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Unverified Commit d114d80f authored by Patrick von Platen's avatar Patrick von Platen Committed by GitHub
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[Stable Diffusion Inpainting] Allow standard text-to-img checkpoints to be... 

[Stable Diffusion Inpainting] Allow standard text-to-img checkpoints to be useable for SD inpainting (#3533)

* Add default to inpaint

* Make sure controlnet also works with normal sd for inpaint

* Add tests

* improve

* Correct encode images function

* Correct inpaint controlnet

* Improve text2img inpanit

* make style

* up

* up

* up

* up

* fix more
parent e5215dee
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Showing with 456 additions and 113 deletions
src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py
View file @ d114d80f
......@@ -50,49 +50,59 @@ logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> # !pip install opencv-python transformers accelerate
>>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, UniPCMultistepScheduler
>>> # !pip install transformers accelerate
>>> from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel, DDIMScheduler
>>> from diffusers.utils import load_image
>>> import numpy as np
>>> import torch
>>> import cv2
>>> from PIL import Image
>>> init_image = load_image(
... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
... )
>>> init_image = init_image.resize((512, 512))
>>> generator = torch.Generator(device="cpu").manual_seed(1)
>>> mask_image = load_image(
... "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
... )
>>> mask_image = mask_image.resize((512, 512))
>>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
>>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
>>> init_image = load_image(img_url).resize((512, 512))
>>> mask_image = load_image(mask_url).resize((512, 512))
>>> def make_inpaint_condition(image, image_mask):
... image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
... image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
>>> image = np.array(init_image)
... assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
... image[image_mask > 0.5] = -1.0 # set as masked pixel
... image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
... image = torch.from_numpy(image)
... return image
>>> # get canny image
>>> image = cv2.Canny(image, 100, 200)
>>> image = image[:, :, None]
>>> image = np.concatenate([image, image, image], axis=2)
>>> canny_image = Image.fromarray(image)
>>> # load control net and stable diffusion inpainting
>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
>>> control_image = make_inpaint_condition(init_image, mask_image)
>>> controlnet = ControlNetModel.from_pretrained(
... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
... )
>>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
... "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16
... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... )
>>> # speed up diffusion process with faster scheduler and memory optimization
>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
>>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
>>> pipe.enable_model_cpu_offload()
>>> # generate image
>>> generator = torch.manual_seed(0)
>>> image = pipe(
... "spiderman",
... num_inference_steps=30,
... "a beautiful man",
... num_inference_steps=20,
... generator=generator,
... eta=1.0,
... image=init_image,
... mask_image=mask_image,
... control_image=canny_image,
... control_image=control_image,
... ).images[0]
```
"""
......@@ -226,6 +236,17 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
In addition the pipeline inherits the following loading methods:
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
<Tip>
This pipeline can be used both with checkpoints that have been specifically fine-tuned for inpainting, such as
[runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)
as well as default text-to-image stable diffusion checkpoints, such as
[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
Default text-to-image stable diffusion checkpoints might be preferable for controlnets that have been fine-tuned on
those, such as [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
</Tip>
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
......@@ -597,6 +618,16 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def check_inputs(
self,
prompt,
......@@ -812,6 +843,8 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
image=None,
timestep=None,
is_strength_max=True,
return_noise=False,
return_image_latents=False,
):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
......@@ -826,32 +859,28 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
"However, either the image or the noise timestep has not been provided."
)
if latents is None:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
if is_strength_max:
# if strength is 100% then simply initialise the latents to noise
latents = noise
else:
# otherwise initialise latents as init image + noise
if return_image_latents or (latents is None and not is_strength_max):
image = image.to(device=device, dtype=dtype)
if isinstance(generator, list):
image_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(batch_size)
]
else:
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
image_latents = self.vae.config.scaling_factor * image_latents
image_latents = self._encode_vae_image(image=image, generator=generator)
latents = self.scheduler.add_noise(image_latents, noise, timestep)
if latents is None:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
outputs = (latents,)
if return_noise:
outputs += (noise,)
if return_image_latents:
outputs += (image_latents,)
return outputs
def _default_height_width(self, height, width, image):
# NOTE: It is possible that a list of images have different
......@@ -891,17 +920,7 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
# encode the mask image into latents space so we can concatenate it to the latents
if isinstance(generator, list):
masked_image_latents = [
self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(batch_size)
]
masked_image_latents = torch.cat(masked_image_latents, dim=0)
else:
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
......@@ -930,6 +949,21 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
return mask, masked_image_latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(image.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
image_latents = self.vae.config.scaling_factor * image_latents
return image_latents
# override DiffusionPipeline
def save_pretrained(
self,
......@@ -954,6 +988,7 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
] = None,
height: Optional[int] = None,
width: Optional[int] = None,
strength: float = 1.0,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
......@@ -990,6 +1025,13 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
strength (`float`, *optional*, defaults to 1.):
Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
`strength`. The number of denoising steps depends on the amount of noise initially added. When
`strength` is 1, added noise will be maximum and the denoising process will run for the full number of
iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
portion of the reference `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
......@@ -1145,13 +1187,25 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
assert False
# 4. Preprocess mask and image - resizes image and mask w.r.t height and width
mask, masked_image, init_image = prepare_mask_and_masked_image(
image, mask_image, height, width, return_image=True
)
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
timesteps, num_inference_steps = self.get_timesteps(
num_inference_steps=num_inference_steps, strength=strength, device=device
)
# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
is_strength_max = strength == 1.0
# 6. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
num_channels_unet = self.unet.config.in_channels
return_image_latents = num_channels_unet == 4
latents_outputs = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
......@@ -1160,10 +1214,19 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
device,
generator,
latents,
image=init_image,
timestep=latent_timestep,
is_strength_max=is_strength_max,
return_noise=True,
return_image_latents=return_image_latents,
)
if return_image_latents:
latents, noise, image_latents = latents_outputs
else:
latents, noise = latents_outputs
# 7. Prepare mask latent variables
mask, masked_image = prepare_mask_and_masked_image(image, mask_image, height, width)
mask, masked_image_latents = self.prepare_mask_latents(
mask,
masked_image,
......@@ -1213,7 +1276,9 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
# predict the noise residual
if num_channels_unet == 9:
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
noise_pred = self.unet(
latent_model_input,
t,
......@@ -1232,6 +1297,15 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if num_channels_unet == 4:
init_latents_proper = image_latents[:1]
init_mask = mask[:1]
if i < len(timesteps) - 1:
init_latents_proper = self.scheduler.add_noise(init_latents_proper, noise, torch.tensor([t]))
latents = (1 - init_mask) * init_latents_proper + init_mask * latents
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
......
src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
View file @ d114d80f
......@@ -328,17 +328,7 @@ class PaintByExamplePipeline(DiffusionPipeline):
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
# encode the mask image into latents space so we can concatenate it to the latents
if isinstance(generator, list):
masked_image_latents = [
self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(batch_size)
]
masked_image_latents = torch.cat(masked_image_latents, dim=0)
else:
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
......@@ -367,6 +357,21 @@ class PaintByExamplePipeline(DiffusionPipeline):
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
return mask, masked_image_latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline._encode_vae_image
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(image.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
image_latents = self.vae.config.scaling_factor * image_latents
return image_latents
def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
dtype = next(self.image_encoder.parameters()).dtype
......
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
View file @ d114d80f
......@@ -155,7 +155,7 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool
class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
r"""
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
Pipeline for text-guided image inpainting using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
......@@ -167,6 +167,16 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
as well as the following saving methods:
- *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
<Tip>
It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
text-to-image stable diffusion checkpoints, such as
[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) are also compatible with
this pipeline, but might be less performant.
</Tip>
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
......@@ -266,14 +276,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
# Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4
if unet.config.in_channels != 9:
logger.warning(
f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default,"
f" {self.__class__} assumes that `pipeline.unet` has 9 input channels: 4 for `num_channels_latents`,"
" 1 for `num_channels_mask`, and 4 for `num_channels_masked_image`. If you did not intend to modify"
" this behavior, please check whether you have loaded the right checkpoint."
)
logger.info(f"You have loaded a UNet with {unet.config.in_channels} input channels which.")
self.register_modules(
vae=vae,
......@@ -620,6 +626,8 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
image=None,
timestep=None,
is_strength_max=True,
return_noise=False,
return_image_latents=False,
):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
......@@ -634,32 +642,42 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
"However, either the image or the noise timestep has not been provided."
)
if return_image_latents or (latents is None and not is_strength_max):
image = image.to(device=device, dtype=dtype)
image_latents = self._encode_vae_image(image=image, generator=generator)
if latents is None:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
if is_strength_max:
# if strength is 100% then simply initialise the latents to noise
latents = noise
latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
else:
# otherwise initialise latents as init image + noise
image = image.to(device=device, dtype=dtype)
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
outputs = (latents,)
if return_noise:
outputs += (noise,)
if return_image_latents:
outputs += (image_latents,)
return outputs
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(batch_size)
for i in range(image.shape[0])
]
image_latents = torch.cat(image_latents, dim=0)
else:
image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
image_latents = self.vae.config.scaling_factor * image_latents
latents = self.scheduler.add_noise(image_latents, noise, timestep)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
return image_latents
def prepare_mask_latents(
self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
......@@ -673,17 +691,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
mask = mask.to(device=device, dtype=dtype)
masked_image = masked_image.to(device=device, dtype=dtype)
# encode the mask image into latents space so we can concatenate it to the latents
if isinstance(generator, list):
masked_image_latents = [
self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
for i in range(batch_size)
]
masked_image_latents = torch.cat(masked_image_latents, dim=0)
else:
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
......@@ -916,7 +924,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
# 6. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
latents = self.prepare_latents(
num_channels_unet = self.unet.config.in_channels
return_image_latents = num_channels_unet == 4
latents_outputs = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
......@@ -928,8 +939,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
image=init_image,
timestep=latent_timestep,
is_strength_max=is_strength_max,
return_noise=True,
return_image_latents=return_image_latents,
)
if return_image_latents:
latents, noise, image_latents = latents_outputs
else:
latents, noise = latents_outputs
# 7. Prepare mask latent variables
mask, masked_image_latents = self.prepare_mask_latents(
mask,
......@@ -942,8 +960,12 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
generator,
do_classifier_free_guidance,
)
init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)
init_image = self._encode_vae_image(init_image, generator=generator)
# 8. Check that sizes of mask, masked image and latents match
if num_channels_unet == 9:
# default case for runwayml/stable-diffusion-inpainting
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
......@@ -954,6 +976,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
" `pipeline.unet` or your `mask_image` or `image` input."
)
elif num_channels_unet != 4:
raise ValueError(
f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
)
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
......@@ -967,6 +993,8 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
# concat latents, mask, masked_image_latents in the channel dimension
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
if num_channels_unet == 9:
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
# predict the noise residual
......@@ -986,6 +1014,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMi
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if num_channels_unet == 4:
init_latents_proper = image_latents[:1]
init_mask = mask[:1]
if i < len(timesteps) - 1:
init_latents_proper = self.scheduler.add_noise(init_latents_proper, noise, torch.tensor([t]))
latents = (1 - init_mask) * init_latents_proper + init_mask * latents
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
......
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
View file @ d114d80f
......@@ -123,7 +123,6 @@ class StableDiffusionInpaintPipelineLegacy(
"""
_optional_components = ["feature_extractor"]
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.__init__
def __init__(
self,
vae: AutoencoderKL,
......@@ -137,6 +136,13 @@ class StableDiffusionInpaintPipelineLegacy(
):
super().__init__()
deprecation_message = (
f"The class {self.__class__} is deprecated and will be removed in v1.0.0. You can achieve exactly the same functionality"
"by loading your model into `StableDiffusionInpaintPipeline` instead. See https://github.com/huggingface/diffusers/pull/3533"
"for more information."
)
deprecate("legacy is outdated", "1.0.0", deprecation_message, standard_warn=False)
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`"
......
tests/pipelines/controlnet/test_controlnet_inpaint.py
View file @ d114d80f
......@@ -163,6 +163,78 @@ class ControlNetInpaintPipelineFastTests(PipelineLatentTesterMixin, PipelineTest
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
class ControlNetSimpleInpaintPipelineFastTests(ControlNetInpaintPipelineFastTests):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"controlnet": controlnet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
class MultiControlNetInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableDiffusionControlNetInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
......@@ -376,3 +448,60 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
)
assert np.abs(expected_image - image).max() < 9e-2
def test_inpaint(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(33)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
)
init_image = init_image.resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
)
mask_image = mask_image.resize((512, 512))
prompt = "a handsome man with ray-ban sunglasses"
def make_inpaint_condition(image, image_mask):
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
image[image_mask > 0.5] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
control_image = make_inpaint_condition(init_image, mask_image)
output = pipe(
prompt,
image=init_image,
mask_image=mask_image,
control_image=control_image,
guidance_scale=9.0,
eta=1.0,
generator=generator,
num_inference_steps=20,
output_type="np",
)
image = output.images[0]
assert image.shape == (512, 512, 3)
expected_image = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"
)
assert np.abs(expected_image - image).max() < 9e-2
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py
View file @ d114d80f
......@@ -232,6 +232,82 @@ class StableDiffusionInpaintPipelineFastTests(PipelineLatentTesterMixin, Pipelin
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
pipeline_class = StableDiffusionInpaintPipeline
params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
image_params = frozenset([])
# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = PNDMScheduler(skip_prk_steps=True)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionInpaintPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.4925, 0.4967, 0.4100, 0.5234, 0.5322, 0.4532, 0.5805, 0.5877, 0.4151])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@unittest.skip("skipped here because area stays unchanged due to mask")
def test_stable_diffusion_inpaint_lora(self):
...
@slow
@require_torch_gpu
class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
......@@ -403,6 +479,22 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
expected_slice = np.array([0.0021, 0.2350, 0.3712, 0.0575, 0.2485, 0.3451, 0.1857, 0.3156, 0.3943])
assert np.abs(expected_slice - image_slice).max() < 3e-3
def test_stable_diffusion_simple_inpaint_ddim(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.5157, 0.6858, 0.6873, 0.4619, 0.6416, 0.6898, 0.3702, 0.5960, 0.6935])
assert np.abs(expected_slice - image_slice).max() < 6e-4
@nightly
@require_torch_gpu
......
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