1222 lines
53 KiB
Python
1222 lines
53 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from logging import getLogger
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from math import ceil
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from typing import Callable, List, Optional, Tuple, Union
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import numpy as np
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import PIL
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import torch
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from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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from diffusers.utils import PIL_INTERPOLATION, deprecate
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from transformers import CLIPImageProcessor, CLIPTokenizer
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from ...chain.tile import make_tile_mask
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from ...constants import LATENT_CHANNELS, LATENT_FACTOR
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from ...params import Size
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from ..utils import (
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expand_latents,
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parse_regions,
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random_seed,
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repair_nan,
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resize_latent_shape,
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)
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from .base import OnnxStableDiffusionBasePipeline
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logger = getLogger(__name__)
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# inpaint constants
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NUM_UNET_INPUT_CHANNELS = 9
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DEFAULT_WINDOW = 32
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DEFAULT_STRIDE = 8
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def preprocess(image):
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if isinstance(image, torch.Tensor):
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return image
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elif isinstance(image, PIL.Image.Image):
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image = [image]
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if isinstance(image[0], PIL.Image.Image):
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w, h = image[0].size
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w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64
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image = [
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np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
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for i in image
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]
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image = np.concatenate(image, axis=0)
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image = np.array(image).astype(np.float32) / 255.0
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image = image.transpose(0, 3, 1, 2)
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image = 2.0 * image - 1.0
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image = torch.from_numpy(image)
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elif isinstance(image[0], torch.Tensor):
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image = torch.cat(image, dim=0)
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return image
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def prepare_mask_and_masked_image(image, mask, latents_shape):
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image = np.array(
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image.convert("RGB").resize((latents_shape[1] * 8, latents_shape[0] * 8))
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)
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image = image[None].transpose(0, 3, 1, 2)
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image = image.astype(np.float32) / 127.5 - 1.0
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image_mask = np.array(
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mask.convert("L").resize((latents_shape[1] * 8, latents_shape[0] * 8))
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)
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masked_image = image * (image_mask < 127.5)
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mask = mask.resize(
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(latents_shape[1], latents_shape[0]), PIL_INTERPOLATION["nearest"]
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)
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mask = np.array(mask.convert("L"))
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mask = mask.astype(np.float32) / 255.0
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mask = mask[None, None]
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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return mask, masked_image
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class OnnxStableDiffusionPanoramaPipeline(OnnxStableDiffusionBasePipeline):
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def __init__(
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self,
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vae_encoder: OnnxRuntimeModel,
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vae_decoder: OnnxRuntimeModel,
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text_encoder: OnnxRuntimeModel,
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tokenizer: CLIPTokenizer,
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unet: OnnxRuntimeModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: OnnxRuntimeModel,
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feature_extractor: CLIPImageProcessor,
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requires_safety_checker: bool = True,
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window: Optional[int] = None,
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stride: Optional[int] = None,
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):
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super().__init__(
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vae_encoder=vae_encoder,
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vae_decoder=vae_decoder,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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requires_safety_checker=requires_safety_checker,
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)
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self.window = window or DEFAULT_WINDOW
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self.stride = stride or DEFAULT_STRIDE
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def get_views(
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self, panorama_height: int, panorama_width: int, window_size: int, stride: int
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) -> Tuple[List[Tuple[int, int, int, int]], Tuple[int, int]]:
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# Here, we define the mappings F_i (see Eq. 7 in the MultiDiffusion paper https://arxiv.org/abs/2302.08113)
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panorama_height /= 8
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panorama_width /= 8
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num_blocks_height = ceil(abs((panorama_height - window_size) / stride)) + 1
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num_blocks_width = ceil(abs((panorama_width - window_size) / stride)) + 1
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total_num_blocks = int(num_blocks_height * num_blocks_width)
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logger.debug(
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"panorama generated %s views, %s by %s blocks",
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total_num_blocks,
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num_blocks_height,
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num_blocks_width,
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)
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views = []
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for i in range(total_num_blocks):
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h_start = int((i // num_blocks_width) * stride)
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h_end = h_start + window_size
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w_start = int((i % num_blocks_width) * stride)
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w_end = w_start + window_size
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views.append((h_start, h_end, w_start, w_end))
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return (views, (h_end * 8, w_end * 8))
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@torch.no_grad()
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def text2img(
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self,
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prompt: Union[str, List[str]] = None,
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height: Optional[int] = 512,
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width: Optional[int] = 512,
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num_inference_steps: Optional[int] = 50,
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guidance_scale: Optional[float] = 7.5,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: Optional[float] = 0.0,
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generator: Optional[np.random.RandomState] = None,
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latents: Optional[np.ndarray] = None,
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prompt_embeds: Optional[np.ndarray] = None,
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negative_prompt_embeds: Optional[np.ndarray] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
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callback_steps: int = 1,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
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`Image`, or tensor representing an image batch which will be upscaled. *
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 7.5):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
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is less than `1`).
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`np.random.RandomState`, *optional*):
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One or a list of [numpy generator(s)](TODO) to make generation deterministic.
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latents (`np.ndarray`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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prompt_embeds (`np.ndarray`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`np.ndarray`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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callback (`Callable`, *optional*):
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A function that will be called every `callback_steps` steps during inference. The function will be
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called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
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callback_steps (`int`, *optional*, defaults to 1):
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The frequency at which the `callback` function will be called. If not specified, the callback will be
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called at every step.
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Returns:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
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[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
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When returning a tuple, the first element is a list with the generated images, and the second element is a
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list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
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(nsfw) content, according to the `safety_checker`.
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"""
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# check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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height,
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width,
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callback_steps,
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negative_prompt,
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prompt_embeds,
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negative_prompt_embeds,
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)
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# define call parameters
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if generator is None:
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generator = np.random
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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prompt, regions = parse_regions(prompt)
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prompt_embeds = self._encode_prompt(
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prompt,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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)
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# 3.b. Encode region prompts
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region_embeds: List[np.ndarray] = []
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for _top, _left, _bottom, _right, _weight, _feather, region_prompt in regions:
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if region_prompt.endswith("+"):
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region_prompt = region_prompt[:-1] + " " + prompt
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region_prompt_embeds = self._encode_prompt(
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region_prompt,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt,
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)
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region_embeds.append(region_prompt_embeds)
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# get the initial random noise unless the user supplied it
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latents_dtype = prompt_embeds.dtype
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latents_shape = (
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batch_size * num_images_per_prompt,
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LATENT_CHANNELS,
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height // LATENT_FACTOR,
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width // LATENT_FACTOR,
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)
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if latents is None:
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latents = generator.randn(*latents_shape).astype(latents_dtype)
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elif latents.shape != latents_shape:
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raise ValueError(
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f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
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)
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# set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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latents = latents * np.float64(self.scheduler.init_noise_sigma)
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(
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inspect.signature(self.scheduler.step).parameters.keys()
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)
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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timestep_dtype = next(
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(
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input.type
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for input in self.unet.model.get_inputs()
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if input.name == "timestep"
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),
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"tensor(float)",
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)
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timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
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# panorama additions
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views, resize = self.get_views(height, width, self.window, self.stride)
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logger.trace("panorama resized latents to %s", resize)
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count = np.zeros(resize_latent_shape(latents, resize))
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value = np.zeros(resize_latent_shape(latents, resize))
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# adjust latents
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latents = expand_latents(
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latents,
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random_seed(generator),
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Size(resize[1], resize[0]),
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sigma=self.scheduler.init_noise_sigma,
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)
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for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
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last = i == (len(self.scheduler.timesteps) - 1)
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count.fill(0)
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value.fill(0)
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for h_start, h_end, w_start, w_end in views:
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# get the latents corresponding to the current view coordinates
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latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
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# expand the latents if we are doing classifier free guidance
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latent_model_input = (
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np.concatenate([latents_for_view] * 2)
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if do_classifier_free_guidance
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else latents_for_view
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)
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latent_model_input = self.scheduler.scale_model_input(
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torch.from_numpy(latent_model_input), t
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)
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latent_model_input = latent_model_input.cpu().numpy()
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# predict the noise residual
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timestep = np.array([t], dtype=timestep_dtype)
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noise_pred = self.unet(
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sample=latent_model_input,
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timestep=timestep,
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encoder_hidden_states=prompt_embeds,
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)
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noise_pred = noise_pred[0]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
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noise_pred = noise_pred_uncond + guidance_scale * (
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noise_pred_text - noise_pred_uncond
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)
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# compute the previous noisy sample x_t -> x_t-1
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scheduler_output = self.scheduler.step(
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torch.from_numpy(noise_pred),
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t,
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torch.from_numpy(latents_for_view),
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**extra_step_kwargs,
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)
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latents_view_denoised = scheduler_output.prev_sample.numpy()
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value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
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count[:, :, h_start:h_end, w_start:w_end] += 1
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if not last:
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for r, region in enumerate(regions):
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top, left, bottom, right, weight, feather, prompt = region
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logger.debug(
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"running region prompt: %s, %s, %s, %s, %s, %s, %s",
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top,
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left,
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bottom,
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right,
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weight,
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feather,
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prompt,
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)
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# convert coordinates to latent space
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h_start = top // LATENT_FACTOR
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h_end = bottom // LATENT_FACTOR
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w_start = left // LATENT_FACTOR
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w_end = right // LATENT_FACTOR
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# get the latents corresponding to the current view coordinates
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latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]
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logger.trace(
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"region latent shape: [:,:,%s:%s,%s:%s] -> %s",
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h_start,
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h_end,
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w_start,
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w_end,
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latents_for_region.shape,
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)
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# expand the latents if we are doing classifier free guidance
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latent_region_input = (
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np.concatenate([latents_for_region] * 2)
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if do_classifier_free_guidance
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else latents_for_region
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)
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latent_region_input = self.scheduler.scale_model_input(
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torch.from_numpy(latent_region_input), t
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)
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latent_region_input = latent_region_input.cpu().numpy()
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# predict the noise residual
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timestep = np.array([t], dtype=timestep_dtype)
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region_noise_pred = self.unet(
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sample=latent_region_input,
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timestep=timestep,
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encoder_hidden_states=region_embeds[r],
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)
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region_noise_pred = region_noise_pred[0]
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# perform guidance
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if do_classifier_free_guidance:
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region_noise_pred_uncond, region_noise_pred_text = np.split(
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region_noise_pred, 2
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)
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region_noise_pred = (
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region_noise_pred_uncond
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+ guidance_scale
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* (region_noise_pred_text - region_noise_pred_uncond)
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)
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# compute the previous noisy sample x_t -> x_t-1
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scheduler_output = self.scheduler.step(
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torch.from_numpy(region_noise_pred),
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t,
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torch.from_numpy(latents_for_region),
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**extra_step_kwargs,
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)
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latents_region_denoised = scheduler_output.prev_sample.numpy()
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|
|
|
if feather[0] > 0.0:
|
|
mask = make_tile_mask(
|
|
(h_end - h_start, w_end - w_start),
|
|
(h_end - h_start, w_end - w_start),
|
|
feather[0],
|
|
feather[1],
|
|
)
|
|
mask = np.expand_dims(mask, axis=0)
|
|
mask = np.repeat(mask, 4, axis=0)
|
|
mask = np.expand_dims(mask, axis=0)
|
|
else:
|
|
mask = 1
|
|
|
|
if weight >= 100.0:
|
|
value[:, :, h_start:h_end, w_start:w_end] = (
|
|
latents_region_denoised * mask
|
|
)
|
|
count[:, :, h_start:h_end, w_start:w_end] = mask
|
|
else:
|
|
value[:, :, h_start:h_end, w_start:w_end] += (
|
|
latents_region_denoised * weight * mask
|
|
)
|
|
count[:, :, h_start:h_end, w_start:w_end] += weight * mask
|
|
|
|
# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
|
|
latents = np.where(count > 0, value / count, value)
|
|
latents = repair_nan(latents)
|
|
|
|
# call the callback, if provided
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
# remove extra margins
|
|
latents = latents[
|
|
:, :, 0 : (height // LATENT_FACTOR), 0 : (width // LATENT_FACTOR)
|
|
]
|
|
|
|
latents = np.clip(latents, -4, +4)
|
|
latents = 1 / 0.18215 * latents
|
|
# image = self.vae_decoder(latent_sample=latents)[0]
|
|
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
|
|
image = np.concatenate(
|
|
[
|
|
self.vae_decoder(latent_sample=latents[i : i + 1])[0]
|
|
for i in range(latents.shape[0])
|
|
]
|
|
)
|
|
|
|
image = np.clip(image / 2 + 0.5, 0, 1)
|
|
image = image.transpose((0, 2, 3, 1))
|
|
|
|
if self.safety_checker is not None:
|
|
safety_checker_input = self.feature_extractor(
|
|
self.numpy_to_pil(image), return_tensors="np"
|
|
).pixel_values.astype(image.dtype)
|
|
|
|
images, has_nsfw_concept = [], []
|
|
for i in range(image.shape[0]):
|
|
image_i, has_nsfw_concept_i = self.safety_checker(
|
|
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
|
|
)
|
|
images.append(image_i)
|
|
has_nsfw_concept.append(has_nsfw_concept_i[0])
|
|
image = np.concatenate(images)
|
|
else:
|
|
has_nsfw_concept = None
|
|
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def img2img(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
image: Union[np.ndarray, PIL.Image.Image] = None,
|
|
strength: float = 0.8,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 7.5,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: Optional[float] = 0.0,
|
|
generator: Optional[np.random.RandomState] = None,
|
|
prompt_embeds: Optional[np.ndarray] = None,
|
|
negative_prompt_embeds: Optional[np.ndarray] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
|
|
`Image`, or tensor representing an image batch which will be upscaled. *
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
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 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). 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.
|
|
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`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`np.random.RandomState`, *optional*):
|
|
One or a list of [numpy generator(s)](TODO) to make generation deterministic.
|
|
latents (`np.ndarray`, *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`.
|
|
prompt_embeds (`np.ndarray`, *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 (`np.ndarray`, *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.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
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 will be called. If not specified, the callback will be
|
|
called at every step.
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
|
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
|
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
|
(nsfw) content, according to the `safety_checker`.
|
|
"""
|
|
|
|
height = image.height
|
|
width = image.width
|
|
|
|
# check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if strength < 0 or strength > 1:
|
|
raise ValueError(
|
|
f"The value of strength should in [0.0, 1.0] but is {strength}"
|
|
)
|
|
|
|
if generator is None:
|
|
generator = np.random
|
|
|
|
# set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps)
|
|
|
|
# prep image
|
|
image = preprocess(image).cpu().numpy()
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
prompt_embeds = self._encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
)
|
|
|
|
# get the initial random noise unless the user supplied it
|
|
latents_dtype = prompt_embeds.dtype
|
|
image = image.astype(latents_dtype)
|
|
|
|
# encode the init image into latents and scale the latents
|
|
latents = self.vae_encoder(sample=image)[0]
|
|
latents = 0.18215 * latents
|
|
|
|
if isinstance(prompt, str):
|
|
prompt = [prompt]
|
|
if len(prompt) > latents.shape[0] and len(prompt) % latents.shape[0] == 0:
|
|
# expand init_latents for batch_size
|
|
deprecation_message = (
|
|
f"You have passed {len(prompt)} text prompts (`prompt`), but only {latents.shape[0]} initial"
|
|
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
|
|
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
|
|
" your script to pass as many initial images as text prompts to suppress this warning."
|
|
)
|
|
deprecate(
|
|
"len(prompt) != len(image)",
|
|
"1.0.0",
|
|
deprecation_message,
|
|
standard_warn=False,
|
|
)
|
|
additional_image_per_prompt = len(prompt) // latents.shape[0]
|
|
latents = np.concatenate(
|
|
[latents] * additional_image_per_prompt * num_images_per_prompt, axis=0
|
|
)
|
|
elif len(prompt) > latents.shape[0] and len(prompt) % latents.shape[0] != 0:
|
|
raise ValueError(
|
|
f"Cannot duplicate `image` of batch size {latents.shape[0]} to {len(prompt)} text prompts."
|
|
)
|
|
else:
|
|
latents = np.concatenate([latents] * num_images_per_prompt, axis=0)
|
|
|
|
# get the original timestep using init_timestep
|
|
offset = self.scheduler.config.get("steps_offset", 0)
|
|
init_timestep = int(num_inference_steps * strength) + offset
|
|
init_timestep = min(init_timestep, num_inference_steps)
|
|
|
|
timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
|
|
timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
|
|
|
|
noise = generator.randn(*latents.shape).astype(latents_dtype)
|
|
latents = self.scheduler.add_noise(
|
|
torch.from_numpy(latents),
|
|
torch.from_numpy(noise),
|
|
torch.from_numpy(timesteps),
|
|
)
|
|
latents = latents.numpy()
|
|
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
accepts_eta = "eta" in set(
|
|
inspect.signature(self.scheduler.step).parameters.keys()
|
|
)
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
t_start = max(num_inference_steps - init_timestep + offset, 0)
|
|
timesteps = self.scheduler.timesteps[t_start:].numpy()
|
|
|
|
timestep_dtype = next(
|
|
(
|
|
input.type
|
|
for input in self.unet.model.get_inputs()
|
|
if input.name == "timestep"
|
|
),
|
|
"tensor(float)",
|
|
)
|
|
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
|
|
|
|
# panorama additions
|
|
views, resize = self.get_views(height, width, self.window, self.stride)
|
|
logger.trace("panorama resized latents to %s", resize)
|
|
|
|
count = np.zeros(resize_latent_shape(latents, resize))
|
|
value = np.zeros(resize_latent_shape(latents, resize))
|
|
|
|
# adjust latents
|
|
latents = expand_latents(
|
|
latents,
|
|
random_seed(generator),
|
|
Size(resize[1], resize[0]),
|
|
sigma=self.scheduler.init_noise_sigma,
|
|
)
|
|
|
|
for i, t in enumerate(self.progress_bar(timesteps)):
|
|
count.fill(0)
|
|
value.fill(0)
|
|
|
|
for h_start, h_end, w_start, w_end in views:
|
|
# get the latents corresponding to the current view coordinates
|
|
latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = (
|
|
np.concatenate([latents_for_view] * 2)
|
|
if do_classifier_free_guidance
|
|
else latents_for_view
|
|
)
|
|
latent_model_input = self.scheduler.scale_model_input(
|
|
torch.from_numpy(latent_model_input), t
|
|
)
|
|
latent_model_input = latent_model_input.cpu().numpy()
|
|
|
|
# predict the noise residual
|
|
timestep = np.array([t], dtype=timestep_dtype)
|
|
noise_pred = self.unet(
|
|
sample=latent_model_input,
|
|
timestep=timestep,
|
|
encoder_hidden_states=prompt_embeds,
|
|
)
|
|
noise_pred = noise_pred[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (
|
|
noise_pred_text - noise_pred_uncond
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
scheduler_output = self.scheduler.step(
|
|
torch.from_numpy(noise_pred),
|
|
t,
|
|
torch.from_numpy(latents_for_view),
|
|
**extra_step_kwargs,
|
|
)
|
|
latents_view_denoised = scheduler_output.prev_sample.numpy()
|
|
|
|
value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
|
|
count[:, :, h_start:h_end, w_start:w_end] += 1
|
|
|
|
# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
|
|
latents = np.where(count > 0, value / count, value)
|
|
|
|
# call the callback, if provided
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
# remove extra margins
|
|
latents = latents[
|
|
:, :, 0 : (height // LATENT_FACTOR), 0 : (width // LATENT_FACTOR)
|
|
]
|
|
|
|
latents = 1 / 0.18215 * latents
|
|
# image = self.vae_decoder(latent_sample=latents)[0]
|
|
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
|
|
image = np.concatenate(
|
|
[
|
|
self.vae_decoder(latent_sample=latents[i : i + 1])[0]
|
|
for i in range(latents.shape[0])
|
|
]
|
|
)
|
|
|
|
image = np.clip(image / 2 + 0.5, 0, 1)
|
|
image = image.transpose((0, 2, 3, 1))
|
|
|
|
if self.safety_checker is not None:
|
|
safety_checker_input = self.feature_extractor(
|
|
self.numpy_to_pil(image), return_tensors="np"
|
|
).pixel_values.astype(image.dtype)
|
|
|
|
images, has_nsfw_concept = [], []
|
|
for i in range(image.shape[0]):
|
|
image_i, has_nsfw_concept_i = self.safety_checker(
|
|
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
|
|
)
|
|
images.append(image_i)
|
|
has_nsfw_concept.append(has_nsfw_concept_i[0])
|
|
image = np.concatenate(images)
|
|
else:
|
|
has_nsfw_concept = None
|
|
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def inpaint(
|
|
self,
|
|
prompt: Union[str, List[str]],
|
|
image: PIL.Image.Image,
|
|
mask_image: PIL.Image.Image,
|
|
height: Optional[int] = 512,
|
|
width: Optional[int] = 512,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: float = 0.0,
|
|
generator: Optional[np.random.RandomState] = None,
|
|
latents: Optional[np.ndarray] = None,
|
|
prompt_embeds: Optional[np.ndarray] = None,
|
|
negative_prompt_embeds: Optional[np.ndarray] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
|
|
callback_steps: int = 1,
|
|
):
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
Args:
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide 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 (`PIL.Image.Image`):
|
|
`Image`, or 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)`.
|
|
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 50):
|
|
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 7.5):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). 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.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
|
if `guidance_scale` is less than `1`).
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
|
[`schedulers.DDIMScheduler`], will be ignored for others.
|
|
generator (`np.random.RandomState`, *optional*):
|
|
A np.random.RandomState to make generation deterministic.
|
|
latents (`np.ndarray`, *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`.
|
|
prompt_embeds (`np.ndarray`, *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 (`np.ndarray`, *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.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that will be called every `callback_steps` steps during inference. The function will be
|
|
called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
|
called at every step.
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
|
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
|
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
|
(nsfw) content, according to the `safety_checker`.
|
|
"""
|
|
|
|
# check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
|
|
# define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if generator is None:
|
|
generator = np.random
|
|
|
|
# set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps)
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
prompt_embeds = self._encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
)
|
|
|
|
num_channels_latents = LATENT_CHANNELS
|
|
latents_shape = (
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height // LATENT_FACTOR,
|
|
width // LATENT_FACTOR,
|
|
)
|
|
latents_dtype = prompt_embeds.dtype
|
|
if latents is None:
|
|
latents = generator.randn(*latents_shape).astype(latents_dtype)
|
|
else:
|
|
if latents.shape != latents_shape:
|
|
raise ValueError(
|
|
f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
|
|
)
|
|
|
|
# prepare mask and masked_image
|
|
mask, masked_image = prepare_mask_and_masked_image(
|
|
image, mask_image, latents_shape[-2:]
|
|
)
|
|
mask = mask.astype(latents.dtype)
|
|
masked_image = masked_image.astype(latents.dtype)
|
|
|
|
masked_image_latents = self.vae_encoder(sample=masked_image)[0]
|
|
masked_image_latents = 0.18215 * masked_image_latents
|
|
|
|
# duplicate mask and masked_image_latents for each generation per prompt
|
|
mask = mask.repeat(batch_size * num_images_per_prompt, 0)
|
|
masked_image_latents = masked_image_latents.repeat(
|
|
batch_size * num_images_per_prompt, 0
|
|
)
|
|
|
|
mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
|
|
masked_image_latents = (
|
|
np.concatenate([masked_image_latents] * 2)
|
|
if do_classifier_free_guidance
|
|
else masked_image_latents
|
|
)
|
|
|
|
num_channels_mask = mask.shape[1]
|
|
num_channels_masked_image = masked_image_latents.shape[1]
|
|
|
|
unet_input_channels = NUM_UNET_INPUT_CHANNELS
|
|
if (
|
|
num_channels_latents + num_channels_mask + num_channels_masked_image
|
|
!= unet_input_channels
|
|
):
|
|
raise ValueError(
|
|
"Incorrect configuration settings! The config of `pipeline.unet` expects"
|
|
f" {unet_input_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
|
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
|
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
|
|
" `pipeline.unet` or your `mask_image` or `image` input."
|
|
)
|
|
|
|
# set timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * np.float64(self.scheduler.init_noise_sigma)
|
|
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
|
# and should be between [0, 1]
|
|
accepts_eta = "eta" in set(
|
|
inspect.signature(self.scheduler.step).parameters.keys()
|
|
)
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
timestep_dtype = next(
|
|
(
|
|
input.type
|
|
for input in self.unet.model.get_inputs()
|
|
if input.name == "timestep"
|
|
),
|
|
"tensor(float)",
|
|
)
|
|
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
|
|
|
|
# panorama additions
|
|
views, resize = self.get_views(height, width, self.window, self.stride)
|
|
logger.trace("panorama resized latents to %s", resize)
|
|
|
|
count = np.zeros(resize_latent_shape(latents, resize))
|
|
value = np.zeros(resize_latent_shape(latents, resize))
|
|
|
|
# adjust latents
|
|
latents = expand_latents(
|
|
latents,
|
|
random_seed(generator),
|
|
Size(resize[1], resize[0]),
|
|
sigma=self.scheduler.init_noise_sigma,
|
|
)
|
|
|
|
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
|
|
count.fill(0)
|
|
value.fill(0)
|
|
|
|
for h_start, h_end, w_start, w_end in views:
|
|
# get the latents corresponding to the current view coordinates
|
|
latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
|
|
mask_for_view = mask[:, :, h_start:h_end, w_start:w_end]
|
|
masked_latents_for_view = masked_image_latents[
|
|
:, :, h_start:h_end, w_start:w_end
|
|
]
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = (
|
|
np.concatenate([latents_for_view] * 2)
|
|
if do_classifier_free_guidance
|
|
else latents_for_view
|
|
)
|
|
# concat latents, mask, masked_image_latnets in the channel dimension
|
|
latent_model_input = self.scheduler.scale_model_input(
|
|
torch.from_numpy(latent_model_input), t
|
|
)
|
|
latent_model_input = latent_model_input.cpu().numpy()
|
|
latent_model_input = np.concatenate(
|
|
[latent_model_input, mask_for_view, masked_latents_for_view], axis=1
|
|
)
|
|
|
|
# predict the noise residual
|
|
timestep = np.array([t], dtype=timestep_dtype)
|
|
noise_pred = self.unet(
|
|
sample=latent_model_input,
|
|
timestep=timestep,
|
|
encoder_hidden_states=prompt_embeds,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (
|
|
noise_pred_text - noise_pred_uncond
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
scheduler_output = self.scheduler.step(
|
|
torch.from_numpy(noise_pred),
|
|
t,
|
|
torch.from_numpy(latents_for_view),
|
|
**extra_step_kwargs,
|
|
)
|
|
latents_view_denoised = scheduler_output.prev_sample.numpy()
|
|
|
|
value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
|
|
count[:, :, h_start:h_end, w_start:w_end] += 1
|
|
|
|
# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
|
|
latents = np.where(count > 0, value / count, value)
|
|
|
|
# call the callback, if provided
|
|
if callback is not None and i % callback_steps == 0:
|
|
callback(i, t, latents)
|
|
|
|
# remove extra margins
|
|
latents = latents[
|
|
:, :, 0 : (height // LATENT_FACTOR), 0 : (width // LATENT_FACTOR)
|
|
]
|
|
|
|
latents = 1 / 0.18215 * latents
|
|
# image = self.vae_decoder(latent_sample=latents)[0]
|
|
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
|
|
image = np.concatenate(
|
|
[
|
|
self.vae_decoder(latent_sample=latents[i : i + 1])[0]
|
|
for i in range(latents.shape[0])
|
|
]
|
|
)
|
|
|
|
image = np.clip(image / 2 + 0.5, 0, 1)
|
|
image = image.transpose((0, 2, 3, 1))
|
|
|
|
if self.safety_checker is not None:
|
|
safety_checker_input = self.feature_extractor(
|
|
self.numpy_to_pil(image), return_tensors="np"
|
|
).pixel_values.astype(image.dtype)
|
|
# safety_checker does not support batched inputs yet
|
|
images, has_nsfw_concept = [], []
|
|
for i in range(image.shape[0]):
|
|
image_i, has_nsfw_concept_i = self.safety_checker(
|
|
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
|
|
)
|
|
images.append(image_i)
|
|
has_nsfw_concept.append(has_nsfw_concept_i[0])
|
|
image = np.concatenate(images)
|
|
else:
|
|
has_nsfw_concept = None
|
|
|
|
if output_type == "pil":
|
|
image = self.numpy_to_pil(image)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
)
|
|
|
|
def __call__(
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
if "image" in kwargs or (
|
|
len(args) > 1
|
|
and (
|
|
isinstance(args[1], np.ndarray) or isinstance(args[1], PIL.Image.Image)
|
|
)
|
|
):
|
|
if "mask_image" in kwargs or (
|
|
len(args) > 2
|
|
and (
|
|
isinstance(args[1], np.ndarray)
|
|
or isinstance(args[1], PIL.Image.Image)
|
|
)
|
|
):
|
|
logger.debug("running inpaint panorama pipeline")
|
|
return self.inpaint(*args, **kwargs)
|
|
else:
|
|
logger.debug("running img2img panorama pipeline")
|
|
return self.img2img(*args, **kwargs)
|
|
else:
|
|
logger.debug("running txt2img panorama pipeline")
|
|
return self.text2img(*args, **kwargs)
|
|
|
|
def set_window_size(self, window: int, stride: int):
|
|
self.window = window
|
|
self.stride = stride
|