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| import gradio as gr | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| # Check if a GPU is available, otherwise fall back to CPU safely | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| torch_dtype = torch.float16 if device == "cuda" else torch.float32 | |
| print(f"--- Current running device is: {device.upper()} ---") | |
| # Load the photorealistic epiCRealism XL pipeline | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "glides/epicrealismxl", | |
| torch_dtype=torch_dtype | |
| ) | |
| pipe.to(device) | |
| def generate_image(prompt, negative_prompt, guidance_scale): | |
| # Safe guard: SDXL models take too long on free CPUs and will timeout. | |
| # This prevents your space from crashing while waiting for the GPU grant. | |
| if device == "cpu": | |
| return None | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale | |
| ).images[0] | |
| return image | |
| # Building a clean, lightweight Gradio interface | |
| demo = gr.Interface( | |
| fn=generate_image, | |
| inputs=[ | |
| gr.Textbox(label="Prompt", placeholder="A stunning photorealistic portrait of..."), | |
| gr.Textbox(label="Negative Prompt", value="blurry, bad quality, distorted, lowres, digital art, anime"), | |
| gr.Slider(minimum=1, maximum=15, value=7.5, step=0.5, label="Guidance Scale") | |
| ], | |
| outputs=gr.Image(type="filepath", label="Generated Output Image") | |
| ) | |
| demo.launch() |