Create app.py

app.py

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+# Ref: https://huggingface.co/spaces/multimodalart/cosxl
+import gradio as gr
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+import spaces 
+import torch 
+
+model_id = "aipicasso/emi-2"
+token=os.envron["TOKEN"]
+
+scheduler = EulerAncestralDiscreteScheduler.from_pretraind(model_id)
+pipe_normal = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.bfloat16)
+pipe_normal.to("cuda")
+
+@spaces.GPU
+def run_normal(prompt, negative_prompt="", guidance_scale=7, progress=gr.Progress(track_tqdm=True)):
+    return pipe_normal(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=20).images[0]
+
+normal_examples = ["portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography", "backlit photography of a dog", "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece"]
+with gr.Blocks(css=css) as demo:
+    gr.Markdown('''# Emi 2
+    Official demo for Emi 2
+    ''')
+    with gr.Group():
+        with gr.Row():
+          prompt_normal = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt, e.g.: backlit photography of a dog")
+          button_normal = gr.Button("Generate", min_width=120)
+        output_normal = gr.Image(label="Your result image", interactive=False)
+        with gr.Accordion("Advanced Settings", open=False):
+          negative_prompt_normal = gr.Textbox(label="Negative Prompt")
+          guidance_scale_normal = gr.Number(label="Guidance Scale", value=7)
+    gr.Examples(examples=normal_examples, fn=run_normal, inputs=[prompt_normal], outputs=[output_normal], cache_examples=True) 
+    
+    gr.on(
+        triggers=[
+            button_normal.click,
+            prompt_normal.submit
+        ],
+        fn=run_normal,
+        inputs=[prompt_normal, negative_prompt_normal, guidance_scale_normal],
+        outputs=[output_normal],
+    )
+if __name__ == "__main__":
+    demo.launch(share=True)