support for Textual Embeddings?

[AG-w]

Edited: I'm not sure if it's an actual fix, but It works fine after I added with devices.autocast():

                with devices.autocast():
                    cond = prompt_parser.get_learned_conditioning(shared.sd_model,input,p.steps)

=====

if you try to put negative weight on embedding, it will show error

      File "H:\AItest\stable-diffusion-webui\extensions\sd-webui-negpip\scripts\negpip.py", line 268, in denoiser_callback
        for step, regions in self.hr_conds_all[0] if self.hrp and self.hr else  self.conds_all[0]:
    TypeError: 'NoneType' object is not subscriptable

   Traceback (most recent call last):
      File "H:\AItest\stable-diffusion-webui\modules\scripts.py", line 808, in process_batch
        script.process_batch(p, *script_args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\extensions\sd-webui-negpip\scripts\negpip.py", line 213, in process_batch
        self.conds_all = calcconds(nip)
      File "H:\AItest\stable-diffusion-webui\extensions\sd-webui-negpip\scripts\negpip.py", line 205, in calcconds
        conds, contokens = conddealer(targets)
      File "H:\AItest\stable-diffusion-webui\extensions\sd-webui-negpip\scripts\negpip.py", line 179, in conddealer
        cond = prompt_parser.get_learned_conditioning(shared.sd_model,input,p.steps)
      File "H:\AItest\stable-diffusion-webui\modules\prompt_parser.py", line 188, in get_learned_conditioning
        conds = model.get_learned_conditioning(texts)
      File "H:\AItest\stable-diffusion-webui\scripts\v2.py", line 36, in get_learned_conditioning_with_prior
        cond = ldm.models.diffusion.ddpm.LatentDiffusion.get_learned_conditioning_original(self, c)
      File "H:\AItest\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 669, in get_learned_conditioning
        c = self.cond_stage_model(c)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
        return forward_call(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\modules\sd_hijack_clip.py", line 234, in forward
        z = self.process_tokens(tokens, multipliers)
      File "H:\AItest\stable-diffusion-webui\modules\sd_hijack_clip.py", line 276, in process_tokens
        z = self.encode_with_transformers(tokens)
      File "H:\AItest\stable-diffusion-webui\modules\sd_hijack_clip.py", line 331, in encode_with_transformers
        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
        return forward_call(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\transformers\models\clip\modeling_clip.py", line 822, in forward
        return self.text_model(
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
        return forward_call(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\transformers\models\clip\modeling_clip.py", line 740, in forward
        encoder_outputs = self.encoder(
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
        return forward_call(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\transformers\models\clip\modeling_clip.py", line 654, in forward
        layer_outputs = encoder_layer(
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
        return forward_call(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\transformers\models\clip\modeling_clip.py", line 382, in forward
        hidden_states = self.layer_norm1(hidden_states)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
        return forward_call(*args, **kwargs)
      File "H:\AItest\stable-diffusion-webui\extensions-builtin\Lora\networks.py", line 545, in network_LayerNorm_forward
        return originals.LayerNorm_forward(self, input)
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\normalization.py", line 201, in forward
        return F.layer_norm(
      File "H:\AItest\stable-diffusion-webui\venv\lib\site-packages\torch\nn\functional.py", line 2546, in layer_norm
        return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
    RuntimeError: expected scalar type Float but found Half