| 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 = 'heisenberg3376/ddpm-celebahq-finetuned-Vintage-Faces-2epochs' | |
| 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_scale", minimum=0, maximum=30, value=3) ] | |
| 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, share=True) |