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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import numpy as np | |
| from diffusers import StableDiffusionDepth2ImgPipeline | |
| from pathlib import Path | |
| dept2img = StableDiffusionDepth2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-depth", | |
| torch_dtype=torch.float16, | |
| ).to("cuda") | |
| def pad_image(input_image): | |
| pad_w, pad_h = np.max(((2, 2), np.ceil( | |
| np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size | |
| im_padded = Image.fromarray( | |
| np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) | |
| w, h = im_padded.size | |
| if w == h: | |
| return im_padded | |
| elif w > h: | |
| new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0)) | |
| new_image.paste(im_padded, (0, (w - h) // 2)) | |
| return new_image | |
| else: | |
| new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0)) | |
| new_image.paste(im_padded, ((h - w) // 2, 0)) | |
| return new_image | |
| def predict(input_image, prompt, negative_prompt, steps, num_samples, scale, seed, strength, depth_image=None): | |
| depth = None | |
| if depth_image is not None: | |
| depth_image = pad_image(depth_image) | |
| depth = np.array(depth_image.convert("L")) | |
| depth = depth.astype(np.float32) / 255.0 | |
| depth = depth[None, None] | |
| depth = torch.from_numpy(depth) | |
| init_image = input_image.convert("RGB") | |
| image = pad_image(init_image) # resize to integer multiple of 32 | |
| image = image.resize((512, 512)) | |
| result = dept2img( | |
| image=image, | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| depth_image=depth, | |
| seed=seed, | |
| strength=strength, | |
| num_inference_steps=steps, | |
| guidance_scale=scale, | |
| num_images_per_prompt=num_samples, | |
| ) | |
| return result['images'] | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("## Stable Diffusion 2 Depth2Img") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="pil") | |
| depth_image = gr.Image( | |
| source='upload', type="pil", label="Depth image Optional", value=None) | |
| prompt = gr.Textbox(label="Prompt") | |
| negative_prompt = gr.Textbox(label="Negative Pompt") | |
| run_button = gr.Button(label="Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider( | |
| label="Images", minimum=1, maximum=4, value=1, step=1) | |
| steps = gr.Slider(label="Steps", minimum=1, | |
| maximum=50, value=50, step=1) | |
| scale = gr.Slider( | |
| label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1 | |
| ) | |
| strength = gr.Slider( | |
| label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01 | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=2147483647, | |
| step=1, | |
| randomize=True, | |
| ) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style( | |
| grid=[2], height="auto") | |
| gr.Examples( | |
| examples=[ | |
| ["./examples/baby.jpg", "high definition photo of a baby astronaut space walking at the international space station with earth seeing from above in the background", | |
| "", 50, 4, 9.0, 123123123, 0.8, None], | |
| ["./examples/gol.jpg", "professional photo of a Elmo jumping between two high rises, beautiful colorful city landscape in the background", | |
| "", 50, 4, 9.0, 1734133747, 0.9, None] | |
| ], | |
| inputs=[input_image, prompt, negative_prompt, steps, | |
| num_samples, scale, seed, strength, depth_image], | |
| outputs=[gallery], | |
| fn=predict, | |
| cache_examples=True, | |
| ) | |
| run_button.click(fn=predict, inputs=[input_image, prompt, negative_prompt, | |
| steps, num_samples, scale, seed, strength, depth_image], outputs=[gallery]) | |
| block.launch(show_api=False) |