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Support concat of clip outputs for longer prompt #48

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Support concat of clip outputs for longer prompt #48

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40 changes: 39 additions & 1 deletion gradio_app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,44 @@ def encode_cropped_prompt_77tokens(txt: str):
return text_cond


@torch.inference_mode()
def encode_cropped_prompt(txt: str, max_length=225):
memory_management.load_models_to_gpu(text_encoder)
cond_ids = tokenizer(
txt,
padding="max_length",
max_length=max_length + 2,
truncation=True,
return_tensors="pt",
).input_ids.to(device=text_encoder.device)
if max_length + 2 > tokenizer.model_max_length:
input_ids = cond_ids.squeeze(0)
id_list = list(range(1, max_length + 2 - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2))
text_cond_list = []
for i in id_list:
# Encode each chunk than concatenate their result
ids_chunk = (
input_ids[0].unsqueeze(0),
input_ids[i : i + tokenizer.model_max_length - 2],
input_ids[-1].unsqueeze(0),
)
if torch.all(ids_chunk[1] == tokenizer.pad_token_id):
break
text_cond = text_encoder(torch.concat(ids_chunk).unsqueeze(0)).last_hidden_state
if text_cond_list == []:
# BOS token
text_cond_list.append(text_cond[:, :1])
text_cond_list.append(text_cond[:, 1:tokenizer.model_max_length - 1])
# EOS token
text_cond_list.append(text_cond[:, -1:])
text_cond = torch.concat(text_cond_list, dim=1)
else:
text_cond = text_encoder(
cond_ids, attention_mask=None
).last_hidden_state
return text_cond.flatten(0, 1).unsqueeze(0)


@torch.inference_mode()
def pytorch2numpy(imgs):
results = []
Expand Down Expand Up @@ -126,7 +164,7 @@ def process(input_fg, prompt, input_undo_steps, image_width, image_height, seed,
concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor

memory_management.load_models_to_gpu(text_encoder)
conds = encode_cropped_prompt_77tokens(prompt)
conds = encode_cropped_prompt(prompt)
unconds = encode_cropped_prompt_77tokens(n_prompt)

memory_management.load_models_to_gpu(unet)
Expand Down