| import torch | |
| from libs.base_utils import do_resize_content | |
| from imagedream.ldm.util import ( | |
| instantiate_from_config, | |
| get_obj_from_str, | |
| ) | |
| from omegaconf import OmegaConf | |
| from PIL import Image | |
| import numpy as np | |
| class TwoStagePipeline(object): | |
| def __init__( | |
| self, | |
| stage1_model_config, | |
| stage2_model_config, | |
| stage1_sampler_config, | |
| stage2_sampler_config, | |
| device="cuda", | |
| dtype=torch.float16, | |
| resize_rate=1, | |
| ) -> None: | |
| """ | |
| only for two stage generate process. | |
| - the first stage was condition on single pixel image, gererate multi-view pixel image, based on the v2pp config | |
| - the second stage was condition on multiview pixel image generated by the first stage, generate the final image, based on the stage2-test config | |
| """ | |
| self.resize_rate = resize_rate | |
| self.stage1_model = instantiate_from_config(OmegaConf.load(stage1_model_config.config).model) | |
| self.stage1_model.load_state_dict(torch.load(stage1_model_config.resume, map_location="cpu"), strict=False) | |
| self.stage1_model = self.stage1_model.to(device).to(dtype) | |
| self.stage2_model = instantiate_from_config(OmegaConf.load(stage2_model_config.config).model) | |
| sd = torch.load(stage2_model_config.resume, map_location="cpu") | |
| self.stage2_model.load_state_dict(sd, strict=False) | |
| self.stage2_model = self.stage2_model.to(device).to(dtype) | |
| self.stage1_model.device = device | |
| self.stage2_model.device = device | |
| self.device = device | |
| self.dtype = dtype | |
| self.stage1_sampler = get_obj_from_str(stage1_sampler_config.target)( | |
| self.stage1_model, device=device, dtype=dtype, **stage1_sampler_config.params | |
| ) | |
| self.stage2_sampler = get_obj_from_str(stage2_sampler_config.target)( | |
| self.stage2_model, device=device, dtype=dtype, **stage2_sampler_config.params | |
| ) | |
| def stage1_sample( | |
| self, | |
| pixel_img, | |
| prompt="3D assets", | |
| neg_texts="uniform low no texture ugly, boring, bad anatomy, blurry, pixelated, obscure, unnatural colors, poor lighting, dull, and unclear.", | |
| step=50, | |
| scale=5, | |
| ddim_eta=0.0, | |
| ): | |
| if type(pixel_img) == str: | |
| pixel_img = Image.open(pixel_img) | |
| if isinstance(pixel_img, Image.Image): | |
| if pixel_img.mode == "RGBA": | |
| background = Image.new('RGBA', pixel_img.size, (0, 0, 0, 0)) | |
| pixel_img = Image.alpha_composite(background, pixel_img).convert("RGB") | |
| else: | |
| pixel_img = pixel_img.convert("RGB") | |
| else: | |
| raise | |
| uc = self.stage1_sampler.model.get_learned_conditioning([neg_texts]).to(self.device) | |
| stage1_images = self.stage1_sampler.i2i( | |
| self.stage1_sampler.model, | |
| self.stage1_sampler.size, | |
| prompt, | |
| uc=uc, | |
| sampler=self.stage1_sampler.sampler, | |
| ip=pixel_img, | |
| step=step, | |
| scale=scale, | |
| batch_size=self.stage1_sampler.batch_size, | |
| ddim_eta=ddim_eta, | |
| dtype=self.stage1_sampler.dtype, | |
| device=self.stage1_sampler.device, | |
| camera=self.stage1_sampler.camera, | |
| num_frames=self.stage1_sampler.num_frames, | |
| pixel_control=(self.stage1_sampler.mode == "pixel"), | |
| transform=self.stage1_sampler.image_transform, | |
| offset_noise=self.stage1_sampler.offset_noise, | |
| ) | |
| stage1_images = [Image.fromarray(img) for img in stage1_images] | |
| stage1_images.pop(self.stage1_sampler.ref_position) | |
| return stage1_images | |
| def stage2_sample(self, pixel_img, stage1_images, scale=5, step=50): | |
| if type(pixel_img) == str: | |
| pixel_img = Image.open(pixel_img) | |
| if isinstance(pixel_img, Image.Image): | |
| if pixel_img.mode == "RGBA": | |
| background = Image.new('RGBA', pixel_img.size, (0, 0, 0, 0)) | |
| pixel_img = Image.alpha_composite(background, pixel_img).convert("RGB") | |
| else: | |
| pixel_img = pixel_img.convert("RGB") | |
| else: | |
| raise | |
| stage2_images = self.stage2_sampler.i2iStage2( | |
| self.stage2_sampler.model, | |
| self.stage2_sampler.size, | |
| "3D assets", | |
| self.stage2_sampler.uc, | |
| self.stage2_sampler.sampler, | |
| pixel_images=stage1_images, | |
| ip=pixel_img, | |
| step=step, | |
| scale=scale, | |
| batch_size=self.stage2_sampler.batch_size, | |
| ddim_eta=0.0, | |
| dtype=self.stage2_sampler.dtype, | |
| device=self.stage2_sampler.device, | |
| camera=self.stage2_sampler.camera, | |
| num_frames=self.stage2_sampler.num_frames, | |
| pixel_control=(self.stage2_sampler.mode == "pixel"), | |
| transform=self.stage2_sampler.image_transform, | |
| offset_noise=self.stage2_sampler.offset_noise, | |
| ) | |
| stage2_images = [Image.fromarray(img) for img in stage2_images] | |
| return stage2_images | |
| def set_seed(self, seed): | |
| self.stage1_sampler.seed = seed | |
| self.stage2_sampler.seed = seed | |
| def __call__(self, pixel_img, prompt="3D assets", scale=5, step=50): | |
| pixel_img = do_resize_content(pixel_img, self.resize_rate) | |
| stage1_images = self.stage1_sample(pixel_img, prompt, scale=scale, step=step) | |
| stage2_images = self.stage2_sample(pixel_img, stage1_images, scale=scale, step=step) | |
| return { | |
| "ref_img": pixel_img, | |
| "stage1_images": stage1_images, | |
| "stage2_images": stage2_images, | |
| } | |
| if __name__ == "__main__": | |
| stage1_config = OmegaConf.load("configs/nf7_v3_SNR_rd_size_stroke.yaml").config | |
| stage2_config = OmegaConf.load("configs/stage2-v2-snr.yaml").config | |
| stage2_sampler_config = stage2_config.sampler | |
| stage1_sampler_config = stage1_config.sampler | |
| stage1_model_config = stage1_config.models | |
| stage2_model_config = stage2_config.models | |
| pipeline = TwoStagePipeline( | |
| stage1_model_config, | |
| stage2_model_config, | |
| stage1_sampler_config, | |
| stage2_sampler_config, | |
| ) | |
| img = Image.open("assets/astronaut.png") | |
| rt_dict = pipeline(img) | |
| stage1_images = rt_dict["stage1_images"] | |
| stage2_images = rt_dict["stage2_images"] | |
| np_imgs = np.concatenate(stage1_images, 1) | |
| np_xyzs = np.concatenate(stage2_images, 1) | |
| Image.fromarray(np_imgs).save("pixel_images.png") | |
| Image.fromarray(np_xyzs).save("xyz_images.png") | |