sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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pipe = AutoPipelineForImage2Image.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
image.thumbnail((768, 768))

image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0] enable_model_cpu_offload < source > ( gpu_id = 0 ) Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet. enable_sequential_cpu_offload < source > ( gpu_id = 0 ) Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower. KandinskyV22ControlnetImg2ImgPipeline class diffusers.KandinskyV22ControlnetImg2ImgPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (DDIMScheduler) —
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —
MoVQ Decoder to generate the image from the latents. Pipeline for image-to-image generation using Kandinsky This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( image_embeds: Union image: Union negative_image_embeds: Union hint: FloatTensor height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 strength: float = 0.3 num_images_per_prompt: int = 1 generator: Union = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —
The clip image embeddings for text prompt, that will be used to condition the image generation. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) —
Image, or tensor representing an image batch, that will be used as the starting point for the
process. Can also accept image latents as image, if passing latents directly, it will not be encoded
again. strength (float, optional, defaults to 0.8) —
Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image
will be used as a starting point, adding more noise to it the larger the strength. The number of
denoising steps depends on the amount of noise initially added. When strength is 1, added noise will
be maximum and the denoising process will run for the full number of iterations specified in
num_inference_steps. A value of 1, therefore, essentially ignores image. hint (torch.FloatTensor) —
The controlnet condition. negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —
The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) —
The height in pixels of the generated image. width (int, optional, defaults to 512) —
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 100) —
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 4.0) —
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale is defined as w of equation 2. of Imagen
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,
usually at the expense of lower image quality. num_images_per_prompt (int, optional, defaults to 1) —
The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) —
One or a list of torch generator(s)
to make generation deterministic. output_type (str, optional, defaults to "pil") —
The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np"
(np.array) or "pt" (torch.Tensor). 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. return_dict (bool, optional, defaults to True) —
Whether or not to return a ImagePipelineOutput instead of a plain tuple. Returns
ImagePipelineOutput or tuple
Function invoked when calling the pipeline for generation. Examples: KandinskyV22InpaintPipeline class diffusers.KandinskyV22InpaintPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (DDIMScheduler) —
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —
MoVQ Decoder to generate the image from the latents. Pipeline for text-guided image inpainting using Kandinsky2.1 This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( image_embeds: Union image: Union mask_image: Union negative_image_embeds: Union height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 num_images_per_prompt: int = 1 generator: Union = None latents: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —
The clip image embeddings for text prompt, that will be used to condition the image generation. image (PIL.Image.Image) —
Image, or tensor representing an image batch which will be inpainted, i.e. parts of the image will
be masked out with mask_image and repainted according to prompt. mask_image (np.array) —
Tensor representing an image batch, to mask image. White pixels in the mask will be repainted, while
black pixels will be preserved. If mask_image is a PIL image, it will be converted to a single
channel (luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3,
so the expected shape would be (B, H, W, 1). negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —
The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) —
The height in pixels of the generated image. width (int, optional, defaults to 512) —
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 100) —
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 4.0) —
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale is defined as w of equation 2. of Imagen
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,
usually at the expense of lower image quality. num_images_per_prompt (int, optional, defaults to 1) —
The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) —
One or a list of torch generator(s)
to make generation deterministic. latents (torch.FloatTensor, optional) —
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 will ge generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") —
The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np"
(np.array) or "pt" (torch.Tensor). return_dict (bool, optional, defaults to True) —
Whether or not to return a ImagePipelineOutput instead of a plain tuple. callback_on_step_end (Callable, optional) —
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) —
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 pipeline class. Returns
ImagePipelineOutput or tuple
Function invoked when calling the pipeline for generation. Examples: KandinskyV22InpaintCombinedPipeline class diffusers.KandinskyV22InpaintCombinedPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) —
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —
MoVQ Decoder to generate the image from the latents. prior_prior (PriorTransformer) —
The canonincal unCLIP prior to approximate the image embedding from the text embedding. prior_image_encoder (CLIPVisionModelWithProjection) —
Frozen image-encoder. prior_text_encoder (CLIPTextModelWithProjection) —
Frozen text-encoder. prior_tokenizer (CLIPTokenizer) —
Tokenizer of class

pipe = AutoPipelineForImage2Image.from_pretrained(

"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16

)

pipe.enable_model_cpu_offload()

prompt = "A fantasy landscape, Cinematic lighting"

negative_prompt = "low quality, bad quality"

url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)

image = Image.open(BytesIO(response.content)).convert("RGB")

image.thumbnail((768, 768))

image = pipe(prompt=prompt, image=original_image, num_inference_steps=25).images[0] enable_model_cpu_offload < source > ( gpu_id = 0 ) Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared

to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward

method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with

enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet. enable_sequential_cpu_offload < source > ( gpu_id = 0 ) Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,

text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a

torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower. KandinskyV22ControlnetImg2ImgPipeline class diffusers.KandinskyV22ControlnetImg2ImgPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (DDIMScheduler) —

A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —

Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —

MoVQ Decoder to generate the image from the latents. Pipeline for image-to-image generation using Kandinsky This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the

library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( image_embeds: Union image: Union negative_image_embeds: Union hint: FloatTensor height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 strength: float = 0.3 num_images_per_prompt: int = 1 generator: Union = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —

The clip image embeddings for text prompt, that will be used to condition the image generation. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) —

Image, or tensor representing an image batch, that will be used as the starting point for the

process. Can also accept image latents as image, if passing latents directly, it will not be encoded

again. strength (float, optional, defaults to 0.8) —

Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image

will be used as a starting point, adding more noise to it the larger the strength. The number of

denoising steps depends on the amount of noise initially added. When strength is 1, added noise will

be maximum and the denoising process will run for the full number of iterations specified in

num_inference_steps. A value of 1, therefore, essentially ignores image. hint (torch.FloatTensor) —

The controlnet condition. negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —

The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) —

The height in pixels of the generated image. width (int, optional, defaults to 512) —

The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 100) —

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 4.0) —

Guidance scale as defined in Classifier-Free Diffusion Guidance.

guidance_scale is defined as w of equation 2. of Imagen

Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,

usually at the expense of lower image quality. num_images_per_prompt (int, optional, defaults to 1) —

The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) —

One or a list of torch generator(s)

to make generation deterministic. output_type (str, optional, defaults to "pil") —

The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np"

(np.array) or "pt" (torch.Tensor). 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. return_dict (bool, optional, defaults to True) —

Whether or not to return a ImagePipelineOutput instead of a plain tuple. Returns

ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation. Examples: KandinskyV22InpaintPipeline class diffusers.KandinskyV22InpaintPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (DDIMScheduler) —

A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —

Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —

MoVQ Decoder to generate the image from the latents. Pipeline for text-guided image inpainting using Kandinsky2.1 This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the

library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( image_embeds: Union image: Union mask_image: Union negative_image_embeds: Union height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 num_images_per_prompt: int = 1 generator: Union = None latents: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —

The clip image embeddings for text prompt, that will be used to condition the image generation. image (PIL.Image.Image) —

Image, or tensor representing an image batch which will be inpainted, i.e. parts of the image will

be masked out with mask_image and repainted according to prompt. mask_image (np.array) —

Tensor representing an image batch, to mask image. White pixels in the mask will be repainted, while

black pixels will be preserved. If mask_image is a PIL image, it will be converted to a single

channel (luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3,

so the expected shape would be (B, H, W, 1). negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) —

The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) —

The height in pixels of the generated image. width (int, optional, defaults to 512) —

The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 100) —

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 4.0) —

Guidance scale as defined in Classifier-Free Diffusion Guidance.

guidance_scale is defined as w of equation 2. of Imagen

Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,

usually at the expense of lower image quality. num_images_per_prompt (int, optional, defaults to 1) —

The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) —

One or a list of torch generator(s)

to make generation deterministic. latents (torch.FloatTensor, optional) —

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 will ge generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") —

The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np"

(np.array) or "pt" (torch.Tensor). return_dict (bool, optional, defaults to True) —

Whether or not to return a ImagePipelineOutput instead of a plain tuple. callback_on_step_end (Callable, optional) —

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) —

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 pipeline class. Returns

ImagePipelineOutput or tuple

Function invoked when calling the pipeline for generation. Examples: KandinskyV22InpaintCombinedPipeline class diffusers.KandinskyV22InpaintCombinedPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) —

A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) —

Conditional U-Net architecture to denoise the image embedding. movq (VQModel) —

MoVQ Decoder to generate the image from the latents. prior_prior (PriorTransformer) —

The canonincal unCLIP prior to approximate the image embedding from the text embedding. prior_image_encoder (CLIPVisionModelWithProjection) —

Frozen image-encoder. prior_text_encoder (CLIPTextModelWithProjection) —

Frozen text-encoder. prior_tokenizer (CLIPTokenizer) —

Tokenizer of class