RuntimeError in forge, magic prompt

[tazztone]

not sure if this is supposed to work on forge in the first place. but when trying magic prompt i get this error:
(i got more than enough spare VRAM)

WARNING:dynamicprompts.generators.magicprompt:First load of MagicPrompt may take a while.
C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(
Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
*** Error running process: C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\extensions\sd-dynamic-prompts\scripts\dynamic_prompting.py
    Traceback (most recent call last):
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\modules\scripts.py", line 844, in process
        script.process(p, *script_args)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\extensions\sd-dynamic-prompts\sd_dynamic_prompts\dynamic_prompting.py", line 480, in process
        all_prompts, all_negative_prompts = generate_prompts(
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\extensions\sd-dynamic-prompts\sd_dynamic_prompts\helpers.py", line 93, in generate_prompts
        all_prompts = prompt_generator.generate(prompt, num_prompts, seeds=seeds) or [""]
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\dynamicprompts\generators\magicprompt.py", line 169, in generate
        magic_prompts = self._generate_magic_prompts(prompts)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\extensions\sd-dynamic-prompts\sd_dynamic_prompts\magic_prompt.py", line 32, in _generate_magic_prompts
        for prompt in super()._generate_magic_prompts(list(orig_prompts))
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\dynamicprompts\generators\magicprompt.py", line 215, in _generate_magic_prompts
        prompts = self._generator(
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\pipelines\text_generation.py", line 262, in __call__
        return super().__call__(text_inputs, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\pipelines\base.py", line 1238, in __call__
        outputs = list(final_iterator)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\pipelines\pt_utils.py", line 124, in __next__
        item = next(self.iterator)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\pipelines\pt_utils.py", line 125, in __next__
        processed = self.infer(item, **self.params)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\pipelines\base.py", line 1164, in forward
        model_outputs = self._forward(model_inputs, **forward_params)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\pipelines\text_generation.py", line 351, in _forward
        generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
        return func(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\generation\utils.py", line 2024, in generate
        result = self._sample(
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\generation\utils.py", line 2982, in _sample
        outputs = self(**model_inputs, return_dict=True)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\models\gpt2\modeling_gpt2.py", line 1315, in forward
        transformer_outputs = self.transformer(
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\transformers\models\gpt2\modeling_gpt2.py", line 1027, in forward
        inputs_embeds = self.wte(input_ids)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\module.py", line 1532, in _wrapped_call_impl
        return self._call_impl(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\module.py", line 1541, in _call_impl
        return forward_call(*args, **kwargs)
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\modules\sparse.py", line 163, in forward
        return F.embedding(
      File "C:\_stability_matrix\Data\Packages\stable-diffusion-webui-forge\venv\lib\site-packages\torch\nn\functional.py", line 2264, in embedding
        return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
    RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument index in method wrapper_CUDA__index_select)

---

[tazztone]

claude 3.5 told me how to fix it

1. i asked how do i fix this error?.
2. it told me to mod the file magic_prompt.py.
3. i pasted the file's content and asked how to modify to fix it.

amazingly it gave me this working code to replace with:

import re
from itertools import zip_longest
import torch
from dynamicprompts.generators.magicprompt import MagicPromptGenerator
from sd_dynamic_prompts.special_syntax import (
    append_chunks,
    remove_a1111_special_syntax_chunks,
)

def massage_prompt(prompt: str) -> str:
    # Coalesce repeated punctuation to a single instance
    prompt = re.sub(r"([.,])\1+", r"\1", prompt)
    # Remove leading/trailing whitespace
    prompt = prompt.strip()
    return prompt

class SpecialSyntaxAwareMagicPromptGenerator(MagicPromptGenerator):

    """
    Magic Prompt generator that is aware of A1111 special syntax (LoRA, hypernet, etc.).
    """
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def _generate_magic_prompts(self, orig_prompts: list[str]) -> list[str]:
        orig_prompts, chunks = zip(
            *(remove_a1111_special_syntax_chunks(p) for p in orig_prompts),
        )
        
        # Move input to the correct device
        input_ids = self.tokenizer(list(orig_prompts), return_tensors="pt", padding=True).input_ids
        input_ids = input_ids.to(self.device)
        
        # Generate the magic prompts
        with torch.no_grad():
            output = self.model.generate(
                input_ids,
                max_length=100,
                num_return_sequences=1,
                no_repeat_ngram_size=2
            )
        
        # Decode the output
        magic_prompts = self.tokenizer.batch_decode(output, skip_special_tokens=True)
        
        # Massage the prompts
        magic_prompts = [massage_prompt(prompt) for prompt in magic_prompts]
        
        # Append chunks and return
        return [
            append_chunks(prompt, chunk)
            for prompt, chunk in zip_longest(magic_prompts, chunks, fillvalue=None)
        ]

so if anyone has the same error as me and wants to fix it: just overwrite/replace the above code into \stable-diffusion-webui-forge\extensions\sd-dynamic-prompts\sd_dynamic_prompts\magic_prompt.py, save it and restart forge

[chaewai]

Thanks for sharing the fix, had the same problem!