| """Native visual HVS adapter used by visual initialization.""" |
| from .registry import MODELS |
| class Actor: |
| def __init__(self,name,out,max_pixels=65536): |
| import torch |
| from transformers import AutoProcessor,Qwen2_5_VLForConditionalGeneration |
| self.name=name;self.profile=MODELS[name];self.kind=self.profile['kind'];self.out=out;self.max_pixels=max_pixels |
| assert self.kind=='vl' and not self.profile['audio'] |
| self.proc=AutoProcessor.from_pretrained(self.profile['path'],local_files_only=True,min_pixels=3136,max_pixels=max_pixels,use_fast=True) |
| self.model=Qwen2_5_VLForConditionalGeneration.from_pretrained(self.profile['path'],local_files_only=True,torch_dtype=torch.bfloat16,attn_implementation='sdpa').cuda().eval() |
| ip=self.proc.image_processor;ip.min_pixels=3136;ip.max_pixels=max_pixels |
| if isinstance(getattr(ip,'size',None),dict):ip.size.update(shortest_edge=3136,longest_edge=max_pixels) |
| self.model.requires_grad_(False) |
| def prepare_inputs(self,msgs,pics,audio=None): |
| assert audio is None |
| text=self.proc.apply_chat_template(msgs,tokenize=False,add_generation_prompt=True) |
| batch=dict(self.proc(text=[text],images=pics,return_tensors='pt',padding=True)) |
| assert batch['input_ids'].shape[1]<=4096 |
| return batch |
| def generate(self,msgs,pics,audio=None,instruction='',pose=None): |
| import torch |
| x={k:v.cuda() for k,v in self.prepare_inputs(msgs,pics,audio).items()};n=x['input_ids'].shape[1] |
| with torch.inference_mode(),torch.autocast('cuda',dtype=torch.bfloat16): |
| y=self.model.generate(**x,max_new_tokens=512,do_sample=False,use_cache=True,pad_token_id=self.proc.tokenizer.eos_token_id) |
| return self.proc.decode(y[0,n:],skip_special_tokens=True) |
|
|
