Rename app1.py to app.py

app1.py → app.py

@@ -1,49 +1,47 @@
-import gradio as gr
-import torch
-from annotator.util import resize_image, HWC3
-from annotator.canny import CannyDetector
-from cldm.model import create_model, load_state_dict
-from cldm.ddim_hacked import DDIMSampler
-
-# Initialize the model and other components
-apply_canny = CannyDetector()
-model = create_model('./models/cldm_v21_512_latctrl_coltrans.yaml').cpu()
-model.load_state_dict(load_state_dict('xywwww/scene_diffusion/checkpoints/epoch=25-step=112553.ckpt', location='cuda'), strict=False)
-model = model.cuda()
-ddim_sampler = DDIMSampler(model)
-
-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)
-        H, W, C = img.shape
-        # detected_map = apply_canny(img, low_threshold, high_threshold)
-        # detected_map = HWC3(detected_map)
-        # Add the rest of the processing logic here
-
-def create_demo(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image()
-                prompt = gr.Textbox(label="Prompt", submit_btn=True)
-                a_prompt = gr.Textbox(label="Additional Prompt")
-                n_prompt = gr.Textbox(label="Negative Prompt")
-                with gr.Accordion("Advanced options", open=False):
-                    num_samples = gr.Slider(label="Number of images", minimum=1, maximum=10, value=1, step=1)
-                    image_resolution = gr.Slider(label="Image resolution", minimum=256, maximum=1024, value=512, step=256)
-                    ddim_steps = gr.Slider(label="DDIM Steps", minimum=1, maximum=100, value=50, step=1)
-                    guess_mode = gr.Checkbox(label="Guess Mode")
-                    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.Image()
-        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)
-    return demo
-
-demo = create_demo(process)
+import gradio as gr
+import torch
+from annotator.util import resize_image, HWC3
+from cldm.model import create_model, load_state_dict
+from cldm.ddim_hacked import DDIMSampler
+
+# Initialize the model and other components
+model = create_model('./models/cldm_v21_512_latctrl_coltrans.yaml').cpu()
+model.load_state_dict(load_state_dict('xywwww/scene_diffusion/checkpoints/epoch=25-step=112553.ckpt', location='cuda'), strict=False)
+model = model.cuda()
+ddim_sampler = DDIMSampler(model)
+
+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)
+        H, W, C = img.shape
+        # detected_map = apply_canny(img, low_threshold, high_threshold)
+        # detected_map = HWC3(detected_map)
+        # Add the rest of the processing logic here
+
+def create_demo(process):
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image()
+                prompt = gr.Textbox(label="Prompt", submit_btn=True)
+                a_prompt = gr.Textbox(label="Additional Prompt")
+                n_prompt = gr.Textbox(label="Negative Prompt")
+                with gr.Accordion("Advanced options", open=False):
+                    num_samples = gr.Slider(label="Number of images", minimum=1, maximum=10, value=1, step=1)
+                    image_resolution = gr.Slider(label="Image resolution", minimum=256, maximum=1024, value=512, step=256)
+                    ddim_steps = gr.Slider(label="DDIM Steps", minimum=1, maximum=100, value=50, step=1)
+                    guess_mode = gr.Checkbox(label="Guess Mode")
+                    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.Image()
+        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)
+    return demo
+
+demo = create_demo(process)
 demo.launch()