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app.py

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+# Load an image
+import gradio as gr
+from gradio.components import *
+import torch
+from torch import autocast
+from torchvision import transforms as T
+
+from boomerang import *
+
+def main(image, prompt, percent_noise): # percent_noise = 0.5, 0.02, 0.999
+
+  # Convert image to float and preprocess it.
+  # From huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py
+  transform = T.Compose([T.PILToTensor(),
+                        T.ConvertImageDtype(torch.float),
+                        T.Normalize([0.5], [0.5])])
+  
+  tensor = transform(image).half().to(pipe.device)
+  tensor = torch.unsqueeze(tensor, 0)
+
+  # Project image into the latent space
+  clean_z = pipe.vae.encode(tensor).latent_dist.mode() # From huggingface/diffusers/blob/main/src/diffusers/models/vae.py
+  clean_z = 0.18215 * clean_z
+
+  # Add noise to the latent variable
+  # (this is the forward diffusion process)
+  noise = torch.randn(clean_z.shape).to(pipe.device)
+  timestep = torch.Tensor([int(pipe.scheduler.config.num_train_timesteps * percent_noise)]).to(pipe.device).long()
+  z = pipe.scheduler.add_noise(clean_z, noise, timestep).half()
+
+  # Run the diffusion model
+  with autocast('cuda'):
+    # The 'num_inference_steps=100' arguments means that, if you were running
+    # the full diffusion model (i.e., percent_noise = 0.999), it would be sampling
+    # at 2x frequency compared to the normal stable diffusion model (which uses 50 steps).
+    # This way, running percent_noise = 0.5 yields 50 inference steps,
+    # and running percent_noise = 0.2 yields 20 inference steps, etc.
+    return pipe(prompt=prompt, latents=z, num_inference_steps=100, percent_noise=percent_noise).images[0]
+    
+    
+    
+    
+  
+  
+gr.Interface(fn=main, 
+             inputs=[gr.Image(type="pil", shape=(512, 512)), "text", gr.Slider(0.02, 0.999, value=0.7, label='percent noise')],
+             outputs=gr.Image(type="pil", shape=(512,512)),
+             examples=[["original.png", "person", 0.7],
+                       ['cat.png', 'cat', 0.9],
+                       ['bedroom.jpg', 'bathroom', 0.8],
+                       ['einstein.jpg', 'dog', 0.8],
+                       ['oprah.jpeg', 'pirate', 0.8]]).launch()