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update sd inpainting pipeline

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Qing 2022-10-15 22:32:25 +08:00
parent b92e9d8da6
commit 3c87b050d9
5 changed files with 178 additions and 112 deletions
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36
lama_cleaner/model/sd.py
View File

@ -4,9 +4,8 @@ import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import PNDMScheduler, DDIMScheduler
from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler
from loguru import logger
from transformers import FeatureExtractionMixin, ImageFeatureExtractionMixin
from lama_cleaner.helper import norm_img
@ -39,30 +38,6 @@ from lama_cleaner.schema import Config, SDSampler
# mask = torch.from_numpy(mask)
# return mask
class DummyFeatureExtractorOutput:
def __init__(self, pixel_values):
self.pixel_values = pixel_values
def to(self, device):
return self
class DummyFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
return DummyFeatureExtractorOutput(torch.empty(0, 3))
class DummySafetyChecker:
def __init__(self, *args, **kwargs):
pass
def __call__(self, clip_input, images):
return images, False
class SD(InpaintModel):
pad_mod = 64 # current diffusers only support 64 https://github.com/huggingface/diffusers/pull/505
min_size = 512
@ -74,8 +49,7 @@ class SD(InpaintModel):
if kwargs['sd_disable_nsfw']:
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(dict(
feature_extractor=DummyFeatureExtractor(),
safety_checker=DummySafetyChecker(),
safety_checker=None,
))
self.model = StableDiffusionInpaintPipeline.from_pretrained(
@ -94,7 +68,7 @@ class SD(InpaintModel):
self.model.text_encoder = self.model.text_encoder.to(torch.device('cpu'), non_blocking=True)
self.model.text_encoder = self.model.text_encoder.to(torch.float32, non_blocking=True )
self.callbacks = kwargs.pop("callbacks", None)
self.callback = kwargs.pop("callback", None)
@torch.cuda.amp.autocast()
def forward(self, image, mask, config: Config):
@ -133,6 +107,8 @@ class SD(InpaintModel):
"skip_prk_steps": True,
}
scheduler = PNDMScheduler(**PNDM_kwargs)
elif config.sd_sampler == SDSampler.k_lms:
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
else:
raise ValueError(config.sd_sampler)
@ -156,7 +132,7 @@ class SD(InpaintModel):
num_inference_steps=config.sd_steps,
guidance_scale=config.sd_guidance_scale,
output_type="np.array",
callbacks=self.callbacks,
callback=self.callback,
).images[0]
output = (output * 255).round().astype("uint8")

228
lama_cleaner/model/sd_pipeline.py
View File

@ -5,9 +5,10 @@ import numpy as np
import torch
import PIL
from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, PNDMScheduler
from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker, StableDiffusionPipelineOutput
from diffusers.utils import logging
from diffusers.utils import logging, deprecate
from diffusers.configuration_utils import FrozenDict
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
@ -59,7 +60,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offsensive or harmful.
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
@ -71,13 +72,37 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler],
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("pt")
logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
@ -113,7 +138,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
back to computing attention in one step.
"""
# set slice_size = `None` to disable `set_attention_slice`
self.enable_attention_slice(None)
self.enable_attention_slicing(None)
@torch.no_grad()
def __call__(
@ -124,11 +149,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
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[torch.Generator] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callbacks: List[Callable[[int], None]] = None
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
@ -141,8 +170,9 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
process. This is the image whose masked region will be inpainted.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
converted to a single channel (luminance) before use.
replaced by noise and therefore 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)`.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
@ -157,6 +187,11 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
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.
@ -165,10 +200,16 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
deterministic.
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 `nd.array`.
[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`:
@ -187,58 +228,39 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
offset = 0
if accepts_offset:
offset = 1
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# preprocess image
init_image = preprocess_image(init_image).to(self.device)
# encode the init image into latents and scale the latents
init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents
# Expand init_latents for batch_size
init_latents = torch.cat([init_latents] * batch_size)
init_latents_orig = init_latents
# preprocess mask
mask = preprocess_mask(mask_image).to(self.device)
mask = torch.cat([mask] * batch_size)
# 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
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, dtype=torch.long, device=self.device)
# add noise to latents using the timesteps
noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
self.scheduler.set_timesteps(num_inference_steps)
# get prompt text embeddings
text_input = self.tokenizer(
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_encoder_device = self.text_encoder.device
text_input_ids = text_inputs.input_ids
text_embeddings = self.text_encoder(text_input.input_ids.to(text_encoder_device, non_blocking=True))[0].to(self.device, non_blocking=True)
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
text_encoder_device = self.text_encoder.device
text_embeddings = self.text_encoder(text_input_ids.to(text_encoder_device))[0].to(self.device)
# duplicate text embeddings for each generation per prompt
text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
# 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`
@ -246,17 +268,80 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(text_encoder_device, non_blocking=True))[0].to(self.device, non_blocking=True)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(text_encoder_device))[0].to(self.device)
# duplicate unconditional embeddings for each generation per prompt
uncond_embeddings = uncond_embeddings.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
# 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
@ -267,10 +352,18 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
extra_step_kwargs["eta"] = eta
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
# 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
@ -281,25 +374,28 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
# 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, t)
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
latents = (init_latents_proper * mask) + (latents * (1 - mask))
if callbacks is not None:
for callback in callbacks:
callback(i)
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
# scale and decode the image latents with vae
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()
# run safety checker
safety_cheker_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_cheker_input.pixel_values)
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)

1
lama_cleaner/schema.py
View File

@ -17,6 +17,7 @@ class LDMSampler(str, Enum):
class SDSampler(str, Enum):
ddim = "ddim"
pndm = "pndm"
k_lms = "k_lms"
class Config(BaseModel):

6
lama_cleaner/server.py
View File

@ -82,7 +82,7 @@ def get_image_ext(img_bytes):
return w
def diffuser_callback(step: int):
def diffuser_callback(i, t, latents):
pass
# socketio.emit('diffusion_step', {'diffusion_step': step})
@ -129,7 +129,7 @@ def process():
)
if config.sd_seed == -1:
config.sd_seed = random.randint(1, 9999999)
config.sd_seed = random.randint(1, 999999999)
logger.info(f"Origin image shape: {original_shape}")
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
@ -223,7 +223,7 @@ def main(args):
sd_disable_nsfw=args.sd_disable_nsfw,
sd_cpu_textencoder=args.sd_cpu_textencoder,
sd_run_local=args.sd_run_local,
callbacks=[diffuser_callback],
callback=diffuser_callback,
)
if args.gui:

19
lama_cleaner/tests/test_model.py
View File

@ -171,7 +171,7 @@ def test_sd(strategy, sampler):
sd_run_local=False,
sd_disable_nsfw=False,
sd_cpu_textencoder=False,
callbacks=[callback])
callback=callback)
cfg = get_config(strategy, prompt='a cat sitting on a bench', sd_steps=sd_steps)
cfg.sd_sampler = sampler
@ -193,9 +193,10 @@ def test_sd(strategy, sampler):
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
@pytest.mark.parametrize("sampler", [SDSampler.ddim, SDSampler.pndm, SDSampler.k_lms])
@pytest.mark.parametrize("disable_nsfw", [True, False])
def test_sd_run_local(strategy, sampler, disable_nsfw):
@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}")
@ -207,7 +208,7 @@ def test_sd_run_local(strategy, sampler, disable_nsfw):
hf_access_token=None,
sd_run_local=True,
sd_disable_nsfw=disable_nsfw,
sd_cpu_textencoder=True,
sd_cpu_textencoder=cpu_textencoder,
)
cfg = get_config(strategy, prompt='a cat sitting on a bench', sd_steps=sd_steps)
cfg.sd_sampler = sampler
@ -215,19 +216,11 @@ def test_sd_run_local(strategy, sampler, disable_nsfw):
assert_equal(
model,
cfg,
f"sd_{strategy.capitalize()}_{sampler}_local_result.png",
f"sd_{strategy.capitalize()}_{sampler}_local_disablensfw_{disable_nsfw}_cputextencoder_{cpu_textencoder}_result.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)
assert_equal(
model,
cfg,
f"sd_{strategy.capitalize()}_{sampler}_blur_mask_local_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]