Add application file

app.py

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+import gradio as gr
+
+import torch
+from diffusers import DiffusionPipeline
+from diffusers import EulerDiscreteScheduler
+
+
+pipeline = DiffusionPipeline.from_pretrained("recoilme/ColorfulXL-Lightning",variant="fp16"#, torch_dtype=torch.float16
+                                             , use_safetensors=True)#.to("cuda")
+pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")
+
+
+def generate(prompt, negative_prompt, width, height, sample_steps):
+    return pipeline(prompt=prompt, guidance_scale=0, negative_prompt="", width=width, height=height, num_inference_steps=sample_steps).images[0]
+
+with gr.Blocks() as interface:
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    prompt = gr.Textbox(label="Prompt", info="What do you want?", value="girl sitting on a small hill looking at night sky, back view, distant exploding moon, nights darkness, intricate circuits and sensors, photographic realism style, detailed textures, peacefulness, mysterious.", lines=4, interactive=True)
+                with gr.Column():
+                    generate_button = gr.Button("Generate")
+                    output = gr.Image()
+            with gr.Row():
+                with gr.Accordion(label="Advanced Settings", open=False):
+                    with gr.Row():
+                        with gr.Column():
+                            width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=576, minimum=512, maximum=1280, step=64, interactive=True)
+                            height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=832, minimum=512, maximum=1280, step=64, interactive=True)
+                        with gr.Column():
+                            sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=5, minimum=3, maximum=10, step=1, interactive=True)
+    
+            with gr.Row():
+                about_text = """
+                Based on: Stable Diffusion XL Image Generation interface built by Noa Roggendorff.
+                
+                You can enter a prompt and negative prompt, adjust the image size and sampling steps, and click the "Generate" button to generate an image.
+                """
+                gr.Markdown(about_text)
+        
+        generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output])
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+diffusers
+torch
+gradio
+accelerate
+transformers