:tada: init

.gitignore

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+### Example user template template
+### Example user template
+
+# IntelliJ project files
+.idea
+*.iml
+out
+gen

app.py

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+import time
+
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (
+    DDPMScheduler,
+    DDIMScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+    FlowMatchEulerDiscreteScheduler,
+)
+from diffusers.utils.torch_utils import randn_tensor
+
+SCHEDULERS = {
+    "DDPMScheduler": DDPMScheduler,
+    "DDIMScheduler": DDIMScheduler,
+    "EulerAncestralDiscreteScheduler": EulerAncestralDiscreteScheduler,
+    "DPMSolverMultistepScheduler": DPMSolverMultistepScheduler,
+    "FlowMatchEulerDiscreteScheduler": FlowMatchEulerDiscreteScheduler,
+}
+
+
+def inference(
+        image_pil: Image.Image,
+        scheduler_name: str,
+        per_step_time: int = 1,
+        n_total_steps: int = 1000,
+):
+    scheduler = SCHEDULERS[scheduler_name]()
+    scheduler.set_timesteps(num_inference_steps=n_total_steps)
+    timesteps = torch.flip(scheduler.timesteps, dims=[0])
+
+    image_processor = VaeImageProcessor()
+    image_tensor = image_processor.preprocess(image_pil)
+
+    # Fix seed
+    generator = torch.Generator().manual_seed(1117)
+    noise = randn_tensor(image_tensor.shape, generator)
+
+    for i, t in enumerate(timesteps):
+        noised_image_tensor = scheduler.add_noise(image_tensor, noise, timesteps=t)
+        noised_image_pil = image_processor.postprocess(noised_image_tensor)[0]
+        time.sleep(per_step_time)
+
+        # language=HTML
+        info_html = f"""
+            <div class="info-step">
+              <span class="step-number">Step {i + 1}</span> / {n_total_steps}
+            </div>
+        """
+
+        yield noised_image_pil, info_html
+
+
+if __name__ == '__main__':
+    demo = gr.Interface(
+        title='Noisescope',
+        description='',
+        fn=inference,
+        inputs=[
+            gr.Image(type='pil', label='Input Image'),
+            gr.Dropdown(list(SCHEDULERS.keys()), value='DDPMScheduler', label='Scheduler'),
+            gr.Radio(choices=[0, 1, 2], value=0, label='Per-Step time'),
+            gr.Radio(choices=[10, 25, 50, 100, 1000], value=50, label='Total Steps'),
+        ],
+        outputs=[
+            gr.Image(type='pil', label='Noised Image'),
+            gr.HTML(label='Timestep Info'),
+        ],
+        # language=css
+        css="""
+            body { font-family: Arial, sans-serif; background-color: #f0f0f5; }
+            h1 { color: #3c3c3c; }
+            .gradio-container { max-width: 800px; margin: auto; padding: 20px; background: white; border-radius: 10px; box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.1); }
+            .info-step { padding: 10px; background: #3c3c3c; color: white; border-radius: 5px; margin-bottom: 10px; }
+            .step-number { font-weight: bold; color: #FFD700; }
+        """
+    )
+    demo.launch()

requirements.txt

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+diffusers
+transformers
+accelerate
+safetensors