Update app.py

app.py

@@ -1,206 +1,206 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-from PIL import Image
-import numpy as np
-import cv2
-from rembg import remove
-
-# Загрузка моделей
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    controlnet=controlnet,
-    # torch_dtype=torch.float16
-).to("cuda")
-
-def generate_background(image_path, prompt, negative_prompt):
-    # Удаление фона
-    image = Image.open(image_path).convert("RGBA")
-    output_image = remove(image)
+# import gradio as gr
+# import torch
+# from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+# from PIL import Image
+# import numpy as np
+# import cv2
+# from rembg import remove
+
+# # Загрузка моделей
+# controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble")
+# pipe = StableDiffusionControlNetPipeline.from_pretrained(
+#     "runwayml/stable-diffusion-v1-5",
+#     controlnet=controlnet,
+#     # torch_dtype=torch.float16
+# ).to("cuda")
+
+# def generate_background(image_path, prompt, negative_prompt):
+#     # Удаление фона
+#     image = Image.open(image_path).convert("RGBA")
+#     output_image = remove(image)
     
-    # Преобразование изображения объекта в контурное изображение
-    foreground = output_image.convert("L")
-    _, contour = cv2.threshold(np.array(foreground), 127, 255, cv2.THRESH_BINARY)
-    contour_image = Image.fromarray(contour)
+#     # Преобразование изображения объекта в контурное изображение
+#     foreground = output_image.convert("L")
+#     _, contour = cv2.threshold(np.array(foreground), 127, 255, cv2.THRESH_BINARY)
+#     contour_image = Image.fromarray(contour)
     
-    # Генерация фона
-    generator = torch.Generator(device="cuda").manual_seed(1024)
-    result = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=contour_image,
-        generator=generator,
-        num_inference_steps=50
-    )
+#     # Генерация фона
+#     generator = torch.Generator(device="cuda").manual_seed(1024)
+#     result = pipe(
+#         prompt=prompt,
+#         negative_prompt=negative_prompt,
+#         image=contour_image,
+#         generator=generator,
+#         num_inference_steps=50
+#     )
     
-    background = result.images[0].convert("RGBA")
+#     background = result.images[0].convert("RGBA")
     
-    # Изменение размера фона до размера переднего плана
-    background = background.resize(output_image.size)
+#     # Изменение размера фона до размера переднего плана
+#     background = background.resize(output_image.size)
     
-    # Наложение изображений
-    composite = Image.alpha_composite(background, output_image)
+#     # Наложение изображений
+#     composite = Image.alpha_composite(background, output_image)
     
-    return composite
-
-# Определение интерфейса Gradio
-iface = gr.Interface(
-    fn=generate_background,
-    inputs=[
-        gr.Image(type="filepath", label="Загрузите изображение"),
-        gr.Textbox(lines=2, placeholder="Введите позитивный промт", label="Позитивный промт"),
-        gr.Textbox(lines=2, placeholder="Введите негативный промт", label="Негативный промт")
-    ],
-    outputs=gr.Image(type="pil", label="Результат")
-)
-
-# Запуск интерфейса
-iface.launch()
+#     return composite
+
+# # Определение интерфейса Gradio
+# iface = gr.Interface(
+#     fn=generate_background,
+#     inputs=[
+#         gr.Image(type="filepath", label="Загрузите изображение"),
+#         gr.Textbox(lines=2, placeholder="Введите позитивный промт", label="Позитивный промт"),
+#         gr.Textbox(lines=2, placeholder="Введите негативный промт", label="Негативный промт")
+#     ],
+#     outputs=gr.Image(type="pil", label="Результат")
+# )
+
+# # Запуск интерфейса
+# iface.launch()
 
-# import gradio as gr
-# import numpy as np
-# import random
-# from diffusers import DiffusionPipeline
-# import torch
+import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
 
-# device = "cuda" if torch.cuda.is_available() else "cpu"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# if torch.cuda.is_available():
-#     torch.cuda.max_memory_allocated(device=device)
-#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-#     pipe.enable_xformers_memory_efficient_attention()
-#     pipe = pipe.to(device)
-# else: 
-#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-#     pipe = pipe.to(device)
+if torch.cuda.is_available():
+    torch.cuda.max_memory_allocated(device=device)
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
+else: 
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = pipe.to(device)
 
-# MAX_SEED = np.iinfo(np.int32).max
-# MAX_IMAGE_SIZE = 1024
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
 
-# def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
 
-#     if randomize_seed:
-#         seed = random.randint(0, MAX_SEED)
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
         
-#     generator = torch.Generator().manual_seed(seed)
+    generator = torch.Generator().manual_seed(seed)
     
-#     image = pipe(
-#         prompt = prompt, 
-#         negative_prompt = negative_prompt,
-#         guidance_scale = guidance_scale, 
-#         num_inference_steps = num_inference_steps, 
-#         width = width, 
-#         height = height,
-#         generator = generator
-#     ).images[0] 
+    image = pipe(
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
+    ).images[0] 
     
-#     return image
-
-# examples = [
-#     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-#     "An astronaut riding a green horse",
-#     "A delicious ceviche cheesecake slice",
-# ]
-
-# css="""
-# #col-container {
-#     margin: 0 auto;
-#     max-width: 520px;
-# }
-# """
-
-# if torch.cuda.is_available():
-#     power_device = "GPU"
-# else:
-#     power_device = "CPU"
-
-# with gr.Blocks(css=css) as demo:
+    return image
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
+with gr.Blocks(css=css) as demo:
     
-#     with gr.Column(elem_id="col-container"):
-#         gr.Markdown(f"""
-#         # Text-to-Image Gradio Template
-#         Currently running on {power_device}.
-#         """)
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Text-to-Image Gradio Template
+        Currently running on {power_device}.
+        """)
         
-#         with gr.Row():
+        with gr.Row():
             
-#             prompt = gr.Text(
-#                 label="Prompt",
-#                 show_label=False,
-#                 max_lines=1,
-#                 placeholder="Enter your prompt",
-#                 container=False,
-#             )
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
             
-#             run_button = gr.Button("Run", scale=0)
+            run_button = gr.Button("Run", scale=0)
         
-#         result = gr.Image(label="Result", show_label=False)
+        result = gr.Image(label="Result", show_label=False)
 
-#         with gr.Accordion("Advanced Settings", open=False):
+        with gr.Accordion("Advanced Settings", open=False):
             
-#             negative_prompt = gr.Text(
-#                 label="Negative prompt",
-#                 max_lines=1,
-#                 placeholder="Enter a negative prompt",
-#                 visible=False,
-#             )
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
             
-#             seed = gr.Slider(
-#                 label="Seed",
-#                 minimum=0,
-#                 maximum=MAX_SEED,
-#                 step=1,
-#                 value=0,
-#             )
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
             
-#             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
-#             with gr.Row():
+            with gr.Row():
                 
-#                 width = gr.Slider(
-#                     label="Width",
-#                     minimum=256,
-#                     maximum=MAX_IMAGE_SIZE,
-#                     step=32,
-#                     value=512,
-#                 )
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
                 
-#                 height = gr.Slider(
-#                     label="Height",
-#                     minimum=256,
-#                     maximum=MAX_IMAGE_SIZE,
-#                     step=32,
-#                     value=512,
-#                 )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
             
-#             with gr.Row():
+            with gr.Row():
                 
-#                 guidance_scale = gr.Slider(
-#                     label="Guidance scale",
-#                     minimum=0.0,
-#                     maximum=10.0,
-#                     step=0.1,
-#                     value=0.0,
-#                 )
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=0.0,
+                )
                 
-#                 num_inference_steps = gr.Slider(
-#                     label="Number of inference steps",
-#                     minimum=1,
-#                     maximum=12,
-#                     step=1,
-#                     value=2,
-#                 )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=2,
+                )
         
-#         gr.Examples(
-#             examples = examples,
-#             inputs = [prompt]
-#         )
-
-#     run_button.click(
-#         fn = infer,
-#         inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-#         outputs = [result]
-#     )
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+
+    run_button.click(
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
+    )
 
-# demo.queue().launch()
+demo.queue().launch()