hmm

app-ok.py

@@ -1,294 +0,0 @@
-#!/usr/bin/env python
-
-import json
-import os
-import random
-from typing import Tuple
-import uuid
-
-from diffusers import DiffusionPipeline
-import gradio as gr
-import numpy as np
-from PIL import Image
-import spaces
-import torch
-
-from gradio_imagefeed import ImageFeed
-
-
-DEFAULT_STYLE = "Photograph"
-DEFAULT_NEGATIVE = (
-    "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon,"
-    " drawing, anime, asian, bad anatomy:1.4), text, close up, cropped, out of frame,"
-    " worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated,"
-    " extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation,"
-    " deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned"
-    " face, disfigured, gross proportions, malformed limbs, missing arms, missing legs,"
-    " extra arms, extra legs, fused fingers, too many fingers, long neck, {negative}"
-)
-STYLES = {
-    "Photograph": (
-        (
-            "realistic photograph of {positive}, ultra fine detail, lifelike,"
-            " high-resolution, sharp, realistic colors, photorealistic, Nikon, 35mm"
-        ),
-        DEFAULT_NEGATIVE,
-    ),
-    "Cinematic": (
-        (
-            "cinematic photograph of {positive}, 35mm photograph, film, bokeh,"
-            " professional, 4k, highly detailed"
-        ),
-        DEFAULT_NEGATIVE,
-    ),
-    "Still Photo": (
-        (
-            "cinematic still photograph of {positive}, emotional, harmonious, vignette,"
-            " highly detailed, bokeh, cinemascope, moody, epic, gorgeous, film grain,"
-            " grainy, high resolution"
-        ),
-        DEFAULT_NEGATIVE,
-    ),
-}
-
-
-def apply_style(name: str, pos: str, neg: str) -> Tuple[str, str]:
-    try:
-        def_pos, def_neg = STYLES[name]
-    except KeyError:
-        def_pos, def_neg = "{positive}", "{negative}"
-    finally:
-        pos = def_pos.replace("{positive}", pos).strip().strip(",")
-        neg = def_neg.replace("{negative}", ", " + neg).strip().strip(",")
-        return (pos, neg)
-
-
-DESCRIPTION = ""
-MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = "lazy"
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "1") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "1") == "1"
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-NUM_IMAGES_PER_PROMPT = 2
-
-if torch.cuda.is_available():
-    pipe = DiffusionPipeline.from_pretrained(
-        "SG161222/RealVisXL_V3.0_Turbo",
-        torch_dtype=torch.float16,
-        use_safetensors=True,
-        add_watermarker=False,
-        variant="fp16",
-    )
-    pipe2 = DiffusionPipeline.from_pretrained(
-        "SG161222/RealVisXL_V2.02_Turbo",
-        torch_dtype=torch.float16,
-        use_safetensors=True,
-        add_watermarker=False,
-        variant="fp16",
-    )
-    if ENABLE_CPU_OFFLOAD:
-        pipe.enable_model_cpu_offload()
-        pipe2.enable_model_cpu_offload()
-    else:
-        pipe.to(device)
-        pipe2.to(device)
-        print("Loaded on Device!")
-
-    if USE_TORCH_COMPILE:
-        pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=True)
-        pipe2.unet = torch.compile(pipe2.unet, mode="max-autotune", fullgraph=True)
-        print("Model Compiled!")
-
-
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-@spaces.GPU(enable_queue=True)
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    style: str = DEFAULT_STYLE,
-    seed: int = 0,
-    width: int = 896,
-    height: int = 1152,
-    guidance_scale: float = 3,
-    randomize_seed: bool = False,
-    use_resolution_binning: bool = True,
-    progress=gr.Progress(track_tqdm=True),
-):
-
-    
-    print("prompt1 = ", prompt)
-    print("negative_prompt1 = ", negative_prompt)
-    
-    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)
-
-    print("prompt2 = ", prompt)
-    print("negative_prompt2 = ", negative_prompt)
-    
-    print("seed = ", seed)
-    
-    options = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "width": width,
-        "height": height,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": 25,
-        "generator": generator,
-        "num_images_per_prompt": NUM_IMAGES_PER_PROMPT,
-        "use_resolution_binning": use_resolution_binning,
-        "output_type": "pil",
-    }
-
-    print("options = ")
-    print(options)
-
-    images = pipe(**options).images + pipe2(**options).images
-
-    image_paths = [save_image(img) for img in images]
-    return image_paths, seed
-
-
-examples = [
-    (
-        "college life of 21 year old, depth of field, bokeh, shallow"
-        " focus, minimalism, fujifilm xh2s with Canon EF lens, cinematic --ar 85:128"
-        " --v 6.0 --style raw"
-    ),
-]
-
-css = """
-"""
-
-with gr.Blocks(css=css, theme="rawrsor1/Everforest") as demo:
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=4,
-                placeholder="Enter a Prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run")
-        result = ImageFeed(label="Result")
-        # result = gr.Gallery(label="Result", columns=1, preview=True)
-
-    with gr.Accordion("Advanced", open=False):
-        use_negative_prompt = gr.Checkbox(
-            label="Use Negative", value=True, visible=True
-        )
-        negative_prompt = gr.Text(
-            label="Negative Prompt",
-            max_lines=4,
-            placeholder="",
-            value="",
-            visible=True,
-        )
-        with gr.Row():
-            num_inference_steps = gr.Slider(
-                label="Steps",
-                minimum=10,
-                maximum=60,
-                step=1,
-                value=30,
-            )
-        with gr.Row():
-            num_images_per_prompt = gr.Slider(
-                label="Image Count",
-                minimum=1,
-                maximum=5,
-                step=1,
-                value=2,
-            )
-        seed = gr.Slider(
-            label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, visible=True
-        )
-        randomize_seed = gr.Checkbox(label="New Seed", value=True)
-        with gr.Row(visible=True):
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=2048,
-                step=16,
-                value=896,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=2048,
-                step=16,
-                value=1152,
-            )
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance",
-                minimum=0.1,
-                maximum=20.0,
-                step=0.1,
-                value=6,
-            )
-    with gr.Row(visible=True):
-        style_selection = gr.Radio(
-            show_label=True,
-            container=True,
-            interactive=True,
-            choices=list(STYLES.keys()),
-            value=DEFAULT_STYLE,
-            label="Style",
-        )
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result, seed],
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-        api_name=False,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            negative_prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            style_selection,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-        ],
-        outputs=[result, seed],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()

app2.py → app-original.py

@@ -119,7 +119,7 @@ if torch.cuda.is_available():
         pipe.to(device)    
         pipe2.to(device)    
         print("Loaded on Device!")
-    
+
     if USE_TORCH_COMPILE:
         pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
         pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
@@ -149,9 +149,6 @@ def generate(
     use_resolution_binning: bool = True,
     progress=gr.Progress(track_tqdm=True),
 ):
-    if check_text(prompt, negative_prompt):
-        raise ValueError("Prompt contains restricted words.")
-    
     prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
@@ -172,7 +169,10 @@ def generate(
         "use_resolution_binning": use_resolution_binning,
         "output_type": "pil",
     }
-    
+    print("options = ", options)
+
+
+
     images = pipe(**options).images + pipe2(**options).images
 
     image_paths = [save_image(img) for img in images]
@@ -189,7 +189,7 @@ css = '''
 .gradio-container{max-width: 700px !important}
 h1{text-align:center}
 '''
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+with gr.Blocks() as demo:
     gr.Markdown(DESCRIPTION)
     gr.DuplicateButton(
         value="Duplicate Space for private use",
@@ -201,7 +201,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
-                max_lines=1,
+                max_lines=4,
                 placeholder="Enter your prompt",
                 container=False,
             )
@@ -211,7 +211,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True, visible=True)
         negative_prompt = gr.Text(
             label="Negative prompt",
-            max_lines=1,
+            max_lines=4,
             placeholder="Enter a negative prompt",
             value="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck",
             visible=True,
@@ -311,4 +311,4 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+    demo.queue(max_size=20).launch()

app.py

@@ -1,101 +1,78 @@
 #!/usr/bin/env python
 
+import json
 import os
 import random
+from typing import Tuple
 import uuid
-import json
 
+from diffusers import DiffusionPipeline
 import gradio as gr
 import numpy as np
 from PIL import Image
 import spaces
 import torch
-from diffusers import DiffusionPipeline
-from typing import Tuple
-
-#Check for the Model Base..//
-
-
-
-bad_words = json.loads(os.getenv('BAD_WORDS', "[]"))
-bad_words_negative = json.loads(os.getenv('BAD_WORDS_NEGATIVE', "[]"))
-default_negative = os.getenv("default_negative","")
-
-def check_text(prompt, negative=""):
-    return False
-
-style_list = [
-
-    {
-        "name": "2560 x 1440",
-        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
-    },
-
-    {
-        "name": "Photo",
-        "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",
-    },   
-
-    {
-        "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",
-    },
-
-    {
-        "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",
-    },
-    {
-        "name": "3D Model",
-        "prompt": "professional 3d model {prompt}. octane render, highly detailed, volumetric, dramatic lighting",
-        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
-    },
-    {
-        "name": "(No style)",
-        "prompt": "{prompt}",
-        "negative_prompt": "",
-    },
-]
-
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-STYLE_NAMES = list(styles.keys())
-DEFAULT_STYLE_NAME = "2560 x 1440"
-
-def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    if not negative:
-        negative = ""
-    return p.replace("{prompt}", positive), n + negative
-
-
-
-    
-
-DESCRIPTION = """## MidJourney
-
-Drop your best results in the community: [rb.gy/klkbs7](http://rb.gy/klkbs7), Have you tried the stable hamster space? [rb.gy/hfrm2f](http://rb.gy/hfrm2f)
-"""
-
-
-
-
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"
 
+from gradio_imagefeed import ImageFeed
+
+
+DEFAULT_STYLE = "Photograph"
+DEFAULT_NEGATIVE = (
+    "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon,"
+    " drawing, anime, asian, bad anatomy:1.4), text, close up, cropped, out of frame,"
+    " worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated,"
+    " extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation,"
+    " deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned"
+    " face, disfigured, gross proportions, malformed limbs, missing arms, missing legs,"
+    " extra arms, extra legs, fused fingers, too many fingers, long neck"
+)
+STYLES = {
+    "Photograph": (
+        (
+            "realistic photograph of {positive}, ultra fine detail, lifelike,"
+            " high-resolution, sharp, realistic colors, photorealistic, Nikon, 35mm"
+        ),
+        DEFAULT_NEGATIVE,
+    ),
+    "Cinematic": (
+        (
+            "cinematic photograph of {positive}, 35mm photograph, film, bokeh,"
+            " professional, 4k, highly detailed"
+        ),
+        DEFAULT_NEGATIVE,
+    ),
+    "Still Photo": (
+        (
+            "cinematic still photograph of {positive}, emotional, harmonious, vignette,"
+            " highly detailed, bokeh, cinemascope, moody, epic, gorgeous, film grain,"
+            " grainy, high resolution"
+        ),
+        DEFAULT_NEGATIVE,
+    ),
+}
+
+
+def apply_style(name: str, pos: str, neg: str) -> Tuple[str, str]:
+    try:
+        def_pos, def_neg = STYLES[name]
+    except KeyError:
+        def_pos, def_neg = "{positive}", ""
+    finally:
+        pos = def_pos.replace("{positive}", pos).strip().strip(",")
+        neg = def_neg + (", " + neg).strip().strip(",")
+        return (pos, neg)
+
+
+DESCRIPTION = ""
 MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
+CACHE_EXAMPLES = False  # "lazy"
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
 USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
 ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-NUM_IMAGES_PER_PROMPT = 1
+NUM_IMAGES_PER_PROMPT = 2
 
 if torch.cuda.is_available():
     pipe = DiffusionPipeline.from_pretrained(
@@ -103,60 +80,60 @@ if torch.cuda.is_available():
         torch_dtype=torch.float16,
         use_safetensors=True,
         add_watermarker=False,
-        variant="fp16"
+        variant="fp16",
     )
     pipe2 = DiffusionPipeline.from_pretrained(
         "SG161222/RealVisXL_V2.02_Turbo",
         torch_dtype=torch.float16,
         use_safetensors=True,
         add_watermarker=False,
-        variant="fp16"
+        variant="fp16",
     )
     if ENABLE_CPU_OFFLOAD:
         pipe.enable_model_cpu_offload()
         pipe2.enable_model_cpu_offload()
     else:
-        pipe.to(device)    
-        pipe2.to(device)    
+        pipe.to(device)
+        pipe2.to(device)
         print("Loaded on Device!")
-    
+
     if USE_TORCH_COMPILE:
         pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
         pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
         print("Model Compiled!")
 
+
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
     return unique_name
 
+
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     return seed
 
+
 @spaces.GPU(enable_queue=True)
 def generate(
     prompt: str,
     negative_prompt: str = "",
     use_negative_prompt: bool = False,
-    style: str = DEFAULT_STYLE_NAME,
+    style: str = DEFAULT_STYLE,
     seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
+    width: int = 896,
+    height: int = 1152,
     guidance_scale: float = 3,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True,
     progress=gr.Progress(track_tqdm=True),
 ):
+
     prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
 
-    if not use_negative_prompt:
-        negative_prompt = ""  # type: ignore
-    negative_prompt += default_negative    
-
     options = {
         "prompt": prompt,
         "negative_prompt": negative_prompt,
@@ -169,51 +146,46 @@ def generate(
         "use_resolution_binning": use_resolution_binning,
         "output_type": "pil",
     }
-    print("options = ", options)
-
-
 
     images = pipe(**options).images + pipe2(**options).images
-
     image_paths = [save_image(img) for img in images]
+
     return image_paths, seed
 
+
 examples = [
-    "A closeup of a cat, a window, in a rustic cabin, close up, with a shallow depth of field, with a vintage film grain, in the style of Annie Leibovitz and in the style of Wes Anderson. --ar 85:128 --v 6.0 --style raw",
-    "Daria Morgendorffer the main character of the animated series Daria, serious expression, very excites sultry look, so hot girl, beautiful charismatic girl, so hot shot, a woman wearing eye glasses, gorgeous figure, interesting shapes, life-size figures",
-    "Dark green large leaves of anthurium, close up, photography, aerial view, in the style of unsplash, hasselblad h6d400c  --ar 85:128 --v 6.0 --style raw",
-    "Closeup of blonde woman depth of field, bokeh, shallow focus, minimalism, fujifilm xh2s with Canon EF lens, cinematic --ar 85:128 --v 6.0 --style raw"
+    (
+        "college life of 21 year old, depth of field, bokeh, shallow"
+        " focus, minimalism, fujifilm xh2s with Canon EF lens, cinematic --ar 85:128"
+        " --v 6.0 --style raw"
+    ),
 ]
 
-css = '''
-.gradio-container{max-width: 700px !important}
-h1{text-align:center}
-'''
-with gr.Blocks() as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
+css = ""
+
+with gr.Blocks(css=css, theme="rawrsor1/Everforest") as demo:
     with gr.Group():
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
                 max_lines=4,
-                placeholder="Enter your prompt",
+                placeholder="Enter a Prompt",
                 container=False,
             )
             run_button = gr.Button("Run")
-        result = gr.Gallery(label="Result", columns=1, preview=True)
-    with gr.Accordion("Advanced options", open=False):
-        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True, visible=True)
+        #result = ImageFeed(label="Result")
+        result = gr.Gallery(label="Result", columns=2, preview=True)
+
+    with gr.Accordion("Advanced", open=False):
+        use_negative_prompt = gr.Checkbox(
+            label="Use Negative", value=True, visible=True
+        )
         negative_prompt = gr.Text(
-            label="Negative prompt",
+            label="Negative Prompt",
             max_lines=4,
-            placeholder="Enter a negative prompt",
-            value="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck",
+            placeholder="",
+            value="",
             visible=True,
         )
         with gr.Row():
@@ -226,39 +198,34 @@ with gr.Blocks() as demo:
             )
         with gr.Row():
             num_images_per_prompt = gr.Slider(
-                label="Images",
+                label="Image Count",
                 minimum=1,
                 maximum=5,
                 step=1,
                 value=2,
             )
         seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-            visible=True
+            label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, visible=True
         )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        randomize_seed = gr.Checkbox(label="New Seed", value=True)
         with gr.Row(visible=True):
             width = gr.Slider(
                 label="Width",
                 minimum=512,
                 maximum=2048,
-                step=8,
-                value=1024,
+                step=16,
+                value=896,
             )
             height = gr.Slider(
                 label="Height",
                 minimum=512,
                 maximum=2048,
-                step=8,
-                value=1024,
+                step=16,
+                value=1152,
             )
         with gr.Row():
             guidance_scale = gr.Slider(
-                label="Guidance Scale",
+                label="Guidance",
                 minimum=0.1,
                 maximum=20.0,
                 step=0.1,
@@ -269,9 +236,9 @@ with gr.Blocks() as demo:
             show_label=True,
             container=True,
             interactive=True,
-            choices=STYLE_NAMES,
-            value=DEFAULT_STYLE_NAME,
-            label="Image Style",
+            choices=list(STYLES.keys()),
+            value=DEFAULT_STYLE,
+            label="Style",
         )
     gr.Examples(
         examples=examples,