199 lines
6.4 KiB
Python
199 lines
6.4 KiB
Python
# Project EmbodiedGen
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#
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# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied. See the License for the specific language governing
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# permissions and limitations under the License.
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import logging
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import os
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import random
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from typing import List, Tuple
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import fire
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import numpy as np
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import torch
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from diffusers.utils import make_image_grid
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from kolors.pipelines.pipeline_controlnet_xl_kolors_img2img import (
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StableDiffusionXLControlNetImg2ImgPipeline,
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)
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from PIL import Image, ImageEnhance, ImageFilter
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from torchvision import transforms
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from embodied_gen.data.datasets import Asset3dGenDataset
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from embodied_gen.models.texture_model import build_texture_gen_pipe
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def get_init_noise_image(image: Image.Image) -> Image.Image:
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blurred_image = image.convert("L").filter(
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ImageFilter.GaussianBlur(radius=3)
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)
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enhancer = ImageEnhance.Contrast(blurred_image)
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image_decreased_contrast = enhancer.enhance(factor=0.5)
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return image_decreased_contrast
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def infer_pipe(
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index_file: str,
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controlnet_ckpt: str = None,
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uid: str = None,
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prompt: str = None,
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controlnet_cond_scale: float = 0.4,
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control_guidance_end: float = 0.9,
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strength: float = 1.0,
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num_inference_steps: int = 50,
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guidance_scale: float = 10,
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ip_adapt_scale: float = 0,
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ip_img_path: str = None,
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sub_idxs: List[List[int]] = None,
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num_images_per_prompt: int = 3, # increase if want similar images.
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device: str = "cuda",
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save_dir: str = "infer_vis",
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seed: int = None,
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target_hw: tuple[int, int] = (512, 512),
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pipeline: StableDiffusionXLControlNetImg2ImgPipeline = None,
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) -> str:
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# sub_idxs = [[0, 1, 2], [3, 4, 5]] # None for single image.
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if sub_idxs is None:
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sub_idxs = [[random.randint(0, 5)]] # 6 views.
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target_hw = [2 * size for size in target_hw]
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transform_list = [
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transforms.Resize(
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target_hw, interpolation=transforms.InterpolationMode.BILINEAR
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),
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transforms.CenterCrop(target_hw),
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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]
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image_transform = transforms.Compose(transform_list)
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control_transform = transforms.Compose(transform_list[:-1])
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grid_hw = (target_hw[0] * len(sub_idxs), target_hw[1] * len(sub_idxs[0]))
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dataset = Asset3dGenDataset(
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index_file, target_hw=grid_hw, sub_idxs=sub_idxs
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)
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if uid is None:
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uid = random.choice(list(dataset.meta_info.keys()))
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if prompt is None:
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prompt = dataset.meta_info[uid]["capture"]
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if isinstance(prompt, List) or isinstance(prompt, Tuple):
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prompt = ", ".join(map(str, prompt))
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# prompt += "high quality, ultra-clear, high resolution, best quality, 4k"
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# prompt += "高品质,清晰,细节"
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prompt += ", high quality, high resolution, best quality"
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# prompt += ", with diffuse lighting, showing no reflections."
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logger.info(f"Inference with prompt: {prompt}")
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negative_prompt = "nsfw,阴影,低分辨率,伪影、模糊,霓虹灯,高光,镜面反射"
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control_image = dataset.fetch_sample_grid_images(
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uid,
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attrs=["image_view_normal", "image_position", "image_mask"],
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sub_idxs=sub_idxs,
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transform=control_transform,
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)
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color_image = dataset.fetch_sample_grid_images(
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uid,
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attrs=["image_color"],
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sub_idxs=sub_idxs,
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transform=image_transform,
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)
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normal_pil, position_pil, mask_pil, color_pil = dataset.visualize_item(
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control_image,
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color_image,
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save_dir=save_dir,
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)
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if pipeline is None:
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pipeline = build_texture_gen_pipe(
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base_ckpt_dir="./weights",
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controlnet_ckpt=controlnet_ckpt,
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ip_adapt_scale=ip_adapt_scale,
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device=device,
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)
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if ip_adapt_scale > 0 and ip_img_path is not None and len(ip_img_path) > 0:
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ip_image = Image.open(ip_img_path).convert("RGB")
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ip_image = ip_image.resize(target_hw[::-1])
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ip_image = [ip_image]
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pipeline.set_ip_adapter_scale([ip_adapt_scale])
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else:
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ip_image = None
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generator = None
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if seed is not None:
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generator = torch.Generator(device).manual_seed(seed)
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torch.manual_seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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init_image = get_init_noise_image(normal_pil)
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# init_image = get_init_noise_image(color_pil)
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images = []
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row_num, col_num = 2, 3
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img_save_paths = []
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while len(images) < col_num:
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image = pipeline(
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prompt=prompt,
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image=init_image,
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controlnet_conditioning_scale=controlnet_cond_scale,
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control_guidance_end=control_guidance_end,
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strength=strength,
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control_image=control_image[None, ...],
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negative_prompt=negative_prompt,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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num_images_per_prompt=num_images_per_prompt,
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ip_adapter_image=ip_image,
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generator=generator,
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).images
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images.extend(image)
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grid_image = [normal_pil, position_pil, color_pil] + images[:col_num]
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# save_dir = os.path.join(save_dir, uid)
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os.makedirs(save_dir, exist_ok=True)
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for idx in range(col_num):
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rgba_image = Image.merge("RGBA", (*images[idx].split(), mask_pil))
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img_save_path = os.path.join(save_dir, f"color_sample{idx}.png")
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rgba_image.save(img_save_path)
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img_save_paths.append(img_save_path)
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sub_idxs = "_".join(
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[str(item) for sublist in sub_idxs for item in sublist]
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)
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save_path = os.path.join(
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save_dir, f"sample_idx{str(sub_idxs)}_ip{ip_adapt_scale}.jpg"
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)
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make_image_grid(grid_image, row_num, col_num).save(save_path)
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logger.info(f"Visualize in {save_path}")
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return img_save_paths
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def entrypoint() -> None:
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fire.Fire(infer_pipe)
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if __name__ == "__main__":
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entrypoint()
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