update app

app.py

@@ -17,30 +17,30 @@ resolution = 512
 # sd_dreambooth_model._diffusion_model = db_diffusion_model
 
 # checkpoint of the converted Stable Diffusion from KerasCV
-# model_ckpt = "nielsgl/dreambooth-keras-pug-ace-sd2.1"
-# pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt)
-# pipeline.to("cuda")
+model_ckpt = "nielsgl/dreambooth-keras-pug-ace-sd2.1"
+pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt)
+pipeline.to("cuda")
 
-# unique_id = "puggieace"
-# class_label = "dog"
-# prompt = f"A photo of {unique_id} {class_label} in a bucket"
-# image = pipeline(prompt, num_inference_steps=50).images[0]
+unique_id = "puggieace"
+class_label = "dog"
+prompt = f"A photo of {unique_id} {class_label} on the beach"
+image = pipeline(prompt, num_inference_steps=50).images[0]
 
 # generate images
-# def infer(prompt, negative_prompt, guidance_scale=10, num_inference_steps=50):
-#     neg = negative_prompt if negative_prompt else None
-#     imgs = []
-#     while len(imgs) != 2:
-#         next_prompt = pipeline(prompt, negative_prompt=neg, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=5)
-#         for img, is_neg in zip(next_prompt.images, next_prompt.nsfw_content_detected):
-#             if not is_neg:
-#                 imgs.append(img)
-#             if len(imgs) == 2:
-#                 break
+def infer(prompt, negative_prompt, guidance_scale=10, num_inference_steps=50):
+    neg = negative_prompt if negative_prompt else None
+    imgs = []
+    while len(imgs) != 2:
+        next_prompt = pipeline(prompt, negative_prompt=neg, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, num_images_per_prompt=5)
+        for img, is_neg in zip(next_prompt.images, next_prompt.nsfw_content_detected):
+            if not is_neg:
+                imgs.append(img)
+            if len(imgs) == 2:
+                break
             
-#     return imgs
+    return imgs
     
-# output = gr.Gallery(label="Outputs").style(grid=(1,2))
+output = gr.Gallery(label="Outputs").style(grid=(1,2))
 
 # customize interface
 title = "KerasCV Stable Diffusion Demo on images of Ace."
@@ -161,12 +161,12 @@ The following hyperparameters were used during training for Stable Diffusion v1.
 with gr.Blocks() as card_interface:
     gr.Markdown(model_card)
 
-# demo_interface = gr.Interface(infer, inputs=[gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut"),
-#         gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"),
-#         # gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
-#         gr.Number(label='Guidance scale', value=12),
-#         gr.Slider(label="Inference Steps",value=50),
-#         ], outputs=[output], title=title, description=description, examples=examples).queue()
+demo_interface = gr.Interface(infer, inputs=[gr.Textbox(label="Positive Prompt", value="a photo of puggieace dog getting a haircut"),
+        gr.Textbox(label="Negative Prompt", value="bad anatomy, blurry"),
+        # gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
+        gr.Number(label='Guidance scale', value=12),
+        gr.Slider(label="Inference Steps",value=50),
+        ], outputs=[output], title=title, description=description, examples=examples).queue()
 
 
-gr.TabbedInterface([card_interface], ["Model Card"]).launch()
+gr.TabbedInterface([card_interface, demo_interface], ["Model Card", "Demo 🤗"]).launch()