Update app.py

app.py

@@ -1,63 +1,98 @@
+# import torch
+# import random
+# import numpy as np
+# import gradio as gr
+# from pytorch_lightning import seed_everything
+# from annotator.util import resize_image, HWC3
+# from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+
+# # # Load the controlnet model
+# # controlnet = ControlNetModel.from_pretrained("CompVis/controlnet")
+
+# # # Load the pipeline
+# # pipe = StableDiffusionControlNetPipeline.from_pretrained(
+# #     "CompVis/stable-diffusion-v1-4",
+# #     controlnet=controlnet
+# # ).to("cuda")
+# controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+# pipe = StableDiffusionControlNetPipeline.from_pretrained(
+#     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+# )
+
+# def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold):
+#     with torch.no_grad():
+#         img = resize_image(HWC3(input_image), image_resolution)
+        
+#         if seed == -1:
+#             seed = random.randint(0, 65535)
+#         seed_everything(seed)
+
+#         # Generate images using the pipeline
+#         generator = torch.Generator("cuda").manual_seed(seed)
+#         images = pipe(prompt=prompt + ', ' + a_prompt, num_inference_steps=ddim_steps, guidance_scale=scale, generator=generator, num_images_per_prompt=num_samples).images
+
+#         results = [np.array(image) for image in images]
+#     return results
+
+# block = gr.Blocks().queue()
+# with block:
+#     with gr.Row():
+#         gr.Markdown("## Scene Diffusion with ControlNet")
+#     with gr.Row():
+#         with gr.Column():
+#             input_image = gr.Image(label="Image")
+#             prompt = gr.Textbox(label="Prompt")
+#             a_prompt = gr.Textbox(label="Additional Prompt")
+#             n_prompt = gr.Textbox(label="Negative Prompt")
+#             num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+#             image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
+#             ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+#             guess_mode = gr.Checkbox(label='Guess Mode', value=False)
+#             strength = gr.Slider(label="Strength", minimum=0.0, maximum=1.0, value=0.5, step=0.1)
+#             scale = gr.Slider(label="Scale", minimum=0.1, maximum=30.0, value=10.0, step=0.1)
+#             seed = gr.Slider(label="Seed", minimum=0, maximum=10000, value=42, step=1)
+#             eta = gr.Slider(label="ETA", minimum=0.0, maximum=1.0, value=0.0, step=0.1)
+#             low_threshold = gr.Slider(label="Canny Low Threshold", minimum=1, maximum=255, value=100, step=1)
+#             high_threshold = gr.Slider(label="Canny High Threshold", minimum=1, maximum=255, value=200, step=1)
+#             submit = gr.Button("Generate")
+#         with gr.Column():
+#             output_image = gr.Gallery(label='Output', show_label=False, elem_id="gallery")
+#     submit.click(fn=process, inputs=[input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold], outputs=output_image)
+
+# demo = block
+# demo.launch()
 import torch
-import random
-import numpy as np
+from diffusers import StableDiffusionControlNetPipeline
+from diffusers import ControlNetModel
 import gradio as gr
-from pytorch_lightning import seed_everything
-from annotator.util import resize_image, HWC3
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from PIL import Image
+import numpy as np
 
-# # Load the controlnet model
-# controlnet = ControlNetModel.from_pretrained("CompVis/controlnet")
+# 初始化 ControlNet 模型和 Stable Diffusion Pipeline
+controlnet = ControlNetModel.from_pretrained("controlnet/checkpoints/ControlNetModel")
+pipeline = StableDiffusionControlNetPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", controlnet=controlnet).to("cuda")
 
-# # Load the pipeline
-# pipe = StableDiffusionControlNetPipeline.from_pretrained(
-#     "CompVis/stable-diffusion-v1-4",
-#     controlnet=controlnet
-# ).to("cuda")
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+# 定义图像生成函数
+def generate_image(prompt: str, input_image: Image.Image):
+    # 可以在这里根据传入的图像做一些预处理（例如，使用控制网络或图像生成模型）
+    
+    # 将输入图像转换为合适的格式
+    input_image = input_image.convert("RGB")
+    
+    # 生成图像
+    result_image = pipeline(prompt=prompt, init_image=input_image, strength=0.75).images[0]
+    return result_image
+
+# 创建 Gradio 界面
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Enter a prompt", placeholder="e.g. a futuristic city at sunset"),  # 提示框
+        gr.Image(label="Upload an Image", type="pil")  # 图像上传框
+    ],
+    outputs=gr.Image(label="Generated Image"),  # 输出生成的图像
+    live=True
 )
 
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold):
-    with torch.no_grad():
-        img = resize_image(HWC3(input_image), image_resolution)
-        
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-        # Generate images using the pipeline
-        generator = torch.Generator("cuda").manual_seed(seed)
-        images = pipe(prompt=prompt + ', ' + a_prompt, num_inference_steps=ddim_steps, guidance_scale=scale, generator=generator, num_images_per_prompt=num_samples).images
-
-        results = [np.array(image) for image in images]
-    return results
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown("## Scene Diffusion with ControlNet")
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(label="Image")
-            prompt = gr.Textbox(label="Prompt")
-            a_prompt = gr.Textbox(label="Additional Prompt")
-            n_prompt = gr.Textbox(label="Negative Prompt")
-            num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
-            image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
-            ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
-            guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-            strength = gr.Slider(label="Strength", minimum=0.0, maximum=1.0, value=0.5, step=0.1)
-            scale = gr.Slider(label="Scale", minimum=0.1, maximum=30.0, value=10.0, step=0.1)
-            seed = gr.Slider(label="Seed", minimum=0, maximum=10000, value=42, step=1)
-            eta = gr.Slider(label="ETA", minimum=0.0, maximum=1.0, value=0.0, step=0.1)
-            low_threshold = gr.Slider(label="Canny Low Threshold", minimum=1, maximum=255, value=100, step=1)
-            high_threshold = gr.Slider(label="Canny High Threshold", minimum=1, maximum=255, value=200, step=1)
-            submit = gr.Button("Generate")
-        with gr.Column():
-            output_image = gr.Gallery(label='Output', show_label=False, elem_id="gallery")
-    submit.click(fn=process, inputs=[input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold], outputs=output_image)
-
-demo = block
-demo.launch()
+# 启动 Gradio 应用
+iface.launch()