_rasterize_svg
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# Load model and processor
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model = AutoModelForCausalLM.from_pretrained(
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"starvector/starvector-8b-im2svg",
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trust_remote_code=True,
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torch_dtype=torch.float16
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).cuda()
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processor = AutoProcessor.from_pretrained("starvector/starvector-8b-im2svg")
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def generate_svg(input_data, input_type):
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if input_type == "image":
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# Process image input
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image = processor(input_data, return_tensors="pt")['pixel_values'].cuda()
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raw_svg = model.generate_im2svg({"image": image}, max_length=4000)[0]
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else:
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# Process text input
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raw_svg = model.generate_text2svg(input_data, max_length=4000)[0]
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svg_code, raster_image = process_and_rasterize_svg(raw_svg)
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return svg_code, raster_image
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with gr.Blocks() as demo:
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gr.Markdown("# 💫 StarVector SVG Generator")
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with gr.Tab("Image to SVG"):
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gr.Markdown("Upload an image to convert to SVG")
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with gr.Row():
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image_input = gr.Image(type="pil", label="Input Image")
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image_output = gr.Image(label="SVG Preview")
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svg_code = gr.Code(label="Generated SVG Code")
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image_button = gr.Button("Convert to SVG")
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with gr.Tab("Text to SVG"):
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gr.Markdown("Enter text to generate SVG")
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with gr.Row():
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text_input = gr.Textbox(label="Text Prompt")
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text_output = gr.Image(label="SVG Preview")
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text_svg_code = gr.Code(label="Generated SVG Code")
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text_button = gr.Button("Generate SVG")
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image_button.click(
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fn=lambda x: generate_svg(x, "image"),
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inputs=image_input,
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outputs=[svg_code, image_output]
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)
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text_button.click(
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fn=lambda x: generate_svg(x, "text"),
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inputs=text_input,
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outputs=[text_svg_code, text_output]
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)
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demo.launch()
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requirements.txt
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@@ -0,0 +1,5 @@
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gradio
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transformers
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torch
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accelerate
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starvector
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