Function get_text_embedding

src/composenW.py:92–112

(prompts)

        layers_modified[i] = 'model.diffusion_model' + layers_modified[i] + '.weight'

    def get_text_embedding(prompts):
        with torch.no_grad():
            uc = []
            for text in prompts:
                tokens = tokenizer(text,
                                   truncation=True,
                                   max_length=77,
                                   return_length=True,
                                   return_overflowing_tokens=False,
                                   padding="max_length",
                                   return_tensors="pt")

                tokens = tokens["input_ids"]
                end = torch.nonzero(tokens == 49407)[:, 1].min()
                if 'photo of a' in text[:15]:
                    print(text)
                    uc.append((model.get_learned_conditioning(1 * [text])[:, 4:end+1]).reshape(-1, 768))
                else:
                    uc.append((model.get_learned_conditioning(1 * [text])[:, 1:end+1]).reshape(-1, 768))

        return torch.cat(uc, 0)

    tokenizer = model.cond_stage_model.tokenizer
    embeds = []