elements depending on the configuration (XLNetConfig) and inputs. input_ids: TFModelInputType | None = None Use in Transformers. loss (tf.Tensor of shape (1,), optional, returned when labels is provided) Classification loss. eprint={2302.05543}, training: bool = False Simply click on one of the following spaces to play around with ControlNet: "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png", "monochrome, lowres, bad anatomy, worst quality, low quality", "a photo of sks mr potato head, best quality, extremely detailed", "https://huggingface.co/datasets/YiYiXu/controlnet-testing/resolve/main/", "fusing/stable-diffusion-v1-5-controlnet-openpose", "super-hero character, best quality, extremely detailed", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png", # zero out middle columns of image where pose will be overlayed, "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png", Running ControlNet with multiple conditionings, "a giant standing in a fantasy landscape, best quality", Adding Conditional Control to Text-to-Image Diffusion Models. mems: typing.Optional[torch.Tensor] = None ) Directly manipulating pose skeleton should also work. attention_mask: np.ndarray | tf.Tensor | None = None token_ids_0: typing.List[int] target_mapping. ControlNet is a neural network structure to control diffusion models by adding extra conditions. mem_len = 512 Output type of TFXLNetForQuestionAnsweringSimple. **kwargs elements depending on the configuration (XLNetConfig) and inputs. transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput or tuple(tf.Tensor), transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput or tuple(tf.Tensor). The "trainable" one learns your condition. cls_token = '' logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) Classification scores (before SoftMax). With these techniques combined the generation process takes only ~3 seconds on a V100 GPU and consumes just ~4 GBs of VRAM for a single image On free services like Google Colab, generation takes about 5s on the default GPU (T4), whereas the original implementation requires 17s to create the same result! Analytical cookies are used to understand how visitors interact with the website. The "locked" one preserves your model. You can generate images more efficiently. start_logits (torch.FloatTensor of shape (batch_size, sequence_length,)) Span-start scores (before SoftMax). 2023/02/12 - Now you can play with any community model by Transferring the ControlNet. True, one has to make sure that the train batches are correctly pre-processed, e.g. transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput or tuple(tf.Tensor), transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput or tuple(tf.Tensor). it requires a CUDA toolchain with the same version as PyTorch. attn_type = 'bi' However, ControlNet can be trained to augment any compatible Stable Diffusion model (such as CompVis/stable-diffusion-v1-4) or stabilityai/stable-diffusion-2-1. How to Spot a Best AI Tools for Artists and Creators (2023). Note in order to perform standard auto-regressive language modeling a token has inputs_embeds: np.ndarray | tf.Tensor | None = None like 372. As a result, addressing these challenges becomes essential in unlocking the full potential of LLMs and making their benefits more widely accessible. ControlNet is a neural network structure to control diffusion models by adding extra conditions. It is used to ) refer to this superclass for more information regarding those methods. Parameters . primaryClass={cs.CV} output_hidden_states: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None A grayscale image with black representing deep areas and white representing shallow areas. Training a ControlNet is as easy as (or even easier than) training a simple pix2pix. We also use third-party cookies that help us analyze and understand how you use this website. token_ids_1: typing.Optional[typing.List[int]] = None Were excited to see what the community builds on top of this pipeline. The only difference is that the model will "try harder" to guess what is in the control map even if you do not provide the prompt. The "trainable" one learns your condition. ; n_layer (int, optional, defaults to 24) Number of hidden . In this mode, the ControlNet encoder will try best to recognize the content of the input control map, like depth map, edge map, scribbles, etc, even if you remove all prompts. layer_norm_eps = 1e-12 XLNet is not a traditional autoregressive model but uses a training strategy that builds on that. However, instead of using the Stable Diffusion 1.5, we are going to load the Mr Potato Head model into our pipeline - Mr Potato Head is a Stable Diffusion model fine-tuned with Mr Potato Head concept using Dreambooth . Instead of loading our pipeline directly to GPU, we instead enable smart CPU offloading which loss: tf.Tensor | None = None The ControlNet+SD1.5 model to control SD using semantic segmentation. We welcome you to run the code snippets shown in the sections below with this Colab Notebook. logits (torch.FloatTensor of shape (batch_size, num_predict, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). Stable Diffusion 1.5 + ControlNet (using fake scribbles). logits (tf.Tensor of shape (batch_size, num_choices)) num_choices is the second dimension of the input tensors. Throughout the examples, we explored multiple facets of the StableDiffusionControlNetPipeline to show how easy and intuitive it is play around with ControlNet via Diffusers. **kwargs A monochrome image with white edges on a black background. ) In this post, we are going to use our beloved Mr Potato Head as an example to show how to use ControlNet with DreamBooth. Thank haofanwang for making ControlNet-for-Diffusers! It is done so that the locked parameter copy can preserve the vast knowledge learned from a large dataset, whereas the trainable copy is employed to learn task-specific aspects. ---------------- Running App Files Files Community 7 . We still provide a prompt to guide the image generation process, just like what we would normally do with a Stable Diffusion image-to-image pipeline. The ControlNet+SD1.5 model to control SD using human scribbles. The token used is the sep_token. Put the yaml file with model because it is sd2.1 v_prediction model. transformers.models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput or tuple(torch.FloatTensor), transformers.models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput or tuple(torch.FloatTensor). perm_mask: typing.Optional[torch.Tensor] = None This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. tokenizer_file = None Make sure that SD models are put in "ControlNet/models" and detectors are put in "ControlNet/annotator/ckpts". after the pound sign (#) when it creates any internal routing links. keeping the same image generation quality. elements depending on the configuration (XLNetConfig) and inputs. lllyasviel/ControlNet-v1-1 Hugging Face have to make changes to the config to have a successful training run. Changing the default Adam optimizer to DeepSpeeds Adam unk_token = '' We will explore different use cases with the StableDiffusionControlNetPipeline in this blog post. The paper proposed 8 different conditioning models that are all supported in Diffusers! blog/controlnet.md at main huggingface/blog GitHub Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM. Civitai | Multi Controlnet Video to Video Show case (EbSynth This default configuration requires ~38GB VRAM. Split the target video into frames using EbSynth Utility and create a mask for background transparency. ) Now let's make Mr Potato posing for Johannes Vermeer! Third-party model: HED boundary detection. target_mapping: np.ndarray | tf.Tensor | None = None end_logits: FloatTensor = None ( This makes it fairly simple **kwargs output_hidden_states: Optional[bool] = None For example, we can fine-tune a model with DreamBooth, and use it to render ourselves into different scenes. ) These blocks can be independently retrieved using a block table during attention computation, leading to more efficient memory utilization. attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: np.ndarray | tf.Tensor | None = None ( loss (tf.Tensor of shape (1,), optional, returned when labels is provided) ControlNet is a neural network structure to control diffusion models by adding extra conditions. head_mask: np.ndarray | tf.Tensor | None = None This script use the exactly same scribble-based model but use a simple algorithm to synthesize scribbles from input images. attentions: Tuple[tf.Tensor] | None = None Please do not upload image to that drawing canvas. We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. eos_token = '' The abstract from the paper is the following: With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves Before training, all zero convolutions output zeros, and ControlNet will not cause any distortion. margin, including question answering, natural language inference, sentiment analysis, and document ranking. It achieves throughput levels that are 24 times higher than those of HuggingFace Transformers while maintaining the same model architecture and without necessitating any modifications. hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
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