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Optional image input to work with IP Adapters. output_type (str, optional, defaults to "pil") — |
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) — |
Whether or not to return a StableDiffusionPipelineOutput instead of a |
plain tuple. callback (Callable, optional) — |
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) — |
The frequency at which the callback function is called. If not specified, the callback is called at |
every step. cross_attention_kwargs (dict, optional) — |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under |
self.processor in |
diffusers.models.attention_processor. circular_padding (bool, optional, defaults to False) — |
If set to True, circular padding is applied to ensure there are no stitching artifacts. Circular |
padding allows the model to seamlessly generate a transition from the rightmost part of the image to |
the leftmost part, maintaining consistency in a 360-degree sense. clip_skip (int, optional) — |
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. 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 |
second element is a list of bools indicating whether the corresponding generated image contains |
“not-safe-for-work” (nsfw) content. |
The call function to the pipeline for generation. Examples: Copied >>> import torch |
>>> from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler |
>>> model_ckpt = "stabilityai/stable-diffusion-2-base" |
>>> scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") |
>>> pipe = StableDiffusionPanoramaPipeline.from_pretrained( |
... model_ckpt, scheduler=scheduler, torch_dtype=torch.float16 |
... ) |
>>> pipe = pipe.to("cuda") |
>>> prompt = "a photo of the dolomites" |
>>> image = pipe(prompt).images[0] disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to |
computing decoding in one step. enable_vae_slicing < source > ( ) Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. encode_prompt < source > ( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_... |
prompt to be encoded |
device — (torch.device): |
torch device num_images_per_prompt (int) — |
number of images that should be generated per prompt do_classifier_free_guidance (bool) — |
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) — |
The prompt or prompts not to guide the image generation. If not defined, one has to pass |
negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is |
less than 1). prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not |
provided, text embeddings will be generated from prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt |
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input |
argument. lora_scale (float, optional) — |
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) — |
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. Encodes the prompt into text encoder hidden states. StableDiffusionPipelineOutput class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters ... |
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected (List[bool]) — |
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or |
None if safety checking could not be performed. Output class for Stable Diffusion pipelines. |
Adapt a model to a new task Many diffusion systems share the same components, allowing you to adapt a pretrained model for one task to an entirely different task. This guide will show you how to adapt a pretrained text-to-image model for inpainting by initializing and modifying the architecture of a pretrained UNet2DCo... |
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True) |
pipeline.unet.config["in_channels"] |
4 Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like runwayml/stable-diffusion-inpainting: Copied from diffusers import StableDiffusionPipeline |
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True) |
pipeline.unet.config["in_channels"] |
9 To adapt your text-to-image model for inpainting, you’ll need to change the number of in_channels from 4 to 9. Initialize a UNet2DConditionModel with the pretrained text-to-image model weights, and change in_channels to 9. Changing the number of in_channels means you need to set ignore_mismatched_sizes=True and low_c... |
model_id = "runwayml/stable-diffusion-v1-5" |
unet = UNet2DConditionModel.from_pretrained( |
model_id, |
subfolder="unet", |
in_channels=9, |
low_cpu_mem_usage=False, |
ignore_mismatched_sizes=True, |
use_safetensors=True, |
) The pretrained weights of the other components from the text-to-image model are initialized from their checkpoints, but the input channel weights (conv_in.weight) of the unet are randomly initialized. It is important to finetune the model for inpainting because otherwise the model returns noise. |
Using Diffusers for reinforcement learning |
Support for one RL model and related pipelines is included in the experimental source of diffusers. |
More models and examples coming soon! |
Diffuser Value-guided Planning |
You can run the model from Planning with Diffusion for Flexible Behavior Synthesis with Diffusers. |
The script is located in the RL Examples folder. |
Or, run this example in Colab |
class diffusers.experimental.ValueGuidedRLPipeline |
< |
source |
> |
( |
value_function: UNet1DModel |
unet: UNet1DModel |
scheduler: DDPMScheduler |
env |
) |
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