debug zerogpu

app.py

@@ -1,154 +1,72 @@
-import gradio as gr
-import numpy as np
-import random
+#!/usr/bin/env python
 
-import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import gradio as gr
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+from app_canny import create_demo as create_demo_canny
 
-    generator = torch.Generator().manual_seed(seed)
+from model import Model
+from settings import ALLOW_CHANGING_BASE_MODEL, DEFAULT_MODEL_ID, SHOW_DUPLICATE_BUTTON
 
-    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]
+DESCRIPTION = "# Material Authoring Demo v0.1. Under Construction"
 
-    return image, seed
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
+model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="Canny")
 
-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: 640px;
-}
-"""
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=SHOW_DUPLICATE_BUTTON,
+    )
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+    with gr.Tabs():
+        with gr.Tab("Canny"):
+            create_demo_canny(model.process_canny)
+        with gr.Tab("Texnet"):
+            create_demo_canny(model.process_canny)
+        with gr.Tab("Matnet"):
+            create_demo_canny(model.process_canny)
 
+    with gr.Accordion(label="Base model", open=False):
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=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,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
+            with gr.Column(scale=5):
+                current_base_model = gr.Text(label="Current base model")
+            with gr.Column(scale=1):
+                check_base_model_button = gr.Button("Check current base model")
+        with gr.Row():
+            with gr.Column(scale=5):
+                new_base_model_id = gr.Text(
+                    label="New base model",
+                    max_lines=1,
+                    placeholder="stable-diffusion-v1-5/stable-diffusion-v1-5",
+                    info="The base model must be compatible with Stable Diffusion v1.5.",
+                    interactive=ALLOW_CHANGING_BASE_MODEL,
                 )
+            with gr.Column(scale=1):
+                change_base_model_button = gr.Button("Change base model", interactive=ALLOW_CHANGING_BASE_MODEL)
+        if not ALLOW_CHANGING_BASE_MODEL:
+            gr.Markdown(
+                """The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space."""
+            )
 
-        gr.Examples(examples=examples, inputs=[prompt])
+    check_base_model_button.click(
+        fn=lambda: model.base_model_id,
+        outputs=current_base_model,
+        queue=False,
+        api_name="check_base_model",
+    )
     gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        triggers=[new_base_model_id.submit, change_base_model_button.click],
+        fn=model.set_base_model,
+        inputs=new_base_model_id,
+        outputs=current_base_model,
+        api_name=False,
+        concurrency_id="main",
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()

app_.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-import torch
-
-from app_canny import create_demo as create_demo_canny
-
-from model import Model
-from settings import ALLOW_CHANGING_BASE_MODEL, DEFAULT_MODEL_ID, SHOW_DUPLICATE_BUTTON
-
-DESCRIPTION = "# Material Authoring Demo v0.1. Under Construction"
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="Canny")
-
-with gr.Blocks() as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=SHOW_DUPLICATE_BUTTON,
-    )
-
-    with gr.Tabs():
-        with gr.Tab("Canny"):
-            create_demo_canny(model.process_canny)
-        with gr.Tab("Texnet"):
-            create_demo_canny(model.process_canny)
-        with gr.Tab("Matnet"):
-            create_demo_canny(model.process_canny)
-
-    with gr.Accordion(label="Base model", open=False):
-        with gr.Row():
-            with gr.Column(scale=5):
-                current_base_model = gr.Text(label="Current base model")
-            with gr.Column(scale=1):
-                check_base_model_button = gr.Button("Check current base model")
-        with gr.Row():
-            with gr.Column(scale=5):
-                new_base_model_id = gr.Text(
-                    label="New base model",
-                    max_lines=1,
-                    placeholder="stable-diffusion-v1-5/stable-diffusion-v1-5",
-                    info="The base model must be compatible with Stable Diffusion v1.5.",
-                    interactive=ALLOW_CHANGING_BASE_MODEL,
-                )
-            with gr.Column(scale=1):
-                change_base_model_button = gr.Button("Change base model", interactive=ALLOW_CHANGING_BASE_MODEL)
-        if not ALLOW_CHANGING_BASE_MODEL:
-            gr.Markdown(
-                """The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space."""
-            )
-
-    check_base_model_button.click(
-        fn=lambda: model.base_model_id,
-        outputs=current_base_model,
-        queue=False,
-        api_name="check_base_model",
-    )
-    gr.on(
-        triggers=[new_base_model_id.submit, change_base_model_button.click],
-        fn=model.set_base_model,
-        inputs=new_base_model_id,
-        outputs=current_base_model,
-        api_name=False,
-        concurrency_id="main",
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()

app_sd.py

@@ -0,0 +1,154 @@
+import gradio as gr
+import numpy as np
+import random
+
+import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+@spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+    prompt,
+    negative_prompt,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    guidance_scale,
+    num_inference_steps,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_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]
+
+    return image, seed
+
+
+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: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image Gradio Template")
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+
+            run_button = gr.Button("Run", scale=0, variant="primary")
+
+        result = gr.Image(label="Result", show_label=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,
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  # Replace with defaults that work for your model
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  # Replace with defaults that work for your model
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=0.0,  # Replace with defaults that work for your model
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=2,  # Replace with defaults that work for your model
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

model.py

@@ -4,6 +4,7 @@ import numpy as np
 import PIL.Image
 import torch
 from controlnet_aux.util import HWC3
+import spaces #[uncomment to use ZeroGPU]
 from diffusers import (
     ControlNetModel,
     DiffusionPipeline,
@@ -122,6 +123,7 @@ class Model:
             image=control_image,
         ).images
 
+    @spaces.GPU #[uncomment to use ZeroGPU]
     @torch.inference_mode()
     def process_canny(
         self,