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output directory update

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  1. output/vitonhd-512/unpaired/00654_00.jpg +0 -0
  2. output/vitonhd-512/unpaired/01265_00.jpg +0 -0
  3. output/vitonhd-512/unpaired/01985_00.jpg +0 -0
  4. output/vitonhd-512/unpaired/02023_00.jpg +0 -0
  5. output/vitonhd-512/unpaired/02532_00.jpg +0 -0
  6. output/vitonhd-512/unpaired/02944_00.jpg +0 -0
  7. output/vitonhd-512/unpaired/03191_00.jpg +0 -0
  8. output/vitonhd-512/unpaired/03921_00.jpg +0 -0
  9. output/vitonhd-512/unpaired/05006_00.jpg +0 -0
  10. output/vitonhd-512/unpaired/05378_00.jpg +0 -0
  11. output/vitonhd-512/unpaired/07342_00.jpg +0 -0
  12. output/vitonhd-512/unpaired/08088_00.jpg +0 -0
  13. output/vitonhd-512/unpaired/08239_00.jpg +0 -0
  14. output/vitonhd-512/unpaired/08650_00.jpg +0 -0
  15. output/vitonhd-512/unpaired/08839_00.jpg +0 -0
  16. output/vitonhd-512/unpaired/11085_00.jpg +0 -0
  17. output/vitonhd-512/unpaired/12345_00.jpg +0 -0
  18. output/vitonhd-512/unpaired/12419_00.jpg +0 -0
  19. output/vitonhd-512/unpaired/12562_00.jpg +0 -0
  20. output/vitonhd-512/unpaired/14651_00.jpg +0 -0
  21. sample_inference.ipynb +14 -26
  22. trained_output/vitonhd-384/unpaired/00654_00.jpg +0 -0
  23. trained_output/vitonhd-384/unpaired/01265_00.jpg +0 -0
  24. trained_output/vitonhd-384/unpaired/01985_00.jpg +0 -0
  25. trained_output/vitonhd-384/unpaired/02023_00.jpg +0 -0
  26. trained_output/vitonhd-384/unpaired/02532_00.jpg +0 -0
  27. trained_output/vitonhd-384/unpaired/02944_00.jpg +0 -0
  28. trained_output/vitonhd-384/unpaired/03191_00.jpg +0 -0
  29. trained_output/vitonhd-384/unpaired/03921_00.jpg +0 -0
  30. trained_output/vitonhd-384/unpaired/05006_00.jpg +0 -0
  31. trained_output/vitonhd-384/unpaired/05378_00.jpg +0 -0
  32. trained_output/vitonhd-384/unpaired/07342_00.jpg +0 -0
  33. trained_output/vitonhd-384/unpaired/08088_00.jpg +0 -0
  34. trained_output/vitonhd-384/unpaired/08239_00.jpg +0 -0
  35. trained_output/vitonhd-384/unpaired/08650_00.jpg +0 -0
  36. trained_output/vitonhd-384/unpaired/08839_00.jpg +0 -0
  37. trained_output/vitonhd-384/unpaired/11085_00.jpg +0 -0
  38. trained_output/vitonhd-384/unpaired/12345_00.jpg +0 -0
  39. trained_output/vitonhd-384/unpaired/12419_00.jpg +0 -0
  40. trained_output/vitonhd-384/unpaired/12562_00.jpg +0 -0
  41. trained_output/vitonhd-384/unpaired/14651_00.jpg +0 -0
output/vitonhd-512/unpaired/00654_00.jpg CHANGED
output/vitonhd-512/unpaired/01265_00.jpg CHANGED
output/vitonhd-512/unpaired/01985_00.jpg CHANGED
output/vitonhd-512/unpaired/02023_00.jpg CHANGED
output/vitonhd-512/unpaired/02532_00.jpg CHANGED
output/vitonhd-512/unpaired/02944_00.jpg CHANGED
output/vitonhd-512/unpaired/03191_00.jpg CHANGED
output/vitonhd-512/unpaired/03921_00.jpg CHANGED
output/vitonhd-512/unpaired/05006_00.jpg CHANGED
output/vitonhd-512/unpaired/05378_00.jpg CHANGED
output/vitonhd-512/unpaired/07342_00.jpg CHANGED
output/vitonhd-512/unpaired/08088_00.jpg CHANGED
output/vitonhd-512/unpaired/08239_00.jpg CHANGED
output/vitonhd-512/unpaired/08650_00.jpg CHANGED
output/vitonhd-512/unpaired/08839_00.jpg CHANGED
output/vitonhd-512/unpaired/11085_00.jpg CHANGED
output/vitonhd-512/unpaired/12345_00.jpg CHANGED
output/vitonhd-512/unpaired/12419_00.jpg CHANGED
output/vitonhd-512/unpaired/12562_00.jpg CHANGED
output/vitonhd-512/unpaired/14651_00.jpg CHANGED
sample_inference.ipynb CHANGED
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  "id": "a069151e",
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@@ -253,7 +253,7 @@
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  },
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  "cell_type": "code",
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- "execution_count": 5,
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  "id": "a729bf46",
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@@ -311,13 +299,13 @@
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  " args.__dict__= {\n",
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  " \"dataset_name\": \"vitonhd\",\n",
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  " \"data_root_path\": \"./sample_dataset\",\n",
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- " \"output_dir\": \"./trained_output\",\n",
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  " \"seed\": 555,\n",
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  " \"batch_size\": 1,\n",
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  " \"num_inference_steps\": 50,\n",
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  " \"guidance_scale\": 2.5,\n",
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  " \"width\": 384,\n",
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- " \"height\": 384,\n",
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  " \"repaint\": True,\n",
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  " \"eval_pair\": False,\n",
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  " \"concat_eval_results\": True,\n",
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 2,
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  "id": "bab24c29",
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  "metadata": {},
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  "outputs": [
 
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  {
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  "cell_type": "code",
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+ "execution_count": 3,
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  "id": "a069151e",
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  "metadata": {},
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  "outputs": [
 
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  {
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  "cell_type": "code",
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+ "execution_count": null,
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  "id": "a729bf46",
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  "metadata": {},
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  "outputs": [
 
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  " args.__dict__= {\n",
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  " \"dataset_name\": \"vitonhd\",\n",
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  " \"data_root_path\": \"./sample_dataset\",\n",
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+ " \"output_dir\": \"./output\",\n",
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  " \"seed\": 555,\n",
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  " \"batch_size\": 1,\n",
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  " \"num_inference_steps\": 50,\n",
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  " \"guidance_scale\": 2.5,\n",
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  " \"width\": 384,\n",
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+ " \"height\": 512,\n",
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  " \"repaint\": True,\n",
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  " \"eval_pair\": False,\n",
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  " \"concat_eval_results\": True,\n",
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