Adjust parameters to address render issues.

- Disable tiling on the first pass to produce less dense patterns.
- Increase inference steps to 50 to generate higher quality / higher variety images.
- Increase guidance scale to generate higher variety images.
- Decrease img2img strength to 0.3 to ensure the img2img pass tiles effectively.

handler.py

@@ -6,13 +6,14 @@ import torch
 from utils import pipes, tiling, wallpaper, images
 
 desired_output_width = 1024
-inference_steps = 25
-img2img_strength = 0.5
+inference_steps = 50
+guidance_scale = 10
+img2img_strength = 0.3
 
 
 class EndpointHandler:
     def __init__(self, path=""):
-        self.sdxl_pipe = pipes.create_stable_diffusion_xl_pipeline("cuda:0")
+        self.sdxl_pipe = pipes.create_stable_diffusion_xl_pipeline("cuda:0", enable_tiling=False)
         self.sdxl_img2img_pipe = pipes.create_stable_diffusion_xl_img2img_pipe("cuda:1")
         print("post __init__ memory summary")
         print(torch.cuda.memory_summary())
@@ -29,7 +30,7 @@ class EndpointHandler:
         original_image_size = tiling.compute_input_tile_width_for_desired_output(desired_output_width)
 
         original_image = self.sdxl_pipe(prompt=prompt, num_inference_steps=inference_steps, width=original_image_size,
-                                        height=original_image_size).images[0]
+                                        height=original_image_size, guidance_scale=guidance_scale).images[0]
         print("post-SDXL memory summary")
         print(torch.cuda.memory_summary())
         inner_rotated_tile = images.extract_inner_rotated_tile_from_image(original_image)

utils/pipes.py

@@ -5,14 +5,15 @@ from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipelin
 from utils import tiling
 
 
-def create_stable_diffusion_xl_pipeline(device):
+def create_stable_diffusion_xl_pipeline(device, enable_tiling=True):
     vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
     pipe = StableDiffusionXLPipeline.from_pretrained(
         "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16",
         use_safetensors=True
     )
-    pipe.vae.disable_tiling()
-    tiling.enable_circular_tiling([pipe.vae, pipe.text_encoder, pipe.unet])
+    if enable_tiling:
+        pipe.vae.disable_tiling()
+        tiling.enable_circular_tiling([pipe.vae, pipe.text_encoder, pipe.unet])
     pipe = pipe.to(device)
     return pipe