From cf917864831ef0d8f73b9aeafe5201b0be3a106a Mon Sep 17 00:00:00 2001
From: number_cruncher <lennart.oestreich@stud.tu-darmstadt.de>
Date: Tue, 8 Apr 2025 18:38:31 +0200
Subject: [PATCH] after str
---
BE2_GAN_and_Diffusion_colab_3.ipynb | 11 +++--------
1 file changed, 3 insertions(+), 8 deletions(-)
diff --git a/BE2_GAN_and_Diffusion_colab_3.ipynb b/BE2_GAN_and_Diffusion_colab_3.ipynb
index f4a2eee..6d75e77 100644
--- a/BE2_GAN_and_Diffusion_colab_3.ipynb
+++ b/BE2_GAN_and_Diffusion_colab_3.ipynb
@@ -44738,16 +44738,11 @@
"source": [
"<font color='red'>Question</font> \n",
"What are the differences between the UNet used for the cGAN generator and the one defined above ? \n",
- "Indicate the differences in the architecture by analyzing both models \\_\\_str\\_\\_."
+ "Indicate the differences in the architecture by analyzing both models \\_\\_str\\_\\_.\n",
+ "\n",
+ "The UNet2DModel uses a 1 channel input and the UNet from before a 3 channels input. Furthermore UNet2dModel uses a conditioning on time steps as it is necessary for diffusion. Also it uses SiLu activations instead of LeakyRelu/Relu/tanh. On top of that UNet2dModel uses GroupNorm and the other model uses BatchNorm2d "
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {
--
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