Update app.py

app.py

@@ -10,35 +10,33 @@ from typing import Tuple
 import torch
 from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
-
 DESCRIPTION = """# InterDiffusion-4.0
 ### [https://huggingface.co/cutycat2000x/InterDiffusion-4.0](https://huggingface.co/cutycat2000x/InterDiffusion-4.0)"""
 
+MAX_SEED = np.iinfo(np.int32).max
+DEFAULT_STYLE_NAME = "(LoRA)"
 
 def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
+    filename = str(uuid.uuid4()) + ".png"
+    img.save(filename)
+    return filename
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-
-MAX_SEED = np.iinfo(np.int32).max
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU, This may not work on CPU.</p>"
-
-MAX_SEED = np.iinfo(np.int32).max
-
-USE_TORCH_COMPILE = 0
-ENABLE_CPU_OFFLOAD = 0
-
+    return random.randint(0, MAX_SEED) if randomize_seed else seed
 
+style_list = [
+    {
+        "name": DEFAULT_STYLE_NAME,
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+]
+styles = {s["name"]: (s["prompt"], s["negative_prompt"]) for s in style_list}
+STYLE_NAMES = list(styles.keys())
 
+def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive), n + negative
 
 if torch.cuda.is_available():
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -46,55 +44,18 @@ if torch.cuda.is_available():
         torch_dtype=torch.float16,
         use_safetensors=True,
     )
-    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)  
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.load_lora_weights("cutycat2000x/LoRA2", weight_name="lora.safetensors", adapter_name="adapt")
     pipe.set_adapters("adapt")
     pipe.to("cuda")
 
-
-
-
-    
-style_list = [
-    {
-        "name": "(LoRA)",
-        "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 = "(LoRA)"
-
-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
-
 @spaces.GPU(enable_queue=True)
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    style: str = DEFAULT_STYLE_NAME,
-    use_negative_prompt: bool = False,
-    num_inference_steps: int = 30,
-    num_images_per_prompt: int = 2,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    randomize_seed: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-):
-
-    
-    seed = int(randomize_seed_fn(seed, randomize_seed))
+def generate(prompt, negative_prompt, style, use_negative_prompt, num_inference_steps,
+             num_images_per_prompt, seed, width, height, guidance_scale, randomize_seed, progress=gr.Progress(track_tqdm=True)):
 
+    seed = randomize_seed_fn(seed, randomize_seed)
     if not use_negative_prompt:
-        negative_prompt = ""  # type: ignore
+        negative_prompt = ""
     prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
 
     images = pipe(
@@ -106,160 +67,83 @@ def generate(
         num_inference_steps=num_inference_steps,
         num_images_per_prompt=num_images_per_prompt,
         cross_attention_kwargs={"scale": 0.65},
-        output_type="pil",
+        output_type="pil"
     ).images
+
     image_paths = [save_image(img) for img in images]
-    print(image_paths)
     return image_paths, seed
 
 examples = [
     'a smiling girl with sparkles in her eyes, walking in a garden, in the morning --style anime',
-    'firewatch landscape, Graphic Novel, Pastel Art, Poster, Golden Hour, Electric Colors, 4k, RGB, Geometric, Volumetric, Lumen Global Illumination, Ray Tracing Reflections, Twisted Rays, Glowing Edges, RTX --raw',
+    'firewatch landscape, Graphic Novel, Pastel Art...',
     'Cat on a tree sitting in between parrots.',
-    'cat, 4k, 8k, hyperrealistic, realistic, High-resolution, unreal engine 5, rtx, 16k, taken on a sony camera, Cinematic, dramatic lighting',
-    'cinimatic closeup of burning skull',
+    'cat, 4k, hyperrealistic, Cinematic, unreal engine 5',
+    'cinematic closeup of burning skull',
     'frozen elsa',
     'A rainbow tree, anime style, tree in focus',
-    'A cat holding a sign that reads "Hello World" in cursive text',
-    'Odette the butterfly goddess wearing a green skirt wondering in the cosmos'
+    'A cat holding a sign that reads "Hello World"',
+    'Odette the butterfly goddess wondering in the cosmos'
 ]
 
 css = '''
 .gradio-container{max-width: 560px !important}
 h1{text-align:center}
-footer {
-    visibility: hidden
-}
+footer { visibility: hidden }
 '''
 
 with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo:
     gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=False,
-    )
 
     with gr.Group():
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+            prompt = gr.Textbox(
+                label="Prompt", placeholder="Enter your prompt", lines=1
             )
             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=False, visible=True)
-        negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                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="Images",
-                minimum=1,
-                maximum=5,
-                step=1,
-                value=2,
-            )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-            visible=True
-        )
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+        negative_prompt = gr.Textbox(label="Negative prompt", lines=1, visible=True)
+        num_inference_steps = gr.Slider(label="Steps", minimum=10, maximum=60, step=1, value=30)
+        num_images_per_prompt = gr.Slider(label="Images", minimum=1, maximum=5, step=1, value=2)
+        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row(visible=True):
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=2048,
-                step=8,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=2048,
-                step=8,
-                value=1024,
-            )
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                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=STYLE_NAMES,
-                value=DEFAULT_STYLE_NAME,
-                label="Image Style",
-            )
-        
+        width = gr.Slider(label="Width", minimum=512, maximum=2048, step=8, value=1024)
+        height = gr.Slider(label="Height", minimum=512, maximum=2048, step=8, value=1024)
+        guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=6.0)
+        style_selection = gr.Radio(label="Image Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
 
     gr.Examples(
         examples=examples,
         inputs=prompt,
         outputs=[result, seed],
         fn=generate,
-        cache_examples=False,
+        cache_examples=False
     )
 
     use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
+        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,
-        ],
+    prompt.submit(
         fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            style_selection,
-            use_negative_prompt,
-            num_inference_steps,
-            num_images_per_prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-        ],
+        inputs=[prompt, negative_prompt, style_selection, use_negative_prompt,
+                num_inference_steps, num_images_per_prompt, seed,
+                width, height, guidance_scale, randomize_seed],
         outputs=[result, seed],
-        api_name="run",
     )
 
-
+    run_button.click(
+        fn=generate,
+        inputs=[prompt, negative_prompt, style_selection, use_negative_prompt,
+                num_inference_steps, num_images_per_prompt, seed,
+                width, height, guidance_scale, randomize_seed],
+        outputs=[result, seed],
+    )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch(show_api=False, debug=False)
+    demo.queue(max_size=20).launch(show_api=False, debug=False)