Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import torch
+from torch import autocast
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+model = "linaqruf/animagine-xl"
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    model,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    variant="fp16",
+    )
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to('cuda')
+
+def launch(prompt, negative_prompt):
+    prompt += " ,awesome, pixel art"
+    negative_prompt += ", lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
+    image = pipe(prompt,
+                 negative_prompt=negative_prompt,
+                 width=1024,
+                 height=1024,
+                 guidance_scale=12,
+                 target_size=(1024, 1024),
+                 original_size=(4096, 4096),
+                 num_inference_steps=50)
+    return image.images[0]  # Assuming this is how you get the resulting image
+
+iface = gr.Interface(fn=launch,
+                     inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="Negative Prompt")],
+                     outputs=gr.Image(type='pil'),
+                     title="Generate Images",
+                     description="Enter a prompt and a negative prompt to generate an image.")
+iface.launch()