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import gradio as gr |
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import numpy as np |
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import random |
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import os |
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from PIL import Image |
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import spaces |
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import torch |
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from transformers import pipeline |
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from diffusers import StableDiffusionDepth2ImgPipeline |
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model_id_depth = "depth-anything/Depth-Anything-V2-Large-hf" |
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if torch.cuda.is_available(): |
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pipe_depth = pipeline(task="depth-estimation", model=model_id_depth, device="cuda") |
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else: |
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pipe_depth = pipeline(task="depth-estimation", model=model_id_depth) |
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model_id_depth2image = "stabilityai/stable-diffusion-2-depth" |
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if torch.cuda.is_available(): |
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pipe_depth2image = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id_depth2image, torch_dtype=torch.float16).to("cuda") |
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else: |
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pipe_depth2image = StableDiffusionDepth2ImgPipeline.from_pretrained(model_id_depth2image) |
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max_seed = np.iinfo(np.int32).max |
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max_image_size = 1344 |
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example_files = [os.path.join('assets/examples', filename) for filename in sorted(os.listdir('assets/examples'))] |
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@spaces.GPU |
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def infer( |
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init_image, |
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prompt, |
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negative_prompt, |
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seed, |
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randomize_seed, |
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width, |
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height, |
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guidance_scale, |
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num_inference_steps): |
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if randomize_seed: |
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seed = random.randint(0, max_seed) |
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init_image = Image.fromarray(np.uint8(init_image)) |
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predicted_depth = pipe_depth(init_image)["predicted_depth"] |
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image = pipe_depth2image( |
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prompt=prompt, |
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image=init_image, |
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depth_map=predicted_depth, |
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negative_prompt=negative_prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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height=height, |
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width=width, |
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generator=torch.Generator().manual_seed(seed) |
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).images[0] |
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return image, seed |
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with gr.Blocks() as demo: |
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gr.Markdown("# Demo [Depth2Image](https://huggingface.co/stabilityai/stable-diffusion-2-depth) with depth map estimated by [Depth Anything V2](https://huggingface.co/depth-anything/Depth-Anything-V2-Large-hf).") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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with gr.Row(): |
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init_image = gr.Image(label="Input Image", type='numpy') |
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result = gr.Image(label="Result") |
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with gr.Accordion("Advanced Settings", open=False): |
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negative_prompt = gr.Text( |
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label="Negative Prompt", |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=max_seed, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=max_image_size, |
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step=64, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=max_image_size, |
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step=64, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.0, |
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maximum=10.0, |
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step=0.1, |
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value=7.5, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=5, |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit, negative_prompt.submit], |
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fn=infer, |
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inputs=[init_image, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], |
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outputs=[result, seed] |
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) |
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examples = gr.Examples( |
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examples=example_files, inputs=[init_image], outputs=[result, seed] |
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) |
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demo.queue().launch() |
