INNER CODE UNIT · Python
text_inputs
FoundationVision/Liquid · evaluation/app.py:197
text_inputs = [message]*4 # generate 4 samples once
uncondition_text_inputs = ['<unconditional><boi>']*len(text_inputs)
for i in range(len(text_inputs)):
text_inputs[i] = text_inputs[i]+' Generate an image based on this description.<boi>'
ori_batchsize = len(text_inputs)
if guidance_scale>1:
model_inputs = tokenizer(text_inputs+uncondition_text_inputs, return_tensors="pt",padding=True).to("cuda:0")
else:
model_inputs = tokenizer(text_inputs, return_tensors="pt",padding=True).to("cuda:0")
with torch.no_grad():
sampling_kwargs={'temperature': temperature, 'top_k': top_K, 'top_p': top_P, 'sample_logits': True}
input_ids = model_inputs['input_ids']
cur_len = input_ids.shape[1]
model_kwargs = {'attention_mask':model_inputs['attention_mask'] , 'use_cache': True}
model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)