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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/line-art-model'
+prefix = ''
+     
+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>Line Art Model</h1>
+              </div>
+              <p>
+               Demo for <a href="https://huggingface.co/Duskfallcrew/line-art-model">Line Art Model</a> Stable Diffusion modelRunning on Free CPU, if there's a queue make sure you duplicate the space to your own and if you got the funds upgrade to GPU.  No prefix tokens. If you like what you see consider donating here: <a href="https://ko-fi.com/DUSKFALLcrew">Ko-Fi Duskfallcrew </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/line-art-model/settings'>Settings</a></b>"} after duplicating the space<br><br>
+              <a style="display:inline-block" href="https://huggingface.co/spaces/Duskfallcrew/line-art-model?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 ()", 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()