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app.py

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+from transformers import pipeline, Wav2Vec2Processor, AutoModelForCTC
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+
+model_id = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token='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', revision="fp16", torch_dtype=torch.float16)
+device = 'cuda'
+#has_cuda = torch.cuda.is_available()
+#device = torch.device('cpu' if not has_cuda else 'cuda')    
+pipe = pipe.to(device)
+
+def convert(prompt):
+        with autocast("cuda"):
+                image = pipe(prompt)["sample"][0]
+        return image
+
+gr.Interface(convert,
+             inputs = [gr.inputs.Textbox(label="Enter text")],    
+             outputs = [gr.outputs.Image(label="Generated Image")], 
+             title="Text to Image Generation").launch()