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sayakpaul
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diffusers-qa-chatbot-artifacts

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--learnable_property="object" \
--placeholder_token="<cat-toy>" \
--initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 \
--scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="textual_inversion_cat" \
--push_to_hub After training is complete, you can use your newly trained model for inference like: PyTorch Flax Copied from diffusers import StableDiffusionPipeline
import torch
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
pipeline.load_textual_inversion("sd-concepts-library/cat-toy")
image = pipeline("A <cat-toy> train", num_inference_steps=50).images[0]
image.save("cat-train.png") Next steps Congratulations on training your own Textual Inversion model! πŸŽ‰ To learn more about how to use your new model, the following guides may be helpful: Learn how to load Textual Inversion embeddings and also use them as negative embeddings. Learn how to use Textual Inversion for in...
ControlNet ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the Con...
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) β€”
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) β€”
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) β€”
A UNet2DConditionModel to denoise the encoded image latents. controlnet (ControlNetModel or List[ControlNetModel]) β€”
Provides additional conditioning to the unet during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
additional conditioning. scheduler (SchedulerMixin) β€”
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. safety_checker (StableDiffusionSafetyChecker) β€”
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the model card for more details
about a model’s potential harms. feature_extractor (CLIPImageProcessor) β€”
A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights from_single_file...
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], List[np.ndarray], β€”
List[List[torch.FloatTensor]], List[List[np.ndarray]] or List[List[PIL.Image.Image]]):
The ControlNet input condition to provide guidance to the unet for generation. If the type is
specified as torch.FloatTensor, it is passed to ControlNet as is. PIL.Image.Image can also be
accepted as an image. The dimensions of the output image defaults to image’s dimensions. If height
and/or width are passed, image is resized accordingly. If multiple ControlNets are specified in
init, images must be passed as a list such that each element of the list can be correctly batched for
input to a single ControlNet. When prompt is a list, and if a list of images is passed for a single ControlNet,
each will be paired with each prompt in the prompt list. This also applies to multiple ControlNets,
where a list of image lists can be passed to batch for each prompt and each ControlNet. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The height in pixels of the generated image. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 50) β€”
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. timesteps (List[int], optional) β€”
Custom timesteps to use for the denoising process with schedulers which support a timesteps argument
in their set_timesteps method. If not defined, the default behavior when num_inference_steps is
passed will be used. Must be in descending order. guidance_scale (float, optional, defaults to 7.5) β€”
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) β€”
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_images_per_prompt (int, optional, defaults to 1) β€”
The number of images to generate per prompt. eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) β€”
A torch.Generator to make
generation deterministic. latents (torch.FloatTensor, optional) β€”
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random generator. prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
ip_adapter_image β€” (PipelineImageInput, optional): Optional image input to work with IP Adapters. ip_adapter_image_embeds (List[torch.FloatTensor], optional) β€”
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.
Each element should be a tensor of shape (batch_size, num_images, emb_dim). It should contain the negative image embedding
if do_classifier_free_guidance is set to True.
If not provided, embeddings are computed from the ip_adapter_image input argument. output_type (str, optional, defaults to "pil") β€”
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple. callback (Callable, optional) β€”
A function that calls every callback_steps steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor). callback_steps (int, optional, defaults to 1) β€”
The frequency at which the callback function is called. If not specified, the callback is called at
every step. cross_attention_kwargs (dict, optional) β€”
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in
self.processor. controlnet_conditioning_scale (float or List[float], optional, defaults to 1.0) β€”
The outputs of the ControlNet are multiplied by controlnet_conditioning_scale before they are added
to the residual in the original unet. If multiple ControlNets are specified in init, you can set
the corresponding scale as a list. guess_mode (bool, optional, defaults to False) β€”
The ControlNet encoder tries to recognize the content of the input image even if you remove all
prompts. A guidance_scale value between 3.0 and 5.0 is recommended. control_guidance_start (float or List[float], optional, defaults to 0.0) β€”
The percentage of total steps at which the ControlNet starts applying. control_guidance_end (float or List[float], optional, defaults to 1.0) β€”
The percentage of total steps at which the ControlNet stops applying. clip_skip (int, optional) β€”
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. callback_on_step_end (Callable, optional) β€”
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by
callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) β€”
The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list
will be passed as callback_kwargs argument. You will only be able to include variables listed in the
._callback_tensor_inputs attribute of your pipeine class. Returns
StableDiffusionPipelineOutput or tuple
If return_dict is True, StableDiffusionPipelineOutput is returned,
otherwise a tuple is returned where the first element is a list with the generated images and the