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import spaces
import torch
import numpy as np
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import natten
import gradio as gr
from PIL import Image
"""
IMPORT MODEL
"""
#model generate depth image
depth_image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16)
depth_model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf", torch_dtype=torch.float16)
depth_model = depth_model.cuda()
#model generate segment image
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_dinat_large")
model = model.cuda()
#model generate image
#load depth controlnet, segmentation controlnet
controlnets = [
ControlNetModel.from_pretrained("Lam-Hung/controlnet_depth_interior", torch_dtype=torch.float16, use_safetensors=True),
ControlNetModel.from_pretrained("Lam-Hung/controlnet_segment_interior", torch_dtype=torch.float16, use_safetensors=True)
]
#load stable diffusion 1.5 and controlnets
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
"SG161222/Realistic_Vision_V5.1_noVAE", controlnet= controlnets, torch_dtype=torch.float16, use_safetensors=True
)
# take UniPCMultistepScheduler for faster inference
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline.load_lora_weights('Lam-Hung/controlnet_lora_interior', weight_name= "pytorch_lora_weights.safetensors", adapter_name="interior")
pipeline.to("cuda")
"""
IMPORT FUNCTION
"""
def ade_palette() -> list[list[int]]:
"""ADE20K palette that maps each class to RGB values."""
return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
[102, 255, 0], [92, 0, 255]]
@torch.inference_mode()
@spaces.GPU
def get_depth_image(image: Image) -> Image:
"""
create depth image
"""
image_to_depth = depth_image_processor(images=image, return_tensors="pt").to("cuda")
with torch.no_grad():
depth_map = depth_model(**image_to_depth).predicted_depth
width, height = image.size
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1).float(),
size=(height, width),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
image = torch.cat([depth_map] * 3, dim=1)
image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
return image
@torch.inference_mode()
@spaces.GPU
def get_segmentation_of_room(image: Image):
#-> tuple[np.ndarray, Image]:
"""
create instance segmentation image
"""
# Semantic Segmentation
with torch.inference_mode():
semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
semantic_inputs = {key: value.to("cuda") for key, value in semantic_inputs.items()}
semantic_outputs = model(**semantic_inputs)
# pass through image_processor for postprocessing
predicted_semantic_map = \
processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]
predicted_semantic_map = predicted_semantic_map.cpu()
color_seg = np.zeros((predicted_semantic_map.shape[0], predicted_semantic_map.shape[1], 3), dtype=np.uint8)
palette = np.array(ade_palette())
for label, color in enumerate(palette):
color_seg[predicted_semantic_map == label, :] = color
color_seg = color_seg.astype(np.uint8)
seg_image = Image.fromarray(color_seg).convert('RGB')
return seg_image
@torch.inference_mode()
@spaces.GPU
def interior_inference(image,
prompt,
negative_prompt="window, door, low resolution, banner, logo, watermark, text, deformed, blurry, out of focus, surreal, ugly, beginner",
num_inference_steps=25,
depth_weight=0.9,
segment_weight=0.9,
lora_weight=0.7,
seed= 123):
depth_image = get_depth_image(image)
segmentation_image = get_segmentation_of_room(image)
prompt = prompt + " interior design, 4K, high resolution, photorealistic"
image_interior = pipeline(
prompt,
negative_prompt = negative_prompt,
image = [depth_image, segmentation_image],
num_inference_steps = num_inference_steps,
generator = torch.manual_seed(seed),
#lora_scale if enable_lora
cross_attention_kwargs={"scale": lora_weight},
controlnet_conditioning_scale=[depth_weight, segment_weight],
).images[0]
return image_interior
interface = gr.Interface(
fn = interior_inference,
inputs = [
gr.Image(type = "pil", label = "Empty room image", show_label = True),
gr.Textbox(label = "Enter your prompt", lines = 3, placeholder = "Enter your prompt here"),
],
outputs=[
gr.Image(type = "pil", label = "Interior design", show_label = True),
],
additional_inputs=[
gr.Textbox(label = "Negative prompt", lines = 3, placeholder = "Enter your negative prompt here"),
gr.Slider(label = "Number of inference steps", minimum = 1, maximum = 100, value = 25, step = 1),
gr.Slider(label = "Depth weight", minimum = 0, maximum = 1, value = 0.9, step = 0.01),
gr.Slider(label = "Segment weight", minimum = 0, maximum = 1, value = 0.9, step = 0.01),
gr.Slider(label = "Lora weight", minimum = 0, maximum = 1, value = 0.7, step = 0.01),
gr.Number(label = "Seed", value = 123),
],
title="INTERIOR DESIGN",
description="**We will design your empty room become the beautiful room",
)
interface.launch() |