add sd1.5
This commit is contained in:
Qing
parent
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@ -7,8 +7,6 @@ import torch
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from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler
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from loguru import logger
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from lama_cleaner.helper import norm_img
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from lama_cleaner.model.base import InpaintModel
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from lama_cleaner.schema import Config, SDSampler
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@ -38,12 +36,22 @@ from lama_cleaner.schema import Config, SDSampler
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# mask = torch.from_numpy(mask)
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# return mask
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class CPUTextEncoderWrapper:
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def __init__(self, text_encoder):
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self.text_encoder = text_encoder.to(torch.device('cpu'), non_blocking=True)
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self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
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def __call__(self, x):
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input_device = x.device
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return [self.text_encoder(x.to(self.text_encoder.device))[0].to(input_device)]
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class SD(InpaintModel):
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pad_mod = 64 # current diffusers only support 64 https://github.com/huggingface/diffusers/pull/505
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pad_mod = 8 # current diffusers only support 64 https://github.com/huggingface/diffusers/pull/505
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from .sd_pipeline import StableDiffusionInpaintPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
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model_kwargs = {"local_files_only": kwargs['sd_run_local']}
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if kwargs['sd_disable_nsfw']:
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@ -65,8 +73,7 @@ class SD(InpaintModel):
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if kwargs['sd_cpu_textencoder']:
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logger.info("Run Stable Diffusion TextEncoder on CPU")
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self.model.text_encoder = self.model.text_encoder.to(torch.device('cpu'), non_blocking=True)
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self.model.text_encoder = self.model.text_encoder.to(torch.float32, non_blocking=True )
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self.model.text_encoder = CPUTextEncoderWrapper(self.model.text_encoder)
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self.callback = kwargs.pop("callback", None)
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@ -99,7 +106,6 @@ class SD(InpaintModel):
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)
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elif config.sd_sampler == SDSampler.pndm:
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PNDM_kwargs = {
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"tensor_format": "pt",
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"beta_end": 0.012,
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@ -124,15 +130,19 @@ class SD(InpaintModel):
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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_kwargs = {
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self.image_key: PIL.Image.fromarray(image),
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}
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output = self.model(
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prompt=config.prompt,
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init_image=PIL.Image.fromarray(image),
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
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strength=config.sd_strength,
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num_inference_steps=config.sd_steps,
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guidance_scale=config.sd_guidance_scale,
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output_type="np.array",
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callback=self.callback,
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**_kwargs
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).images[0]
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output = (output * 255).round().astype("uint8")
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@ -185,7 +195,9 @@ class SD(InpaintModel):
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class SD14(SD):
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model_id_or_path = "CompVis/stable-diffusion-v1-4"
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image_key = "init_image"
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class SD15(SD):
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model_id_or_path = "CompVis/stable-diffusion-v1-5"
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model_id_or_path = "runwayml/stable-diffusion-inpainting"
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image_key = "image"
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@ -1,406 +0,0 @@
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import inspect
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from typing import List, Optional, Union, Callable
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import numpy as np
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import torch
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import PIL
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from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler
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from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker, StableDiffusionPipelineOutput
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from diffusers.utils import logging, deprecate
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from diffusers.configuration_utils import FrozenDict
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from tqdm.auto import tqdm
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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logger = logging.get_logger(__name__)
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def preprocess_image(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL.Image.LANCZOS)
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.0 * image - 1.0
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def preprocess_mask(mask):
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mask = mask.convert("L")
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w, h = mask.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = np.tile(mask, (4, 1, 1))
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mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
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mask = 1 - mask # repaint white, keep black
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mask = torch.from_numpy(mask)
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return mask
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class StableDiffusionInpaintPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder. Stable Diffusion uses the text portion of
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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safety_checker ([`StableDiffusionSafetyChecker`]):
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Classification module that estimates whether generated images could be considered offensive or harmful.
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Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
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feature_extractor ([`CLIPFeatureExtractor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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):
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super().__init__()
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logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
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if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
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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("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
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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 safety_checker is None:
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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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self.register_modules(
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vae=vae,
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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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)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `set_attention_slice`
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self.enable_attention_slicing(None)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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init_image: Union[torch.FloatTensor, PIL.Image.Image],
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mask_image: Union[torch.FloatTensor, PIL.Image.Image],
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strength: float = 0.8,
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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[torch.Generator] = 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, torch.FloatTensor], None]] = None,
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callback_steps: Optional[int] = 1,
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**kwargs,
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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]`):
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The prompt or prompts to guide the image generation.
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init_image (`torch.FloatTensor` or `PIL.Image.Image`):
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`Image`, or tensor representing an image batch, that will be used as the starting point for the
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process. This is the image whose masked region will be inpainted.
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mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
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`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
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replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
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PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
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contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
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strength (`float`, *optional*, defaults to 0.8):
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Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
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is 1, the denoising process will be run on the masked area for the full number of iterations specified
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in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
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noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
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the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
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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. Ignored when not using guidance (i.e., ignored
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if `guidance_scale` 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 (`torch.Generator`, *optional*):
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
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deterministic.
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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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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if strength < 0 or strength > 1:
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raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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if (callback_steps is None) or (
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callback_steps is not None 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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# set timesteps
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self.scheduler.set_timesteps(num_inference_steps)
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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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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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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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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_encoder_device = self.text_encoder.device
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text_embeddings = self.text_encoder(text_input_ids.to(text_encoder_device))[0].to(self.device)
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# duplicate text embeddings for each generation per prompt
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text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
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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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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""]
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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]
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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 = text_input_ids.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="pt",
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)
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uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(text_encoder_device))[0].to(self.device)
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# duplicate unconditional embeddings for each generation per prompt
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uncond_embeddings = uncond_embeddings.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
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|
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# For classifier free guidance, we need to do two forward passes.
|
||||
# Here we concatenate the unconditional and text embeddings into a single batch
|
||||
# to avoid doing two forward passes
|
||||
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
||||
|
||||
# preprocess image
|
||||
if not isinstance(init_image, torch.FloatTensor):
|
||||
init_image = preprocess_image(init_image)
|
||||
|
||||
# encode the init image into latents and scale the latents
|
||||
latents_dtype = text_embeddings.dtype
|
||||
init_image = init_image.to(device=self.device, dtype=latents_dtype)
|
||||
init_latent_dist = self.vae.encode(init_image).latent_dist
|
||||
init_latents = init_latent_dist.sample(generator=generator)
|
||||
init_latents = 0.18215 * init_latents
|
||||
|
||||
# Expand init_latents for batch_size and num_images_per_prompt
|
||||
init_latents = torch.cat([init_latents] * batch_size * num_images_per_prompt, dim=0)
|
||||
init_latents_orig = init_latents
|
||||
|
||||
# preprocess mask
|
||||
if not isinstance(mask_image, torch.FloatTensor):
|
||||
mask_image = preprocess_mask(mask_image)
|
||||
mask_image = mask_image.to(device=self.device, dtype=latents_dtype)
|
||||
mask = torch.cat([mask_image] * batch_size * num_images_per_prompt)
|
||||
|
||||
# check sizes
|
||||
if not mask.shape == init_latents.shape:
|
||||
raise ValueError("The mask and init_image should be the same size!")
|
||||
|
||||
# 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[-init_timestep]
|
||||
timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device)
|
||||
|
||||
# add noise to latents using the timesteps
|
||||
noise = torch.randn(init_latents.shape, generator=generator, device=self.device, dtype=latents_dtype)
|
||||
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
|
||||
|
||||
# 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
|
||||
|
||||
latents = init_latents
|
||||
|
||||
t_start = max(num_inference_steps - init_timestep + offset, 0)
|
||||
|
||||
# Some schedulers like PNDM have timesteps as arrays
|
||||
# It's more optimized to move all timesteps to correct device beforehand
|
||||
timesteps = self.scheduler.timesteps[t_start:].to(self.device)
|
||||
|
||||
for i, t in tqdm(enumerate(timesteps)):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
||||
# masking
|
||||
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
|
||||
|
||||
latents = (init_latents_proper * mask) + (latents * (1 - mask))
|
||||
|
||||
# call the callback, if provided
|
||||
if callback is not None and i % callback_steps == 0:
|
||||
callback(i, t, latents)
|
||||
|
||||
latents = 1 / 0.18215 * latents
|
||||
image = self.vae.decode(latents).sample
|
||||
|
||||
image = (image / 2 + 0.5).clamp(0, 1)
|
||||
image = image.cpu().permute(0, 2, 3, 1).numpy()
|
||||
|
||||
if self.safety_checker is not None:
|
||||
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
|
||||
self.device
|
||||
)
|
||||
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_checker_input.pixel_values)
|
||||
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)
|
||||
@ -2,12 +2,12 @@ from lama_cleaner.model.fcf import FcF
|
||||
from lama_cleaner.model.lama import LaMa
|
||||
from lama_cleaner.model.ldm import LDM
|
||||
from lama_cleaner.model.mat import MAT
|
||||
from lama_cleaner.model.sd import SD14
|
||||
from lama_cleaner.model.sd import SD14, SD15
|
||||
from lama_cleaner.model.zits import ZITS
|
||||
from lama_cleaner.model.opencv2 import OpenCV2
|
||||
from lama_cleaner.schema import Config
|
||||
|
||||
models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.4": SD14, "cv2": OpenCV2}
|
||||
models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.4": SD14, "sd1.5": SD15, "cv2": OpenCV2}
|
||||
|
||||
|
||||
class ModelManager:
|
||||
|
||||
@ -10,7 +10,7 @@ def parse_args():
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
default="lama",
|
||||
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.4", "cv2"],
|
||||
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.4", "sd1.5", "cv2"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hf_access_token",
|
||||
|
||||
@ -159,10 +159,10 @@ def test_fcf(strategy):
|
||||
|
||||
|
||||
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
|
||||
@pytest.mark.parametrize("sampler", [SDSampler.ddim, SDSampler.pndm])
|
||||
@pytest.mark.parametrize("sampler", [SDSampler.ddim, SDSampler.pndm, SDSampler.k_lms])
|
||||
def test_sd(strategy, sampler):
|
||||
def callback(step: int):
|
||||
print(f"sd_step_{step}")
|
||||
def callback(i, t, latents):
|
||||
print(f"sd_step_{i}")
|
||||
|
||||
sd_steps = 50
|
||||
model = ModelManager(name="sd1.4",
|
||||
@ -197,8 +197,8 @@ def test_sd(strategy, sampler):
|
||||
@pytest.mark.parametrize("disable_nsfw", [True, False])
|
||||
@pytest.mark.parametrize("cpu_textencoder", [True, False])
|
||||
def test_sd_run_local(strategy, sampler, disable_nsfw, cpu_textencoder):
|
||||
def callback(step: int):
|
||||
print(f"sd_step_{step}")
|
||||
def callback(i, t, latents):
|
||||
print(f"sd_step_{i}")
|
||||
|
||||
sd_steps = 50
|
||||
model = ModelManager(
|
||||
@ -222,6 +222,40 @@ def test_sd_run_local(strategy, sampler, disable_nsfw, cpu_textencoder):
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
|
||||
@pytest.mark.parametrize("sampler", [SDSampler.ddim, SDSampler.pndm, SDSampler.k_lms])
|
||||
def test_runway_sd_1_5(strategy, sampler):
|
||||
def callback(i, t, latents):
|
||||
print(f"sd_step_{i}")
|
||||
|
||||
sd_steps = 20
|
||||
model = ModelManager(name="sd1.5",
|
||||
device=device,
|
||||
hf_access_token=None,
|
||||
sd_run_local=True,
|
||||
sd_disable_nsfw=True,
|
||||
sd_cpu_textencoder=True,
|
||||
callback=callback)
|
||||
cfg = get_config(strategy, prompt='a cat sitting on a bench', sd_steps=sd_steps)
|
||||
cfg.sd_sampler = sampler
|
||||
|
||||
assert_equal(
|
||||
model,
|
||||
cfg,
|
||||
f"runway_sd_{strategy.capitalize()}_{sampler}_result.png",
|
||||
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
||||
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
|
||||
)
|
||||
|
||||
assert_equal(
|
||||
model,
|
||||
cfg,
|
||||
f"runway_sd_{strategy.capitalize()}_{sampler}_blur_mask_result.png",
|
||||
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
||||
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask_blur.png",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"strategy", [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP]
|
||||
)
|
||||
|
||||
@ -10,5 +10,5 @@ pytest
|
||||
yacs
|
||||
markupsafe==2.0.1
|
||||
scikit-image==0.19.3
|
||||
diffusers==0.5.1
|
||||
diffusers==0.6.0
|
||||
transformers==4.21.0
|
||||
|
||||
Loading…
Reference in New Issue
Block a user