commit

app.py

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+import gradio as gr
+import torch, torchvision
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image, ImageColor
+from diffusers import DDPMPipeline
+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'
+image_pipe = DDPMPipeline.from_pretrained(pipeline_name).to(device)
+
+scheduler = DDIMScheduler.from_pretrained(pipeline_name)
+scheduler.set_timesteps(num_inference_steps=20)
+
+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()
+    return error
+
+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
+
+inputs = [
+    gr.ColorPicker(label="Color", value = '55FFAA'),
+    gr.Slider(label="Guidance Loss Scale", minimum=0, maximum=30, value=1)
+]
+outputs = gr.Image(label="result")
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=inputs,
+    outputs=outputs,
+    examples=[
+        ["#BB2266", 3],["#44CCAA", 5]
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch(enable_queue=True)

requirements.txt

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+diffusers 
+torch 
+numpy 
+torchvision 
+gradio 
+pillow