Open LDS666888 opened 1 week ago
I use the following command to generate the image:
torchrun --nproc_per_node=1 --nnodes=1 --node_rank=0 --master_addr="localhost" --master_port=59726 test_dex.py --which_cond d ex --bs 1 --cond_weight 1 --sd_ckpt "F:\data_enhancement\HOIDiffusion-main\stable_difussion_model\sd-v1-4.ckpt" --cond_tau 1 --adapter_ckpt "F:\data_enhancement\HOIDiffusion-main\midas _models\t2iadapter_depth_sd14v1.pth" --cond_inp_type image --input "F:\data_enhancement\HOIDiffusion-main\output\depth" --file "F:\data_enhancement\HOIDiffusion-main\output\train.csv" --outdir "F:\data_enhancement\HOIDiffusion-main\test_dex_outdir
The Settings in inference_base.py are as follows:
def get_adapters(opt, cond_type: ExtraCondition): adapter = {} cond_weight = getattr(opt, f'{cond_type.name}_weight', None) if cond_weight is None: cond_weight = getattr(opt, 'cond_weight') adapter['cond_weight'] = cond_weight adapter['model'] = CoAdapter(w1 = 0, w2 = 1, w3 = 0).to(opt.device) ckpt_path = getattr(opt, f'{cond_type.name}_adapter_ckpt', None) if ckpt_path is None: ckpt_path = getattr(opt, 'adapter_ckpt') state_dict = read_state_dict(ckpt_path) new_state_dict = {} for k, v in state_dict.items(): if k.startswith('adapter.'): new_state_dict[k[len('adapter.'):]] = v else: new_state_dict[k] = v # 如果某些键名没有前缀,可以手动添加 for k, v in state_dict .items(): if not k.startswith('depth_ada.'): new_state_dict['depth_ada.' + k] = v del new_state_dict[k] adapter['model'].load_state_dict(new_state_dict) return adapter
The resulting picture had the wrong number of fingers, and some of the hands did not touch the object:
From command, It seems you directly use the original sd1.4 and depth condition model from t2i-adapter to generate. It's hard without any training.
I use the following command to generate the image:
The Settings in inference_base.py are as follows:
The resulting picture had the wrong number of fingers, and some of the hands did not touch the object: