add experimental patch with latent mirroring
This commit is contained in:
parent
ff11d75784
commit
dcaadf1a31
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@ -18,9 +18,11 @@ from ..params import DeviceParams, ImageParams
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from ..server import ModelTypes, ServerContext
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from ..torch_before_ort import InferenceSession
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from ..utils import run_gc
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from .patches.scheduler import SchedulerPatch
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from .patches.unet import UNetWrapper
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from .patches.vae import VAEWrapper
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from .pipelines.controlnet import OnnxStableDiffusionControlNetPipeline
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from .pipelines.highres import OnnxStableDiffusionHighresPipeline
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from .pipelines.lpw import OnnxStableDiffusionLongPromptWeightingPipeline
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from .pipelines.panorama import OnnxStableDiffusionPanoramaPipeline
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from .pipelines.panorama_xl import ORTStableDiffusionXLPanoramaPipeline
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@ -53,6 +55,7 @@ logger = getLogger(__name__)
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available_pipelines = {
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"controlnet": OnnxStableDiffusionControlNetPipeline,
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"highres": OnnxStableDiffusionHighresPipeline,
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"img2img": OnnxStableDiffusionImg2ImgPipeline,
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"img2img-sdxl": ORTStableDiffusionXLImg2ImgPipeline,
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"inpaint": OnnxStableDiffusionInpaintPipeline,
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@ -651,9 +654,13 @@ def patch_pipeline(
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if not params.is_lpw() and not params.is_xl():
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pipe._encode_prompt = expand_prompt.__get__(pipe, pipeline)
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logger.debug("patching pipeline scheduler")
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original_scheduler = pipe.scheduler
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pipe.scheduler = SchedulerPatch(original_scheduler)
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logger.debug("patching pipeline UNet")
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original_unet = pipe.unet
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pipe.unet = UNetWrapper(server, original_unet, params.is_xl())
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logger.debug("patched UNet with wrapper")
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if hasattr(pipe, "vae_decoder"):
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original_decoder = pipe.vae_decoder
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@ -13,18 +13,17 @@
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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.configuration_utils import FrozenDict
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from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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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, logging
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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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@ -37,8 +36,9 @@ from ..utils import (
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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 = logging.get_logger(__name__)
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logger = getLogger(__name__)
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# inpaint constants
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@ -96,18 +96,7 @@ def prepare_mask_and_masked_image(image, mask, latents_shape):
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return mask, masked_image
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class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
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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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_optional_components = ["safety_checker", "feature_extractor"]
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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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@ -122,65 +111,7 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
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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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self.window = window or DEFAULT_WINDOW
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self.stride = stride or DEFAULT_STRIDE
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if (
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hasattr(scheduler.config, "steps_offset")
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and scheduler.config.steps_offset != 1
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):
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate(
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"steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
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)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if (
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hasattr(scheduler.config, "clip_sample")
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and scheduler.config.clip_sample is True
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):
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
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" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
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" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
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" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
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" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
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)
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deprecate(
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"clip_sample not set", "1.0.0", deprecation_message, standard_warn=False
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)
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new_config = dict(scheduler.config)
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new_config["clip_sample"] = False
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scheduler._internal_dict = FrozenDict(new_config)
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if safety_checker is None and requires_safety_checker:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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if safety_checker is not None and feature_extractor is None:
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raise ValueError(
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"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
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" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
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)
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self.register_modules(
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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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@ -189,173 +120,11 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
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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.register_to_config(requires_safety_checker=requires_safety_checker)
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def _encode_prompt(
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self,
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prompt: Union[str, List[str]],
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num_images_per_prompt: Optional[int],
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do_classifier_free_guidance: bool,
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negative_prompt: Optional[str],
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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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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`):
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prompt to be encoded
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`):
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` is less than `1`).
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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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"""
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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 prompt_embeds is None:
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# get prompt text embeddings
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="np",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(
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prompt, padding="max_length", return_tensors="np"
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).input_ids
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if not np.array_equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder(
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input_ids=text_input_ids.astype(np.int32)
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)[0]
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prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt] * batch_size
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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max_length = prompt_embeds.shape[1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="np",
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)
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negative_prompt_embeds = self.text_encoder(
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input_ids=uncond_input.input_ids.astype(np.int32)
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)[0]
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if do_classifier_free_guidance:
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negative_prompt_embeds = np.repeat(
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negative_prompt_embeds, num_images_per_prompt, axis=0
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)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
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return prompt_embeds
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def check_inputs(
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self,
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prompt: Union[str, List[str]],
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height: Optional[int],
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width: Optional[int],
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callback_steps: int,
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negative_prompt: Optional[str] = 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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):
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(
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f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
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)
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if (callback_steps is None) or (
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callback_steps is not None
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and (not isinstance(callback_steps, int) or callback_steps <= 0)
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):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (
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not isinstance(prompt, str) and not isinstance(prompt, list)
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):
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raise ValueError(
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f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
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)
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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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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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@ -993,7 +762,6 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
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)
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for i, t in enumerate(self.progress_bar(timesteps)):
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last = i == (len(timesteps) - 1)
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count.fill(0)
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value.fill(0)
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@ -835,7 +835,6 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
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# 8. Denoising loop
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num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
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for i, t in enumerate(self.progress_bar(timesteps)):
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last = i == (len(timesteps) - 1)
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count.fill(0)
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value.fill(0)
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@ -100,8 +100,8 @@ export function HorizontalBody(props: BodyProps) {
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separator
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snap
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>
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<TabGroup direction={props.direction} />
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<Box className='box-history' sx={layout.history.style}>
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<TabGroup direction={props.direction} panelClass='scroll-controls' />
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<Box className='scroll-history' sx={layout.history.style}>
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<ImageHistory width={props.width} />
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</Box>
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</Allotment>;
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@ -113,7 +113,7 @@ export function VerticalBody(props: BodyProps) {
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return <Stack direction={layout.direction} spacing={STANDARD_SPACING}>
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<TabGroup direction={props.direction} />
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<Divider flexItem variant='middle' orientation={layout.divider} />
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<Box className='box-history' sx={layout.history.style}>
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<Box sx={layout.history.style}>
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<ImageHistory width={props.width} />
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</Box>
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</Stack>;
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@ -121,6 +121,7 @@ export function VerticalBody(props: BodyProps) {
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export interface TabGroupProps {
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direction: Layout;
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panelClass?: string;
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}
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export function TabGroup(props: TabGroupProps) {
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@ -138,25 +139,25 @@ export function TabGroup(props: TabGroupProps) {
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{TAB_LABELS.map((name) => <Tab key={name} label={t(`tab.${name}`)} value={name} />)}
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</TabList>
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</Box>
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<TabPanel value='txt2img'>
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<TabPanel className={props.panelClass} value='txt2img'>
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<Txt2Img />
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</TabPanel>
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<TabPanel value='img2img'>
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<TabPanel className={props.panelClass} value='img2img'>
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<Img2Img />
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</TabPanel>
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<TabPanel value='inpaint'>
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<TabPanel className={props.panelClass} value='inpaint'>
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<Inpaint />
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</TabPanel>
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<TabPanel value='upscale'>
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<TabPanel className={props.panelClass} value='upscale'>
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<Upscale />
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</TabPanel>
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<TabPanel value='blend'>
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<TabPanel className={props.panelClass} value='blend'>
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<Blend />
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</TabPanel>
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<TabPanel value='models'>
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<TabPanel className={props.panelClass} value='models'>
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<Models />
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</TabPanel>
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<TabPanel value='settings'>
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<TabPanel className={props.panelClass} value='settings'>
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<Settings />
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</TabPanel>
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</TabContext>
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|
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@ -2,7 +2,12 @@
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height: 90vb;
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}
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.box-history {
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.scroll-history {
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max-height: 90vh;
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overflow-y: auto;
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}
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.scroll-controls {
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max-height: 85vh;
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overflow-y: auto;
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}
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