huggingface / huggingface/diffusers
UNet-only adapters will still cause problems with pipeline.set_adapters() after torch.compile because the compiled graph expects adapters to exist in all components.
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Descripción
2025-10-03 07:47:56 - main - INFO - loading adapter detail_enhancer
No LoRA keys associated to CLIPTextModel found with the prefix='text_encoder'. This is safe to ignore if LoRA state dict didn't originally have any CLIPTextModel related params. You can also try specifying prefix=None to resolve the warning. Otherwise, open an issue if you think it's unexpected: https://github.com/huggingface/diffusers/issues/new
No LoRA keys associated to CLIPTextModelWithProjection found with the prefix='text_encoder_2'. This is safe to ignore if LoRA state dict didn't originally have any CLIPTextModelWithProjection related params. You can also try specifying prefix=None to resolve the warning. Otherwise, open an issue if you think it's unexpected: https://github.com/huggingface/diffusers/issues/new
This warning confirms what we've been discussing: detail_enhancer (and likely nijistyle and chibi_rr) only have UNet weights, not text encoder weights.
The warnings are saying the LoRA files don't contain any parameters for the text encoders - they're UNet-only adapters.
Why This Causes Issues After Compilation
When you call pipeline.set_adapters(['detail_enhancer']):
Without compilation:
Pipeline checks each component
Applies adapter to UNet ✓
Skips text encoders (no weights found) ✓
Works fine
With compilation:
The compiled graph expects a consistent structure
If you compile after loading adapters that ARE in text encoders, the graph expects text encoder adapters
When you try to use detail_enhancer which has no text encoder weights, the compiled code path fails
Guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
Línea de trabajo
Empieza reproduciendo la interacción entre pipeline.set_adapters() y torch.compile descrita en la issue, comparando adaptadores con y sin pesos del codificador de texto. Lee los puntos de entrada para la carga de adaptadores y para la pipeline compilada para determinar el comportamiento esperado y define una prueba de regresión para adaptadores UNet-only.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- python, pytorch
- Área
- machine-learning
- Tipo de issue
- Error
- Dificultad
- 5/5
- Tiempo estimado
- Más de una semana
- Estado de actividad
- Estancado
- Claridad
- Necesita aclaración
- Aptitud para principiantes
- 30/100