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sayakpaul
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diffusers-qa-chatbot-artifacts

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(img2text) mode. 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. data_type (int, optional, defaults to 1) β€”
The data type (either 0 or 1). Only used if you are loading a checkpoint which supports a data type
embedding; this is added for compatibility with the
UniDiffuser-v1 checkpoint. 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. guidance_scale (float, optional, defaults to 8.0) 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). Used in
text-conditioned image generation (text2img) mode. num_images_per_prompt (int, optional, defaults to 1) β€”
The number of images to generate per prompt. Used in text2img (text-conditioned image generation) and
img mode. If the mode is joint and both num_images_per_prompt and num_prompts_per_image are
supplied, min(num_images_per_prompt, num_prompts_per_image) samples are generated. num_prompts_per_image (int, optional, defaults to 1) β€”
The number of prompts to generate per image. Used in img2text (image-conditioned text generation) and
text mode. If the mode is joint and both num_images_per_prompt and num_prompts_per_image are
supplied, min(num_images_per_prompt, num_prompts_per_image) samples are generated. 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 joint
image-text 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. This assumes
a full set of VAE, CLIP, and text latents, if supplied, overrides the value of prompt_latents,
vae_latents, and clip_latents. prompt_latents (torch.FloatTensor, optional) β€”
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text
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. vae_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. clip_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. Used in text-conditioned
image generation (text2img) mode. 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 be generated from the negative_prompt input argument. Used
in text-conditioned image generation (text2img) mode. 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 ImageTextPipelineOutput 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. Returns
ImageTextPipelineOutput or tuple
If return_dict is True, ImageTextPipelineOutput 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 generated texts.
The call function to the pipeline for generation. 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. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling 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. enable_vae_tiling < source > ( ) Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images. 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_skip: Optional = None ) Parameters prompt (str or List[str], optional...
prompt to be encoded
device β€” (torch.device):
torch device num_images_per_prompt (int) β€”
number of images that should be generated per prompt do_classifier_free_guidance (bool) β€”
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) β€”
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) β€”
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) β€”
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) β€”
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. 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. Encodes the prompt into text encoder hidden states. reset_mode < source > ( ) Removes a manually set mode; after calling this, the pipeline will infer the mode from inputs. set_image_mode < source > ( ) Manually set the gen...
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). text (List[str] or List[List[str]]) β€”
List of generated text strings of length batch_size or a list of list of strings whose outer list has
length batch_size. Output class for joint image-text pipelines.
OpenVINO πŸ€— Optimum provides Stable Diffusion pipelines compatible with OpenVINO to perform inference on a variety of Intel processors (see the full list of supported devices). You’ll need to install πŸ€— Optimum Intel with the --upgrade-strategy eager option to ensure optimum-intel is using the latest version: Copied ...
model_id = "runwayml/stable-diffusion-v1-5"
pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
prompt = "sailing ship in storm by Rembrandt"
image = pipeline(prompt).images[0]
# Don't forget to save the exported model
pipeline.save_pretrained("openvino-sd-v1-5") To further speed-up inference, statically reshape the model. If you change any parameters such as the outputs height or width, you’ll need to statically reshape your model again. Copied # Define the shapes related to the inputs and desired outputs
batch_size, num_images, height, width = 1, 1, 512, 512
# Statically reshape the model
pipeline.reshape(batch_size, height, width, num_images)
# Compile the model before inference
pipeline.compile()
image = pipeline(
prompt,
height=height,
width=width,
num_images_per_prompt=num_images,