Update app.py

app.py

@@ -88,41 +88,65 @@ def resize_crop(image, size=512):
     return image
 
 
-async def predict(init_image, prompt, strength, steps, seed=1231231):
-    init_image = None
-    if init_image is not None:
-        init_image = resize_crop(init_image)
-        generator = torch.manual_seed(seed)
-        last_time = time.time()
+# async def predict(init_image, prompt, strength, steps, seed=1231231):
+#     # init_image = None
+#     if init_image is not None:
+#         init_image = resize_crop(init_image)
+#         generator = torch.manual_seed(seed)
+#         last_time = time.time()
     
-        if int(steps * strength) < 1:
-            steps = math.ceil(1 / max(0.10, strength))
+#         if int(steps * strength) < 1:
+#             steps = math.ceil(1 / max(0.10, strength))
             
-        results = i2i_pipe(
-            prompt=prompt,
-            image=init_image,
-            generator=generator,
-            num_inference_steps=steps,
-            guidance_scale=0.0,
-            strength=strength,
-            width=512,
-            height=512,
-            output_type="pil",
-        )
-    else:
-        generator = torch.manual_seed(seed)
-        last_time = time.time()
-        t2i_pipe.safety_checker = None
-        t2i_pipe.requires_safety_checker = False
-        results = t2i_pipe(
-            prompt=prompt,
-            generator=generator,
-            num_inference_steps=steps,
-            guidance_scale=0.0,
-            width=512,
-            height=512,
-            output_type="pil",
-        )
+#         results = i2i_pipe(
+#             prompt=prompt,
+#             image=init_image,
+#             generator=generator,
+#             num_inference_steps=steps,
+#             guidance_scale=0.0,
+#             strength=strength,
+#             width=512,
+#             height=512,
+#             output_type="pil",
+#         )
+#     else:
+#         generator = torch.manual_seed(seed)
+#         last_time = time.time()
+#         t2i_pipe.safety_checker = None
+#         t2i_pipe.requires_safety_checker = False
+#         results = t2i_pipe(
+#             prompt=prompt,
+#             generator=generator,
+#             num_inference_steps=steps,
+#             guidance_scale=0.0,
+#             width=512,
+#             height=512,
+#             output_type="pil",
+#         )
+#     print(f"Pipe took {time.time() - last_time} seconds")
+#     nsfw_content_detected = (
+#         results.nsfw_content_detected[0]
+#         if "nsfw_content_detected" in results
+#         else False
+#     )
+#     if nsfw_content_detected:
+#         gr.Warning("NSFW content detected.")
+#         return Image.new("RGB", (512, 512))
+#     return results.images[0]
+async def predict(prompt, strength, steps, seed=1231231):
+    generator = torch.manual_seed(seed)
+    last_time = time.time()
+    t2i_pipe.safety_checker = None
+    t2i_pipe.requires_safety_checker = False
+    results = t2i_pipe(
+        prompt=prompt,
+        generator=generator,
+        num_inference_steps=steps,
+        guidance_scale=0.0,
+        width=512,
+        height=512,
+        output_type="pil",
+    )
     print(f"Pipe took {time.time() - last_time} seconds")
     nsfw_content_detected = (
         results.nsfw_content_detected[0]
@@ -134,7 +158,6 @@ async def predict(init_image, prompt, strength, steps, seed=1231231):
         return Image.new("RGB", (512, 512))
     return results.images[0]
 
-
 css = """
 #container{
     margin: 0 auto;
@@ -217,20 +240,20 @@ with gr.Blocks(css=css) as demo:
         #     ```
         #     """
         #     )
-        image_input = None
-        inputs = [image_input, prompt, strength, steps, seed]
+        inputs = [prompt, strength, steps, seed]
+        # inputs = [image_input, prompt, strength, steps, seed]
         generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False)
         prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False)
         steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
         seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
         strength.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        image_input.change(
-            fn=lambda x: x,
-            inputs=image_input,
-            outputs=init_image_state,
-            show_progress=False,
-            queue=False,
-        )
+        # image_input.change(
+        #     fn=lambda x: x,
+        #     inputs=image_input,
+        #     outputs=init_image_state,
+        #     show_progress=False,
+        #     queue=False,
+        # )
 
 demo.queue()
 demo.launch()