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app.py

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+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
+import gradio as gr
+import torch
+from PIL import Image
+
+model_id = 'Duskfallcrew/neon-isometric'
+prefix = 'dskise'
+     
+scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
+
+pipe = StableDiffusionPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+  scheduler=scheduler)
+
+pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+  scheduler=scheduler)
+
+if torch.cuda.is_available():
+  pipe = pipe.to("cuda")
+  pipe_i2i = pipe_i2i.to("cuda")
+
+def error_str(error, title="Error"):
+    return f"""#### {title}
+            {error}"""  if error else ""
+
+def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
+
+  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
+  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
+
+  try:
+    if img is not None:
+      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
+    else:
+      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
+  except Exception as e:
+    return None, error_str(e)
+
+def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
+
+    result = pipe(
+      prompt,
+      negative_prompt = neg_prompt,
+      num_inference_steps = int(steps),
+      guidance_scale = guidance,
+      width = width,
+      height = height,
+      generator = generator)
+    
+    return result.images[0]
+
+def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
+
+    ratio = min(height / img.height, width / img.width)
+    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
+    result = pipe_i2i(
+        prompt,
+        negative_prompt = neg_prompt,
+        init_image = img,
+        num_inference_steps = int(steps),
+        strength = strength,
+        guidance_scale = guidance,
+        width = width,
+        height = height,
+        generator = generator)
+        
+    return result.images[0]
+
+css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
+"""
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        f"""
+            <div class="main-div">
+              <div>
+                <h1>Neon Isometric</h1>
+              </div>
+              <p>
+               Demo for <a href="https://huggingface.co/Duskfallcrew/neon-isometric">Neon Isometric</a> Stable Diffusion model.  <br> All samples and info are here:
+ <a href="hhttps://civitai.com/user/duskfallcrew"> On Civit </a> <br> 
+If you want to donate towards costs and don't want to subscribe:
+ <a href="hhttps://ko-fi.com/DUSKFALLcrew> Coffee Is nice! </a> <br>
+               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
+              </p>
+              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/Duskfallcrew/neon-isometric/settings'>Settings</a></b>"} after duplicating the space<br><br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/Duskfallcrew/neon-isometric?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
+            </div>
+        """
+    )
+    with gr.Row():
+        
+        with gr.Column(scale=55):
+          with gr.Group():
+              with gr.Row():
+                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
+                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
+
+              image_out = gr.Image(height=512)
+          error_output = gr.Markdown()
+
+        with gr.Column(scale=45):
+          with gr.Tab("Options"):
+            with gr.Group():
+              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
+              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (dskise)", value=prefix, visible=prefix)
+
+              with gr.Row():
+                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
+                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
+
+              with gr.Row():
+                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
+                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
+
+              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
+
+          with gr.Tab("Image to image"):
+              with gr.Group():
+                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
+                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
+
+    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
+
+    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
+    outputs = [image_out, error_output]
+    prompt.submit(inference, inputs=inputs, outputs=outputs)
+    generate.click(inference, inputs=inputs, outputs=outputs)
+
+    gr.HTML("""
+    <div style="border-top: 1px solid #303030;">
+      <br>
+      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
+    </div>
+    """)
+
+demo.queue(concurrency_count=1)
+demo.launch()