309 lines
14 KiB
Python
309 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import copy
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import os.path
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import random
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from collections import OrderedDict
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import gradio as gr
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import torch
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import torchvision.transforms.functional as TF
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from scepter.modules.model.network.diffusion.diffusion import GaussianDiffusion
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from scepter.modules.model.utils.data_utils import crop_back
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.file_system import FS
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from .diffusion_inference import DiffusionInference, get_model
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class LargenInference(DiffusionInference):
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'''
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define vae, unet, text-encoder, tuner, refiner components
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support to load the components dynamicly.
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create and load model when run this model at the first time.
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'''
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def __init__(self, logger=None):
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self.logger = logger
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self.is_redefine_paras = True
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self.loaded_model = {}
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self.loaded_model_name = [
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'diffusion_model', 'first_stage_model', 'cond_stage_model'
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]
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self.diffusion_insclass = GaussianDiffusion
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def redefine_paras(self, cfg):
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if cfg.get('PRETRAINED_MODEL', None):
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assert FS.isfile(cfg.PRETRAINED_MODEL)
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with FS.get_from(cfg.PRETRAINED_MODEL,
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wait_finish=True) as local_path:
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if local_path.endswith('safetensors'):
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from safetensors.torch import load_file as load_safetensors
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sd = load_safetensors(local_path)
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else:
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sd = torch.load(local_path, map_location='cpu')
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if 'model' in sd:
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sd = sd['model']
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first_stage_model_path = os.path.join(
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os.path.dirname(local_path), 'first_stage_model.pth')
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cond_stage_model_path = os.path.join(
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os.path.dirname(local_path), 'cond_stage_model.pth')
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diffusion_model_path = os.path.join(
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os.path.dirname(local_path), 'diffusion_model.pth')
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if (not os.path.exists(first_stage_model_path)
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or not os.path.exists(cond_stage_model_path)
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or not os.path.exists(diffusion_model_path)):
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self.logger.info(
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'Now read the whole model and rearrange the modules, it may take several mins.'
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)
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first_stage_model = OrderedDict()
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cond_stage_model = OrderedDict()
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diffusion_model = OrderedDict()
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for k, v in sd.items():
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if k.startswith('first_stage_model.'):
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first_stage_model[k.replace(
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'first_stage_model.', '')] = v
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elif k.startswith('conditioner.'):
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cond_stage_model[k.replace('conditioner.', '')] = v
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elif k.startswith('cond_stage_model.'):
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if k.startswith('cond_stage_model.model.'):
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cond_stage_model[k.replace(
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'cond_stage_model.model.', '')] = v
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else:
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cond_stage_model[k.replace(
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'cond_stage_model.', '')] = v
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elif k.startswith('model.diffusion_model.'):
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diffusion_model[k.replace('model.diffusion_model.',
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'')] = v
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elif k.startswith('model.'):
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diffusion_model[k.replace('model.', '')] = v
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else:
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continue
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if cfg.have('FIRST_STAGE_MODEL'):
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with open(first_stage_model_path + 'cache', 'wb') as f:
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torch.save(first_stage_model, f)
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os.rename(first_stage_model_path + 'cache',
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first_stage_model_path)
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self.logger.info(
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'First stage model has been processed.')
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if cfg.have('COND_STAGE_MODEL'):
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with open(cond_stage_model_path + 'cache', 'wb') as f:
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torch.save(cond_stage_model, f)
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os.rename(cond_stage_model_path + 'cache',
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cond_stage_model_path)
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self.logger.info(
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'Cond stage model has been processed.')
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if cfg.have('DIFFUSION_MODEL'):
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with open(diffusion_model_path + 'cache', 'wb') as f:
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torch.save(diffusion_model, f)
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os.rename(diffusion_model_path + 'cache',
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diffusion_model_path)
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self.logger.info('Diffusion model has been processed.')
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if not cfg.FIRST_STAGE_MODEL.get('PRETRAINED_MODEL', None):
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cfg.FIRST_STAGE_MODEL.PRETRAINED_MODEL = first_stage_model_path
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else:
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cfg.FIRST_STAGE_MODEL.RELOAD_MODEL = first_stage_model_path
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if not cfg.COND_STAGE_MODEL.get('PRETRAINED_MODEL', None):
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cfg.COND_STAGE_MODEL.PRETRAINED_MODEL = cond_stage_model_path
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else:
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cfg.COND_STAGE_MODEL.RELOAD_MODEL = cond_stage_model_path
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if not cfg.DIFFUSION_MODEL.get('PRETRAINED_MODEL', None):
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cfg.DIFFUSION_MODEL.PRETRAINED_MODEL = diffusion_model_path
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else:
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cfg.DIFFUSION_MODEL.RELOAD_MODEL = diffusion_model_path
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return cfg
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@torch.no_grad()
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def __call__(self,
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input,
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num_samples=1,
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intermediate_callback=None,
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refine_strength=0,
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img_to_img_strength=0,
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cat_uc=True,
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tuner_model=None,
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control_model=None,
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largen_state=False,
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**kwargs):
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if not largen_state:
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raise gr.Error('LARGEN model must be used with LAR-Gen settings')
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value_input = copy.deepcopy(self.input)
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value_input.update(input)
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print(value_input)
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height, width = value_input['target_size_as_tuple']
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value_output = copy.deepcopy(self.output)
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batch, batch_uc = self.get_batch(value_input, num_samples=1)
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# first stage encode
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task = kwargs.get('largen_task', 'Text_Guided_Inpainting')
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image_scale = kwargs.get('largen_image_scale', 1.0)
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tar_image = kwargs.get('largen_tar_image', None)
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tar_mask = kwargs.get('largen_tar_mask', None)
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masked_image = kwargs.get('largen_masked_image', None)
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ref_image = kwargs.get('largen_ref_image', None)
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ref_mask = kwargs.get('largen_ref_mask', None)
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ref_clip = kwargs.get('largen_ref_clip', None)
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base_image = kwargs.get('largen_base_image', None)
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extra_sizes = kwargs.get('largen_extra_sizes', None)
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bbox_yyxx = kwargs.get('largen_bbox_yyxx', None)
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device = we.device_id
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tar_image = tar_image.to(device)
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tar_mask = tar_mask.to(device)
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masked_image = masked_image.to(device)
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if 'Subject' in task:
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ref_image = ref_image.to(device)
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ref_mask = ref_mask.to(device)
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ref_clip = ref_clip.to(device)
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self.dynamic_load(self.first_stage_model, 'first_stage_model')
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tar_x0 = self.encode_first_stage(tar_image)
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masked_x0 = self.encode_first_stage(masked_image)
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b, _, h, w = tar_x0.shape
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tar_mask_latent = TF.resize(tar_mask, (h, w), antialias=True)
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tar_mask_latent = (tar_mask_latent > 0.5).float()
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batch.update({
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'tar_x0': tar_x0,
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'tar_mask_latent': tar_mask_latent,
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'masked_x0': masked_x0,
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'task': task
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})
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batch_uc.update({
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'tar_x0': tar_x0,
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'tar_mask_latent': tar_mask_latent,
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'masked_x0': masked_x0,
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'task': task
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})
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if 'Subject' in task and ref_image is not None:
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ref_x0 = self.encode_first_stage(ref_image)
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batch.update({
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'ref_ip': ref_clip,
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'ref_detail': ref_clip,
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'ref_x0': ref_x0,
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'ref_mask': ref_mask,
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'image_scale': image_scale,
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})
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batch_uc.update({
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'ref_ip': torch.zeros_like(ref_clip),
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'ref_detail': ref_clip,
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'ref_x0': ref_x0,
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'ref_mask': ref_mask,
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'image_scale': image_scale,
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})
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self.dynamic_unload(self.first_stage_model,
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'first_stage_model',
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skip_loaded=False)
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# cond stage
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self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
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function_name, dtype = self.get_function_info(self.cond_stage_model)
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with torch.autocast('cuda',
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enabled=dtype == 'float16',
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dtype=getattr(torch, dtype)):
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context = getattr(get_model(self.cond_stage_model),
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function_name)(batch)
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null_context = getattr(get_model(self.cond_stage_model),
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function_name)(batch_uc)
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self.dynamic_unload(self.cond_stage_model,
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'cond_stage_model',
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skip_loaded=False)
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# get noise
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seed = kwargs.pop('seed', -1)
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g = torch.Generator(device=we.device_id)
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seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
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g.manual_seed(seed)
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if 'seed' in value_output:
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value_output['seed'] = seed
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for sample_id in range(num_samples):
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if self.diffusion_model is not None:
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noise = torch.empty(
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1,
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4,
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height // self.first_stage_model['paras']['size_factor'],
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width // self.first_stage_model['paras']['size_factor'],
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device=we.device_id).normal_(generator=g)
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self.dynamic_load(self.diffusion_model, 'diffusion_model')
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# UNet use input n_prompt
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function_name, dtype = self.get_function_info(
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self.diffusion_model)
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with torch.autocast('cuda',
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enabled=dtype == 'float16',
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dtype=getattr(torch, dtype)):
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latent = self.diffusion.sample(
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noise=noise,
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x=None,
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denoising_strength=1.0,
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refine_strength=refine_strength,
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solver=value_input.get('sample', 'ddim'),
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model=get_model(self.diffusion_model),
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model_kwargs=[{
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'cond': context
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}, {
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'cond': null_context
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}],
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steps=value_input.get('sample_steps', 50),
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guide_scale=value_input.get('guide_scale', 7.5),
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guide_rescale=value_input.get('guide_rescale', 0.5),
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discretization=value_input.get('discretization',
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'trailing'),
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show_progress=True,
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seed=seed,
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condition_fn=None,
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clamp=None,
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percentile=None,
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t_max=None,
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t_min=None,
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discard_penultimate_step=None,
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intermediate_callback=intermediate_callback,
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cat_uc=cat_uc,
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**kwargs)
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self.dynamic_unload(self.diffusion_model,
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'diffusion_model',
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skip_loaded=True)
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if 'latent' in value_output:
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if value_output['latent'] is None or (
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isinstance(value_output['latent'], list)
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and len(value_output['latent']) < 1):
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value_output['latent'] = []
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value_output['latent'].append(latent)
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self.dynamic_load(self.first_stage_model, 'first_stage_model')
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x_samples = self.decode_first_stage(latent).float()
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self.dynamic_unload(self.first_stage_model,
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'first_stage_model',
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skip_loaded=False)
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images = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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if base_image is not None:
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stitch_images = []
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for img in images:
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stitch_img = crop_back(img, copy.deepcopy(base_image),
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extra_sizes, bbox_yyxx)
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stitch_images.append(stitch_img)
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images = torch.stack(stitch_images, dim=0)
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if 'images' in value_output:
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if value_output['images'] is None or (
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isinstance(value_output['images'], list)
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and len(value_output['images']) < 1):
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value_output['images'] = []
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value_output['images'].append(images)
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for k, v in value_output.items():
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if isinstance(v, list):
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value_output[k] = torch.cat(v, dim=0)
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if isinstance(v, torch.Tensor):
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value_output[k] = v.cpu()
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return value_output
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