[Community Pipeline] Add multilingual stable diffusion to community pipelines (#1142)
* Add multilingual_stable_diffusion.py file * Add multilingual stable diffusion to examples README file * Update examples/community/README.md Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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@ -18,9 +18,11 @@ If a community doesn't work as expected, please open an issue and ping the autho
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| Composable Stable Diffusion| Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |
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| Seed Resizing Stable Diffusion| Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. | [Seed Resizing](#seed-resizing) | - | [Mark Rich](https://github.com/MarkRich) |
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| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image| [Imagic Stable Diffusion](#imagic-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |
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| Multilingual Stable Diffusion| Stable Diffusion Pipeline that supports prompts in 50 different languages. | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline) | - | [Juan Carlos Piñeros](https://github.com/juancopi81) |
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| Image to Image Inpainting Stable Diffusion | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting| [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | - | [Alex McKinney](https://github.com/vvvm23) |
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To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
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```py
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="filename_in_the_community_folder")
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@ -502,6 +504,74 @@ image = res.images[0]
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image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))
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```
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### Multilingual Stable Diffusion Pipeline
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The following code can generate an images from texts in different languages using the pre-trained [mBART-50 many-to-one multilingual machine translation model](https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt) and Stable Diffusion.
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```python
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from PIL import Image
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import torch
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from diffusers import DiffusionPipeline
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from transformers import (
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pipeline,
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MBart50TokenizerFast,
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MBartForConditionalGeneration,
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)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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device_dict = {"cuda": 0, "cpu": -1}
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# helper function taken from: https://huggingface.co/blog/stable_diffusion
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def image_grid(imgs, rows, cols):
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assert len(imgs) == rows*cols
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w, h = imgs[0].size
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grid = Image.new('RGB', size=(cols*w, rows*h))
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grid_w, grid_h = grid.size
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for i, img in enumerate(imgs):
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grid.paste(img, box=(i%cols*w, i//cols*h))
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return grid
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# Add language detection pipeline
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language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
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language_detection_pipeline = pipeline("text-classification",
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model=language_detection_model_ckpt,
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device=device_dict[device])
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# Add model for language translation
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trans_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
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trans_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device)
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diffuser_pipeline = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="multilingual_stable_diffusion",
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detection_pipeline=language_detection_pipeline,
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translation_model=trans_model,
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translation_tokenizer=trans_tokenizer,
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revision="fp16",
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torch_dtype=torch.float16,
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)
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diffuser_pipeline.enable_attention_slicing()
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diffuser_pipeline = diffuser_pipeline.to(device)
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prompt = ["a photograph of an astronaut riding a horse",
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"Una casa en la playa",
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"Ein Hund, der Orange isst",
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"Un restaurant parisien"]
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output = diffuser_pipeline(prompt)
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images = output.images
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grid = image_grid(images, rows=2, cols=2)
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```
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This example produces the following images:
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### Image to Image Inpainting Stable Diffusion
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Similar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.
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@ -534,4 +604,4 @@ pipe = pipe.to("cuda")
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prompt = "Your prompt here!"
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image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
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```
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```
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@ -0,0 +1,436 @@
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import inspect
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from typing import Callable, List, Optional, Union
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import torch
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from diffusers.configuration_utils import FrozenDict
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from diffusers.utils import deprecate, logging
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from transformers import (
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CLIPFeatureExtractor,
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CLIPTextModel,
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CLIPTokenizer,
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MBart50TokenizerFast,
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MBartForConditionalGeneration,
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pipeline,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def detect_language(pipe, prompt, batch_size):
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"""helper function to detect language(s) of prompt"""
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if batch_size == 1:
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preds = pipe(prompt, top_k=1, truncation=True, max_length=128)
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return preds[0]["label"]
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else:
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detected_languages = []
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for p in prompt:
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preds = pipe(p, top_k=1, truncation=True, max_length=128)
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detected_languages.append(preds[0]["label"])
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return detected_languages
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def translate_prompt(prompt, translation_tokenizer, translation_model, device):
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"""helper function to translate prompt to English"""
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encoded_prompt = translation_tokenizer(prompt, return_tensors="pt").to(device)
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generated_tokens = translation_model.generate(**encoded_prompt, max_new_tokens=1000)
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en_trans = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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return en_trans[0]
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class MultilingualStableDiffusion(DiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion in different languages.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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detection_pipeline ([`pipeline`]):
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Transformers pipeline to detect prompt's language.
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translation_model ([`MBartForConditionalGeneration`]):
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Model to translate prompt to English, if necessary. Please refer to the
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[model card](https://huggingface.co/docs/transformers/model_doc/mbart) for details.
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translation_tokenizer ([`MBart50TokenizerFast`]):
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Tokenizer of the translation model.
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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def __init__(
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self,
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detection_pipeline: pipeline,
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translation_model: MBartForConditionalGeneration,
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translation_tokenizer: MBart50TokenizerFast,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if safety_checker is None:
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logger.warn(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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self.register_modules(
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detection_pipeline=detection_pipeline,
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translation_model=translation_model,
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translation_tokenizer=translation_tokenizer,
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `attention slicing`
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self.enable_attention_slicing(None)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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height: int = 512,
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width: int = 512,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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latents: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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**kwargs,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation. Can be in different languages.
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height (`int`, *optional*, defaults to 512):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if (callback_steps is None) or (
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callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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# detect language and translate if necessary
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prompt_language = detect_language(self.detection_pipeline, prompt, batch_size)
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if batch_size == 1 and prompt_language != "en":
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prompt = translate_prompt(prompt, self.translation_tokenizer, self.translation_model, self.device)
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if isinstance(prompt, list):
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for index in range(batch_size):
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if prompt_language[index] != "en":
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p = translate_prompt(
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prompt[index], self.translation_tokenizer, self.translation_model, self.device
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)
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prompt[index] = p
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# get prompt text embeddings
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = text_embeddings.shape
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text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
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text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||||
# corresponds to doing no classifier free guidance.
|
||||
do_classifier_free_guidance = guidance_scale > 1.0
|
||||
# get unconditional embeddings for classifier free guidance
|
||||
if do_classifier_free_guidance:
|
||||
uncond_tokens: List[str]
|
||||
if negative_prompt is None:
|
||||
uncond_tokens = [""] * batch_size
|
||||
elif type(prompt) is not type(negative_prompt):
|
||||
raise TypeError(
|
||||
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
||||
f" {type(prompt)}."
|
||||
)
|
||||
elif isinstance(negative_prompt, str):
|
||||
# detect language and translate it if necessary
|
||||
negative_prompt_language = detect_language(self.detection_pipeline, negative_prompt, batch_size)
|
||||
if negative_prompt_language != "en":
|
||||
negative_prompt = translate_prompt(
|
||||
negative_prompt, self.translation_tokenizer, self.translation_model, self.device
|
||||
)
|
||||
if isinstance(negative_prompt, str):
|
||||
uncond_tokens = [negative_prompt]
|
||||
elif batch_size != len(negative_prompt):
|
||||
raise ValueError(
|
||||
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
||||
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
||||
" the batch size of `prompt`."
|
||||
)
|
||||
else:
|
||||
# detect language and translate it if necessary
|
||||
if isinstance(negative_prompt, list):
|
||||
negative_prompt_languages = detect_language(self.detection_pipeline, negative_prompt, batch_size)
|
||||
for index in range(batch_size):
|
||||
if negative_prompt_languages[index] != "en":
|
||||
p = translate_prompt(
|
||||
negative_prompt[index], self.translation_tokenizer, self.translation_model, self.device
|
||||
)
|
||||
negative_prompt[index] = p
|
||||
uncond_tokens = negative_prompt
|
||||
|
||||
max_length = text_input_ids.shape[-1]
|
||||
uncond_input = self.tokenizer(
|
||||
uncond_tokens,
|
||||
padding="max_length",
|
||||
max_length=max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
|
||||
|
||||
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
||||
seq_len = uncond_embeddings.shape[1]
|
||||
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
|
||||
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
# For classifier free guidance, we need to do two forward passes.
|
||||
# Here we concatenate the unconditional and text embeddings into a single batch
|
||||
# to avoid doing two forward passes
|
||||
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
||||
|
||||
# get the initial random noise unless the user supplied it
|
||||
|
||||
# Unlike in other pipelines, latents need to be generated in the target device
|
||||
# for 1-to-1 results reproducibility with the CompVis implementation.
|
||||
# However this currently doesn't work in `mps`.
|
||||
latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
|
||||
latents_dtype = text_embeddings.dtype
|
||||
if latents is None:
|
||||
if self.device.type == "mps":
|
||||
# randn does not work reproducibly on mps
|
||||
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
|
||||
self.device
|
||||
)
|
||||
else:
|
||||
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
|
||||
else:
|
||||
if latents.shape != latents_shape:
|
||||
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
|
||||
latents = latents.to(self.device)
|
||||
|
||||
# set timesteps
|
||||
self.scheduler.set_timesteps(num_inference_steps)
|
||||
|
||||
# Some schedulers like PNDM have timesteps as arrays
|
||||
# It's more optimized to move all timesteps to correct device beforehand
|
||||
timesteps_tensor = self.scheduler.timesteps.to(self.device)
|
||||
|
||||
# scale the initial noise by the standard deviation required by the scheduler
|
||||
latents = latents * self.scheduler.init_noise_sigma
|
||||
|
||||
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
||||
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
||||
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
||||
# and should be between [0, 1]
|
||||
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
extra_step_kwargs = {}
|
||||
if accepts_eta:
|
||||
extra_step_kwargs["eta"] = eta
|
||||
|
||||
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
||||
|
||||
# call the callback, if provided
|
||||
if callback is not None and i % callback_steps == 0:
|
||||
callback(i, t, latents)
|
||||
|
||||
latents = 1 / 0.18215 * latents
|
||||
image = self.vae.decode(latents).sample
|
||||
|
||||
image = (image / 2 + 0.5).clamp(0, 1)
|
||||
|
||||
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
||||
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
||||
|
||||
if self.safety_checker is not None:
|
||||
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
|
||||
self.device
|
||||
)
|
||||
image, has_nsfw_concept = self.safety_checker(
|
||||
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
|
||||
)
|
||||
else:
|
||||
has_nsfw_concept = None
|
||||
|
||||
if output_type == "pil":
|
||||
image = self.numpy_to_pil(image)
|
||||
|
||||
if not return_dict:
|
||||
return (image, has_nsfw_concept)
|
||||
|
||||
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
||||
Loading…
Reference in New Issue