pre-commit

app_base.py

@@ -4,10 +4,13 @@ import gradio as gr
 import PIL.Image
 
 from model import ADAPTER_NAMES, Model
-from utils import MAX_SEED, randomize_seed_fn, styles, style_names, apply_style
-
-
-default_style_name = "Photographic"
+from utils import (
+    DEFAULT_STYLE_NAME,
+    MAX_SEED,
+    STYLE_NAMES,
+    apply_style,
+    randomize_seed_fn,
+)
 
 
 def create_demo(model: Model) -> gr.Blocks:
@@ -16,7 +19,7 @@ def create_demo(model: Model) -> gr.Blocks:
         prompt: str,
         negative_prompt: str,
         adapter_name: str,
-        style_name: str = default_style_name,
+        style_name: str = DEFAULT_STYLE_NAME,
         num_inference_steps: int = 30,
         guidance_scale: float = 5.0,
         adapter_conditioning_scale: float = 1.0,
@@ -26,7 +29,7 @@ def create_demo(model: Model) -> gr.Blocks:
         progress=gr.Progress(track_tqdm=True),
     ) -> list[PIL.Image.Image]:
         prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
-        
+
         return model.run(
             image=image,
             prompt=prompt,
@@ -54,7 +57,7 @@ def create_demo(model: Model) -> gr.Blocks:
                         label="Negative prompt",
                         value="",
                     )
-                    style = gr.Dropdown(choices=style_names, value=default_style_name, label="Style")
+                    style = gr.Dropdown(choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Style")
                     num_inference_steps = gr.Slider(
                         label="Number of steps",
                         minimum=1,

app_sketch.py

@@ -6,20 +6,23 @@ import torch
 import torchvision.transforms.functional as TF
 
 from model import Model
-from utils import MAX_SEED, randomize_seed_fn, styles, style_names, apply_style
-
+from utils import (
+    DEFAULT_STYLE_NAME,
+    MAX_SEED,
+    STYLE_NAMES,
+    apply_style,
+    randomize_seed_fn,
+)
 
 SKETCH_ADAPTER_NAME = "TencentARC/t2i-adapter-sketch-sdxl-1.0"
 
-default_style_name = "Photographic"
-
 
 def create_demo(model: Model) -> gr.Blocks:
     def run(
         image: PIL.Image.Image,
         prompt: str,
         negative_prompt: str,
-        style_name: str = default_style_name,
+        style_name: str = DEFAULT_STYLE_NAME,
         num_steps: int = 25,
         guidance_scale: float = 5,
         adapter_conditioning_scale: float = 0.8,
@@ -63,7 +66,7 @@ def create_demo(model: Model) -> gr.Blocks:
                     prompt = gr.Textbox(label="Prompt")
                     run_button = gr.Button("Run")
                 with gr.Accordion("Advanced options", open=False):
-                    style = gr.Dropdown(choices=style_names, value=default_style_name, label="Style")
+                    style = gr.Dropdown(choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME, label="Style")
                     negative_prompt = gr.Textbox(label="Negative prompt")
                     num_steps = gr.Slider(
                         label="Number of steps",

model.py

@@ -47,10 +47,11 @@ SD_XL_BASE_RATIOS = {
     "3.0": (1728, 576),
 }
 
+
 def find_closest_aspect_ratio(target_width, target_height):
     target_ratio = target_width / target_height
     closest_ratio = None
-    min_difference = float('inf')
+    min_difference = float("inf")
 
     for ratio_str, (width, height) in SD_XL_BASE_RATIOS.items():
         ratio = width / height
@@ -260,7 +261,9 @@ class Model:
                 variant="fp16",
             ).to(self.device)
             self.pipe.enable_xformers_memory_efficient_attention()
-            self.pipe.load_lora_weights("stabilityai/stable-diffusion-xl-base-1.0", weight_name="sd_xl_offset_example-lora_1.0.safetensors")
+            self.pipe.load_lora_weights(
+                "stabilityai/stable-diffusion-xl-base-1.0", weight_name="sd_xl_offset_example-lora_1.0.safetensors"
+            )
             self.pipe.fuse_lora(lora_scale=0.4)
         else:
             self.preprocessor = None  # type: ignore

utils.py

@@ -44,20 +44,22 @@ style_list = [
     {
         "name": "Neonpunk",
         "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
-        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured"
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
     },
     {
         "name": "Manga",
         "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
-        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style"
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
     },
 ]
 
 styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-style_names = list(styles.keys())
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Photographic"
+
 
 def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
-    p, n = styles.get(style_name, "Photographic")
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
     return p.replace("{prompt}", positive), n + negative