pre-commit

app.py

@@ -7,64 +7,72 @@ import numpy as np
 import PIL.Image
 import torch
 import torchvision.transforms.functional as TF
-from diffusers import EulerAncestralDiscreteScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter, AutoencoderKL
+from diffusers import (
+    AutoencoderKL,
+    EulerAncestralDiscreteScheduler,
+    StableDiffusionXLAdapterPipeline,
+    T2IAdapter,
+)
 
 DESCRIPTION = "# T2I-Adapter-SDXL Sketch"
 
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
-styles = [
+style_list = [
     {
         "name": "Cinematic",
         "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
-        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"
+        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
     },
     {
         "name": "3D Model",
         "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
-        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting"
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
     },
     {
         "name": "Anime",
         "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
-        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast"
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
     },
     {
         "name": "Digital Art",
         "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
-        "negative_prompt": "photo, photorealistic, realism, ugly"
+        "negative_prompt": "photo, photorealistic, realism, ugly",
     },
     {
         "name": "Photographic",
         "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
-        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly"
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
     },
     {
         "name": "Pixel art",
         "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
-        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic"
+        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
     },
     {
         "name": "Fantasy art",
         "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
-        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white"
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
     },
 ]
 
-styles = {k['name']: (k['prompt'], k['negative_prompt']) for k in styles}
-default_style = styles['Photographic']
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+default_style = styles["Photographic"]
 style_names = list(styles.keys())
 
 
 def apply_style(style, positive, negative=""):
     p, n = styles.get(style, default_style)
-    return p.replace('{prompt}', positive), n + negative
+    return p.replace("{prompt}", positive), n + negative
+
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 if torch.cuda.is_available():
     model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-    adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16")
+    adapter = T2IAdapter.from_pretrained(
+        "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
+    )
     scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
     pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
         model_id,
@@ -135,14 +143,8 @@ with gr.Blocks() as demo:
             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,
-                    label="Style"
-                )
-                negative_prompt = gr.Textbox(
-                    label="Negative prompt", value=""
-                )
+                style = gr.Dropdown(choices=style_names, value=default_style, label="Style")
+                negative_prompt = gr.Textbox(label="Negative prompt", value="")
                 num_steps = gr.Slider(
                     label="Number of steps",
                     minimum=1,
@@ -162,14 +164,14 @@ with gr.Blocks() as demo:
                     minimum=0.5,
                     maximum=1,
                     step=0.1,
-                    value=.8,
+                    value=0.8,
                 )
                 cond_tau = gr.Slider(
                     label="Fraction of timesteps for which adapter should be applied",
                     minimum=0.5,
                     maximum=1,
                     step=0.1,
-                    value=.8,
+                    value=0.8,
                 )
                 seed = gr.Slider(
                     label="Seed",

requirements.txt

@@ -1,8 +1,8 @@
+accelerate==0.22.0
 git+https://github.com/huggingface/diffusers@t2iadapterxl
-accelerate
-safetensors
 gradio==3.42.0
 Pillow==10.0.0
+safetensors==0.3.3
 torch==2.0.1
+torchvision==0.15.2
 transformers==4.33.0
-torchvision