Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from PIL import Image
+from io import BytesIO
+import torch
+import os
+from diffusers import DiffusionPipeline, DDIMScheduler
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
+has_cuda = torch.cuda.is_available()
+device = torch.device('cpu' if not has_cuda else 'cuda')
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+        safety_checker=None,
+    use_auth_token=MY_SECRET_TOKEN,
+    custom_pipeline="imagic_stable_diffusion",
+    scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
+).to(device)
+
+generator = th.Generator("cuda").manual_seed(0)
+
+def infer(prompt, init_image):
+    res = pipe.train(
+        prompt,
+        init_image,
+        guidance_scale=7.5,
+        num_inference_steps=50,
+        generator=generator)
+    
+    res = pipe(alpha=1)
+
+    return res.images[0]
+
+
+prompt_input = gr.Textbox()
+image_init = gr.Image(source="upload", type="filepath")
+image_output = gr.Image()
+
+demo = gr.Interface(fn=infer, inputs=[prompt_input, image_init], outputs=image_output)
+demo.launch()