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@@ -20,18 +20,18 @@ Given a target image, WeavePrompt automatically generates and refines text promp
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  **WeavePrompt** is a research and development project designed to evaluate and refine text-to-image generation prompts across multiple state-of-the-art image generation models. The primary goal is to optimize prompts such that the generated images align closely with a given reference image, improving both fidelity and semantic consistency.
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- The process involves generating images from identical prompts using various image generation models, comparing the results to a reference image through a recognition and similarity evaluation pipeline, and iteratively adjusting the prompt to minimize perceptual differences. This feedback loop continues for a set number of iterations, progressively enhancing prompt effectiveness.
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  To achieve this, **WeavePrompt** integrates advanced tools:
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- **Image recognition** is powered by meta-llama/Llama-4-Scout-17B-16E-Instruct.
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- **Similarity evaluation** uses the **LPIPS (alex)** metric for perceptual comparison.
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- **Image generation models** under evaluation include:
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- - FLUX family: FLUX.1 [pro], [dev], [schnell], and FLUX.1 with LoRAs
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- - Google models: Imagen 4, Imagen 4 Ultra, and Gemini 2.5 Flash Image
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- - Other models: Stable Diffusion 3.5 Large and Qwen Image
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  By systematically combining prompt optimization with multi-model evaluation, **WeavePrompt** aims to advance the understanding of cross-model prompt effectiveness and improve controllability in image generation tasks.
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  **WeavePrompt** is a research and development project designed to evaluate and refine text-to-image generation prompts across multiple state-of-the-art image generation models. The primary goal is to optimize prompts such that the generated images align closely with a given reference image, improving both fidelity and semantic consistency.
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+ **Procedure**: the process involves generating images from identical prompts using various image generation models, comparing the results to a reference image through a recognition and similarity evaluation pipeline, and iteratively adjusting the prompt to minimize perceptual differences. This feedback loop continues for a set number of iterations, progressively enhancing prompt effectiveness.
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  To achieve this, **WeavePrompt** integrates advanced tools:
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+ - **Image recognition** is powered by meta-llama/Llama-4-Scout-17B-16E-Instruct.
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+ - **Similarity evaluation** uses the **LPIPS (alex)** metric for perceptual comparison.
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+ - **Image generation models** under evaluation include:
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+ - FLUX family: FLUX.1 [pro], [dev], [schnell], and FLUX.1 with LoRAs
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+ - Google models: Imagen 4, Imagen 4 Ultra, and Gemini 2.5 Flash Image
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+ - Other models: Stable Diffusion 3.5 Large and Qwen Image
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  By systematically combining prompt optimization with multi-model evaluation, **WeavePrompt** aims to advance the understanding of cross-model prompt effectiveness and improve controllability in image generation tasks.
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