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@@ -79,7 +79,7 @@ <h1>Project 5: Fun with Diffusion Models</h1>
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<sectionid="part-0">
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<h2>Part 0 – Prompting and Sampling</h2>
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To demonstrate the usage of the DeepFloyd IF diffusion model, below are a few examples of different prompts using 20 inference steps with stage 1 of the model, which generates images at 64x64 resolution:
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To demonstrate the usage of the DeepFloyd IF diffusion model, below are a few examples of different prompts using 20 inference steps with stage 1 of the model, which generates images at 64x64 resolution. Using a seed value of 180:
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<divclass="subsection" id="part-0-images">
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<h3>Images generated with num_inference_steps=20</h3>
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Starting with pure noise, we can obtain random denoise images by setting the starting index of <code>strided_timestamps</code> to 0, and using the prompt <code>'a high quality photo'</code>. Below are a few examples:
Using γ = 7 and the conditional & unconditional prompts be <code>'a high quality photo'</code>and the null prompt, we the following sample images:
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By setting <code>scale = 7</code> (γ = 7) and the conditional & unconditional prompts be <code>'a high quality photo'</code>& the null prompt<code>''</code>, we get the following sample images:
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<divclass="subsection">
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<h3>5 Images with Prompt "a high quality photo" (γ = 7)</h3>
<h2>Part 1.7 – SDEdit-Style Edits and Inpainting</h2>
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<h2>Part 1.7 – Image-to-image Translation</h2>
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Similar to how we added noise to an existing image before denoising the result in part 1.4, we can use the <code>iterative_denoise_cfg</code> function to get a result that is of higher quality, as opposed to merely a prediction of the original. By adjusting the starting amount of noise to the Campanile with the timestamp index <code>i_start</code>, where a higher index means less noise, we get a series of edits to the that gradually go from entirely new to resembling the original image:
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<!-- 1.7: Edits of Campanile -->
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@@ -471,6 +471,10 @@ <h3>1.7.1 – Edits of the Campanile (Noise Levels [1, 3, 5, 7, 10, 20])</h3>
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