Revert to FAL

app.py

@@ -476,59 +476,56 @@ def encode_image(image):
 
 def generate_ai_bg(input_img, prompt):
     # input_img = resize_image(input_img, 0.01)
-    # hf_input_img = encode_image(input_img)
-
-    # handler = fal_client.submit(
-    #     "fal-ai/iclight-v2",
-    #     arguments={
-    #         "prompt": prompt,
-    #         "image_url": hf_input_img
-    #     },
-    #     webhook_url="https://optional.webhook.url/for/results",
-    # )
+    hf_input_img = encode_image(input_img)
+
+    handler = fal_client.submit(
+        "fal-ai/iclight-v2",
+        arguments={
+            "prompt": prompt,
+            "image_url": hf_input_img
+        },
+        webhook_url="https://optional.webhook.url/for/results",
+    )
 
-    # request_id = handler.request_id
+    request_id = handler.request_id
 
-    # status = fal_client.status("fal-ai/iclight-v2", request_id, with_logs=True)
+    status = fal_client.status("fal-ai/iclight-v2", request_id, with_logs=True)
 
-    # result = fal_client.result("fal-ai/iclight-v2", request_id)
+    result = fal_client.result("fal-ai/iclight-v2", request_id)
 
-    # relight_img_path = result['images'][0]['url']
+    relight_img_path = result['images'][0]['url']
 
-    # response = requests.get(relight_img_path, stream=True)
+    response = requests.get(relight_img_path, stream=True)
 
-    # relight_img = Image.open(BytesIO(response.content)).convert("RGBA")
+    relight_img = Image.open(BytesIO(response.content)).convert("RGBA")
 
-    from gradio_client import Client, handle_file
+    # from gradio_client import Client, handle_file
 
-    client = Client("lllyasviel/iclight-v2-vary")
+    # client = Client("lllyasviel/iclight-v2-vary")
 
-    result = client.predict(
-            input_fg=handle_file(input_img),
-            bg_source="None",
-            prompt=prompt,
-            image_width=256,
-            image_height=256,
-            num_samples=1,
-            seed=12345,
-            steps=25,
-            n_prompt="lowres, bad anatomy, bad hands, cropped, worst quality",
-            cfg=2,
-            gs=5,
-            enable_hr_fix=True,
-            hr_downscale=0.5,
-            lowres_denoise=0.8,
-            highres_denoise=0.99,
-            api_name="/process"
-    )
-    print(result)
-
-    relight_img_path = result[0][0]['image']
+    # result = client.predict(
+    #         input_fg=handle_file(input_img),
+    #         bg_source="None",
+    #         prompt=prompt,
+    #         image_width=256,
+    #         image_height=256,
+    #         num_samples=1,
+    #         seed=12345,
+    #         steps=25,
+    #         n_prompt="lowres, bad anatomy, bad hands, cropped, worst quality",
+    #         cfg=2,
+    #         gs=5,
+    #         enable_hr_fix=True,
+    #         hr_downscale=0.5,
+    #         lowres_denoise=0.8,
+    #         highres_denoise=0.99,
+    #         api_name="/process"
+    # )
+    # print(result)
 
-    # response = requests.get(relight_img_path, stream=True)
-    relight_img = Image.open(relight_img_path).convert("RGBA")
+    # relight_img_path = result[0][0]['image']
 
-    # relight_img = Image.open(BytesIO(response.content)).convert("RGBA")
+    # relight_img = Image.open(relight_img_path).convert("RGBA")
 
     return relight_img
 
@@ -593,13 +590,13 @@ def blend_details(input_image, relit_image, masked_image, scaling_factor=1):
 @spaces.GPU
 def generate_image(input_image_path, prompt):
 
-    resized_input_img = resize_to_square(input_image_path, 512)
+    resized_input_img = resize_to_square(input_image_path, 256)
 
     resized_input_img_path = '/tmp/gradio/resized_input_img.png'
 
     resized_input_img.convert("RGBA").save(resized_input_img_path, "PNG")
 
-    ai_gen_image = generate_ai_bg(resized_input_img_path, prompt)
+    ai_gen_image = generate_ai_bg(resized_input_img, prompt)
 
     # mask_input_image = run_grounded_sam(resized_input_img)