# -*- coding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import math import os import random import numpy as np import torch import torch.nn.functional as F from PIL import Image import torchvision.transforms as T from scepter.modules.model.registry import DIFFUSIONS, BACKBONES import torchvision.transforms.functional as TF from scepter.modules.model.utils.basic_utils import check_list_of_list from scepter.modules.model.utils.basic_utils import \ pack_imagelist_into_tensor_v2 as pack_imagelist_into_tensor from scepter.modules.model.utils.basic_utils import ( to_device, unpack_tensor_into_imagelist) from scepter.modules.utils.distribute import we from scepter.modules.utils.file_system import FS from scepter.modules.utils.logger import get_logger from scepter.modules.inference.diffusion_inference import DiffusionInference, get_model def process_edit_image(images, masks, tasks): if not isinstance(images, list): images = [images] if not isinstance(masks, list): masks = [masks] if not isinstance(tasks, list): tasks = [tasks] img_tensors = [] mask_tensors = [] for img, mask, task in zip(images, masks, tasks): if mask is None or mask == '': mask = Image.new('L', img.size, 0) img = TF.center_crop(img, [512, 512]) mask = TF.center_crop(mask, [512, 512]) mask = np.asarray(mask) mask = np.where(mask > 128, 1, 0) mask = mask.astype( np.float32) if np.any(mask) else np.ones_like(mask).astype( np.float32) img_tensor = TF.to_tensor(img).to(we.device_id) img_tensor = TF.normalize(img_tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) mask_tensor = TF.to_tensor(mask).to(we.device_id) if task in ['inpainting', 'Try On', 'Inpainting']: mask_indicator = mask_tensor.repeat(3, 1, 1) img_tensor[mask_indicator == 1] = -1.0 img_tensors.append(img_tensor) mask_tensors.append(mask_tensor) return img_tensors, mask_tensors class FluxACEInference(DiffusionInference): def __init__(self, logger=None): if logger is None: logger = get_logger(name='scepter') self.logger = logger self.loaded_model = {} self.loaded_model_name = [ 'diffusion_model', 'first_stage_model', 'cond_stage_model', 'ref_cond_stage_model' ] def init_from_cfg(self, cfg): self.name = cfg.NAME self.is_default = cfg.get('IS_DEFAULT', False) self.use_dynamic_model = cfg.get('USE_DYNAMIC_MODEL', True) module_paras = self.load_default(cfg.get('DEFAULT_PARAS', None)) assert cfg.have('MODEL') self.size_factor = cfg.get('SIZE_FACTOR', 8) self.diffusion_model = self.infer_model( cfg.MODEL.DIFFUSION_MODEL, module_paras.get( 'DIFFUSION_MODEL', None)) if cfg.MODEL.have('DIFFUSION_MODEL') else None self.first_stage_model = self.infer_model( cfg.MODEL.FIRST_STAGE_MODEL, module_paras.get( 'FIRST_STAGE_MODEL', None)) if cfg.MODEL.have('FIRST_STAGE_MODEL') else None self.cond_stage_model = self.infer_model( cfg.MODEL.COND_STAGE_MODEL, module_paras.get( 'COND_STAGE_MODEL', None)) if cfg.MODEL.have('COND_STAGE_MODEL') else None self.ref_cond_stage_model = self.infer_model( cfg.MODEL.REF_COND_STAGE_MODEL, module_paras.get( 'REF_COND_STAGE_MODEL', None)) if cfg.MODEL.have('REF_COND_STAGE_MODEL') else None self.diffusion = DIFFUSIONS.build(cfg.MODEL.DIFFUSION, logger=self.logger) self.interpolate_func = lambda x: (F.interpolate( x.unsqueeze(0), scale_factor=1 / self.size_factor, mode='nearest-exact') if x is not None else None) self.max_seq_length = cfg.get("MAX_SEQ_LENGTH", 4096) if not self.use_dynamic_model: self.dynamic_load(self.first_stage_model, 'first_stage_model') self.dynamic_load(self.cond_stage_model, 'cond_stage_model') if self.ref_cond_stage_model is not None: self.dynamic_load(self.ref_cond_stage_model, 'ref_cond_stage_model') with torch.device("meta"): pretrained_model = self.diffusion_model['cfg'].PRETRAINED_MODEL self.diffusion_model['cfg'].PRETRAINED_MODEL = None diffusers_lora = self.diffusion_model['cfg'].get("DIFFUSERS_LORA_MODEL", None) self.diffusion_model['cfg'].DIFFUSERS_LORA_MODEL = None swift_lora = self.diffusion_model['cfg'].get("SWIFT_LORA_MODEL", None) self.diffusion_model['cfg'].SWIFT_LORA_MODEL = None pretrain_adapter = self.diffusion_model['cfg'].get("PRETRAIN_ADAPTER", None) self.diffusion_model['cfg'].PRETRAIN_ADAPTER = None blackforest_lora = self.diffusion_model['cfg'].get("BLACKFOREST_LORA_MODEL", None) self.diffusion_model['cfg'].BLACKFOREST_LORA_MODEL = None self.diffusion_model['model'] = BACKBONES.build(self.diffusion_model['cfg'], logger=self.logger).eval() # self.dynamic_load(self.diffusion_model, 'diffusion_model') self.diffusion_model['model'].lora_model = diffusers_lora self.diffusion_model['model'].swift_lora_model = swift_lora self.diffusion_model['model'].pretrain_adapter = pretrain_adapter self.diffusion_model['model'].blackforest_lora_model = blackforest_lora self.diffusion_model['model'].load_pretrained_model(pretrained_model) self.diffusion_model['device'] = we.device_id def upscale_resize(self, image, interpolation=T.InterpolationMode.BILINEAR): c, H, W = image.shape scale = max(1.0, math.sqrt(self.max_seq_length / ((H / 16) * (W / 16)))) rH = int(H * scale) // 16 * 16 # ensure divisible by self.d rW = int(W * scale) // 16 * 16 image = T.Resize((rH, rW), interpolation=interpolation, antialias=True)(image) return image @torch.no_grad() def encode_first_stage(self, x, **kwargs): _, dtype = self.get_function_info(self.first_stage_model, 'encode') with torch.autocast('cuda', enabled=dtype in ('float16', 'bfloat16'), dtype=getattr(torch, dtype)): def run_one_image(u): zu = get_model(self.first_stage_model).encode(u) if isinstance(zu, (tuple, list)): zu = zu[0] return zu z = [run_one_image(u.unsqueeze(0) if u.dim() == 3 else u) for u in x] return z @torch.no_grad() def decode_first_stage(self, z): _, dtype = self.get_function_info(self.first_stage_model, 'decode') with torch.autocast('cuda', enabled=dtype in ('float16', 'bfloat16'), dtype=getattr(torch, dtype)): return [get_model(self.first_stage_model).decode(zu) for zu in z] def noise_sample(self, num_samples, h, w, seed, device = None, dtype = torch.bfloat16): noise = torch.randn( num_samples, 16, # allow for packing 2 * math.ceil(h / 16), 2 * math.ceil(w / 16), device="cpu", dtype=dtype, generator=torch.Generator().manual_seed(seed), ).to(device) return noise @torch.no_grad() def __call__(self, image=None, mask=None, prompt='', task=None, negative_prompt='', output_height=1024, output_width=1024, sampler='flow_euler', sample_steps=20, guide_scale=3.5, seed=-1, history_io=None, tar_index=0, # align=0, **kwargs): input_image, input_mask = image, mask seed = seed if seed >= 0 else random.randint(0, 2**32 - 1) if input_image is not None: # assert isinstance(input_image, list) and isinstance(input_mask, list) if task is None: task = [''] * len(input_image) if not isinstance(prompt, list): prompt = [prompt] * len(input_image) prompt = [ pp.replace('{image}', f'{{image{i}}}') if i > 0 else pp for i, pp in enumerate(prompt) ] edit_image, edit_image_mask = process_edit_image( input_image, input_mask, task) image = torch.zeros( size=[3, int(output_height), int(output_width)]) image_mask = torch.ones( size=[1, int(output_height), int(output_width)]) edit_image, edit_image_mask = [edit_image], [edit_image_mask] else: edit_image = edit_image_mask = [[]] image = torch.zeros( size=[3, int(output_height), int(output_width)]) image_mask = torch.ones( size=[1, int(output_height), int(output_width)]) if not isinstance(prompt, list): prompt = [prompt] align = 0 image, image_mask, prompt = [image], [image_mask], [prompt], align = [align for p in prompt] if isinstance(align, int) else align assert check_list_of_list(prompt) and check_list_of_list( edit_image) and check_list_of_list(edit_image_mask) # negative prompt is not used image = to_device(image) ctx = {} # Get Noise Shape self.dynamic_load(self.first_stage_model, 'first_stage_model') x = self.encode_first_stage(image) self.dynamic_unload(self.first_stage_model, 'first_stage_model', skip_loaded=not self.use_dynamic_model) g = torch.Generator(device=we.device_id).manual_seed(seed) noise = [ torch.randn((1, 16, i.shape[2], i.shape[3]), device=we.device_id, dtype=torch.bfloat16).normal_(generator=g) for i in x ] # import pdb;pdb.set_trace() noise, x_shapes = pack_imagelist_into_tensor(noise) ctx['x_shapes'] = x_shapes ctx['align'] = align image_mask = to_device(image_mask, strict=False) cond_mask = [self.interpolate_func(i) for i in image_mask ] if image_mask is not None else [None] * len(image) ctx['x_mask'] = cond_mask # Encode Prompt instruction_prompt = [[pp[-1]] if "{image}" in pp[-1] else ["{image} " + pp[-1]] for pp in prompt] self.dynamic_load(self.cond_stage_model, 'cond_stage_model') function_name, dtype = self.get_function_info(self.cond_stage_model) cont = getattr(get_model(self.cond_stage_model), function_name)(instruction_prompt) cont["context"] = [ct[-1] for ct in cont["context"]] cont["y"] = [ct[-1] for ct in cont["y"]] self.dynamic_unload(self.cond_stage_model, 'cond_stage_model', skip_loaded=not self.use_dynamic_model) ctx.update(cont) # Encode Edit Images self.dynamic_load(self.first_stage_model, 'first_stage_model') edit_image = [to_device(i, strict=False) for i in edit_image] edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask] e_img, e_mask = [], [] for u, m in zip(edit_image, edit_image_mask): if u is None: continue if m is None: m = [None] * len(u) e_img.append(self.encode_first_stage(u, **kwargs)) e_mask.append([self.interpolate_func(i) for i in m]) self.dynamic_unload(self.first_stage_model, 'first_stage_model', skip_loaded=not self.use_dynamic_model) ctx['edit'] = e_img ctx['edit_mask'] = e_mask # Encode Ref Images if guide_scale is not None: guide_scale = torch.full((noise.shape[0],), guide_scale, device=noise.device, dtype=noise.dtype) else: guide_scale = None # Diffusion Process self.dynamic_load(self.diffusion_model, 'diffusion_model') function_name, dtype = self.get_function_info(self.diffusion_model) with torch.autocast('cuda', enabled=dtype in ('float16', 'bfloat16'), dtype=getattr(torch, dtype)): latent = self.diffusion.sample( noise=noise, sampler=sampler, model=get_model(self.diffusion_model), model_kwargs={ "cond": ctx, "guidance": guide_scale, "gc_seg": -1 }, steps=sample_steps, show_progress=True, guide_scale=guide_scale, return_intermediate=None, reverse_scale=-1, **kwargs).float() if self.use_dynamic_model: self.dynamic_unload(self.diffusion_model, 'diffusion_model', skip_loaded=not self.use_dynamic_model) # Decode to Pixel Space self.dynamic_load(self.first_stage_model, 'first_stage_model') samples = unpack_tensor_into_imagelist(latent, x_shapes) x_samples = self.decode_first_stage(samples) self.dynamic_unload(self.first_stage_model, 'first_stage_model', skip_loaded=not self.use_dynamic_model) x_samples = [x.squeeze(0) for x in x_samples] imgs = [ torch.clamp((x_i.float() + 1.0) / 2.0, min=0.0, max=1.0).squeeze(0).permute(1, 2, 0).cpu().numpy() for x_i in x_samples ] imgs = [Image.fromarray((img * 255).astype(np.uint8)) for img in imgs] return imgs