update

app.py

@@ -8,52 +8,61 @@ from diffusers import DDIMScheduler
 
 device = 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'
 
-pipeline_name = 'johnowhitaker/sd-class-wikiart-from-bedrooms'
+# Load the pretrained pipeline
+pipeline_name = 'EeeeeeeH/Trial2'
 image_pipe = DDPMPipeline.from_pretrained(pipeline_name).to(device)
 
+# Set up the scheduler
 scheduler = DDIMScheduler.from_pretrained(pipeline_name)
 scheduler.set_timesteps(num_inference_steps=20)
 
+# The guidance function 
 def color_loss(images, target_color=(0.1, 0.9, 0.5)):
-    target = torch.tensor(target_color).to(images.device) * 2 - 1
-    target = target[None, :, None, None]
-    error = torch.abs(images - target).mean()
+    """Given a target color (R, G, B) return a loss for how far away on average 
+    the images' pixels are from that color. Defaults to a light teal: (0.1, 0.9, 0.5) """
+    target = torch.tensor(target_color).to(images.device) * 2 - 1 # Map target color to (-1, 1)
+    target = target[None, :, None, None] # Get shape right to work with the images (b, c, h, w)
+    error = torch.abs(images - target).mean() # Mean absolute difference between the image pixels and the target color
     return error
 
+# And the core function to generate an image given the relevant inputs
 def generate(color, guidance_loss_scale):
-    target_color = ImageColor.getcolor(color, "RGB")
-    target_color = [a / 255 for a in target_color]
-    x = torch.randn(1, 3, 256, 256).to(device)
-    for i, t in enumerate(scheduler.timesteps):
-        model_input = scheduler.scale_model_input(x, t)
-        with torch.no_grad():
-            noise_pred = image_pipe.unet(model_input, t)["sample"]
-        x = x.detach().requires_grad_()
-        x0 = scheduler.step(noise_pred, t, x).pred_original_sample
-        loss = color_loss(x0, target_color) * guidance_loss_scale
-        cond_grad = -torch.autograd.grad(loss, x)[0]
-        x = x.detach() + cond_grad
-        x = scheduler.step(noise_pred, t, x).prev_sample
-    grid = torchvision.utils.make_grid(x, nrow=4)
-    im = grid.permute(1, 2, 0).cpu().clip(-1, 1) * 0.5 + 0.5
-    im = Image.fromarray(np.array(im * 255).astype(np.uint8))
-    im.save("test.jpeg")
-    return im
+  target_color = ImageColor.getcolor(color, "RGB") # Target color as RGB
+  target_color = [a/255 for a in target_color] # Rescale from (0, 255) to (0, 1)
+  x = torch.randn(1, 3, 256, 256).to(device) 
+  for i, t in enumerate(scheduler.timesteps):
+    model_input = scheduler.scale_model_input(x, t)
+    with torch.no_grad():
+        noise_pred = image_pipe.unet(model_input, t)["sample"]
+    x = x.detach().requires_grad_()
+    x0 = scheduler.step(noise_pred, t, x).pred_original_sample
+    loss = color_loss(x0, target_color) * guidance_loss_scale
+    cond_grad = -torch.autograd.grad(loss, x)[0]
+    x = x.detach() + cond_grad 
+    x = scheduler.step(noise_pred, t, x).prev_sample
+  grid = torchvision.utils.make_grid(x, nrow=4)
+  im = grid.permute(1, 2, 0).cpu().clip(-1, 1)*0.5 + 0.5
+  im = Image.fromarray(np.array(im*255).astype(np.uint8))
+  im.save('test.jpeg')
+  return im
 
+# See the gradio docs for the types of inputs and outputs available
 inputs = [
-    gr.ColorPicker(label="Color", value = '55FFAA'),
-    gr.Slider(label="Guidance Loss Scale", minimum=0, maximum=30, value=1)
+    gr.ColorPicker(label="color", value='55FFAA'), # Add any inputs you need here
+    gr.Slider(label="guidance_scale", minimum=0, maximum=30, value=3)
 ]
 outputs = gr.Image(label="result")
 
+# Setting up a minimal interface to our function:
 demo = gr.Interface(
     fn=generate,
     inputs=inputs,
     outputs=outputs,
     examples=[
-        ["#BB2266", 3],["#44CCAA", 5]
+        ["#BB2266", 3],["#44CCAA", 5] # You can provide some example inputs to get people started
     ],
 )
 
+# And launching
 if __name__ == "__main__":
     demo.launch(enable_queue=True)