add test for "Adapting SDXL" guide

Author: catwell

docs/guides/adapting_sdxl/index.md

@@ -58,9 +58,9 @@ Then, define the inference parameters by setting the appropriate prompt / seed /
 prompt = "a futuristic castle surrounded by a forest, mountains in the background"
 seed = 42
 sdxl.set_inference_steps(50, first_step=0)
-sdxl.set_self_attention_guidance(
-    enable=True, scale=0.75
-)  # Enable self-attention guidance to enhance the quality of the generated images
+
+# Enable self-attention guidance to enhance the quality of the generated images
+sdxl.set_self_attention_guidance(enable=True, scale=0.75)
 
 # ... Inference process
 
@@ -76,10 +76,10 @@ with no_grad():  # Disable gradient calculation for memory-efficient inference
     )
     time_ids = sdxl.default_time_ids
 
-    manual_seed(seed=seed)
+    manual_seed(seed)
 
-    # Using a higher latents inner dim to improve resolution of generated images
-    x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
+    # SDXL typically generates 1024x1024, here we use a higher resolution.
+    x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
 
     # Diffusion process
     for step in sdxl.steps:
@@ -131,8 +131,8 @@ predicted_image.save("vanilla_sdxl.png")
 
         manual_seed(seed=seed)
 
-        # Using a higher latents inner dim to improve resolution of generated images
-        x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
+        # SDXL typically generates 1024x1024, here we use a higher resolution.
+        x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
 
         # Diffusion process
         for step in sdxl.steps:
@@ -213,8 +213,8 @@ manager.add_loras("scifi-lora", tensors=scifi_lora_weights)
 
         manual_seed(seed=seed)
 
-        # Using a higher latents inner dim to improve resolution of generated images
-        x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
+        # SDXL typically generates 1024x1024, here we use a higher resolution.
+        x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
 
         # Diffusion process
         for step in sdxl.steps:
@@ -304,8 +304,8 @@ manager.add_loras("pixel-art-lora", load_from_safetensors("pixel-art-xl-v1.1.saf
 
         manual_seed(seed=seed)
 
-        # Using a higher latents inner dim to improve resolution of generated images
-        x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
+        # SDXL typically generates 1024x1024, here we use a higher resolution.
+        x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
 
         # Diffusion process
         for step in sdxl.steps:
@@ -440,7 +440,7 @@ with torch.no_grad():
         ip_adapter.set_clip_image_embedding(clip_image_embedding)
 
         manual_seed(seed=seed)
-        x = torch.randn(size=(1, 4, 128, 128), device=sdxl.device, dtype=sdxl.dtype)
+        x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
 
         # Diffusion process
         for step in sdxl.steps:
@@ -578,7 +578,7 @@ with torch.no_grad():
         t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
 
         manual_seed(seed=seed)
-        x = torch.randn(size=(1, 4, 128, 128), device=sdxl.device, dtype=sdxl.dtype)
+        x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
 
         # Diffusion process
         for step in sdxl.steps:

scripts/prepare_test_weights.py

@@ -253,6 +253,20 @@ def download_loras():
     )
     download_file("https://sliders.baulab.info/weights/xl_sliders/eyesize.pt", dest_folder, expected_hash="ee170e4d")
 
+    dest_folder = os.path.join(test_weights_dir, "loras")
+    download_file(
+        "https://civitai.com/api/download/models/140624",
+        filename="Sci-fi_Environments_sdxl.safetensors",
+        dest_folder=dest_folder,
+        expected_hash="6a4afda8",
+    )
+    download_file(
+        "https://civitai.com/api/download/models/135931",
+        filename="pixel-art-xl-v1.1.safetensors",
+        dest_folder=dest_folder,
+        expected_hash="71aaa6ca",
+    )
+
 
 def download_preprocessors():
     dest_folder = os.path.join(test_weights_dir, "carolineec", "informativedrawings")