From c7c309cb89dd7bfe4d68c59d032d2edfa315c7f7 Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 30 Aug 2023 13:28:31 +0800 Subject: [PATCH 001/134] backend add outpainting --- lama_cleaner/model/base.py | 101 ++++++++++++-- lama_cleaner/model/g_diffuser_bot.py | 181 +++++++++++++++++++++++++ lama_cleaner/model/utils.py | 10 +- lama_cleaner/schema.py | 5 + lama_cleaner/tests/test_outpainting.py | 70 ++++++++++ 5 files changed, 354 insertions(+), 13 deletions(-) create mode 100644 lama_cleaner/model/g_diffuser_bot.py create mode 100644 lama_cleaner/tests/test_outpainting.py diff --git a/lama_cleaner/model/base.py b/lama_cleaner/model/base.py index 08c27b1..c452690 100644 --- a/lama_cleaner/model/base.py +++ b/lama_cleaner/model/base.py @@ -12,6 +12,7 @@ from lama_cleaner.helper import ( pad_img_to_modulo, switch_mps_device, ) +from lama_cleaner.model.g_diffuser_bot import expand_image, np_img_grey_to_rgb from lama_cleaner.schema import Config, HDStrategy @@ -266,15 +267,93 @@ class DiffusionInpaintModel(InpaintModel): """ # boxes = boxes_from_mask(mask) if config.use_croper: - crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config) - crop_image = self._scaled_pad_forward(crop_img, crop_mask, config) - inpaint_result = image[:, :, ::-1] - inpaint_result[t:b, l:r, :] = crop_image + if config.croper_is_outpainting: + inpaint_result = self._do_outpainting(image, config) + else: + crop_img, crop_mask, (l, t, r, b) = self._apply_cropper( + image, mask, config + ) + crop_image = self._scaled_pad_forward(crop_img, crop_mask, config) + inpaint_result = image[:, :, ::-1] + inpaint_result[t:b, l:r, :] = crop_image else: inpaint_result = self._scaled_pad_forward(image, mask, config) return inpaint_result + def _do_outpainting(self, image, config: Config): + # cropper 和 image 在同一个坐标系下，croper_x/y 可能为负数 + # 从 image 中 crop 出 outpainting 区域 + image_h, image_w = image.shape[:2] + cropper_l = config.croper_x + cropper_t = config.croper_y + cropper_r = config.croper_x + config.croper_width + cropper_b = config.croper_y + config.croper_height + image_l = 0 + image_t = 0 + image_r = image_w + image_b = image_h + + # 类似求 IOU + l = max(cropper_l, image_l) + t = max(cropper_t, image_t) + r = min(cropper_r, image_r) + b = min(cropper_b, image_b) + + assert ( + 0 <= l < r and 0 <= t < b + ), f"cropper and image not overlap, {l},{t},{r},{b}" + + cropped_image = image[t:b, l:r, :] + padding_l = max(0, image_l - cropper_l) + padding_t = max(0, image_t - cropper_t) + padding_r = max(0, cropper_r - image_r) + padding_b = max(0, cropper_b - image_b) + + zero_padding_count = [padding_l, padding_t, padding_r, padding_b].count(0) + + if zero_padding_count not in [0, 3]: + logger.warning( + f"padding count({zero_padding_count}) not 0 or 3, may result in bad edge outpainting" + ) + + expanded_image, mask_image = expand_image( + cropped_image, + left=padding_l, + top=padding_t, + right=padding_r, + bottom=padding_b, + softness=config.sd_outpainting_softness, + space=config.sd_outpainting_space, + ) + + # 最终扩大了的 image, BGR + expanded_cropped_result_image = self._scaled_pad_forward( + expanded_image, mask_image, config + ) + + # RGB -> BGR + outpainting_image = cv2.copyMakeBorder( + image, + left=padding_l, + top=padding_t, + right=padding_r, + bottom=padding_b, + borderType=cv2.BORDER_CONSTANT, + value=0, + )[:, :, ::-1] + + # 把 cropped_result_image 贴到 outpainting_image 上，这一步不需要 blend + paste_t = 0 if config.croper_y < 0 else config.croper_y + paste_l = 0 if config.croper_x < 0 else config.croper_x + + outpainting_image[ + paste_t : paste_t + expanded_cropped_result_image.shape[0], + paste_l : paste_l + expanded_cropped_result_image.shape[1], + :, + ] = expanded_cropped_result_image + return outpainting_image + def _scaled_pad_forward(self, image, mask, config: Config): longer_side_length = int(config.sd_scale * max(image.shape[:2])) origin_size = image.shape[:2] @@ -291,8 +370,14 @@ class DiffusionInpaintModel(InpaintModel): (origin_size[1], origin_size[0]), interpolation=cv2.INTER_CUBIC, ) - original_pixel_indices = mask < 127 - inpaint_result[original_pixel_indices] = image[:, :, ::-1][ - original_pixel_indices - ] + + # blend result, copy from g_diffuser_bot + # mask_rgb = 1.0 - np_img_grey_to_rgb(mask / 255.0) + # inpaint_result = np.clip( + # inpaint_result * (1.0 - mask_rgb) + image * mask_rgb, 0.0, 255.0 + # ) + # original_pixel_indices = mask < 127 + # inpaint_result[original_pixel_indices] = image[:, :, ::-1][ + # original_pixel_indices + # ] return inpaint_result diff --git a/lama_cleaner/model/g_diffuser_bot.py b/lama_cleaner/model/g_diffuser_bot.py new file mode 100644 index 0000000..450d618 --- /dev/null +++ b/lama_cleaner/model/g_diffuser_bot.py @@ -0,0 +1,181 @@ +# code copy from: https://github.com/parlance-zz/g-diffuser-bot +import cv2 +import numpy as np + + +def np_img_grey_to_rgb(data): + if data.ndim == 3: + return data + return np.expand_dims(data, 2) * np.ones((1, 1, 3)) + + +def convolve(data1, data2): # fast convolution with fft + if data1.ndim != data2.ndim: # promote to rgb if mismatch + if data1.ndim < 3: + data1 = np_img_grey_to_rgb(data1) + if data2.ndim < 3: + data2 = np_img_grey_to_rgb(data2) + return ifft2(fft2(data1) * fft2(data2)) + + +def fft2(data): + if data.ndim > 2: # multiple channels + out_fft = np.zeros( + (data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128 + ) + for c in range(data.shape[2]): + c_data = data[:, :, c] + out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho") + out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c]) + else: # single channel + out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) + out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho") + out_fft[:, :] = np.fft.ifftshift(out_fft[:, :]) + + return out_fft + + +def ifft2(data): + if data.ndim > 2: # multiple channels + out_ifft = np.zeros( + (data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128 + ) + for c in range(data.shape[2]): + c_data = data[:, :, c] + out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho") + out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c]) + else: # single channel + out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) + out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho") + out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :]) + + return out_ifft + + +def get_gradient_kernel(width, height, std=3.14, mode="linear"): + window_scale_x = float( + width / min(width, height) + ) # for non-square aspect ratios we still want a circular kernel + window_scale_y = float(height / min(width, height)) + if mode == "gaussian": + x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x + kx = np.exp(-x * x * std) + if window_scale_x != window_scale_y: + y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y + ky = np.exp(-y * y * std) + else: + y = x + ky = kx + return np.outer(kx, ky) + elif mode == "linear": + x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x + if window_scale_x != window_scale_y: + y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y + else: + y = x + return np.clip(1.0 - np.sqrt(np.add.outer(x * x, y * y)) * std / 3.14, 0.0, 1.0) + else: + raise Exception("Error: Unknown mode in get_gradient_kernel: {0}".format(mode)) + + +def image_blur(data, std=3.14, mode="linear"): + width = data.shape[0] + height = data.shape[1] + kernel = get_gradient_kernel(width, height, std, mode=mode) + return np.real(convolve(data, kernel / np.sqrt(np.sum(kernel * kernel)))) + + +def soften_mask(np_rgba_image, softness, space): + if softness == 0: + return np_rgba_image + softness = min(softness, 1.0) + space = np.clip(space, 0.0, 1.0) + original_max_opacity = np.max(np_rgba_image[:, :, 3]) + out_mask = np_rgba_image[:, :, 3] <= 0.0 + blurred_mask = image_blur(np_rgba_image[:, :, 3], 3.5 / softness, mode="linear") + blurred_mask = np.maximum(blurred_mask - np.max(blurred_mask[out_mask]), 0.0) + np_rgba_image[ + :, :, 3 + ] *= blurred_mask # preserve partial opacity in original input mask + np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # renormalize + np_rgba_image[:, :, 3] = np.clip( + np_rgba_image[:, :, 3] - space, 0.0, 1.0 + ) # make space + np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # and renormalize again + np_rgba_image[:, :, 3] *= original_max_opacity # restore original max opacity + return np_rgba_image + + +def expand_image( + cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float +): + origin_h, origin_w = cv2_img.shape[:2] + new_width = cv2_img.shape[1] + left + right + new_height = cv2_img.shape[0] + top + bottom + new_img = np.zeros((new_height, new_width, 4), np.uint8) # expanded image is rgba + + print( + "Expanding input image from {0}x{1} to {2}x{3}".format( + cv2_img.shape[1], cv2_img.shape[0], new_width, new_height + ) + ) + if cv2_img.shape[2] == 3: # rgb input image + new_img[ + top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 0:3 + ] = cv2_img + new_img[ + top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 3 + ] = 255 # fully opaque + elif cv2_img.shape[2] == 4: # rgba input image + new_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = cv2_img + else: + raise Exception( + "Unsupported image format: {0} channels".format(cv2_img.shape[2]) + ) + + if softness > 0.0: + new_img = soften_mask(new_img / 255.0, softness / 100.0, space / 100.0) + new_img = (np.clip(new_img, 0.0, 1.0) * 255.0).astype(np.uint8) + + mask_image = 255.0 - new_img[:, :, 3] # extract mask from alpha channel and invert + rgb_init_image = ( + 0.0 + new_img[:, :, 0:3] + ) # strip mask from init_img leaving only rgb channels + + hard_mask = np.zeros_like(cv2_img[:, :, 0]) + if top != 0: + hard_mask[0 : origin_h // 2, :] = 255 + if bottom != 0: + hard_mask[origin_h // 2 :, :] = 255 + if left != 0: + hard_mask[:, 0 : origin_w // 2] = 255 + if right != 0: + hard_mask[:, origin_w // 2 :] = 255 + hard_mask = cv2.copyMakeBorder( + hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255 + ) + mask_image = np.where(hard_mask > 0, mask_image, 0) + return rgb_init_image.astype(np.uint8), mask_image.astype(np.uint8) + + +if __name__ == "__main__": + from pathlib import Path + + current_dir = Path(__file__).parent.absolute().resolve() + image_path = current_dir.parent / "tests" / "bunny.jpeg" + init_image = cv2.imread(str(image_path)) + init_image, mask_image = expand_image( + init_image, + 200, + 200, + 0, + 0, + 60, + 50, + ) + print(mask_image.dtype, mask_image.min(), mask_image.max()) + print(init_image.dtype, init_image.min(), init_image.max()) + mask_image = mask_image.astype(np.uint8) + init_image = init_image.astype(np.uint8) + cv2.imwrite("expanded_image.png", init_image) + cv2.imwrite("expanded_mask.png", mask_image) diff --git a/lama_cleaner/model/utils.py b/lama_cleaner/model/utils.py index 998db43..e1e9927 100644 --- a/lama_cleaner/model/utils.py +++ b/lama_cleaner/model/utils.py @@ -27,7 +27,7 @@ def make_beta_schedule( if schedule == "linear": betas = ( torch.linspace( - linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64 + linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64 ) ** 2 ) @@ -772,7 +772,7 @@ def conv2d_resample( f=f, up=up, padding=[px0, px1, py0, py1], - gain=up ** 2, + gain=up**2, flip_filter=flip_filter, ) return x @@ -814,7 +814,7 @@ def conv2d_resample( x=x, f=f, padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt], - gain=up ** 2, + gain=up**2, flip_filter=flip_filter, ) if down > 1: @@ -834,7 +834,7 @@ def conv2d_resample( f=(f if up > 1 else None), up=up, padding=[px0, px1, py0, py1], - gain=up ** 2, + gain=up**2, flip_filter=flip_filter, ) x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) @@ -870,7 +870,7 @@ class Conv2dLayer(torch.nn.Module): self.register_buffer("resample_filter", setup_filter(resample_filter)) self.conv_clamp = conv_clamp self.padding = kernel_size // 2 - self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) + self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2)) self.act_gain = activation_funcs[activation].def_gain memory_format = ( diff --git a/lama_cleaner/schema.py b/lama_cleaner/schema.py index 9eba2f5..b710cdc 100644 --- a/lama_cleaner/schema.py +++ b/lama_cleaner/schema.py @@ -55,6 +55,7 @@ class Config(BaseModel): # Crop image to this size before doing sd inpainting # The value is always on the original image scale use_croper: bool = False + croper_is_outpainting: bool = False croper_x: int = None croper_y: int = None croper_height: int = None @@ -78,6 +79,10 @@ class Config(BaseModel): sd_seed: int = 42 sd_match_histograms: bool = False + # out-painting + sd_outpainting_softness: float = 30.0 + sd_outpainting_space: float = 50.0 + # Configs for opencv inpainting # opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07 cv2_flag: str = "INPAINT_NS" diff --git a/lama_cleaner/tests/test_outpainting.py b/lama_cleaner/tests/test_outpainting.py new file mode 100644 index 0000000..ec59399 --- /dev/null +++ b/lama_cleaner/tests/test_outpainting.py @@ -0,0 +1,70 @@ +import os + +os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" +from pathlib import Path + +import pytest +import torch + +from lama_cleaner.model_manager import ModelManager +from lama_cleaner.schema import HDStrategy, SDSampler +from lama_cleaner.tests.test_model import get_config, assert_equal + +current_dir = Path(__file__).parent.absolute().resolve() +save_dir = current_dir / "result" +save_dir.mkdir(exist_ok=True, parents=True) +device = "cuda" if torch.cuda.is_available() else "cpu" +device = torch.device(device) + + +@pytest.mark.parametrize("sd_device", ["mps"]) +@pytest.mark.parametrize( + "rect", + [ + [0, -100, 512, 512 - 128 + 100], + [0, 128, 512, 512 - 128 + 100], + [128, 0, 512 - 128 + 100, 512], + [-100, 0, 512 - 128 + 100, 512], + [0, 0, 512, 512 + 200], + [0, 0, 512 + 200, 512], + [-100, -100, 512 + 200, 512 + 200], + ], +) +def test_sdxl_outpainting(sd_device, rect): + def callback(i, t, latents): + pass + + if sd_device == "cuda" and not torch.cuda.is_available(): + return + + sd_steps = 50 if sd_device == "cuda" else 1 + model = ModelManager( + name="sd1.5", + device=torch.device(sd_device), + hf_access_token="", + sd_run_local=True, + disable_nsfw=True, + sd_cpu_textencoder=False, + callback=callback, + ) + cfg = get_config( + HDStrategy.ORIGINAL, + prompt="a dog sitting on a bench in the park", + sd_steps=30, + use_croper=True, + croper_is_outpainting=True, + croper_x=rect[0], + croper_y=rect[1], + croper_width=rect[2], + croper_height=rect[3], + sd_guidance_scale=14, + sd_sampler=SDSampler.dpm_plus_plus, + ) + + assert_equal( + model, + cfg, + f"sd15_outpainting_dpm++_{'_'.join(map(str, rect))}.png", + img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", + mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", + ) From 7ba8fdbe7660eef9666774609f587a5079c6101e Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 30 Aug 2023 13:28:41 +0800 Subject: [PATCH 002/134] update diffusers to 0.20.1 --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1023d78..8d35bf3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,7 +9,7 @@ pydantic rich loguru yacs -diffusers==0.16.1 +diffusers==0.20.1 transformers==4.27.4 gradio piexif==1.1.3 From 94211a498510d442ac87509380cb517f6177194e Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 30 Aug 2023 21:30:11 +0800 Subject: [PATCH 003/134] kandinsky --- .../components/Settings/ModelSettingBlock.tsx | 6 ++ lama_cleaner/app/src/store/Atoms.tsx | 11 ++- lama_cleaner/const.py | 1 + lama_cleaner/model/controlnet.py | 4 +- lama_cleaner/model/instruct_pix2pix.py | 2 +- lama_cleaner/model/kandinsky.py | 91 ++++++++++++++++++ lama_cleaner/model/sd.py | 2 +- lama_cleaner/model_manager.py | 2 + lama_cleaner/tests/.gitignore | 3 +- lama_cleaner/tests/cat.png | Bin 0 -> 492745 bytes lama_cleaner/tests/test_outpainting.py | 60 ++++++++++-- 11 files changed, 170 insertions(+), 12 deletions(-) create mode 100644 lama_cleaner/model/kandinsky.py create mode 100644 lama_cleaner/tests/cat.png diff --git a/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx b/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx index 4778361..15c7463 100644 --- a/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx +++ b/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx @@ -270,6 +270,12 @@ function ModelSettingBlock() { 'https://arxiv.org/abs/2211.09800', 'https://github.com/timothybrooks/instruct-pix2pix' ) + case AIModel.KANDINSKY21: + return renderModelDesc( + 'Kandinsky 2.1', + 'https://huggingface.co/kandinsky-community/kandinsky-2-1-inpaint', + 'https://huggingface.co/kandinsky-community/kandinsky-2-1-inpaint' + ) default: return <> } diff --git a/lama_cleaner/app/src/store/Atoms.tsx b/lama_cleaner/app/src/store/Atoms.tsx index 4ca0d9e..26f7ba1 100644 --- a/lama_cleaner/app/src/store/Atoms.tsx +++ b/lama_cleaner/app/src/store/Atoms.tsx @@ -17,6 +17,7 @@ export enum AIModel { Mange = 'manga', PAINT_BY_EXAMPLE = 'paint_by_example', PIX2PIX = 'instruct_pix2pix', + KANDINSKY21 = 'kandinsky2.1', } export enum ControlNetMethod { @@ -566,6 +567,13 @@ const defaultHDSettings: ModelsHDSettings = { hdStrategyCropMargin: 128, enabled: true, }, + [AIModel.KANDINSKY21]: { + hdStrategy: HDStrategy.ORIGINAL, + hdStrategyResizeLimit: 768, + hdStrategyCropTrigerSize: 512, + hdStrategyCropMargin: 128, + enabled: false, + }, } export enum SDSampler { @@ -719,7 +727,8 @@ export const isSDState = selector({ settings.model === AIModel.SD15 || settings.model === AIModel.SD2 || settings.model === AIModel.ANYTHING4 || - settings.model === AIModel.REALISTIC_VISION_1_4 + settings.model === AIModel.REALISTIC_VISION_1_4 || + settings.model === AIModel.KANDINSKY21 ) }, }) diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py index a54c847..a576b5d 100644 --- a/lama_cleaner/const.py +++ b/lama_cleaner/const.py @@ -29,6 +29,7 @@ AVAILABLE_MODELS = [ "sd2", "paint_by_example", "instruct_pix2pix", + "kandinsky2.1" ] SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"] diff --git a/lama_cleaner/model/controlnet.py b/lama_cleaner/model/controlnet.py index 6d6347e..9cc3380 100644 --- a/lama_cleaner/model/controlnet.py +++ b/lama_cleaner/model/controlnet.py @@ -218,7 +218,7 @@ class ControlNet(DiffusionInpaintModel): controlnet_conditioning_scale=config.controlnet_conditioning_scale, negative_prompt=config.negative_prompt, generator=torch.manual_seed(config.sd_seed), - output_type="np.array", + output_type="np", callback=self.callback, ).images[0] else: @@ -262,7 +262,7 @@ class ControlNet(DiffusionInpaintModel): mask_image=mask_image, num_inference_steps=config.sd_steps, guidance_scale=config.sd_guidance_scale, - output_type="np.array", + output_type="np", callback=self.callback, height=img_h, width=img_w, diff --git a/lama_cleaner/model/instruct_pix2pix.py b/lama_cleaner/model/instruct_pix2pix.py index 20e24f3..4d176da 100644 --- a/lama_cleaner/model/instruct_pix2pix.py +++ b/lama_cleaner/model/instruct_pix2pix.py @@ -59,7 +59,7 @@ class InstructPix2Pix(DiffusionInpaintModel): num_inference_steps=config.p2p_steps, image_guidance_scale=config.p2p_image_guidance_scale, guidance_scale=config.p2p_guidance_scale, - output_type="np.array", + output_type="np", generator=torch.manual_seed(config.sd_seed) ).images[0] diff --git a/lama_cleaner/model/kandinsky.py b/lama_cleaner/model/kandinsky.py new file mode 100644 index 0000000..cf583bd --- /dev/null +++ b/lama_cleaner/model/kandinsky.py @@ -0,0 +1,91 @@ +import PIL.Image +import cv2 +import numpy as np +import torch + +from lama_cleaner.model.base import DiffusionInpaintModel +from lama_cleaner.model.utils import get_scheduler +from lama_cleaner.schema import Config + + +class Kandinsky(DiffusionInpaintModel): + pad_mod = 64 + min_size = 512 + + def init_model(self, device: torch.device, **kwargs): + from diffusers import AutoPipelineForInpainting + + fp16 = not kwargs.get("no_half", False) + use_gpu = device == torch.device("cuda") and torch.cuda.is_available() + torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 + + model_kwargs = { + "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"]), + "torch_dtype": torch_dtype, + } + + # self.pipe_prior = KandinskyPriorPipeline.from_pretrained( + # self.prior_name, **model_kwargs + # ).to("cpu") + # + # self.model = KandinskyInpaintPipeline.from_pretrained( + # self.model_name, **model_kwargs + # ).to(device) + self.model = AutoPipelineForInpainting.from_pretrained( + self.model_name, **model_kwargs + ).to(device) + + self.callback = kwargs.pop("callback", None) + + def forward(self, image, mask, config: Config): + """Input image and output image have same size + image: [H, W, C] RGB + mask: [H, W, 1] 255 means area to repaint + return: BGR IMAGE + """ + scheduler_config = self.model.scheduler.config + scheduler = get_scheduler(config.sd_sampler, scheduler_config) + self.model.scheduler = scheduler + + generator = torch.manual_seed(config.sd_seed) + if config.sd_mask_blur != 0: + k = 2 * config.sd_mask_blur + 1 + mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis] + mask = mask.astype(np.float32) / 255 + img_h, img_w = image.shape[:2] + + output = self.model( + prompt=config.prompt, + negative_prompt=config.negative_prompt, + image=PIL.Image.fromarray(image), + mask_image=mask[:, :, 0], + height=img_h, + width=img_w, + num_inference_steps=config.sd_steps, + guidance_scale=config.sd_guidance_scale, + output_type="np", + callback=self.callback, + ).images[0] + + output = (output * 255).round().astype("uint8") + output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) + return output + + def forward_post_process(self, result, image, mask, config): + if config.sd_match_histograms: + result = self._match_histograms(result, image[:, :, ::-1], mask) + + if config.sd_mask_blur != 0: + k = 2 * config.sd_mask_blur + 1 + mask = cv2.GaussianBlur(mask, (k, k), 0) + return result, image, mask + + @staticmethod + def is_downloaded() -> bool: + # model will be downloaded when app start, and can't switch in frontend settings + return True + + +class Kandinsky22(Kandinsky): + name = "kandinsky2.2" + model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint" diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index 945c506..0fb565d 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -147,7 +147,7 @@ class SD(DiffusionInpaintModel): mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), num_inference_steps=config.sd_steps, guidance_scale=config.sd_guidance_scale, - output_type="np.array", + output_type="np", callback=self.callback, height=img_h, width=img_w, diff --git a/lama_cleaner/model_manager.py b/lama_cleaner/model_manager.py index 3f29dd3..4dca9e8 100644 --- a/lama_cleaner/model_manager.py +++ b/lama_cleaner/model_manager.py @@ -7,6 +7,7 @@ from lama_cleaner.const import SD15_MODELS from lama_cleaner.helper import switch_mps_device from lama_cleaner.model.controlnet import ControlNet from lama_cleaner.model.fcf import FcF +from lama_cleaner.model.kandinsky import Kandinsky22 from lama_cleaner.model.lama import LaMa from lama_cleaner.model.ldm import LDM from lama_cleaner.model.manga import Manga @@ -33,6 +34,7 @@ models = { "sd2": SD2, "paint_by_example": PaintByExample, "instruct_pix2pix": InstructPix2Pix, + Kandinsky22.name: Kandinsky22, } diff --git a/lama_cleaner/tests/.gitignore b/lama_cleaner/tests/.gitignore index 82fd705..89b7717 100644 --- a/lama_cleaner/tests/.gitignore +++ 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INTERACTIVE_SEG_MODEL_HELP = "Model size: vit_b < vit_l < vit_h. Bigger model size means better segmentation but slower speed." -AVAILABLE_INTERACTIVE_SEG_MODELS = ["vit_b", "vit_l", "vit_h"] +AVAILABLE_INTERACTIVE_SEG_MODELS = ["vit_b", "vit_l", "vit_h", "vit_t"] AVAILABLE_INTERACTIVE_SEG_DEVICES = ["cuda", "cpu", "mps"] REMOVE_BG_HELP = "Enable remove background. Always run on CPU" ANIMESEG_HELP = "Enable anime segmentation. Always run on CPU" diff --git a/lama_cleaner/plugins/interactive_seg.py b/lama_cleaner/plugins/interactive_seg.py index 9cb5371..36cd8d2 100644 --- a/lama_cleaner/plugins/interactive_seg.py +++ b/lama_cleaner/plugins/interactive_seg.py @@ -22,6 +22,10 @@ SEGMENT_ANYTHING_MODELS = { "url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", "md5": "4b8939a88964f0f4ff5f5b2642c598a6", }, + "vit_t": { + "url": "https://github.com/Sanster/models/releases/download/MobileSAM/mobile_sam.pt", + "md5": "f3c0d8cda613564d499310dab6c812cd", + }, } diff --git a/lama_cleaner/plugins/segment_anything/build_sam.py b/lama_cleaner/plugins/segment_anything/build_sam.py index 07abfca..396daf3 100644 --- a/lama_cleaner/plugins/segment_anything/build_sam.py +++ b/lama_cleaner/plugins/segment_anything/build_sam.py @@ -8,7 +8,15 @@ import torch from functools import partial -from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer +from lama_cleaner.plugins.segment_anything.modeling.tiny_vit_sam import TinyViT + +from .modeling import ( + ImageEncoderViT, + MaskDecoder, + PromptEncoder, + Sam, + TwoWayTransformer, +) def build_sam_vit_h(checkpoint=None): @@ -44,11 +52,64 @@ def build_sam_vit_b(checkpoint=None): ) +def build_sam_vit_t(checkpoint=None): + prompt_embed_dim = 256 + image_size = 1024 + vit_patch_size = 16 + image_embedding_size = image_size // vit_patch_size + mobile_sam = Sam( + image_encoder=TinyViT( + img_size=1024, + in_chans=3, + num_classes=1000, + embed_dims=[64, 128, 160, 320], + depths=[2, 2, 6, 2], + num_heads=[2, 4, 5, 10], + window_sizes=[7, 7, 14, 7], + mlp_ratio=4.0, + drop_rate=0.0, + drop_path_rate=0.0, + use_checkpoint=False, + mbconv_expand_ratio=4.0, + local_conv_size=3, + layer_lr_decay=0.8, + ), + prompt_encoder=PromptEncoder( + embed_dim=prompt_embed_dim, + image_embedding_size=(image_embedding_size, image_embedding_size), + input_image_size=(image_size, image_size), + mask_in_chans=16, + ), + mask_decoder=MaskDecoder( + num_multimask_outputs=3, + transformer=TwoWayTransformer( + depth=2, + embedding_dim=prompt_embed_dim, + mlp_dim=2048, + num_heads=8, + ), + transformer_dim=prompt_embed_dim, + iou_head_depth=3, + iou_head_hidden_dim=256, + ), + pixel_mean=[123.675, 116.28, 103.53], + pixel_std=[58.395, 57.12, 57.375], + ) + + mobile_sam.eval() + if checkpoint is not None: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f) + mobile_sam.load_state_dict(state_dict) + return mobile_sam + + sam_model_registry = { "default": build_sam, "vit_h": build_sam, "vit_l": build_sam_vit_l, "vit_b": build_sam_vit_b, + "vit_t": build_sam_vit_t, } diff --git a/lama_cleaner/plugins/segment_anything/modeling/tiny_vit_sam.py b/lama_cleaner/plugins/segment_anything/modeling/tiny_vit_sam.py new file mode 100644 index 0000000..a5127c7 --- /dev/null +++ b/lama_cleaner/plugins/segment_anything/modeling/tiny_vit_sam.py @@ -0,0 +1,822 @@ +# -------------------------------------------------------- +# TinyViT Model Architecture +# Copyright (c) 2022 Microsoft +# Adapted from LeViT and Swin Transformer +# LeViT: (https://github.com/facebookresearch/levit) +# Swin: (https://github.com/microsoft/swin-transformer) +# Build the TinyViT Model +# -------------------------------------------------------- + +import collections +import itertools +import math +import warnings +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from typing import Tuple + + +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return x + return tuple(itertools.repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) + + +def _trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): + # type: (Tensor, float, float, float, float) -> Tensor + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are + applied while sampling the normal with mean/std applied, therefore a, b args + should be adjusted to match the range of mean, std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + with torch.no_grad(): + return _trunc_normal_(tensor, mean, std, a, b) + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class TimmDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(TimmDropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" + + +class Conv2d_BN(torch.nn.Sequential): + def __init__( + self, a, b, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1 + ): + super().__init__() + self.add_module( + "c", torch.nn.Conv2d(a, b, ks, stride, pad, dilation, groups, bias=False) + ) + bn = torch.nn.BatchNorm2d(b) + torch.nn.init.constant_(bn.weight, bn_weight_init) + torch.nn.init.constant_(bn.bias, 0) + self.add_module("bn", bn) + + @torch.no_grad() + def fuse(self): + c, bn = self._modules.values() + w = bn.weight / (bn.running_var + bn.eps) ** 0.5 + w = c.weight * w[:, None, None, None] + b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5 + m = torch.nn.Conv2d( + w.size(1) * self.c.groups, + w.size(0), + w.shape[2:], + stride=self.c.stride, + padding=self.c.padding, + dilation=self.c.dilation, + groups=self.c.groups, + ) + m.weight.data.copy_(w) + m.bias.data.copy_(b) + return m + + +class DropPath(TimmDropPath): + def __init__(self, drop_prob=None): + super().__init__(drop_prob=drop_prob) + self.drop_prob = drop_prob + + def __repr__(self): + msg = super().__repr__() + msg += f"(drop_prob={self.drop_prob})" + return msg + + +class PatchEmbed(nn.Module): + def __init__(self, in_chans, embed_dim, resolution, activation): + super().__init__() + img_size: Tuple[int, int] = to_2tuple(resolution) + self.patches_resolution = (img_size[0] // 4, img_size[1] // 4) + self.num_patches = self.patches_resolution[0] * self.patches_resolution[1] + self.in_chans = in_chans + self.embed_dim = embed_dim + n = embed_dim + self.seq = nn.Sequential( + Conv2d_BN(in_chans, n // 2, 3, 2, 1), + activation(), + Conv2d_BN(n // 2, n, 3, 2, 1), + ) + + def forward(self, x): + return self.seq(x) + + +class MBConv(nn.Module): + def __init__(self, in_chans, out_chans, expand_ratio, activation, drop_path): + super().__init__() + self.in_chans = in_chans + self.hidden_chans = int(in_chans * expand_ratio) + self.out_chans = out_chans + + self.conv1 = Conv2d_BN(in_chans, self.hidden_chans, ks=1) + self.act1 = activation() + + self.conv2 = Conv2d_BN( + self.hidden_chans, + self.hidden_chans, + ks=3, + stride=1, + pad=1, + groups=self.hidden_chans, + ) + self.act2 = activation() + + self.conv3 = Conv2d_BN(self.hidden_chans, out_chans, ks=1, bn_weight_init=0.0) + self.act3 = activation() + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, x): + shortcut = x + + x = self.conv1(x) + x = self.act1(x) + + x = self.conv2(x) + x = self.act2(x) + + x = self.conv3(x) + + x = self.drop_path(x) + + x += shortcut + x = self.act3(x) + + return x + + +class PatchMerging(nn.Module): + def __init__(self, input_resolution, dim, out_dim, activation): + super().__init__() + + self.input_resolution = input_resolution + self.dim = dim + self.out_dim = out_dim + self.act = activation() + self.conv1 = Conv2d_BN(dim, out_dim, 1, 1, 0) + stride_c = 2 + if out_dim == 320 or out_dim == 448 or out_dim == 576: + stride_c = 1 + self.conv2 = Conv2d_BN(out_dim, out_dim, 3, stride_c, 1, groups=out_dim) + self.conv3 = Conv2d_BN(out_dim, out_dim, 1, 1, 0) + + def forward(self, x): + if x.ndim == 3: + H, W = self.input_resolution + B = len(x) + # (B, C, H, W) + x = x.view(B, H, W, -1).permute(0, 3, 1, 2) + + x = self.conv1(x) + x = self.act(x) + + x = self.conv2(x) + x = self.act(x) + x = self.conv3(x) + x = x.flatten(2).transpose(1, 2) + return x + + +class ConvLayer(nn.Module): + def __init__( + self, + dim, + input_resolution, + depth, + activation, + drop_path=0.0, + downsample=None, + use_checkpoint=False, + out_dim=None, + conv_expand_ratio=4.0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + MBConv( + dim, + dim, + conv_expand_ratio, + activation, + drop_path[i] if isinstance(drop_path, list) else drop_path, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, out_dim=out_dim, activation=activation + ) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + if self.downsample is not None: + x = self.downsample(x) + return x + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.norm = nn.LayerNorm(in_features) + self.fc1 = nn.Linear(in_features, hidden_features) + self.fc2 = nn.Linear(hidden_features, out_features) + self.act = act_layer() + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.norm(x) + + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(torch.nn.Module): + def __init__( + self, + dim, + key_dim, + num_heads=8, + attn_ratio=4, + resolution=(14, 14), + ): + super().__init__() + # (h, w) + assert isinstance(resolution, tuple) and len(resolution) == 2 + self.num_heads = num_heads + self.scale = key_dim**-0.5 + self.key_dim = key_dim + self.nh_kd = nh_kd = key_dim * num_heads + self.d = int(attn_ratio * key_dim) + self.dh = int(attn_ratio * key_dim) * num_heads + self.attn_ratio = attn_ratio + h = self.dh + nh_kd * 2 + + self.norm = nn.LayerNorm(dim) + self.qkv = nn.Linear(dim, h) + self.proj = nn.Linear(self.dh, dim) + + points = list(itertools.product(range(resolution[0]), range(resolution[1]))) + N = len(points) + attention_offsets = {} + idxs = [] + for p1 in points: + for p2 in points: + offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1])) + if offset not in attention_offsets: + attention_offsets[offset] = len(attention_offsets) + idxs.append(attention_offsets[offset]) + self.attention_biases = torch.nn.Parameter( + torch.zeros(num_heads, len(attention_offsets)) + ) + self.register_buffer( + "attention_bias_idxs", torch.LongTensor(idxs).view(N, N), persistent=False + ) + + @torch.no_grad() + def train(self, mode=True): + super().train(mode) + if mode and hasattr(self, "ab"): + del self.ab + else: + self.register_buffer( + "ab", + self.attention_biases[:, self.attention_bias_idxs], + persistent=False, + ) + + def forward(self, x): # x (B,N,C) + B, N, _ = x.shape + + # Normalization + x = self.norm(x) + + qkv = self.qkv(x) + # (B, N, num_heads, d) + q, k, v = qkv.view(B, N, self.num_heads, -1).split( + [self.key_dim, self.key_dim, self.d], dim=3 + ) + # (B, num_heads, N, d) + q = q.permute(0, 2, 1, 3) + k = k.permute(0, 2, 1, 3) + v = v.permute(0, 2, 1, 3) + + attn = (q @ k.transpose(-2, -1)) * self.scale + ( + self.attention_biases[:, self.attention_bias_idxs] + if self.training + else self.ab + ) + attn = attn.softmax(dim=-1) + x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh) + x = self.proj(x) + return x + + +class TinyViTBlock(nn.Module): + r"""TinyViT Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int, int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + drop (float, optional): Dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + local_conv_size (int): the kernel size of the convolution between + Attention and MLP. Default: 3 + activation: the activation function. Default: nn.GELU + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + mlp_ratio=4.0, + drop=0.0, + drop_path=0.0, + local_conv_size=3, + activation=nn.GELU, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + assert window_size > 0, "window_size must be greater than 0" + self.window_size = window_size + self.mlp_ratio = mlp_ratio + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + assert dim % num_heads == 0, "dim must be divisible by num_heads" + head_dim = dim // num_heads + + window_resolution = (window_size, window_size) + self.attn = Attention( + dim, head_dim, num_heads, attn_ratio=1, resolution=window_resolution + ) + + mlp_hidden_dim = int(dim * mlp_ratio) + mlp_activation = activation + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=mlp_activation, + drop=drop, + ) + + pad = local_conv_size // 2 + self.local_conv = Conv2d_BN( + dim, dim, ks=local_conv_size, stride=1, pad=pad, groups=dim + ) + + def forward(self, x): + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + res_x = x + if H == self.window_size and W == self.window_size: + x = self.attn(x) + else: + x = x.view(B, H, W, C) + pad_b = (self.window_size - H % self.window_size) % self.window_size + pad_r = (self.window_size - W % self.window_size) % self.window_size + padding = pad_b > 0 or pad_r > 0 + + if padding: + x = F.pad(x, (0, 0, 0, pad_r, 0, pad_b)) + + pH, pW = H + pad_b, W + pad_r + nH = pH // self.window_size + nW = pW // self.window_size + # window partition + x = ( + x.view(B, nH, self.window_size, nW, self.window_size, C) + .transpose(2, 3) + .reshape(B * nH * nW, self.window_size * self.window_size, C) + ) + x = self.attn(x) + # window reverse + x = ( + x.view(B, nH, nW, self.window_size, self.window_size, C) + .transpose(2, 3) + .reshape(B, pH, pW, C) + ) + + if padding: + x = x[:, :H, :W].contiguous() + + x = x.view(B, L, C) + + x = res_x + self.drop_path(x) + + x = x.transpose(1, 2).reshape(B, C, H, W) + x = self.local_conv(x) + x = x.view(B, C, L).transpose(1, 2) + + x = x + self.drop_path(self.mlp(x)) + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, mlp_ratio={self.mlp_ratio}" + ) + + +class BasicLayer(nn.Module): + """A basic TinyViT layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + drop (float, optional): Dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + local_conv_size: the kernel size of the depthwise convolution between attention and MLP. Default: 3 + activation: the activation function. Default: nn.GELU + out_dim: the output dimension of the layer. Default: dim + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + drop=0.0, + drop_path=0.0, + downsample=None, + use_checkpoint=False, + local_conv_size=3, + activation=nn.GELU, + out_dim=None, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + TinyViTBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + drop=drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + local_conv_size=local_conv_size, + activation=activation, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, out_dim=out_dim, activation=activation + ) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +class TinyViT(nn.Module): + def __init__( + self, + img_size=224, + in_chans=3, + num_classes=1000, + embed_dims=[96, 192, 384, 768], + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_sizes=[7, 7, 14, 7], + mlp_ratio=4.0, + drop_rate=0.0, + drop_path_rate=0.1, + use_checkpoint=False, + mbconv_expand_ratio=4.0, + local_conv_size=3, + layer_lr_decay=1.0, + ): + super().__init__() + self.img_size = img_size + self.num_classes = num_classes + self.depths = depths + self.num_layers = len(depths) + self.mlp_ratio = mlp_ratio + + activation = nn.GELU + + self.patch_embed = PatchEmbed( + in_chans=in_chans, + embed_dim=embed_dims[0], + resolution=img_size, + activation=activation, + ) + + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + kwargs = dict( + dim=embed_dims[i_layer], + input_resolution=( + patches_resolution[0] + // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)), + patches_resolution[1] + // (2 ** (i_layer - 1 if i_layer == 3 else i_layer)), + ), + # input_resolution=(patches_resolution[0] // (2 ** i_layer), + # patches_resolution[1] // (2 ** i_layer)), + depth=depths[i_layer], + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint, + out_dim=embed_dims[min(i_layer + 1, len(embed_dims) - 1)], + activation=activation, + ) + if i_layer == 0: + layer = ConvLayer( + conv_expand_ratio=mbconv_expand_ratio, + **kwargs, + ) + else: + layer = BasicLayer( + num_heads=num_heads[i_layer], + window_size=window_sizes[i_layer], + mlp_ratio=self.mlp_ratio, + drop=drop_rate, + local_conv_size=local_conv_size, + **kwargs, + ) + self.layers.append(layer) + + # Classifier head + self.norm_head = nn.LayerNorm(embed_dims[-1]) + self.head = ( + nn.Linear(embed_dims[-1], num_classes) + if num_classes > 0 + else torch.nn.Identity() + ) + + # init weights + self.apply(self._init_weights) + self.set_layer_lr_decay(layer_lr_decay) + self.neck = nn.Sequential( + nn.Conv2d( + embed_dims[-1], + 256, + kernel_size=1, + bias=False, + ), + LayerNorm2d(256), + nn.Conv2d( + 256, + 256, + kernel_size=3, + padding=1, + bias=False, + ), + LayerNorm2d(256), + ) + + def set_layer_lr_decay(self, layer_lr_decay): + decay_rate = layer_lr_decay + + # layers -> blocks (depth) + depth = sum(self.depths) + lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)] + # print("LR SCALES:", lr_scales) + + def _set_lr_scale(m, scale): + for p in m.parameters(): + p.lr_scale = scale + + self.patch_embed.apply(lambda x: _set_lr_scale(x, lr_scales[0])) + i = 0 + for layer in self.layers: + for block in layer.blocks: + block.apply(lambda x: _set_lr_scale(x, lr_scales[i])) + i += 1 + if layer.downsample is not None: + layer.downsample.apply(lambda x: _set_lr_scale(x, lr_scales[i - 1])) + assert i == depth + for m in [self.norm_head, self.head]: + m.apply(lambda x: _set_lr_scale(x, lr_scales[-1])) + + for k, p in self.named_parameters(): + p.param_name = k + + def _check_lr_scale(m): + for p in m.parameters(): + assert hasattr(p, "lr_scale"), p.param_name + + self.apply(_check_lr_scale) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {"attention_biases"} + + def forward_features(self, x): + # x: (N, C, H, W) + x = self.patch_embed(x) + + x = self.layers[0](x) + start_i = 1 + + for i in range(start_i, len(self.layers)): + layer = self.layers[i] + x = layer(x) + B, _, C = x.size() + x = x.view(B, 64, 64, C) + x = x.permute(0, 3, 1, 2) + x = self.neck(x) + return x + + def forward(self, x): + x = self.forward_features(x) + # x = self.norm_head(x) + # x = self.head(x) + return x From d061b07029ebd7778307a3d59e93f53b893f15c2 Mon Sep 17 00:00:00 2001 From: Qing Date: Tue, 14 Nov 2023 14:02:10 +0800 Subject: [PATCH 005/134] update --- lama_cleaner/model/kandinsky.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/lama_cleaner/model/kandinsky.py b/lama_cleaner/model/kandinsky.py index cf583bd..ea9d9c9 100644 --- a/lama_cleaner/model/kandinsky.py +++ b/lama_cleaner/model/kandinsky.py @@ -24,13 +24,6 @@ class Kandinsky(DiffusionInpaintModel): "torch_dtype": torch_dtype, } - # self.pipe_prior = KandinskyPriorPipeline.from_pretrained( - # self.prior_name, **model_kwargs - # ).to("cpu") - # - # self.model = KandinskyInpaintPipeline.from_pretrained( - # self.model_name, **model_kwargs - # ).to(device) self.model = AutoPipelineForInpainting.from_pretrained( self.model_name, **model_kwargs ).to(device) @@ -54,6 +47,7 @@ class Kandinsky(DiffusionInpaintModel): mask = mask.astype(np.float32) / 255 img_h, img_w = image.shape[:2] + # kandinsky 没有 strength output = self.model( prompt=config.prompt, negative_prompt=config.negative_prompt, @@ -65,6 +59,7 @@ class Kandinsky(DiffusionInpaintModel): guidance_scale=config.sd_guidance_scale, output_type="np", callback=self.callback, + generator=generator, ).images[0] output = (output * 255).round().astype("uint8") From 53b9f3e92fd7444c1425e80ccd78273f4e3c4dec Mon Sep 17 00:00:00 2001 From: Qing Date: Tue, 14 Nov 2023 14:02:15 +0800 Subject: [PATCH 006/134] sd add strength --- lama_cleaner/model/sd.py | 1 + 1 file changed, 1 insertion(+) diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index 0fb565d..b29626c 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -146,6 +146,7 @@ class SD(DiffusionInpaintModel): negative_prompt=config.negative_prompt, mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), num_inference_steps=config.sd_steps, + strength=config.sd_strength, guidance_scale=config.sd_guidance_scale, output_type="np", callback=self.callback, From 78c8d8dbdbf06d0b109f9db9a4f7a9739fab9130 Mon Sep 17 00:00:00 2001 From: Qing Date: Tue, 14 Nov 2023 14:04:06 +0800 Subject: [PATCH 007/134] update --- .../pipeline/pipeline_stable_diffusion_controlnet_inpaint.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py index f5883b8..b1d3b87 100644 --- a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py +++ b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py @@ -11,14 +11,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import Union, List, Optional, Callable, Dict, Any # Copy from https://github.com/mikonvergence/ControlNetInpaint/blob/main/src/pipeline_stable_diffusion_controlnet_inpaint.py import torch import PIL.Image import numpy as np +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import * +from diffusers.utils import replace_example_docstring EXAMPLE_DOC_STRING = """ Examples: From 8fbc8059e1d4a1d25db59d3139719ee850be966d Mon Sep 17 00:00:00 2001 From: Qing Date: Tue, 14 Nov 2023 14:19:56 +0800 Subject: [PATCH 008/134] add sdxl --- lama_cleaner/const.py | 2 + lama_cleaner/model/g_diffuser_bot.py | 13 +-- lama_cleaner/model/sdxl.py | 111 +++++++++++++++++++++++++ lama_cleaner/model_manager.py | 2 + lama_cleaner/schema.py | 12 ++- lama_cleaner/tests/test_outpainting.py | 10 ++- lama_cleaner/tests/test_sdxl.py | 110 ++++++++++++++++++++++++ 7 files changed, 246 insertions(+), 14 deletions(-) create mode 100644 lama_cleaner/model/sdxl.py create mode 100644 lama_cleaner/tests/test_sdxl.py diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py index 738f283..809dc99 100644 --- a/lama_cleaner/const.py +++ b/lama_cleaner/const.py @@ -13,6 +13,7 @@ MPS_SUPPORT_MODELS = [ "paint_by_example", "controlnet", "kandinsky2.2", + "sdxl", ] DEFAULT_MODEL = "lama" @@ -23,6 +24,7 @@ AVAILABLE_MODELS = [ "mat", "fcf", "sd1.5", + "sdxl", "anything4", "realisticVision1.4", "cv2", diff --git a/lama_cleaner/model/g_diffuser_bot.py b/lama_cleaner/model/g_diffuser_bot.py index 450d618..bf7ce2a 100644 --- a/lama_cleaner/model/g_diffuser_bot.py +++ b/lama_cleaner/model/g_diffuser_bot.py @@ -151,6 +151,7 @@ def expand_image( hard_mask[:, 0 : origin_w // 2] = 255 if right != 0: hard_mask[:, origin_w // 2 :] = 255 + hard_mask = cv2.copyMakeBorder( hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255 ) @@ -166,12 +167,12 @@ if __name__ == "__main__": init_image = cv2.imread(str(image_path)) init_image, mask_image = expand_image( init_image, - 200, - 200, - 0, - 0, - 60, - 50, + top=100, + right=100, + bottom=100, + left=100, + softness=20, + space=20, ) print(mask_image.dtype, mask_image.min(), mask_image.max()) print(init_image.dtype, init_image.min(), init_image.max()) diff --git a/lama_cleaner/model/sdxl.py b/lama_cleaner/model/sdxl.py new file mode 100644 index 0000000..d55c591 --- /dev/null +++ b/lama_cleaner/model/sdxl.py @@ -0,0 +1,111 @@ +import PIL.Image +import cv2 +import numpy as np +import torch +from loguru import logger + +from lama_cleaner.model.base import DiffusionInpaintModel +from lama_cleaner.model.utils import torch_gc, get_scheduler +from lama_cleaner.schema import Config + + +class SDXL(DiffusionInpaintModel): + name = "sdxl" + pad_mod = 8 + min_size = 512 + + def init_model(self, device: torch.device, **kwargs): + from diffusers.pipelines import AutoPipelineForInpainting + + fp16 = not kwargs.get("no_half", False) + + model_kwargs = { + "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"]) + } + if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False): + logger.info("Disable Stable Diffusion Model NSFW checker") + model_kwargs.update( + dict( + safety_checker=None, + feature_extractor=None, + requires_safety_checker=False, + ) + ) + + use_gpu = device == torch.device("cuda") and torch.cuda.is_available() + torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 + + self.model = AutoPipelineForInpainting.from_pretrained( + "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", + revision="main", + torch_dtype=torch_dtype, + use_auth_token=kwargs["hf_access_token"], + **model_kwargs, + ) + + # https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing + self.model.enable_attention_slicing() + # https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention + if kwargs.get("enable_xformers", False): + self.model.enable_xformers_memory_efficient_attention() + + if kwargs.get("cpu_offload", False) and use_gpu: + logger.info("Enable sequential cpu offload") + self.model.enable_sequential_cpu_offload(gpu_id=0) + else: + self.model = self.model.to(device) + if kwargs["sd_cpu_textencoder"]: + logger.warning("Stable Diffusion XL not support run TextEncoder on CPU") + + self.callback = kwargs.pop("callback", None) + + def forward(self, image, mask, config: Config): + """Input image and output image have same size + image: [H, W, C] RGB + mask: [H, W, 1] 255 means area to repaint + return: BGR IMAGE + """ + + scheduler_config = self.model.scheduler.config + scheduler = get_scheduler(config.sd_sampler, scheduler_config) + self.model.scheduler = scheduler + + if config.sd_mask_blur != 0: + k = 2 * config.sd_mask_blur + 1 + mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis] + + img_h, img_w = image.shape[:2] + + output = self.model( + image=PIL.Image.fromarray(image), + prompt=config.prompt, + negative_prompt=config.negative_prompt, + mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), + num_inference_steps=config.sd_steps, + strength=0.999 if config.sd_strength == 1.0 else config.sd_strength, + guidance_scale=config.sd_guidance_scale, + output_type="np", + callback=self.callback, + height=img_h, + width=img_w, + generator=torch.manual_seed(config.sd_seed), + callback_steps=1 + ).images[0] + + output = (output * 255).round().astype("uint8") + output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) + return output + + def forward_post_process(self, result, image, mask, config): + if config.sd_match_histograms: + result = self._match_histograms(result, image[:, :, ::-1], mask) + + if config.sd_mask_blur != 0: + k = 2 * config.sd_mask_blur + 1 + mask = cv2.GaussianBlur(mask, (k, k), 0) + return result, image, mask + + @staticmethod + def is_downloaded() -> bool: + # model will be downloaded when app start, and can't switch in frontend settings + return True diff --git a/lama_cleaner/model_manager.py b/lama_cleaner/model_manager.py index 4dca9e8..7d5ed46 100644 --- a/lama_cleaner/model_manager.py +++ b/lama_cleaner/model_manager.py @@ -15,6 +15,7 @@ from lama_cleaner.model.mat import MAT from lama_cleaner.model.paint_by_example import PaintByExample from lama_cleaner.model.instruct_pix2pix import InstructPix2Pix from lama_cleaner.model.sd import SD15, SD2, Anything4, RealisticVision14 +from lama_cleaner.model.sdxl import SDXL from lama_cleaner.model.utils import torch_gc from lama_cleaner.model.zits import ZITS from lama_cleaner.model.opencv2 import OpenCV2 @@ -35,6 +36,7 @@ models = { "paint_by_example": PaintByExample, "instruct_pix2pix": InstructPix2Pix, Kandinsky22.name: Kandinsky22, + SDXL.name: SDXL, } diff --git a/lama_cleaner/schema.py b/lama_cleaner/schema.py index b710cdc..1893e5a 100644 --- a/lama_cleaner/schema.py +++ b/lama_cleaner/schema.py @@ -66,8 +66,12 @@ class Config(BaseModel): sd_scale: float = 1.0 # Blur the edge of mask area. The higher the number the smoother blend with the original image sd_mask_blur: int = 0 - # Ignore this value, it's useless for inpainting - sd_strength: float = 0.75 + # Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a + # starting point and more noise is added the higher the `strength`. The number of denoising steps depends + # on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising + # process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 + # essentially ignores `image`. + sd_strength: float = 1.0 # The number of denoising steps. More denoising steps usually lead to a # higher quality image at the expense of slower inference. sd_steps: int = 50 @@ -80,8 +84,8 @@ class Config(BaseModel): sd_match_histograms: bool = False # out-painting - sd_outpainting_softness: float = 30.0 - sd_outpainting_space: float = 50.0 + sd_outpainting_softness: float = 20.0 + sd_outpainting_space: float = 20.0 # Configs for opencv inpainting # opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07 diff --git a/lama_cleaner/tests/test_outpainting.py b/lama_cleaner/tests/test_outpainting.py index e306b30..3b3dbba 100644 --- a/lama_cleaner/tests/test_outpainting.py +++ b/lama_cleaner/tests/test_outpainting.py @@ -57,7 +57,7 @@ def test_outpainting(name, sd_device, rect): croper_y=rect[1], croper_width=rect[2], croper_height=rect[3], - sd_guidance_scale=4, + sd_guidance_scale=8.0, sd_sampler=SDSampler.dpm_plus_plus, ) @@ -75,7 +75,7 @@ def test_outpainting(name, sd_device, rect): @pytest.mark.parametrize( "rect", [ - [-100, -100, 768, 768], + [-128, -128, 768, 768], ], ) def test_kandinsky_outpainting(name, sd_device, rect): @@ -86,7 +86,7 @@ def test_kandinsky_outpainting(name, sd_device, rect): return model = ModelManager( - name=name, + name="sd1.5", device=torch.device(sd_device), hf_access_token="", sd_run_local=True, @@ -105,7 +105,7 @@ def test_kandinsky_outpainting(name, sd_device, rect): croper_y=rect[1], croper_width=rect[2], croper_height=rect[3], - sd_guidance_scale=4, + sd_guidance_scale=7, sd_sampler=SDSampler.dpm_plus_plus, ) @@ -115,4 +115,6 @@ def test_kandinsky_outpainting(name, sd_device, rect): f"{name.replace('.', '_')}_outpainting_dpm++_{'_'.join(map(str, rect))}.png", img_p=current_dir / "cat.png", mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", + fx=1, + fy=1, ) diff --git a/lama_cleaner/tests/test_sdxl.py b/lama_cleaner/tests/test_sdxl.py new file mode 100644 index 0000000..68230a2 --- /dev/null +++ b/lama_cleaner/tests/test_sdxl.py @@ -0,0 +1,110 @@ +import os + +os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" +from pathlib import Path + +import pytest +import torch + +from lama_cleaner.model_manager import ModelManager +from lama_cleaner.schema import HDStrategy, SDSampler +from lama_cleaner.tests.test_model import get_config, assert_equal + +current_dir = Path(__file__).parent.absolute().resolve() +save_dir = current_dir / "result" +save_dir.mkdir(exist_ok=True, parents=True) + + +@pytest.mark.parametrize("sd_device", ["mps"]) +@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) +@pytest.mark.parametrize("sampler", [SDSampler.ddim]) +@pytest.mark.parametrize("cpu_textencoder", [False]) +@pytest.mark.parametrize("disable_nsfw", [True]) +def test_sdxl(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw): + def callback(i, t, latents): + pass + + if sd_device == "cuda" and not torch.cuda.is_available(): + return + + sd_steps = 20 + model = ModelManager( + name="sdxl", + device=torch.device(sd_device), + hf_access_token="", + sd_run_local=False, + disable_nsfw=disable_nsfw, + sd_cpu_textencoder=cpu_textencoder, + callback=callback, + ) + cfg = get_config( + strategy, + prompt="a fox sitting on a bench", + sd_steps=sd_steps, + sd_strength=0.99, + sd_guidance_scale=7.0, + ) + cfg.sd_sampler = sampler + + name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}" + + assert_equal( + model, + cfg, + f"sdxl_{name}.png", + img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", + mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", + fx=2, + fy=2, + ) + + +@pytest.mark.parametrize("sd_device", ["mps"]) +@pytest.mark.parametrize( + "rect", + [ + [-128, -128, 1024, 1024], + ], +) +def test_sdxl_outpainting(sd_device, rect): + def callback(i, t, latents): + pass + + if sd_device == "cuda" and not torch.cuda.is_available(): + return + + model = ModelManager( + name="sdxl", + device=torch.device(sd_device), + hf_access_token="", + sd_run_local=True, + disable_nsfw=True, + sd_cpu_textencoder=False, + callback=callback, + ) + + cfg = get_config( + HDStrategy.ORIGINAL, + prompt="a dog sitting on a bench in the park", + negative_prompt="lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature", + sd_steps=20, + use_croper=True, + croper_is_outpainting=True, + croper_x=rect[0], + croper_y=rect[1], + croper_width=rect[2], + croper_height=rect[3], + sd_strength=1.0, + sd_guidance_scale=8.0, + sd_sampler=SDSampler.ddim, + ) + + assert_equal( + model, + cfg, + f"sdxl_outpainting_dog_ddim_{'_'.join(map(str, rect))}.png", + img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", + mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", + fx=1.5, + fy=1.5, + ) From 2c9a53da8e18d7ec3e4b5ae87089f8c8da5a4ccf Mon Sep 17 00:00:00 2001 From: Qing Date: Tue, 14 Nov 2023 15:24:30 +0800 Subject: [PATCH 009/134] sdxl do not has nsfw --- lama_cleaner/model/sdxl.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/lama_cleaner/model/sdxl.py b/lama_cleaner/model/sdxl.py index d55c591..05fd9cf 100644 --- a/lama_cleaner/model/sdxl.py +++ b/lama_cleaner/model/sdxl.py @@ -22,15 +22,6 @@ class SDXL(DiffusionInpaintModel): model_kwargs = { "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"]) } - if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False): - logger.info("Disable Stable Diffusion Model NSFW checker") - model_kwargs.update( - dict( - safety_checker=None, - feature_extractor=None, - requires_safety_checker=False, - ) - ) use_gpu = device == torch.device("cuda") and torch.cuda.is_available() torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 From bb98c91c8cd940a8fae6e357f9e736deb73275d6 Mon Sep 17 00:00:00 2001 From: Qing Date: Tue, 14 Nov 2023 22:04:16 +0800 Subject: [PATCH 010/134] backend add freeu --- lama_cleaner/const.py | 2 ++ lama_cleaner/model_manager.py | 19 ++++++++++++++++++- lama_cleaner/schema.py | 11 +++++++++++ requirements.txt | 4 ++-- 4 files changed, 33 insertions(+), 3 deletions(-) diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py index 809dc99..233f291 100644 --- a/lama_cleaner/const.py +++ b/lama_cleaner/const.py @@ -33,8 +33,10 @@ AVAILABLE_MODELS = [ "paint_by_example", "instruct_pix2pix", "kandinsky2.2", + "sdxl" ] SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"] +MODELS_SUPPORT_FREEU = SD15_MODELS + ['sd2', "sdxl"] AVAILABLE_DEVICES = ["cuda", "cpu", "mps"] DEFAULT_DEVICE = "cuda" diff --git a/lama_cleaner/model_manager.py b/lama_cleaner/model_manager.py index 7d5ed46..7b5278d 100644 --- a/lama_cleaner/model_manager.py +++ b/lama_cleaner/model_manager.py @@ -3,7 +3,7 @@ import gc from loguru import logger -from lama_cleaner.const import SD15_MODELS +from lama_cleaner.const import SD15_MODELS, MODELS_SUPPORT_FREEU from lama_cleaner.helper import switch_mps_device from lama_cleaner.model.controlnet import ControlNet from lama_cleaner.model.fcf import FcF @@ -65,6 +65,7 @@ class ModelManager: def __call__(self, image, mask, config: Config): self.switch_controlnet_method(control_method=config.controlnet_method) + self.enable_disable_freeu(config) return self.model(image, mask, config) def switch(self, new_name: str, **kwargs): @@ -120,3 +121,19 @@ class ModelManager: self.name, switch_mps_device(self.name, self.device), **self.kwargs ) logger.info(f"Switch ControlNet method from {old_method} to {control_method}") + + def enable_disable_freeu(self, config: Config): + if str(self.model.device) == "mps": + return + + if self.name in MODELS_SUPPORT_FREEU: + if config.sd_freeu: + freeu_config = config.sd_freeu_config + self.model.model.enable_freeu( + s1=freeu_config.s1, + s2=freeu_config.s2, + b1=freeu_config.b1, + b2=freeu_config.b2, + ) + else: + self.model.model.disable_freeu() diff --git a/lama_cleaner/schema.py b/lama_cleaner/schema.py index 1893e5a..5c8651a 100644 --- a/lama_cleaner/schema.py +++ b/lama_cleaner/schema.py @@ -31,6 +31,13 @@ class SDSampler(str, Enum): uni_pc = "uni_pc" +class FREEUConfig(BaseModel): + s1: float = 1.0 + s2: float = 1.0 + b1: float = 1.0 + b2: float = 1.0 + + class Config(BaseModel): class Config: arbitrary_types_allowed = True @@ -87,6 +94,10 @@ class Config(BaseModel): sd_outpainting_softness: float = 20.0 sd_outpainting_space: float = 20.0 + # freeu + sd_freeu: bool = False + sd_freeu_config: FREEUConfig = FREEUConfig() + # Configs for opencv inpainting # opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07 cv2_flag: str = "INPAINT_NS" diff --git a/requirements.txt b/requirements.txt index 8d35bf3..016a179 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,8 +9,8 @@ pydantic rich loguru yacs -diffusers==0.20.1 -transformers==4.27.4 +diffusers==0.23.0 +transformers==4.34.1 gradio piexif==1.1.3 safetensors From 58b931fdb2bbe34b0b51596b33f4a1eefa7e3011 Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 15 Nov 2023 08:50:35 +0800 Subject: [PATCH 011/134] add lcm lora --- lama_cleaner/const.py | 1 + lama_cleaner/model/base.py | 11 ++++++++++- lama_cleaner/model/sd.py | 8 +++----- lama_cleaner/model/sdxl.py | 8 +++----- lama_cleaner/model_manager.py | 12 +++++++++++- lama_cleaner/schema.py | 5 +++++ 6 files changed, 33 insertions(+), 12 deletions(-) diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py index 233f291..30937e6 100644 --- a/lama_cleaner/const.py +++ b/lama_cleaner/const.py @@ -37,6 +37,7 @@ AVAILABLE_MODELS = [ ] SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"] MODELS_SUPPORT_FREEU = SD15_MODELS + ['sd2', "sdxl"] +MODELS_SUPPORT_LCM_LORA = SD15_MODELS + ["sdxl"] AVAILABLE_DEVICES = ["cuda", "cpu", "mps"] DEFAULT_DEVICE = "cuda" diff --git a/lama_cleaner/model/base.py b/lama_cleaner/model/base.py index c452690..d74f4e9 100644 --- a/lama_cleaner/model/base.py +++ b/lama_cleaner/model/base.py @@ -13,7 +13,8 @@ from lama_cleaner.helper import ( switch_mps_device, ) from lama_cleaner.model.g_diffuser_bot import expand_image, np_img_grey_to_rgb -from lama_cleaner.schema import Config, HDStrategy +from lama_cleaner.model.utils import get_scheduler +from lama_cleaner.schema import Config, HDStrategy, SDSampler class InpaintModel: @@ -381,3 +382,11 @@ class DiffusionInpaintModel(InpaintModel): # original_pixel_indices # ] return inpaint_result + + def set_scheduler(self, config: Config): + scheduler_config = self.model.scheduler.config + sd_sampler = config.sd_sampler + if config.sd_lcm_lora: + sd_sampler = SDSampler.lcm + scheduler = get_scheduler(sd_sampler, scheduler_config) + self.model.scheduler = scheduler diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index b29626c..9aa1b54 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -8,7 +8,7 @@ from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel from lama_cleaner.model.utils import torch_gc, get_scheduler -from lama_cleaner.schema import Config +from lama_cleaner.schema import Config, SDSampler class CPUTextEncoderWrapper: @@ -67,6 +67,7 @@ def load_from_local_model(local_model_path, torch_dtype, disable_nsfw=True): class SD(DiffusionInpaintModel): pad_mod = 8 min_size = 512 + lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" def init_model(self, device: torch.device, **kwargs): from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline @@ -129,10 +130,7 @@ class SD(DiffusionInpaintModel): mask: [H, W, 1] 255 means area to repaint return: BGR IMAGE """ - - scheduler_config = self.model.scheduler.config - scheduler = get_scheduler(config.sd_sampler, scheduler_config) - self.model.scheduler = scheduler + self.set_scheduler(config) if config.sd_mask_blur != 0: k = 2 * config.sd_mask_blur + 1 diff --git a/lama_cleaner/model/sdxl.py b/lama_cleaner/model/sdxl.py index 05fd9cf..197ab77 100644 --- a/lama_cleaner/model/sdxl.py +++ b/lama_cleaner/model/sdxl.py @@ -13,6 +13,7 @@ class SDXL(DiffusionInpaintModel): name = "sdxl" pad_mod = 8 min_size = 512 + lcm_lora_id = "latent-consistency/lcm-lora-sdxl" def init_model(self, device: torch.device, **kwargs): from diffusers.pipelines import AutoPipelineForInpainting @@ -56,10 +57,7 @@ class SDXL(DiffusionInpaintModel): mask: [H, W, 1] 255 means area to repaint return: BGR IMAGE """ - - scheduler_config = self.model.scheduler.config - scheduler = get_scheduler(config.sd_sampler, scheduler_config) - self.model.scheduler = scheduler + self.set_scheduler(config) if config.sd_mask_blur != 0: k = 2 * config.sd_mask_blur + 1 @@ -80,7 +78,7 @@ class SDXL(DiffusionInpaintModel): height=img_h, width=img_w, generator=torch.manual_seed(config.sd_seed), - callback_steps=1 + callback_steps=1, ).images[0] output = (output * 255).round().astype("uint8") diff --git a/lama_cleaner/model_manager.py b/lama_cleaner/model_manager.py index 7b5278d..43d9ced 100644 --- a/lama_cleaner/model_manager.py +++ b/lama_cleaner/model_manager.py @@ -3,7 +3,7 @@ import gc from loguru import logger -from lama_cleaner.const import SD15_MODELS, MODELS_SUPPORT_FREEU +from lama_cleaner.const import SD15_MODELS, MODELS_SUPPORT_FREEU, MODELS_SUPPORT_LCM_LORA from lama_cleaner.helper import switch_mps_device from lama_cleaner.model.controlnet import ControlNet from lama_cleaner.model.fcf import FcF @@ -66,6 +66,7 @@ class ModelManager: def __call__(self, image, mask, config: Config): self.switch_controlnet_method(control_method=config.controlnet_method) self.enable_disable_freeu(config) + self.enable_disable_lcm_lora(config) return self.model(image, mask, config) def switch(self, new_name: str, **kwargs): @@ -137,3 +138,12 @@ class ModelManager: ) else: self.model.model.disable_freeu() + + def enable_disable_lcm_lora(self, config: Config): + if self.name in MODELS_SUPPORT_LCM_LORA: + if config.sd_lcm_lora: + if not self.model.model.pipe.get_list_adapters(): + self.model.model.load_lora_weights(self.model.lcm_lora_id) + else: + self.model.model.disable_lora() + diff --git a/lama_cleaner/schema.py b/lama_cleaner/schema.py index 5c8651a..d384a08 100644 --- a/lama_cleaner/schema.py +++ b/lama_cleaner/schema.py @@ -30,6 +30,8 @@ class SDSampler(str, Enum): dpm_plus_plus = "dpm++" uni_pc = "uni_pc" + lcm = "lcm" + class FREEUConfig(BaseModel): s1: float = 1.0 @@ -98,6 +100,9 @@ class Config(BaseModel): sd_freeu: bool = False sd_freeu_config: FREEUConfig = FREEUConfig() + # lcm-lora + sd_lcm_lora: bool = False + # Configs for opencv inpainting # opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07 cv2_flag: str = "INPAINT_NS" From f13bfb90852bc51637d6ccfbb697ae299cd77bae Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 15 Nov 2023 08:58:52 +0800 Subject: [PATCH 012/134] clean code --- lama_cleaner/model/base.py | 9 +++++++++ lama_cleaner/model/kandinsky.py | 9 --------- lama_cleaner/model/sd.py | 9 --------- lama_cleaner/model/sdxl.py | 8 -------- 4 files changed, 9 insertions(+), 26 deletions(-) diff --git a/lama_cleaner/model/base.py b/lama_cleaner/model/base.py index d74f4e9..b61ba8f 100644 --- a/lama_cleaner/model/base.py +++ b/lama_cleaner/model/base.py @@ -390,3 +390,12 @@ class DiffusionInpaintModel(InpaintModel): sd_sampler = SDSampler.lcm scheduler = get_scheduler(sd_sampler, scheduler_config) self.model.scheduler = scheduler + + def forward_post_process(self, result, image, mask, config): + if config.sd_match_histograms: + result = self._match_histograms(result, image[:, :, ::-1], mask) + + if config.sd_mask_blur != 0: + k = 2 * config.sd_mask_blur + 1 + mask = cv2.GaussianBlur(mask, (k, k), 0) + return result, image, mask diff --git a/lama_cleaner/model/kandinsky.py b/lama_cleaner/model/kandinsky.py index ea9d9c9..769e805 100644 --- a/lama_cleaner/model/kandinsky.py +++ b/lama_cleaner/model/kandinsky.py @@ -66,15 +66,6 @@ class Kandinsky(DiffusionInpaintModel): output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output - def forward_post_process(self, result, image, mask, config): - if config.sd_match_histograms: - result = self._match_histograms(result, image[:, :, ::-1], mask) - - if config.sd_mask_blur != 0: - k = 2 * config.sd_mask_blur + 1 - mask = cv2.GaussianBlur(mask, (k, k), 0) - return result, image, mask - @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index 9aa1b54..d56139a 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -157,15 +157,6 @@ class SD(DiffusionInpaintModel): output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output - def forward_post_process(self, result, image, mask, config): - if config.sd_match_histograms: - result = self._match_histograms(result, image[:, :, ::-1], mask) - - if config.sd_mask_blur != 0: - k = 2 * config.sd_mask_blur + 1 - mask = cv2.GaussianBlur(mask, (k, k), 0) - return result, image, mask - @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings diff --git a/lama_cleaner/model/sdxl.py b/lama_cleaner/model/sdxl.py index 197ab77..7615795 100644 --- a/lama_cleaner/model/sdxl.py +++ b/lama_cleaner/model/sdxl.py @@ -85,14 +85,6 @@ class SDXL(DiffusionInpaintModel): output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output - def forward_post_process(self, result, image, mask, config): - if config.sd_match_histograms: - result = self._match_histograms(result, image[:, :, ::-1], mask) - - if config.sd_mask_blur != 0: - k = 2 * config.sd_mask_blur + 1 - mask = cv2.GaussianBlur(mask, (k, k), 0) - return result, image, mask @staticmethod def is_downloaded() -> bool: From 3e7a553273aeef27c7246126712348e283f7eafb Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 15 Nov 2023 09:10:13 +0800 Subject: [PATCH 013/134] add sd_prevent_unmasked_area --- lama_cleaner/model/base.py | 5 +++-- lama_cleaner/model/sd.py | 1 + lama_cleaner/schema.py | 3 +++ lama_cleaner/tests/test_sd_model.py | 12 ++++++++---- 4 files changed, 15 insertions(+), 6 deletions(-) diff --git a/lama_cleaner/model/base.py b/lama_cleaner/model/base.py index b61ba8f..726a2be 100644 --- a/lama_cleaner/model/base.py +++ b/lama_cleaner/model/base.py @@ -67,8 +67,9 @@ class InpaintModel: result, image, mask = self.forward_post_process(result, image, mask, config) - mask = mask[:, :, np.newaxis] - result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255)) + if config.sd_prevent_unmasked_area: + mask = mask[:, :, np.newaxis] + result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255)) return result def forward_post_process(self, result, image, mask, config): diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index d56139a..f19b96c 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -151,6 +151,7 @@ class SD(DiffusionInpaintModel): height=img_h, width=img_w, generator=torch.manual_seed(config.sd_seed), + callback_steps=1, ).images[0] output = (output * 255).round().astype("uint8") diff --git a/lama_cleaner/schema.py b/lama_cleaner/schema.py index d384a08..8b7e172 100644 --- a/lama_cleaner/schema.py +++ b/lama_cleaner/schema.py @@ -103,6 +103,9 @@ class Config(BaseModel): # lcm-lora sd_lcm_lora: bool = False + # preserving the unmasked area at the expense of some more unnatural transitions between the masked and unmasked areas. + sd_prevent_unmasked_area: bool = True + # Configs for opencv inpainting # opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07 cv2_flag: str = "INPAINT_NS" diff --git a/lama_cleaner/tests/test_sd_model.py b/lama_cleaner/tests/test_sd_model.py index 9b80886..50135a0 100644 --- a/lama_cleaner/tests/test_sd_model.py +++ b/lama_cleaner/tests/test_sd_model.py @@ -95,17 +95,20 @@ def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_ns ) -@pytest.mark.parametrize("sd_device", ["cuda"]) +@pytest.mark.parametrize("sd_device", ["mps"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize("sampler", [SDSampler.ddim]) -def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler): +@pytest.mark.parametrize("sd_prevent_unmasked_area", [False, True]) +def test_runway_sd_1_5_negative_prompt( + sd_device, strategy, sampler, sd_prevent_unmasked_area +): def callback(i, t, latents): pass if sd_device == "cuda" and not torch.cuda.is_available(): return - sd_steps = 50 if sd_device == "cuda" else 1 + sd_steps = 50 if sd_device == "cuda" else 20 model = ModelManager( name="sd1.5", device=torch.device(sd_device), @@ -122,6 +125,7 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler): negative_prompt="orange, yellow, small", sd_sampler=sampler, sd_match_histograms=True, + sd_prevent_unmasked_area=sd_prevent_unmasked_area, ) name = f"{sampler}_negative_prompt" @@ -129,7 +133,7 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler): assert_equal( model, cfg, - f"runway_sd_{strategy.capitalize()}_{name}.png", + f"runway_sd_{strategy.capitalize()}_{name}_prevent_unmasked_area_{sd_prevent_unmasked_area}.png", img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", fx=1, From 1571018cfd5ad6cd2a2d9821618660bdd6610048 Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 15 Nov 2023 16:52:44 +0800 Subject: [PATCH 014/134] update --- lama_cleaner/model/kandinsky.py | 1 + .../pipeline/pipeline_stable_diffusion_controlnet_inpaint.py | 4 +--- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/lama_cleaner/model/kandinsky.py b/lama_cleaner/model/kandinsky.py index 769e805..7a89d9a 100644 --- a/lama_cleaner/model/kandinsky.py +++ b/lama_cleaner/model/kandinsky.py @@ -60,6 +60,7 @@ class Kandinsky(DiffusionInpaintModel): output_type="np", callback=self.callback, generator=generator, + callback_steps=1, ).images[0] output = (output * 255).round().astype("uint8") diff --git a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py index b1d3b87..3dfd840 100644 --- a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py +++ b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py @@ -17,10 +17,8 @@ from typing import Union, List, Optional, Callable, Dict, Any import torch import PIL.Image -import numpy as np -from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput -from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import * +from diffusers.pipelines.controlnet.pipeline_controlnet import * from diffusers.utils import replace_example_docstring EXAMPLE_DOC_STRING = """ From b8a030f83a4b5cf1f070ddd457209b29db7fedb3 Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 15 Nov 2023 17:18:32 +0800 Subject: [PATCH 015/134] fix outpainting image padding --- lama_cleaner/model/g_diffuser_bot.py | 59 +++++++++++----------------- 1 file changed, 22 insertions(+), 37 deletions(-) diff --git a/lama_cleaner/model/g_diffuser_bot.py b/lama_cleaner/model/g_diffuser_bot.py index bf7ce2a..a4147af 100644 --- a/lama_cleaner/model/g_diffuser_bot.py +++ b/lama_cleaner/model/g_diffuser_bot.py @@ -85,59 +85,44 @@ def image_blur(data, std=3.14, mode="linear"): return np.real(convolve(data, kernel / np.sqrt(np.sum(kernel * kernel)))) -def soften_mask(np_rgba_image, softness, space): +def soften_mask(mask_img, softness, space): if softness == 0: - return np_rgba_image + return mask_img softness = min(softness, 1.0) space = np.clip(space, 0.0, 1.0) - original_max_opacity = np.max(np_rgba_image[:, :, 3]) - out_mask = np_rgba_image[:, :, 3] <= 0.0 - blurred_mask = image_blur(np_rgba_image[:, :, 3], 3.5 / softness, mode="linear") + original_max_opacity = np.max(mask_img) + out_mask = mask_img <= 0.0 + blurred_mask = image_blur(mask_img, 3.5 / softness, mode="linear") blurred_mask = np.maximum(blurred_mask - np.max(blurred_mask[out_mask]), 0.0) - np_rgba_image[ - :, :, 3 - ] *= blurred_mask # preserve partial opacity in original input mask - np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # renormalize - np_rgba_image[:, :, 3] = np.clip( - np_rgba_image[:, :, 3] - space, 0.0, 1.0 - ) # make space - np_rgba_image[:, :, 3] /= np.max(np_rgba_image[:, :, 3]) # and renormalize again - np_rgba_image[:, :, 3] *= original_max_opacity # restore original max opacity - return np_rgba_image + mask_img *= blurred_mask # preserve partial opacity in original input mask + mask_img /= np.max(mask_img) # renormalize + mask_img = np.clip(mask_img - space, 0.0, 1.0) # make space + mask_img /= np.max(mask_img) # and renormalize again + mask_img *= original_max_opacity # restore original max opacity + return mask_img def expand_image( cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float ): + assert cv2_img.shape[2] == 3 origin_h, origin_w = cv2_img.shape[:2] new_width = cv2_img.shape[1] + left + right new_height = cv2_img.shape[0] + top + bottom - new_img = np.zeros((new_height, new_width, 4), np.uint8) # expanded image is rgba - print( - "Expanding input image from {0}x{1} to {2}x{3}".format( - cv2_img.shape[1], cv2_img.shape[0], new_width, new_height - ) + # TODO: which is better? + # new_img = np.random.randint(0, 255, (new_height, new_width, 3), np.uint8) + new_img = cv2.copyMakeBorder( + cv2_img, top, bottom, left, right, cv2.BORDER_REPLICATE ) - if cv2_img.shape[2] == 3: # rgb input image - new_img[ - top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 0:3 - ] = cv2_img - new_img[ - top : top + cv2_img.shape[0], left : left + cv2_img.shape[1], 3 - ] = 255 # fully opaque - elif cv2_img.shape[2] == 4: # rgba input image - new_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = cv2_img - else: - raise Exception( - "Unsupported image format: {0} channels".format(cv2_img.shape[2]) - ) + mask_img = np.zeros((new_height, new_width), np.uint8) + mask_img[top : top + cv2_img.shape[0], left : left + cv2_img.shape[1]] = 255 if softness > 0.0: - new_img = soften_mask(new_img / 255.0, softness / 100.0, space / 100.0) - new_img = (np.clip(new_img, 0.0, 1.0) * 255.0).astype(np.uint8) + mask_img = soften_mask(mask_img / 255.0, softness / 100.0, space / 100.0) + mask_img = (np.clip(mask_img, 0.0, 1.0) * 255.0).astype(np.uint8) - mask_image = 255.0 - new_img[:, :, 3] # extract mask from alpha channel and invert + mask_image = 255.0 - mask_img # extract mask from alpha channel and invert rgb_init_image = ( 0.0 + new_img[:, :, 0:3] ) # strip mask from init_img leaving only rgb channels @@ -153,7 +138,7 @@ def expand_image( hard_mask[:, origin_w // 2 :] = 255 hard_mask = cv2.copyMakeBorder( - hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255 + hard_mask, top, bottom, left, right, cv2.BORDER_DEFAULT, value=255 ) mask_image = np.where(hard_mask > 0, mask_image, 0) return rgb_init_image.astype(np.uint8), mask_image.astype(np.uint8) From 8f942e27c45600142d344e99094783057d7e1ca1 Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 15 Nov 2023 17:20:44 +0800 Subject: [PATCH 016/134] add test --- lama_cleaner/tests/test_sd_model.py | 30 +++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/lama_cleaner/tests/test_sd_model.py b/lama_cleaner/tests/test_sd_model.py index 50135a0..3acba71 100644 --- a/lama_cleaner/tests/test_sd_model.py +++ b/lama_cleaner/tests/test_sd_model.py @@ -177,6 +177,36 @@ def test_runway_sd_1_5_sd_scale( ) +@pytest.mark.parametrize("sd_device", ["mps"]) +@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) +@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a]) +def test_runway_sd_sd_strength(sd_device, strategy, sampler): + if sd_device == "cuda" and not torch.cuda.is_available(): + return + + sd_steps = 50 if sd_device == "cuda" else 20 + model = ModelManager( + name="sd1.5", + device=torch.device(sd_device), + hf_access_token="", + sd_run_local=True, + disable_nsfw=True, + sd_cpu_textencoder=False, + ) + cfg = get_config( + strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_strength=0.8 + ) + cfg.sd_sampler = sampler + + assert_equal( + model, + cfg, + f"runway_sd_strength_0.8.png", + img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", + mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", + ) + + @pytest.mark.parametrize("sd_device", ["cuda"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize("sampler", [SDSampler.k_euler_a]) From 0cfec489b756ca76f65e71ff6c27b9e19427050b Mon Sep 17 00:00:00 2001 From: Qing Date: Thu, 16 Nov 2023 11:08:34 +0800 Subject: [PATCH 017/134] update --- lama_cleaner/const.py | 12 +++++-- lama_cleaner/model/controlnet.py | 3 +- ...ine_stable_diffusion_controlnet_inpaint.py | 35 ++++++++++++++----- lama_cleaner/model/sd.py | 7 ++-- lama_cleaner/server.py | 10 +++++- lama_cleaner/tests/test_instruct_pix2pix.py | 6 ++-- lama_cleaner/tests/test_sd_model.py | 18 +++++----- 7 files changed, 63 insertions(+), 28 deletions(-) diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py index 30937e6..d6125b7 100644 --- a/lama_cleaner/const.py +++ b/lama_cleaner/const.py @@ -33,10 +33,10 @@ AVAILABLE_MODELS = [ "paint_by_example", "instruct_pix2pix", "kandinsky2.2", - "sdxl" + "sdxl", ] SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"] -MODELS_SUPPORT_FREEU = SD15_MODELS + ['sd2', "sdxl"] +MODELS_SUPPORT_FREEU = SD15_MODELS + ["sd2", "sdxl"] MODELS_SUPPORT_LCM_LORA = SD15_MODELS + ["sdxl"] AVAILABLE_DEVICES = ["cuda", "cpu", "mps"] @@ -110,6 +110,14 @@ QUALITY_HELP = """ Quality of image encoding, 0-100. Default is 95, higher quality will generate larger file size. """ +FREEU_DEFAULT_CONFIGS = { + "sd2": dict(s1=0.9, s2=0.2, b1=1.1, b2=1.2), + "sdxl": dict(s1=0.6, s2=0.4, b1=1.1, b2=1.2), + "sd1.5": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), + "anything4": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), + "realisticVision1.4": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), +} + class RealESRGANModelName(str, Enum): realesr_general_x4v3 = "realesr-general-x4v3" diff --git a/lama_cleaner/model/controlnet.py b/lama_cleaner/model/controlnet.py index 9cc3380..9becdcf 100644 --- a/lama_cleaner/model/controlnet.py +++ b/lama_cleaner/model/controlnet.py @@ -12,8 +12,9 @@ from lama_cleaner.model.utils import torch_gc, get_scheduler from lama_cleaner.schema import Config -class CPUTextEncoderWrapper: +class CPUTextEncoderWrapper(torch.nn.Module): def __init__(self, text_encoder, torch_dtype): + super().__init__() self.config = text_encoder.config self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True) self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True) diff --git a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py index 3dfd840..01d73d0 100644 --- a/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py +++ b/lama_cleaner/model/pipeline/pipeline_stable_diffusion_controlnet_inpaint.py @@ -285,6 +285,28 @@ class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return mask, masked_image_latents + def _default_height_width(self, height, width, image): + if isinstance(image, list): + image = image[0] + + if height is None: + if isinstance(image, PIL.Image.Image): + height = image.height + elif isinstance(image, torch.Tensor): + height = image.shape[3] + + height = (height // 8) * 8 # round down to nearest multiple of 8 + + if width is None: + if isinstance(image, PIL.Image.Image): + width = image.width + elif isinstance(image, torch.Tensor): + width = image.shape[2] + + width = (width // 8) * 8 # round down to nearest multiple of 8 + + return height, width + @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( @@ -402,14 +424,11 @@ class StableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetPipeline # 1. Check inputs. Raise error if not correct self.check_inputs( - prompt, - control_image, - height, - width, - callback_steps, - negative_prompt, - prompt_embeds, - negative_prompt_embeds, + prompt=prompt, + image=control_image, + callback_steps=callback_steps, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, ) # 2. Define call parameters diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index f19b96c..69153c6 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -7,12 +7,13 @@ import torch from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel -from lama_cleaner.model.utils import torch_gc, get_scheduler -from lama_cleaner.schema import Config, SDSampler +from lama_cleaner.model.utils import torch_gc +from lama_cleaner.schema import Config -class CPUTextEncoderWrapper: +class CPUTextEncoderWrapper(torch.nn.Module): def __init__(self, text_encoder, torch_dtype): + super().__init__() self.config = text_encoder.config self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True) self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True) diff --git a/lama_cleaner/server.py b/lama_cleaner/server.py index 755dc62..cb762bf 100644 --- a/lama_cleaner/server.py +++ b/lama_cleaner/server.py @@ -18,7 +18,12 @@ import torch from PIL import Image from loguru import logger -from lama_cleaner.const import SD15_MODELS +from lama_cleaner.const import ( + SD15_MODELS, + FREEU_DEFAULT_CONFIGS, + MODELS_SUPPORT_FREEU, + MODELS_SUPPORT_LCM_LORA, +) from lama_cleaner.file_manager import FileManager from lama_cleaner.model.utils import torch_gc from lama_cleaner.model_manager import ModelManager @@ -421,6 +426,9 @@ def get_server_config(): "isEnableAutoSaving": is_enable_auto_saving, "enableFileManager": is_enable_file_manager, "plugins": list(plugins.keys()), + "freeSupportedModels": MODELS_SUPPORT_FREEU, + "freeuDefaultConfigs": FREEU_DEFAULT_CONFIGS, + "lcmLoraSupportedModels": MODELS_SUPPORT_LCM_LORA, }, 200 diff --git a/lama_cleaner/tests/test_instruct_pix2pix.py b/lama_cleaner/tests/test_instruct_pix2pix.py index 9778160..8813644 100644 --- a/lama_cleaner/tests/test_instruct_pix2pix.py +++ b/lama_cleaner/tests/test_instruct_pix2pix.py @@ -10,13 +10,13 @@ from lama_cleaner.schema import HDStrategy current_dir = Path(__file__).parent.absolute().resolve() save_dir = current_dir / 'result' save_dir.mkdir(exist_ok=True, parents=True) -device = 'cuda' if torch.cuda.is_available() else 'cpu' +device = 'cuda' if torch.cuda.is_available() else 'mps' @pytest.mark.parametrize("disable_nsfw", [True, False]) @pytest.mark.parametrize("cpu_offload", [False, True]) def test_instruct_pix2pix(disable_nsfw, cpu_offload): - sd_steps = 50 if device == 'cuda' else 1 + sd_steps = 50 if device == 'cuda' else 20 model = ModelManager(name="instruct_pix2pix", device=torch.device(device), hf_access_token="", @@ -41,7 +41,7 @@ def test_instruct_pix2pix(disable_nsfw, cpu_offload): @pytest.mark.parametrize("disable_nsfw", [False]) @pytest.mark.parametrize("cpu_offload", [False]) def test_instruct_pix2pix_snow(disable_nsfw, cpu_offload): - sd_steps = 50 if device == 'cuda' else 1 + sd_steps = 50 if device == 'cuda' else 20 model = ModelManager(name="instruct_pix2pix", device=torch.device(device), hf_access_token="", diff --git a/lama_cleaner/tests/test_sd_model.py b/lama_cleaner/tests/test_sd_model.py index 3acba71..c445459 100644 --- a/lama_cleaner/tests/test_sd_model.py +++ b/lama_cleaner/tests/test_sd_model.py @@ -13,11 +13,9 @@ from lama_cleaner.tests.test_model import get_config, assert_equal current_dir = Path(__file__).parent.absolute().resolve() save_dir = current_dir / "result" save_dir.mkdir(exist_ok=True, parents=True) -device = "cuda" if torch.cuda.is_available() else "cpu" -device = torch.device(device) -@pytest.mark.parametrize("sd_device", ["cuda"]) +@pytest.mark.parametrize("sd_device", ["cuda", "mps"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize("sampler", [SDSampler.ddim]) @pytest.mark.parametrize("cpu_textencoder", [True, False]) @@ -56,7 +54,7 @@ def test_runway_sd_1_5_ddim( ) -@pytest.mark.parametrize("sd_device", ["cuda"]) +@pytest.mark.parametrize("sd_device", ["cuda", "mps"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize( "sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a] @@ -95,7 +93,7 @@ def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_ns ) -@pytest.mark.parametrize("sd_device", ["mps"]) +@pytest.mark.parametrize("sd_device", ["cuda", "mps"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize("sampler", [SDSampler.ddim]) @pytest.mark.parametrize("sd_prevent_unmasked_area", [False, True]) @@ -140,7 +138,7 @@ def test_runway_sd_1_5_negative_prompt( ) -@pytest.mark.parametrize("sd_device", ["cuda"]) +@pytest.mark.parametrize("sd_device", ["cuda", "mps"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize("sampler", [SDSampler.k_euler_a]) @pytest.mark.parametrize("cpu_textencoder", [False]) @@ -151,7 +149,7 @@ def test_runway_sd_1_5_sd_scale( if sd_device == "cuda" and not torch.cuda.is_available(): return - sd_steps = 50 if sd_device == "cuda" else 1 + sd_steps = 50 if sd_device == "cuda" else 20 model = ModelManager( name="sd1.5", device=torch.device(sd_device), @@ -177,7 +175,7 @@ def test_runway_sd_1_5_sd_scale( ) -@pytest.mark.parametrize("sd_device", ["mps"]) +@pytest.mark.parametrize("sd_device", ["cuda", "mps"]) @pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) @pytest.mark.parametrize("sampler", [SDSampler.k_euler_a]) def test_runway_sd_sd_strength(sd_device, strategy, sampler): @@ -214,7 +212,7 @@ def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler): if sd_device == "cuda" and not torch.cuda.is_available(): return - sd_steps = 50 if sd_device == "cuda" else 1 + sd_steps = 50 if sd_device == "cuda" else 20 model = ModelManager( name="sd1.5", device=torch.device(sd_device), @@ -246,7 +244,7 @@ def test_local_file_path(sd_device, sampler): if sd_device == "cuda" and not torch.cuda.is_available(): return - sd_steps = 1 if sd_device == "cpu" else 50 + sd_steps = 1 if sd_device == "cpu" else 30 model = ModelManager( name="sd1.5", device=torch.device(sd_device), From bfd33ef37f29c201d40255f22a2f4dc5c4375aa4 Mon Sep 17 00:00:00 2001 From: Qing Date: Thu, 16 Nov 2023 12:45:05 +0800 Subject: [PATCH 018/134] instruct_pix2pix support freeu --- lama_cleaner/const.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/lama_cleaner/const.py b/lama_cleaner/const.py index d6125b7..1b749a8 100644 --- a/lama_cleaner/const.py +++ b/lama_cleaner/const.py @@ -36,9 +36,19 @@ AVAILABLE_MODELS = [ "sdxl", ] SD15_MODELS = ["sd1.5", "anything4", "realisticVision1.4"] -MODELS_SUPPORT_FREEU = SD15_MODELS + ["sd2", "sdxl"] +MODELS_SUPPORT_FREEU = SD15_MODELS + ["sd2", "sdxl", "instruct_pix2pix"] MODELS_SUPPORT_LCM_LORA = SD15_MODELS + ["sdxl"] +FREEU_DEFAULT_CONFIGS = { + "sd2": dict(s1=0.9, s2=0.2, b1=1.1, b2=1.2), + "sdxl": dict(s1=0.6, s2=0.4, b1=1.1, b2=1.2), + "sd1.5": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), + "anything4": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), + "realisticVision1.4": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), + "instruct_pix2pix": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), +} + + AVAILABLE_DEVICES = ["cuda", "cpu", "mps"] DEFAULT_DEVICE = "cuda" @@ -110,14 +120,6 @@ QUALITY_HELP = """ Quality of image encoding, 0-100. Default is 95, higher quality will generate larger file size. """ -FREEU_DEFAULT_CONFIGS = { - "sd2": dict(s1=0.9, s2=0.2, b1=1.1, b2=1.2), - "sdxl": dict(s1=0.6, s2=0.4, b1=1.1, b2=1.2), - "sd1.5": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), - "anything4": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), - "realisticVision1.4": dict(s1=0.9, s2=0.2, b1=1.2, b2=1.4), -} - class RealESRGANModelName(str, Enum): realesr_general_x4v3 = "realesr-general-x4v3" From 20e660aa4a74507988111a33ed86f912357193ec Mon Sep 17 00:00:00 2001 From: Qing Date: Thu, 16 Nov 2023 14:09:08 +0800 Subject: [PATCH 019/134] use from_single_file --- lama_cleaner/model/sd.py | 36 ++--------------------------- lama_cleaner/tests/test_sd_model.py | 13 ++++++++--- 2 files changed, 12 insertions(+), 37 deletions(-) diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index 69153c6..f466625 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -34,37 +34,6 @@ class CPUTextEncoderWrapper(torch.nn.Module): return self.torch_dtype -def load_from_local_model(local_model_path, torch_dtype, disable_nsfw=True): - from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( - download_from_original_stable_diffusion_ckpt, - ) - from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline - - logger.info(f"Converting {local_model_path} to diffusers pipeline") - - pipe = download_from_original_stable_diffusion_ckpt( - local_model_path, - num_in_channels=9, - from_safetensors=local_model_path.endswith("safetensors"), - device="cpu", - ) - - inpaint_pipe = StableDiffusionInpaintPipeline( - vae=pipe.vae, - text_encoder=pipe.text_encoder, - tokenizer=pipe.tokenizer, - unet=pipe.unet, - scheduler=pipe.scheduler, - safety_checker=None if disable_nsfw else pipe.safety_checker, - feature_extractor=None if disable_nsfw else pipe.safety_checker, - requires_safety_checker=not disable_nsfw, - ) - - del pipe - gc.collect() - return inpaint_pipe.to(torch_dtype=torch_dtype) - - class SD(DiffusionInpaintModel): pad_mod = 8 min_size = 512 @@ -92,9 +61,8 @@ class SD(DiffusionInpaintModel): torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 if kwargs.get("sd_local_model_path", None): - self.model = load_from_local_model( - kwargs["sd_local_model_path"], - torch_dtype=torch_dtype, + self.model = StableDiffusionInpaintPipeline.from_single_file( + kwargs["sd_local_model_path"], torch_dtype=torch_dtype, **model_kwargs ) else: self.model = StableDiffusionInpaintPipeline.from_pretrained( diff --git a/lama_cleaner/tests/test_sd_model.py b/lama_cleaner/tests/test_sd_model.py index c445459..971b7d5 100644 --- a/lama_cleaner/tests/test_sd_model.py +++ b/lama_cleaner/tests/test_sd_model.py @@ -240,7 +240,14 @@ def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler): @pytest.mark.parametrize("sd_device", ["cuda", "mps"]) @pytest.mark.parametrize("sampler", [SDSampler.uni_pc]) -def test_local_file_path(sd_device, sampler): +@pytest.mark.parametrize( + "local_model_path", + [ + "/Users/cwq/data/models/sd-v1-5-inpainting.ckpt", + "/Users/cwq/data/models/sd-v1-5-inpainting.safetensors", + ], +) +def test_local_file_path(sd_device, sampler, local_model_path): if sd_device == "cuda" and not torch.cuda.is_available(): return @@ -253,7 +260,7 @@ def test_local_file_path(sd_device, sampler): disable_nsfw=True, sd_cpu_textencoder=False, cpu_offload=True, - sd_local_model_path="/Users/cwq/data/models/sd-v1-5-inpainting.ckpt", + sd_local_model_path=local_model_path, ) cfg = get_config( HDStrategy.ORIGINAL, @@ -262,7 +269,7 @@ def test_local_file_path(sd_device, sampler): ) cfg.sd_sampler = sampler - name = f"device_{sd_device}_{sampler}" + name = f"device_{sd_device}_{sampler}_{Path(local_model_path).stem}" assert_equal( model, From 1d145d1cd64acb76dc461d6568d1f234846e3fbd Mon Sep 17 00:00:00 2001 From: Qing Date: Thu, 16 Nov 2023 21:12:06 +0800 Subject: [PATCH 020/134] add download command --- lama_cleaner/__init__.py | 2 + lama_cleaner/diffusers_utils.py | 28 ++++++++++++++ lama_cleaner/download.py | 24 ++++++++++++ lama_cleaner/model/base.py | 4 ++ lama_cleaner/model/fcf.py | 7 +++- lama_cleaner/model/instruct_pix2pix.py | 36 ++++++++++------- lama_cleaner/model/kandinsky.py | 8 ++++ lama_cleaner/model/lama.py | 5 +++ lama_cleaner/model/ldm.py | 6 +++ lama_cleaner/model/manga.py | 7 +++- lama_cleaner/model/mat.py | 53 +++++++++++++++----------- lama_cleaner/model/paint_by_example.py | 39 ++++++++++--------- lama_cleaner/model/sd.py | 6 +++ lama_cleaner/model/sdxl.py | 10 ++++- lama_cleaner/model/zits.py | 42 ++++++++++++++++---- lama_cleaner/parse_args.py | 21 +++++++++- lama_cleaner/web_config.py | 2 +- 17 files changed, 233 insertions(+), 67 deletions(-) create mode 100644 lama_cleaner/diffusers_utils.py create mode 100644 lama_cleaner/download.py diff --git a/lama_cleaner/__init__.py b/lama_cleaner/__init__.py index a1ce1a1..db72076 100644 --- a/lama_cleaner/__init__.py +++ b/lama_cleaner/__init__.py @@ -11,6 +11,8 @@ from lama_cleaner.parse_args import parse_args def entry_point(): args = parse_args() + if args is None: + return # To make os.environ["XDG_CACHE_HOME"] = args.model_cache_dir works for diffusers # https://github.com/huggingface/diffusers/blob/be99201a567c1ccd841dc16fb24e88f7f239c187/src/diffusers/utils/constants.py#L18 from lama_cleaner.server import main diff --git a/lama_cleaner/diffusers_utils.py b/lama_cleaner/diffusers_utils.py new file mode 100644 index 0000000..d53fa7f --- /dev/null +++ b/lama_cleaner/diffusers_utils.py @@ -0,0 +1,28 @@ +import json +from pathlib import Path +from typing import Dict, List + + +def folder_name_to_show_name(name: str) -> str: + return name.replace("models--", "").replace("--", "/") + + +def _scan_models(cache_dir, class_name: str) -> List[str]: + cache_dir = Path(cache_dir) + res = [] + for it in cache_dir.glob("**/*/model_index.json"): + with open(it, "r", encoding="utf-8") as f: + data = json.load(f) + if data["_class_name"] == class_name: + name = folder_name_to_show_name(it.parent.parent.parent.name) + if name not in res: + res.append(name) + return res + + +def scan_models(cache_dir) -> List[str]: + return _scan_models(cache_dir, "StableDiffusionPipeline") + + +def scan_inpainting_models(cache_dir) -> List[str]: + return _scan_models(cache_dir, "StableDiffusionInpaintPipeline") diff --git a/lama_cleaner/download.py b/lama_cleaner/download.py new file mode 100644 index 0000000..f0f7cb2 --- /dev/null +++ b/lama_cleaner/download.py @@ -0,0 +1,24 @@ +import os + +from loguru import logger +from pathlib import Path + + +def cli_download_model(model: str, model_dir: str): + if os.path.isfile(model_dir): + raise ValueError(f"invalid --model-dir: {model_dir} is a file") + + if not os.path.exists(model_dir): + logger.info(f"Create model cache directory: {model_dir}") + Path(model_dir).mkdir(exist_ok=True, parents=True) + + os.environ["XDG_CACHE_HOME"] = model_dir + + from lama_cleaner.model_manager import models + + if model in models: + logger.info(f"Downloading {model}...") + models[model].download() + logger.info(f"Done.") + else: + logger.info(f"Downloading model from Huggingface: {model}") diff --git a/lama_cleaner/model/base.py b/lama_cleaner/model/base.py index 726a2be..81cdf0c 100644 --- a/lama_cleaner/model/base.py +++ b/lama_cleaner/model/base.py @@ -51,6 +51,10 @@ class InpaintModel: """ ... + @staticmethod + def download(): + ... + def _pad_forward(self, image, mask, config: Config): origin_height, origin_width = image.shape[:2] pad_image = pad_img_to_modulo( diff --git a/lama_cleaner/model/fcf.py b/lama_cleaner/model/fcf.py index 07292c6..a64885a 100644 --- a/lama_cleaner/model/fcf.py +++ b/lama_cleaner/model/fcf.py @@ -14,6 +14,7 @@ from lama_cleaner.helper import ( norm_img, boxes_from_mask, resize_max_size, + download_model, ) from lama_cleaner.model.base import InpaintModel from torch import conv2d, nn @@ -870,7 +871,6 @@ class SpectralTransform(nn.Module): ) def forward(self, x): - x = self.downsample(x) x = self.conv1(x) output = self.fu(x) @@ -1437,7 +1437,6 @@ class SynthesisNetwork(torch.nn.Module): setattr(self, f"b{res}", block) def forward(self, x_global, mask, feats, ws, fname=None, **block_kwargs): - img = None x, img = self.foreword(x_global, ws, feats, img) @@ -1656,6 +1655,10 @@ class FcF(InpaintModel): self.model = load_model(G, FCF_MODEL_URL, device, FCF_MODEL_MD5) self.label = torch.zeros([1, self.model.c_dim], device=device) + @staticmethod + def download(): + download_model(FCF_MODEL_URL, FCF_MODEL_MD5) + @staticmethod def is_downloaded() -> bool: return os.path.exists(get_cache_path_by_url(FCF_MODEL_URL)) diff --git a/lama_cleaner/model/instruct_pix2pix.py b/lama_cleaner/model/instruct_pix2pix.py index 4d176da..27476b7 100644 --- a/lama_cleaner/model/instruct_pix2pix.py +++ b/lama_cleaner/model/instruct_pix2pix.py @@ -4,7 +4,6 @@ import torch from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel -from lama_cleaner.model.utils import set_seed from lama_cleaner.schema import Config @@ -15,18 +14,21 @@ class InstructPix2Pix(DiffusionInpaintModel): def init_model(self, device: torch.device, **kwargs): from diffusers import StableDiffusionInstructPix2PixPipeline - fp16 = not kwargs.get('no_half', False) - model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)} - if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False): + fp16 = not kwargs.get("no_half", False) + + model_kwargs = {"local_files_only": kwargs.get("local_files_only", False)} + if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False): logger.info("Disable Stable Diffusion Model NSFW checker") - model_kwargs.update(dict( - safety_checker=None, - feature_extractor=None, - requires_safety_checker=False - )) + model_kwargs.update( + dict( + safety_checker=None, + feature_extractor=None, + requires_safety_checker=False, + ) + ) - use_gpu = device == torch.device('cuda') and torch.cuda.is_available() + use_gpu = device == torch.device("cuda") and torch.cuda.is_available() torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained( "timbrooks/instruct-pix2pix", @@ -36,15 +38,23 @@ class InstructPix2Pix(DiffusionInpaintModel): ) self.model.enable_attention_slicing() - if kwargs.get('enable_xformers', False): + if kwargs.get("enable_xformers", False): self.model.enable_xformers_memory_efficient_attention() - if kwargs.get('cpu_offload', False) and use_gpu: + if kwargs.get("cpu_offload", False) and use_gpu: logger.info("Enable sequential cpu offload") self.model.enable_sequential_cpu_offload(gpu_id=0) else: self.model = self.model.to(device) + @staticmethod + def download(): + from diffusers import StableDiffusionInstructPix2PixPipeline + + StableDiffusionInstructPix2PixPipeline.from_pretrained( + "timbrooks/instruct-pix2pix", revision="fp16" + ) + def forward(self, image, mask, config: Config): """Input image and output image have same size image: [H, W, C] RGB @@ -60,7 +70,7 @@ class InstructPix2Pix(DiffusionInpaintModel): image_guidance_scale=config.p2p_image_guidance_scale, guidance_scale=config.p2p_guidance_scale, output_type="np", - generator=torch.manual_seed(config.sd_seed) + generator=torch.manual_seed(config.sd_seed), ).images[0] output = (output * 255).round().astype("uint8") diff --git a/lama_cleaner/model/kandinsky.py b/lama_cleaner/model/kandinsky.py index 7a89d9a..12d8209 100644 --- a/lama_cleaner/model/kandinsky.py +++ b/lama_cleaner/model/kandinsky.py @@ -76,3 +76,11 @@ class Kandinsky(DiffusionInpaintModel): class Kandinsky22(Kandinsky): name = "kandinsky2.2" model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint" + + @staticmethod + def download(): + from diffusers import AutoPipelineForInpainting + + AutoPipelineForInpainting.from_pretrained( + "kandinsky-community/kandinsky-2-2-decoder-inpaint" + ) diff --git a/lama_cleaner/model/lama.py b/lama_cleaner/model/lama.py index bdcdf0d..ebbb6c9 100644 --- a/lama_cleaner/model/lama.py +++ b/lama_cleaner/model/lama.py @@ -8,6 +8,7 @@ from lama_cleaner.helper import ( norm_img, get_cache_path_by_url, load_jit_model, + download_model, ) from lama_cleaner.model.base import InpaintModel from lama_cleaner.schema import Config @@ -23,6 +24,10 @@ class LaMa(InpaintModel): name = "lama" pad_mod = 8 + @staticmethod + def download(): + download_model(LAMA_MODEL_URL, LAMA_MODEL_MD5) + def init_model(self, device, **kwargs): self.model = load_jit_model(LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval() diff --git a/lama_cleaner/model/ldm.py b/lama_cleaner/model/ldm.py index a5b6d12..29956ad 100644 --- a/lama_cleaner/model/ldm.py +++ b/lama_cleaner/model/ldm.py @@ -260,6 +260,12 @@ class LDM(InpaintModel): self.model = LatentDiffusion(self.diffusion_model, device) + @staticmethod + def download(): + download_model(LDM_DIFFUSION_MODEL_URL, LDM_DIFFUSION_MODEL_MD5) + download_model(LDM_DECODE_MODEL_URL, LDM_DECODE_MODEL_MD5) + download_model(LDM_ENCODE_MODEL_URL, LDM_ENCODE_MODEL_MD5) + @staticmethod def is_downloaded() -> bool: model_paths = [ diff --git a/lama_cleaner/model/manga.py b/lama_cleaner/model/manga.py index f6e27e5..d61f408 100644 --- a/lama_cleaner/model/manga.py +++ b/lama_cleaner/model/manga.py @@ -7,7 +7,7 @@ import torch import time from loguru import logger -from lama_cleaner.helper import get_cache_path_by_url, load_jit_model +from lama_cleaner.helper import get_cache_path_by_url, load_jit_model, download_model from lama_cleaner.model.base import InpaintModel from lama_cleaner.schema import Config @@ -42,6 +42,11 @@ class Manga(InpaintModel): ) self.seed = 42 + @staticmethod + def download(): + download_model(MANGA_INPAINTOR_MODEL_URL, MANGA_INPAINTOR_MODEL_MD5) + download_model(MANGA_LINE_MODEL_URL, MANGA_LINE_MODEL_MD5) + @staticmethod def is_downloaded() -> bool: model_paths = [ diff --git a/lama_cleaner/model/mat.py b/lama_cleaner/model/mat.py index 3e09bf4..2fbe11c 100644 --- a/lama_cleaner/model/mat.py +++ b/lama_cleaner/model/mat.py @@ -8,7 +8,12 @@ import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as checkpoint -from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img +from lama_cleaner.helper import ( + load_model, + get_cache_path_by_url, + norm_img, + download_model, +) from lama_cleaner.model.base import InpaintModel from lama_cleaner.model.utils import ( setup_filter, @@ -52,7 +57,7 @@ class ModulatedConv2d(nn.Module): ) self.out_channels = out_channels self.kernel_size = kernel_size - self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) + self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2)) self.padding = self.kernel_size // 2 self.up = up self.down = down @@ -213,7 +218,7 @@ class DecBlockFirst(nn.Module): super().__init__() self.fc = FullyConnectedLayer( in_features=in_channels * 2, - out_features=in_channels * 4 ** 2, + out_features=in_channels * 4**2, activation=activation, ) self.conv = StyleConv( @@ -312,7 +317,7 @@ class DecBlock(nn.Module): in_channels=in_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, up=2, use_noise=use_noise, @@ -323,7 +328,7 @@ class DecBlock(nn.Module): in_channels=out_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, use_noise=use_noise, activation=activation, @@ -507,7 +512,7 @@ class Discriminator(torch.nn.Module): self.img_channels = img_channels resolution_log2 = int(np.log2(img_resolution)) - assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4 + assert img_resolution == 2**resolution_log2 and img_resolution >= 4 self.resolution_log2 = resolution_log2 def nf(stage): @@ -543,7 +548,7 @@ class Discriminator(torch.nn.Module): ) self.Dis = nn.Sequential(*Dis) - self.fc0 = FullyConnectedLayer(nf(2) * 4 ** 2, nf(2), activation=activation) + self.fc0 = FullyConnectedLayer(nf(2) * 4**2, nf(2), activation=activation) self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim) def forward(self, images_in, masks_in, c): @@ -562,7 +567,7 @@ class Discriminator(torch.nn.Module): def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512): NF = {512: 64, 256: 128, 128: 256, 64: 512, 32: 512, 16: 512, 8: 512, 4: 512} - return NF[2 ** stage] + return NF[2**stage] class Mlp(nn.Module): @@ -659,7 +664,7 @@ class Conv2dLayerPartial(nn.Module): ) self.weight_maskUpdater = torch.ones(1, 1, kernel_size, kernel_size) - self.slide_winsize = kernel_size ** 2 + self.slide_winsize = kernel_size**2 self.stride = down self.padding = kernel_size // 2 if kernel_size % 2 == 1 else 0 @@ -715,7 +720,7 @@ class WindowAttention(nn.Module): self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 + self.scale = qk_scale or head_dim**-0.5 self.q = FullyConnectedLayer(in_features=dim, out_features=dim) self.k = FullyConnectedLayer(in_features=dim, out_features=dim) @@ -1211,7 +1216,7 @@ class Encoder(nn.Module): self.resolution = [] for idx, i in enumerate(range(res_log2, 3, -1)): # from input size to 16x16 - res = 2 ** i + res = 2**i self.resolution.append(res) if i == res_log2: block = EncFromRGB(img_channels * 2 + 1, nf(i), activation) @@ -1296,7 +1301,7 @@ class DecBlockFirstV2(nn.Module): in_channels=in_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, use_noise=use_noise, activation=activation, @@ -1341,7 +1346,7 @@ class DecBlock(nn.Module): in_channels=in_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, up=2, use_noise=use_noise, @@ -1352,7 +1357,7 @@ class DecBlock(nn.Module): in_channels=out_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, use_noise=use_noise, activation=activation, @@ -1389,7 +1394,7 @@ class Decoder(nn.Module): for res in range(5, res_log2 + 1): setattr( self, - "Dec_%dx%d" % (2 ** res, 2 ** res), + "Dec_%dx%d" % (2**res, 2**res), DecBlock( res, nf(res - 1), @@ -1406,7 +1411,7 @@ class Decoder(nn.Module): def forward(self, x, ws, gs, E_features, noise_mode="random"): x, img = self.Dec_16x16(x, ws, gs, E_features, noise_mode=noise_mode) for res in range(5, self.res_log2 + 1): - block = getattr(self, "Dec_%dx%d" % (2 ** res, 2 ** res)) + block = getattr(self, "Dec_%dx%d" % (2**res, 2**res)) x, img = block(x, img, ws, gs, E_features, noise_mode=noise_mode) return img @@ -1431,7 +1436,7 @@ class DecStyleBlock(nn.Module): in_channels=in_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, up=2, use_noise=use_noise, @@ -1442,7 +1447,7 @@ class DecStyleBlock(nn.Module): in_channels=out_channels, out_channels=out_channels, style_dim=style_dim, - resolution=2 ** res, + resolution=2**res, kernel_size=3, use_noise=use_noise, activation=activation, @@ -1640,7 +1645,7 @@ class SynthesisNet(nn.Module): ): super().__init__() resolution_log2 = int(np.log2(img_resolution)) - assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4 + assert img_resolution == 2**resolution_log2 and img_resolution >= 4 self.num_layers = resolution_log2 * 2 - 3 * 2 self.img_resolution = img_resolution @@ -1781,7 +1786,7 @@ class Discriminator(torch.nn.Module): self.img_channels = img_channels resolution_log2 = int(np.log2(img_resolution)) - assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4 + assert img_resolution == 2**resolution_log2 and img_resolution >= 4 self.resolution_log2 = resolution_log2 if cmap_dim == None: @@ -1812,7 +1817,7 @@ class Discriminator(torch.nn.Module): ) self.Dis = nn.Sequential(*Dis) - self.fc0 = FullyConnectedLayer(nf(2) * 4 ** 2, nf(2), activation=activation) + self.fc0 = FullyConnectedLayer(nf(2) * 4**2, nf(2), activation=activation) self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim) # for 64x64 @@ -1837,7 +1842,7 @@ class Discriminator(torch.nn.Module): self.Dis_stg1 = nn.Sequential(*Dis_stg1) self.fc0_stg1 = FullyConnectedLayer( - nf(2) // 2 * 4 ** 2, nf(2) // 2, activation=activation + nf(2) // 2 * 4**2, nf(2) // 2, activation=activation ) self.fc1_stg1 = FullyConnectedLayer( nf(2) // 2, 1 if cmap_dim == 0 else cmap_dim @@ -1898,6 +1903,10 @@ class MAT(InpaintModel): self.label = torch.zeros([1, self.model.c_dim], device=device).to(self.torch_dtype) # fmt: on + @staticmethod + def download(): + download_model(MAT_MODEL_URL, MAT_MODEL_MD5) + @staticmethod def is_downloaded() -> bool: return os.path.exists(get_cache_path_by_url(MAT_MODEL_URL)) diff --git a/lama_cleaner/model/paint_by_example.py b/lama_cleaner/model/paint_by_example.py index 4b5ae74..a9606a4 100644 --- a/lama_cleaner/model/paint_by_example.py +++ b/lama_cleaner/model/paint_by_example.py @@ -2,11 +2,9 @@ import PIL import PIL.Image import cv2 import torch -from diffusers import DiffusionPipeline from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel -from lama_cleaner.model.utils import set_seed from lama_cleaner.schema import Config @@ -16,35 +14,40 @@ class PaintByExample(DiffusionInpaintModel): min_size = 512 def init_model(self, device: torch.device, **kwargs): - fp16 = not kwargs.get('no_half', False) - use_gpu = device == torch.device('cuda') and torch.cuda.is_available() - torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 - model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)} + from diffusers import DiffusionPipeline - if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False): + fp16 = not kwargs.get("no_half", False) + use_gpu = device == torch.device("cuda") and torch.cuda.is_available() + torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 + model_kwargs = {"local_files_only": kwargs.get("local_files_only", False)} + + if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False): logger.info("Disable Paint By Example Model NSFW checker") - model_kwargs.update(dict( - safety_checker=None, - requires_safety_checker=False - )) + model_kwargs.update( + dict(safety_checker=None, requires_safety_checker=False) + ) self.model = DiffusionPipeline.from_pretrained( - "Fantasy-Studio/Paint-by-Example", - torch_dtype=torch_dtype, - **model_kwargs + "Fantasy-Studio/Paint-by-Example", torch_dtype=torch_dtype, **model_kwargs ) self.model.enable_attention_slicing() - if kwargs.get('enable_xformers', False): + if kwargs.get("enable_xformers", False): self.model.enable_xformers_memory_efficient_attention() # TODO: gpu_id - if kwargs.get('cpu_offload', False) and use_gpu: + if kwargs.get("cpu_offload", False) and use_gpu: self.model.image_encoder = self.model.image_encoder.to(device) self.model.enable_sequential_cpu_offload(gpu_id=0) else: self.model = self.model.to(device) + @staticmethod + def download(): + from diffusers import DiffusionPipeline + + DiffusionPipeline.from_pretrained("Fantasy-Studio/Paint-by-Example") + def forward(self, image, mask, config: Config): """Input image and output image have same size image: [H, W, C] RGB @@ -56,8 +59,8 @@ class PaintByExample(DiffusionInpaintModel): mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), example_image=config.paint_by_example_example_image, num_inference_steps=config.paint_by_example_steps, - output_type='np.array', - generator=torch.manual_seed(config.paint_by_example_seed) + output_type="np.array", + generator=torch.manual_seed(config.paint_by_example_seed), ).images[0] output = (output * 255).round().astype("uint8") diff --git a/lama_cleaner/model/sd.py b/lama_cleaner/model/sd.py index f466625..0c0b190 100644 --- a/lama_cleaner/model/sd.py +++ b/lama_cleaner/model/sd.py @@ -132,6 +132,12 @@ class SD(DiffusionInpaintModel): # model will be downloaded when app start, and can't switch in frontend settings return True + @classmethod + def download(cls): + from diffusers import StableDiffusionInpaintPipeline + + StableDiffusionInpaintPipeline.from_pretrained(cls.model_id_or_path) + class SD15(SD): name = "sd1.5" diff --git a/lama_cleaner/model/sdxl.py b/lama_cleaner/model/sdxl.py index 7615795..f64bfa8 100644 --- a/lama_cleaner/model/sdxl.py +++ b/lama_cleaner/model/sdxl.py @@ -5,7 +5,6 @@ import torch from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel -from lama_cleaner.model.utils import torch_gc, get_scheduler from lama_cleaner.schema import Config @@ -51,6 +50,14 @@ class SDXL(DiffusionInpaintModel): self.callback = kwargs.pop("callback", None) + @staticmethod + def download(): + from diffusers import AutoPipelineForInpainting + + AutoPipelineForInpainting.from_pretrained( + "diffusers/stable-diffusion-xl-1.0-inpainting-0.1" + ) + def forward(self, image, mask, config: Config): """Input image and output image have same size image: [H, W, C] RGB @@ -85,7 +92,6 @@ class SDXL(DiffusionInpaintModel): output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output - @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings diff --git a/lama_cleaner/model/zits.py b/lama_cleaner/model/zits.py index 664ca15..748623e 100644 --- a/lama_cleaner/model/zits.py +++ b/lama_cleaner/model/zits.py @@ -5,7 +5,7 @@ import cv2 import torch import torch.nn.functional as F -from lama_cleaner.helper import get_cache_path_by_url, load_jit_model +from lama_cleaner.helper import get_cache_path_by_url, load_jit_model, download_model from lama_cleaner.schema import Config import numpy as np @@ -171,14 +171,19 @@ def load_image(img, mask, device, sigma256=3.0): try: import skimage + gray_256 = skimage.color.rgb2gray(img_256) edge_256 = skimage.feature.canny(gray_256, sigma=3.0, mask=None).astype(float) # cv2.imwrite("skimage_gray.jpg", (gray_256*255).astype(np.uint8)) # cv2.imwrite("skimage_edge.jpg", (edge_256*255).astype(np.uint8)) except: gray_256 = cv2.cvtColor(img_256, cv2.COLOR_RGB2GRAY) - gray_256_blured = cv2.GaussianBlur(gray_256, ksize=(7, 7), sigmaX=sigma256, sigmaY=sigma256) - edge_256 = cv2.Canny(gray_256_blured, threshold1=int(255*0.1), threshold2=int(255*0.2)) + gray_256_blured = cv2.GaussianBlur( + gray_256, ksize=(7, 7), sigmaX=sigma256, sigmaY=sigma256 + ) + edge_256 = cv2.Canny( + gray_256_blured, threshold1=int(255 * 0.1), threshold2=int(255 * 0.2) + ) # cv2.imwrite("opencv_edge.jpg", edge_256) @@ -233,12 +238,27 @@ class ZITS(InpaintModel): self.sample_edge_line_iterations = 1 def init_model(self, device, **kwargs): - self.wireframe = load_jit_model(ZITS_WIRE_FRAME_MODEL_URL, device, ZITS_WIRE_FRAME_MODEL_MD5) - self.edge_line = load_jit_model(ZITS_EDGE_LINE_MODEL_URL, device, ZITS_EDGE_LINE_MODEL_MD5) + self.wireframe = load_jit_model( + ZITS_WIRE_FRAME_MODEL_URL, device, ZITS_WIRE_FRAME_MODEL_MD5 + ) + self.edge_line = load_jit_model( + ZITS_EDGE_LINE_MODEL_URL, device, ZITS_EDGE_LINE_MODEL_MD5 + ) self.structure_upsample = load_jit_model( ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, device, ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5 ) - self.inpaint = load_jit_model(ZITS_INPAINT_MODEL_URL, device, ZITS_INPAINT_MODEL_MD5) + self.inpaint = load_jit_model( + ZITS_INPAINT_MODEL_URL, device, ZITS_INPAINT_MODEL_MD5 + ) + + @staticmethod + def download(): + download_model(ZITS_WIRE_FRAME_MODEL_URL, ZITS_WIRE_FRAME_MODEL_MD5) + download_model(ZITS_EDGE_LINE_MODEL_URL, ZITS_EDGE_LINE_MODEL_MD5) + download_model( + ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5 + ) + download_model(ZITS_INPAINT_MODEL_URL, ZITS_INPAINT_MODEL_MD5) @staticmethod def is_downloaded() -> bool: @@ -385,12 +405,20 @@ class ZITS(InpaintModel): if score > mask_th: try: import skimage + rr, cc, value = skimage.draw.line_aa( *to_int(line[0:2]), *to_int(line[2:4]) ) lmap[rr, cc] = np.maximum(lmap[rr, cc], value) except: - cv2.line(lmap, to_int(line[0:2][::-1]), to_int(line[2:4][::-1]), (1, 1, 1), 1, cv2.LINE_AA) + cv2.line( + lmap, + to_int(line[0:2][::-1]), + to_int(line[2:4][::-1]), + (1, 1, 1), + 1, + cv2.LINE_AA, + ) lmap = np.clip(lmap * 255, 0, 255).astype(np.uint8) lines_tensor.append(to_tensor(lmap).unsqueeze(0)) diff --git a/lama_cleaner/parse_args.py b/lama_cleaner/parse_args.py index e27d8ea..f672fb7 100644 --- a/lama_cleaner/parse_args.py +++ b/lama_cleaner/parse_args.py @@ -6,13 +6,29 @@ from pathlib import Path from loguru import logger from lama_cleaner.const import * +from lama_cleaner.download import cli_download_model from lama_cleaner.runtime import dump_environment_info +DOWNLOAD_SUBCOMMAND = "download" + + +def download_parse_args(parser): + subparsers = parser.add_subparsers(dest="subcommand") + subparser = subparsers.add_parser(DOWNLOAD_SUBCOMMAND, help="Download models") + subparser.add_argument( + "--model", help="Erase model name(lama/mat...) or model id on huggingface" + ) + subparser.add_argument( + "--model-dir", type=str, default=DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP + ) + def parse_args(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) + download_parse_args(parser) + parser.add_argument("--host", default="127.0.0.1") parser.add_argument("--port", default=8080, type=int) @@ -166,9 +182,12 @@ def parse_args(): ) args = parser.parse_args() - # collect system info to help debug dump_environment_info() + if args.subcommand == DOWNLOAD_SUBCOMMAND: + cli_download_model(args.model, args.model_dir) + return + if args.install_plugins_package: from lama_cleaner.installer import install_plugins_package diff --git a/lama_cleaner/web_config.py b/lama_cleaner/web_config.py index 61efdcc..25d53c5 100644 --- a/lama_cleaner/web_config.py +++ b/lama_cleaner/web_config.py @@ -185,7 +185,7 @@ def main(config_file: str): ) sd_controlnet_method = gr.Radio( SD_CONTROLNET_CHOICES, - lable="ControlNet method", + label="ControlNet method", value=init_config.sd_controlnet_method, ) no_half = gr.Checkbox(init_config.no_half, label=f"{NO_HALF_HELP}") From ef1179a8587096a2c9c8faf8c41024ed5fb6e6b1 Mon Sep 17 00:00:00 2001 From: Qing Date: Thu, 16 Nov 2023 21:45:55 +0800 Subject: [PATCH 021/134] add get_diffusers_models --- lama_cleaner/diffusers_utils.py | 29 +++++++++++++++++++++-------- lama_cleaner/server.py | 9 +++++++++ 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/lama_cleaner/diffusers_utils.py b/lama_cleaner/diffusers_utils.py index d53fa7f..d67c80c 100644 --- a/lama_cleaner/diffusers_utils.py +++ b/lama_cleaner/diffusers_utils.py @@ -7,22 +7,35 @@ def folder_name_to_show_name(name: str) -> str: return name.replace("models--", "").replace("--", "/") -def _scan_models(cache_dir, class_name: str) -> List[str]: +def _scan_models(cache_dir, class_name: List[str]) -> List[str]: cache_dir = Path(cache_dir) res = [] for it in cache_dir.glob("**/*/model_index.json"): with open(it, "r", encoding="utf-8") as f: data = json.load(f) - if data["_class_name"] == class_name: + if data["_class_name"] in class_name: name = folder_name_to_show_name(it.parent.parent.parent.name) if name not in res: res.append(name) return res -def scan_models(cache_dir) -> List[str]: - return _scan_models(cache_dir, "StableDiffusionPipeline") - - -def scan_inpainting_models(cache_dir) -> List[str]: - return _scan_models(cache_dir, "StableDiffusionInpaintPipeline") +def scan_models(cache_dir) -> Dict[str, List[str]]: + return { + "sd": _scan_models(cache_dir, ["StableDiffusionPipeline"]), + "sd_inpaint": _scan_models( + cache_dir, + [ + "StableDiffusionInpaintPipeline", + "StableDiffusionXLInpaintPipeline", + "KandinskyV22InpaintPipeline", + ], + ), + "other": _scan_models( + cache_dir, + [ + "StableDiffusionInstructPix2PixPipeline", + "PaintByExamplePipeline", + ], + ), + } diff --git a/lama_cleaner/server.py b/lama_cleaner/server.py index cb762bf..3088e7f 100644 --- a/lama_cleaner/server.py +++ b/lama_cleaner/server.py @@ -2,6 +2,8 @@ import os import hashlib +from lama_cleaner.diffusers_utils import scan_models + os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" import imghdr @@ -432,6 +434,13 @@ def get_server_config(): }, 200 +@app.route("/sd_models", methods=["GET"]) +def get_diffusers_models(): + from diffusers.utils import DIFFUSERS_CACHE + + return scan_models(DIFFUSERS_CACHE) + + @app.route("/model") def current_model(): return model.name, 200 From 53aea791c58d6595a86a7d804185d676357d080e Mon Sep 17 00:00:00 2001 From: Qing Date: Fri, 17 Nov 2023 08:56:33 +0800 Subject: [PATCH 022/134] wip --- lama_cleaner/app/package.json | 1 - lama_cleaner/app/pnpm-lock.yaml | 840 +++++++++--------- .../app/src/components/Extender/Extender.scss | 166 ++++ .../app/src/components/Extender/Extender.tsx | 408 +++++++++ .../components/Settings/ModelSettingBlock.tsx | 174 ++-- .../src/components/Settings/SettingsModal.tsx | 4 +- .../src/components/SidePanel/SidePanel.scss | 2 +- .../src/components/SidePanel/SidePanel.tsx | 135 ++- lama_cleaner/app/src/store/Atoms.tsx | 73 +- 9 files changed, 1219 insertions(+), 584 deletions(-) create mode 100644 lama_cleaner/app/src/components/Extender/Extender.scss create mode 100644 lama_cleaner/app/src/components/Extender/Extender.tsx diff --git a/lama_cleaner/app/package.json b/lama_cleaner/app/package.json index 91703b8..26568af 100644 --- a/lama_cleaner/app/package.json +++ b/lama_cleaner/app/package.json @@ -84,7 +84,6 @@ "socket.io-client": "^4.5.4", "source-map-loader": "^3.0.0", "style-loader": "^3.3.1", - "tailwindcss": "^3.0.2", "terser-webpack-plugin": "^5.2.5", "typescript": "4.x", "webpack": "^5.64.4", diff --git a/lama_cleaner/app/pnpm-lock.yaml b/lama_cleaner/app/pnpm-lock.yaml index 7647f94..0156ac0 100644 --- a/lama_cleaner/app/pnpm-lock.yaml +++ b/lama_cleaner/app/pnpm-lock.yaml @@ -15,7 +15,7 @@ dependencies: version: registry.npmmirror.com/@radix-ui/colors@0.1.8 '@radix-ui/react-dialog': specifier: 0.1.8-rc.25 - version: registry.npmmirror.com/@radix-ui/react-dialog@0.1.8-rc.25(@types/react@17.0.53)(react-dom@17.0.2)(react@17.0.2) + version: 0.1.8-rc.25(@types/react@17.0.53)(react-dom@17.0.2)(react@17.0.2) '@radix-ui/react-dropdown-menu': specifier: ^2.0.4 version: 2.0.4(@types/react@17.0.53)(react-dom@17.0.2)(react@17.0.2) @@ -33,7 +33,7 @@ dependencies: version: registry.npmmirror.com/@radix-ui/react-scroll-area@1.0.2(react-dom@17.0.2)(react@17.0.2) '@radix-ui/react-select': specifier: 0.1.2-rc.27 - version: registry.npmmirror.com/@radix-ui/react-select@0.1.2-rc.27(@types/react@17.0.53)(react-dom@17.0.2)(react@17.0.2) + version: 0.1.2-rc.27(@types/react@17.0.53)(react-dom@17.0.2)(react@17.0.2) '@radix-ui/react-switch': specifier: ^0.1.5 version: registry.npmmirror.com/@radix-ui/react-switch@0.1.5(react@17.0.2) @@ -57,7 +57,7 @@ dependencies: version: registry.npmmirror.com/@testing-library/react@12.1.5(react-dom@17.0.2)(react@17.0.2) '@testing-library/user-event': specifier: ^13.5.0 - version: registry.npmmirror.com/@testing-library/user-event@13.5.0(@testing-library/dom@8.20.0) + version: registry.npmmirror.com/@testing-library/user-event@13.5.0(@testing-library/dom@9.3.3) '@types/flexsearch': specifier: ^0.7.3 version: registry.npmmirror.com/@types/flexsearch@0.7.3 @@ -120,7 +120,7 @@ dependencies: version: registry.npmmirror.com/eslint@8.32.0 eslint-config-react-app: specifier: ^7.0.1 - version: registry.npmmirror.com/eslint-config-react-app@7.0.1(@babel/plugin-syntax-flow@7.18.6)(@babel/plugin-transform-react-jsx@7.20.13)(eslint@8.32.0)(jest@27.5.1)(typescript@4.9.4) + version: registry.npmmirror.com/eslint-config-react-app@7.0.1(@babel/plugin-syntax-flow@7.23.3)(@babel/plugin-transform-react-jsx@7.22.15)(eslint@8.32.0)(jest@27.5.1)(typescript@4.9.4) eslint-webpack-plugin: specifier: ^3.1.1 version: registry.npmmirror.com/eslint-webpack-plugin@3.2.0(eslint@8.32.0)(webpack@5.75.0) @@ -241,9 +241,6 @@ dependencies: style-loader: specifier: ^3.3.1 version: registry.npmmirror.com/style-loader@3.3.1(webpack@5.75.0) - tailwindcss: - specifier: ^3.0.2 - version: registry.npmmirror.com/tailwindcss@3.2.4(postcss@8.4.21) terser-webpack-plugin: specifier: ^5.2.5 version: registry.npmmirror.com/terser-webpack-plugin@5.3.6(webpack@5.75.0) @@ -266,7 +263,7 @@ dependencies: devDependencies: '@typescript-eslint/eslint-plugin': specifier: ^5.1.0 - version: registry.npmmirror.com/@typescript-eslint/eslint-plugin@5.49.0(@typescript-eslint/parser@5.49.0)(eslint@8.32.0)(typescript@4.9.4) + version: registry.npmmirror.com/@typescript-eslint/eslint-plugin@5.49.0(@typescript-eslint/parser@5.62.0)(eslint@8.32.0)(typescript@4.9.4) eslint-config-airbnb: specifier: ^18.2.1 version: registry.npmmirror.com/eslint-config-airbnb@18.2.1(eslint-plugin-import@2.27.5)(eslint-plugin-jsx-a11y@6.7.1)(eslint-plugin-react-hooks@4.6.0)(eslint-plugin-react@7.32.1)(eslint@8.32.0) @@ -275,7 +272,7 @@ devDependencies: version: registry.npmmirror.com/eslint-config-prettier@8.6.0(eslint@8.32.0) eslint-plugin-import: specifier: ^2.25.2 - 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name: xtend - version: 4.0.2 - engines: {node: '>=0.4'} - dev: false - registry.npmmirror.com/y18n@5.0.8: resolution: {integrity: sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==, registry: https://registry.npmjs.org/, tarball: https://registry.npmmirror.com/y18n/-/y18n-5.0.8.tgz} name: y18n diff --git a/lama_cleaner/app/src/components/Extender/Extender.scss b/lama_cleaner/app/src/components/Extender/Extender.scss new file mode 100644 index 0000000..a31c723 --- /dev/null +++ b/lama_cleaner/app/src/components/Extender/Extender.scss @@ -0,0 +1,166 @@ +@use 'sass:math'; + +$drag-handle-shortside: 12px; +$drag-handle-longside: 40px; +$drag-bar-size: 12px; + +$half-handle-shortside: math.div($drag-handle-shortside, 2); +$half-handle-longside: math.div($drag-handle-longside, 2); +$half-drag-bar-size: math.div($drag-bar-size, 2); + +.extender-border { + outline-color: var(--yellow-accent); + outline-style: dashed; +} + +.info-bar { + position: absolute; + pointer-events: auto; + font-size: 1rem; + padding: 0.2rem 0.8rem; + display: flex; + align-items: center; + justify-content: center; + gap: 12px; + color: var(--text-color); + background-color: var(--page-bg); + border-radius: 9999px; + + border: var(--editor-toolkit-panel-border); + box-shadow: 0 0 0 1px #0000001a, 0 3px 16px #00000014, 0 2px 6px 1px #00000017; + + &:hover { + cursor: move; + } +} + +.extender-wrapper { + position: absolute; + height: 100%; + width: 100%; + z-index: 2; + pointer-events: none; +} + +.extender { + position: relative; + top: 0; + bottom: 0; + left: 0; + right: 0; + z-index: 2; + pointer-events: none; + + // display: flex; + // flex-direction: column; + // align-items: center; + + box-shadow: 0 0 0 9999px rgba(0, 0, 0, 0.5); +} + +.drag-bar { + position: absolute; + pointer-events: auto; + // display: none; + + &.ord-top { + top: 0; + left: 0; + width: 100%; + height: $drag-bar-size; + margin-top: -$half-drag-bar-size; + cursor: ns-resize; + } + &.ord-right { + right: 0; + top: 0; + width: $drag-bar-size; + height: 100%; + margin-right: -$half-drag-bar-size; + cursor: ew-resize; + } + &.ord-bottom { + bottom: 0; + left: 0; + width: 100%; + height: $drag-bar-size; + margin-bottom: -$half-drag-bar-size; + cursor: ns-resize; + } + &.ord-left { + top: 0; + left: 0; + width: $drag-bar-size; + height: 100%; + margin-left: -$half-drag-bar-size; + cursor: ew-resize; + } +} + +.drag-handle { + width: $drag-handle-shortside; + height: $drag-handle-shortside; + z-index: 4; + position: absolute; + display: block; + content: ''; + border: 2px solid var(--yellow-accent); + background-color: var(--yellow-accent-light); + pointer-events: auto; + + &:hover { + background-color: var(--yellow-accent); + } + + &.ord-topleft { + cursor: nw-resize; + top: (-$half-handle-shortside)-1px; + left: (-$half-handle-shortside)-1px; + } + + &.ord-topright { + cursor: ne-resize; + top: -($half-handle-shortside)-1px; + right: -($half-handle-shortside)-1px; + } + + &.ord-bottomright { + cursor: se-resize; + bottom: -($half-handle-shortside)-1px; + right: -($half-handle-shortside)-1px; + } + + &.ord-bottomleft { + cursor: sw-resize; + bottom: -($half-handle-shortside)-1px; + left: -($half-handle-shortside)-1px; + } + + &.ord-top, + &.ord-bottom { + left: calc(50% - $half-handle-shortside); + cursor: ns-resize; + } + + &.ord-top { + top: (-$half-handle-shortside)-1px; + } + + &.ord-bottom { + bottom: -($half-handle-shortside)-1px; + } + + &.ord-left, + &.ord-right { + top: calc(50% - $half-handle-shortside); + cursor: ew-resize; + } + + &.ord-left { + left: (-$half-handle-shortside)-1px; + } + + &.ord-right { + right: -($half-handle-shortside)-1px; + } +} diff --git a/lama_cleaner/app/src/components/Extender/Extender.tsx b/lama_cleaner/app/src/components/Extender/Extender.tsx new file mode 100644 index 0000000..d5ee4c6 --- /dev/null +++ b/lama_cleaner/app/src/components/Extender/Extender.tsx @@ -0,0 +1,408 @@ +import React, { useEffect, useState } from 'react' +import { useRecoilState, useRecoilValue } from 'recoil' +import { + extenderX, + extenderY, + extenderHeight, + extenderWidth, + isInpaintingState, +} from '../../store/Atoms' + +const DOC_MOVE_OPTS = { capture: true, passive: false } + +const DRAG_HANDLE_BORDER = 2 +const DRAG_HANDLE_SHORT = 12 +const DRAG_HANDLE_LONG = 40 + +interface EVData { + initX: number + initY: number + initHeight: number + initWidth: number + startResizeX: number + startResizeY: number + ord: string // top/right/bottom/left +} + +interface Props { + maxHeight: number + maxWidth: number + scale: number + minHeight: number + minWidth: number + show: boolean +} + +const clamp = ( + newPos: number, + newLength: number, + oldPos: number, + oldLength: number, + minLength: number, + maxLength: number +) => { + if (newPos !== oldPos && newLength === oldLength) { + if (newPos < 0) { + return [0, oldLength] + } + if (newPos + newLength > maxLength) { + return [maxLength - oldLength, oldLength] + } + } else { + if (newLength < minLength) { + if (newPos === oldPos) { + return [newPos, minLength] + } + return [newPos + newLength - minLength, minLength] + } + if (newPos < 0) { + return [0, newPos + newLength] + } + if (newPos + newLength > maxLength) { + return [newPos, maxLength - newPos] + } + } + + return [newPos, newLength] +} + +const Extender = (props: Props) => { + const { minHeight, minWidth, maxHeight, maxWidth, scale, show } = props + const [x, setX] = useRecoilState(extenderX) + const [y, setY] = useRecoilState(extenderY) + const [height, setHeight] = useRecoilState(extenderHeight) + const [width, setWidth] = useRecoilState(extenderWidth) + const isInpainting = useRecoilValue(isInpaintingState) + + const [isResizing, setIsResizing] = useState(false) + const [isMoving, setIsMoving] = useState(false) + + useEffect(() => { + setX(Math.round((maxWidth - 512) / 2)) + setY(Math.round((maxHeight - 512) / 2)) + }, [maxHeight, maxWidth]) + + const [evData, setEVData] = useState({ + initX: 0, + initY: 0, + initHeight: 0, + initWidth: 0, + startResizeX: 0, + startResizeY: 0, + ord: 'top', + }) + + const onDragFocus = () => { + console.log('focus') + } + + const clampLeftRight = (newX: number, newWidth: number) => { + return clamp(newX, newWidth, x, width, minWidth, maxWidth) + } + + const clampTopBottom = (newY: number, newHeight: number) => { + return clamp(newY, newHeight, y, height, minHeight, maxHeight) + } + + const onPointerMove = (e: PointerEvent) => { + if (isInpainting) { + return + } + const curX = e.clientX + const curY = e.clientY + + const offsetY = Math.round((curY - evData.startResizeY) / scale) + const offsetX = Math.round((curX - evData.startResizeX) / scale) + + const moveTop = () => { + const newHeight = evData.initHeight - offsetY + const newY = evData.initY + offsetY + const [clampedY, clampedHeight] = clampTopBottom(newY, newHeight) + setHeight(clampedHeight) + setY(clampedY) + } + + const moveBottom = () => { + const newHeight = evData.initHeight + offsetY + const [clampedY, clampedHeight] = clampTopBottom(evData.initY, newHeight) + setHeight(clampedHeight) + setY(clampedY) + } + + const moveLeft = () => { + const newWidth = evData.initWidth - offsetX + const newX = evData.initX + offsetX + const [clampedX, clampedWidth] = clampLeftRight(newX, newWidth) + setWidth(clampedWidth) + setX(clampedX) + } + + const moveRight = () => { + const newWidth = evData.initWidth + offsetX + const [clampedX, clampedWidth] = clampLeftRight(evData.initX, newWidth) + setWidth(clampedWidth) + setX(clampedX) + } + + if (isResizing) { + switch (evData.ord) { + case 'topleft': { + moveTop() + moveLeft() + break + } + case 'topright': { + moveTop() + moveRight() + break + } + case 'bottomleft': { + moveBottom() + moveLeft() + break + } + case 'bottomright': { + moveBottom() + moveRight() + break + } + case 'top': { + moveTop() + break + } + case 'right': { + moveRight() + break + } + case 'bottom': { + moveBottom() + break + } + case 'left': { + moveLeft() + break + } + + default: + break + } + } + + if (isMoving) { + const newX = evData.initX + offsetX + const newY = evData.initY + offsetY + const [clampedX, clampedWidth] = clampLeftRight(newX, evData.initWidth) + const [clampedY, clampedHeight] = clampTopBottom(newY, evData.initHeight) + setWidth(clampedWidth) + setHeight(clampedHeight) + setX(clampedX) + setY(clampedY) + } + } + + const onPointerDone = (e: PointerEvent) => { + if (isResizing) { + setIsResizing(false) + } + + if (isMoving) { + setIsMoving(false) + } + } + + useEffect(() => { + if (isResizing || isMoving) { + document.addEventListener('pointermove', onPointerMove, DOC_MOVE_OPTS) + document.addEventListener('pointerup', onPointerDone, DOC_MOVE_OPTS) + document.addEventListener('pointercancel', onPointerDone, DOC_MOVE_OPTS) + return () => { + document.removeEventListener( + 'pointermove', + onPointerMove, + DOC_MOVE_OPTS + ) + document.removeEventListener('pointerup', onPointerDone, DOC_MOVE_OPTS) + document.removeEventListener( + 'pointercancel', + onPointerDone, + DOC_MOVE_OPTS + ) + } + } + }, [isResizing, isMoving, width, height, evData]) + + const onCropPointerDown = (e: React.PointerEvent) => { + const { ord } = (e.target as HTMLElement).dataset + if (ord) { + setIsResizing(true) + setEVData({ + initX: x, + initY: y, + initHeight: height, + initWidth: width, + startResizeX: e.clientX, + startResizeY: e.clientY, + ord, + }) + } + } + + const createCropSelection = () => { + return ( +
+
+
+
+
+ +
+ +
+ +
+ +
+ +
+
+
+
+
+ ) + } + + const onInfoBarPointerDown = (e: React.PointerEvent) => { + setIsMoving(true) + setEVData({ + initX: x, + initY: y, + initHeight: height, + initWidth: width, + startResizeX: e.clientX, + startResizeY: e.clientY, + ord: '', + }) + } + + const createInfoBar = () => { + return ( +
+
+ {width} x {height} +
+
+ ) + } + + const createBorder = () => { + return ( +
+ ) + } + + return ( +
+
+ {createBorder()} + {createInfoBar()} + {createCropSelection()} +
+
+ ) +} + +export default Extender diff --git a/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx b/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx index 15c7463..2c08948 100644 --- a/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx +++ b/lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx @@ -1,5 +1,6 @@ -import React, { ReactNode, useEffect, useState } from 'react' +import React, { ReactNode, useState } from 'react' import { useRecoilState, useRecoilValue } from 'recoil' +import * as Tabs from '@radix-ui/react-tabs' import { AIModel, CV2Flag, @@ -9,13 +10,21 @@ import { import Selector from '../shared/Selector' import { Switch, SwitchThumb } from '../shared/Switch' import Tooltip from '../shared/Tooltip' -import { LDMSampler } from './HDSettingBlock' +import HDSettingBlock, { LDMSampler } from './HDSettingBlock' import NumberInputSetting from './NumberInputSetting' import SettingBlock from './SettingBlock' +import Flex from '../shared/Layout' +import ManualRunInpaintingSettingBlock from './ManualRunInpaintingSettingBlock' + +const ERASE_TAB = 'erase_tab' +const INPAINTING_DIFFUSION_TAB = 'inpainting_diffusion_tab' +const DIFFUSION_TAB = 'diffusion_tab' +const OTHER_TAB = 'other_tab' function ModelSettingBlock() { const [setting, setSettingState] = useRecoilState(settingState) const isDisableModelSwitch = useRecoilValue(isDisableModelSwitchState) + const [tab, setTab] = useState(ERASE_TAB) const onModelChange = (value: AIModel) => { setSettingState(old => { @@ -190,112 +199,63 @@ function ModelSettingBlock() { } } - const renderPaperCodeBadge = (): ReactNode => { - switch (setting.model) { - case AIModel.LAMA: - return renderModelDesc( - 'Resolution-robust Large Mask Inpainting with Fourier Convolutions', - 'https://arxiv.org/abs/2109.07161', - 'https://github.com/saic-mdal/lama' - ) - case AIModel.LDM: - return renderModelDesc( - 'High-Resolution Image Synthesis with Latent Diffusion Models', - 'https://arxiv.org/abs/2112.10752', - 'https://github.com/CompVis/latent-diffusion' - ) - case AIModel.ZITS: - return renderModelDesc( - 'Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding', - 'https://arxiv.org/abs/2203.00867', - 'https://github.com/DQiaole/ZITS_inpainting' - ) - case AIModel.MAT: - return renderModelDesc( - 'Mask-Aware Transformer for Large Hole Image Inpainting', - 'https://arxiv.org/abs/2203.15270', - 'https://github.com/fenglinglwb/MAT' - ) - case AIModel.FCF: - return renderModelDesc( - 'Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand', - 'https://arxiv.org/abs/2208.03382', - 'https://github.com/SHI-Labs/FcF-Inpainting' - ) - case AIModel.SD15: - return renderModelDesc( - 'Stable Diffusion 1.5', - 'https://ommer-lab.com/research/latent-diffusion-models/', - 'https://github.com/CompVis/stable-diffusion' - ) - case AIModel.ANYTHING4: - return renderModelDesc( - 'andite/anything-v4.0', - 'https://huggingface.co/andite/anything-v4.0', - 'https://huggingface.co/andite/anything-v4.0' - ) - case AIModel.REALISTIC_VISION_1_4: - return renderModelDesc( - 'SG161222/Realistic_Vision_V1.4', - 'https://huggingface.co/SG161222/Realistic_Vision_V1.4', - 'https://huggingface.co/SG161222/Realistic_Vision_V1.4' - ) - case AIModel.SD2: - return renderModelDesc( - 'Stable Diffusion 2', - 'https://ommer-lab.com/research/latent-diffusion-models/', - 'https://github.com/Stability-AI/stablediffusion' - ) - case AIModel.Mange: - return renderModelDesc( - 'Manga Inpainting', - 'https://www.cse.cuhk.edu.hk/~ttwong/papers/mangainpaint/mangainpaint.html', - 'https://github.com/msxie92/MangaInpainting' - ) - case AIModel.CV2: - return renderModelDesc( - 'OpenCV Image Inpainting', - 'https://docs.opencv.org/4.6.0/df/d3d/tutorial_py_inpainting.html', - 'https://docs.opencv.org/4.6.0/df/d3d/tutorial_py_inpainting.html' - ) - case AIModel.PAINT_BY_EXAMPLE: - return renderModelDesc( - 'Paint by Example', - 'https://arxiv.org/abs/2211.13227', - 'https://github.com/Fantasy-Studio/Paint-by-Example' - ) - case AIModel.PIX2PIX: - return renderModelDesc( - 'InstructPix2Pix', - 'https://arxiv.org/abs/2211.09800', - 'https://github.com/timothybrooks/instruct-pix2pix' - ) - case AIModel.KANDINSKY21: - return renderModelDesc( - 'Kandinsky 2.1', - 'https://huggingface.co/kandinsky-community/kandinsky-2-1-inpaint', - 'https://huggingface.co/kandinsky-community/kandinsky-2-1-inpaint' - ) - default: - return <> - } - } - return ( - onModelChange(val as AIModel)} - disabled={isDisableModelSwitch} - /> - } - optionDesc={renderOptionDesc()} - /> + // onModelChange(val as AIModel)} + // disabled={isDisableModelSwitch} + // /> + // } + // optionDesc={renderOptionDesc()} + // /> + + +
Current Model
+
{setting.model}
+ + setTab(val)} + > + + + Erase + + + Inpainting Diffusion + + + Diffusion + + + Other + + + + + + + 123 + 123 + 123 + + ) } diff --git a/lama_cleaner/app/src/components/Settings/SettingsModal.tsx b/lama_cleaner/app/src/components/Settings/SettingsModal.tsx index aaaf683..1cef5bb 100644 --- a/lama_cleaner/app/src/components/Settings/SettingsModal.tsx +++ b/lama_cleaner/app/src/components/Settings/SettingsModal.tsx @@ -28,7 +28,6 @@ interface SettingModalProps { export default function SettingModal(props: SettingModalProps) { const { onClose } = props const [setting, setSettingState] = useRecoilState(settingState) - const isSD = useRecoilValue(isSDState) const isDiffusionModels = useRecoilValue(isDiffusionModelsState) const handleOnClose = () => { @@ -57,9 +56,8 @@ export default function SettingModal(props: SettingModalProps) { show={setting.show} > - {isDiffusionModels ? <> : } + - {isDiffusionModels ? <> : } ) } diff --git a/lama_cleaner/app/src/components/SidePanel/SidePanel.scss b/lama_cleaner/app/src/components/SidePanel/SidePanel.scss index 0175932..f427d52 100644 --- a/lama_cleaner/app/src/components/SidePanel/SidePanel.scss +++ b/lama_cleaner/app/src/components/SidePanel/SidePanel.scss @@ -85,7 +85,7 @@ } .resize-title-tile { - width: 86px; + width: 60px; font-size: 0.5rem; color: var(--text-color-gray); } diff --git a/lama_cleaner/app/src/components/SidePanel/SidePanel.tsx b/lama_cleaner/app/src/components/SidePanel/SidePanel.tsx index 668260f..ff1b644 100644 --- a/lama_cleaner/app/src/components/SidePanel/SidePanel.tsx +++ b/lama_cleaner/app/src/components/SidePanel/SidePanel.tsx @@ -1,6 +1,7 @@ -import React, { FormEvent } from 'react' +import React, { FormEvent, useState } from 'react' import { useRecoilState, useRecoilValue } from 'recoil' import * as PopoverPrimitive from '@radix-ui/react-popover' +import * as Tabs from '@radix-ui/react-tabs' import { useToggle } from 'react-use' import { ControlNetMethod, @@ -10,6 +11,8 @@ import { propmtState, SDSampler, settingState, + SIDE_PANEL_TAB, + SIDE_PANEL_TAB_TYPE, } from '../../store/Atoms' import NumberInputSetting from '../Settings/NumberInputSetting' import SettingBlock from '../Settings/SettingBlock' @@ -23,6 +26,7 @@ const INPUT_WIDTH = 30 const SidePanel = () => { const [open, toggleOpen] = useToggle(true) + const [tab, setTab] = useState(SIDE_PANEL_TAB.inpainting) const [setting, setSettingState] = useRecoilState(settingState) const [negativePrompt, setNegativePrompt] = useRecoilState(negativePropmtState) @@ -95,27 +99,58 @@ const SidePanel = () => { > Config + - {isControlNet && renderConterNetSetting()} - - { - setSettingState(old => { - return { ...old, showCroper: value } - }) - }} + setTab(val as SIDE_PANEL_TAB_TYPE)} + > + + - - - } - /> + In Painting + + + Out Painting + + + - + {isControlNet && tab === SIDE_PANEL_TAB.inpainting ? ( + renderConterNetSetting() + ) : ( + <> + )} + + {tab === SIDE_PANEL_TAB.inpainting ? ( + { + setSettingState(old => { + return { ...old, showCroper: value } + }) + }} + > + + + } + /> + ) : ( + <> + )} + + {tab === SIDE_PANEL_TAB.inpainting ? : <>} {/* { }} /> - { - const val = value.length === 0 ? 0 : parseInt(value, 10) - setSettingState(old => { - return { ...old, sdMaskBlur: val } - }) - }} - /> + {tab === SIDE_PANEL_TAB.inpainting ? ( + { + const val = value.length === 0 ? 0 : parseInt(value, 10) + setSettingState(old => { + return { ...old, sdMaskBlur: val } + }) + }} + /> + ) : ( + <> + )} - { - setSettingState(old => { - return { ...old, sdMatchHistograms: value } - }) - }} - > - - - } - /> + {tab === SIDE_PANEL_TAB.outpainting ? ( + { + setSettingState(old => { + return { ...old, sdMatchHistograms: value } + }) + }} + > + + + } + /> + ) : ( + <> + )} (o: Array): { [K in T]: K } { + return o.reduce((res, key) => { + res[key] = key + return res + }, Object.create(null)) +} + export enum AIModel { LAMA = 'lama', LDM = 'ldm', @@ -17,7 +24,7 @@ export enum AIModel { Mange = 'manga', PAINT_BY_EXAMPLE = 'paint_by_example', PIX2PIX = 'instruct_pix2pix', - KANDINSKY21 = 'kandinsky2.1', + KANDINSKY22 = 'kandinsky2.2', } export enum ControlNetMethod { @@ -351,6 +358,20 @@ export const croperState = atom({ }, }) +export const SIDE_PANEL_TAB = strEnum(['inpainting', 'outpainting']) +export type SIDE_PANEL_TAB_TYPE = keyof typeof SIDE_PANEL_TAB + +export interface SidePanelState { + tab: SIDE_PANEL_TAB_TYPE +} + +export const sidePanelTabState = atom({ + key: 'sidePanelTabState', + default: { + tab: SIDE_PANEL_TAB.inpainting, + }, +}) + export const croperX = selector({ key: 'croperX', get: ({ get }) => get(croperState).x, @@ -387,6 +408,52 @@ export const croperWidth = selector({ }, }) +export const extenderState = atom({ + key: 'extenderState', + default: { + x: 0, + y: 0, + width: 512, + height: 512, + }, +}) + +export const extenderX = selector({ + key: 'extenderX', + get: ({ get }) => get(extenderState).x, + set: ({ get, set }, newValue: any) => { + const rect = get(extenderState) + set(extenderState, { ...rect, x: newValue }) + }, +}) + +export const extenderY = selector({ + key: 'extenderY', + get: ({ get }) => get(extenderState).y, + set: ({ get, set }, newValue: any) => { + const rect = get(extenderState) + set(extenderState, { ...rect, y: newValue }) + }, +}) + +export const extenderHeight = selector({ + key: 'extenderHeight', + get: ({ get }) => get(extenderState).height, + set: ({ get, set }, newValue: any) => { + const rect = get(extenderState) + set(extenderState, { ...rect, height: newValue }) + }, +}) + +export const extenderWidth = selector({ + key: 'extenderWidth', + get: ({ get }) => get(extenderState).width, + set: ({ get, set }, newValue: any) => { + const rect = get(extenderState) + set(extenderState, { ...rect, width: newValue }) + }, +}) + interface ToastAtomState { open: boolean desc: string @@ -567,7 +634,7 @@ const defaultHDSettings: ModelsHDSettings = { hdStrategyCropMargin: 128, enabled: true, }, - [AIModel.KANDINSKY21]: { + [AIModel.KANDINSKY22]: { hdStrategy: HDStrategy.ORIGINAL, hdStrategyResizeLimit: 768, hdStrategyCropTrigerSize: 512, @@ -728,7 +795,7 @@ export const isSDState = selector({ settings.model === AIModel.SD2 || settings.model === AIModel.ANYTHING4 || settings.model === AIModel.REALISTIC_VISION_1_4 || - settings.model === AIModel.KANDINSKY21 + settings.model === AIModel.KANDINSKY22 ) }, }) From a5c241ac02dcf429be7195000d7e4e9d0816bfbb Mon Sep 17 00:00:00 2001 From: Qing Date: Mon, 20 Nov 2023 13:05:28 +0800 Subject: [PATCH 023/134] add migan --- lama_cleaner/model/mi_gan.py | 109 +++++++++++++++++++++++++++++++ lama_cleaner/model_manager.py | 9 ++- lama_cleaner/tests/test_model.py | 20 +++++- 3 files changed, 134 insertions(+), 4 deletions(-) create mode 100644 lama_cleaner/model/mi_gan.py diff --git a/lama_cleaner/model/mi_gan.py b/lama_cleaner/model/mi_gan.py new file mode 100644 index 0000000..1b2ba1d --- /dev/null +++ b/lama_cleaner/model/mi_gan.py @@ -0,0 +1,109 @@ +import os + +import cv2 +import torch + +from lama_cleaner.const import Config +from lama_cleaner.helper import ( + load_jit_model, + download_model, + get_cache_path_by_url, + boxes_from_mask, + resize_max_size, + norm_img, +) +from lama_cleaner.model.base import InpaintModel + +MIGAN_MODEL_URL = os.environ.get( + "MIGAN_MODEL_URL", + "/Users/cwq/code/github/MI-GAN/exported_models/migan_places512/models/migan_traced.pt", +) +MIGAN_MODEL_MD5 = os.environ.get("MIGAN_MODEL_MD5", "76eb3b1a71c400ee3290524f7a11b89c") + + +class MIGAN(InpaintModel): + name = "migan" + min_size = 512 + pad_mod = 512 + pad_to_square = True + + def init_model(self, device, **kwargs): + self.model = load_jit_model(MIGAN_MODEL_URL, device, MIGAN_MODEL_MD5).eval() + + @staticmethod + def download(): + download_model(MIGAN_MODEL_URL, MIGAN_MODEL_MD5) + + @staticmethod + def is_downloaded() -> bool: + return os.path.exists(get_cache_path_by_url(MIGAN_MODEL_URL)) + + @torch.no_grad() + def __call__(self, image, mask, config: Config): + """ + images: [H, W, C] RGB, not normalized + masks: [H, W] + return: BGR IMAGE + """ + if image.shape[0] == 512 and image.shape[1] == 512: + return self._pad_forward(image, mask, config) + + boxes = boxes_from_mask(mask) + crop_result = [] + config.hd_strategy_crop_margin = 128 + for box in boxes: + crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config) + origin_size = crop_image.shape[:2] + resize_image = resize_max_size(crop_image, size_limit=512) + resize_mask = resize_max_size(crop_mask, size_limit=512) + inpaint_result = self._pad_forward(resize_image, resize_mask, config) + + # only paste masked area result + inpaint_result = cv2.resize( + inpaint_result, + (origin_size[1], origin_size[0]), + interpolation=cv2.INTER_CUBIC, + ) + + original_pixel_indices = crop_mask < 127 + inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][ + original_pixel_indices + ] + + crop_result.append((inpaint_result, crop_box)) + + inpaint_result = image[:, :, ::-1] + for crop_image, crop_box in crop_result: + x1, y1, x2, y2 = crop_box + inpaint_result[y1:y2, x1:x2, :] = crop_image + + return inpaint_result + + def forward(self, image, mask, config: Config): + """Input images and output images have same size + images: [H, W, C] RGB + masks: [H, W] mask area == 255 + return: BGR IMAGE + """ + + image = norm_img(image) # [0, 1] + image = image * 2 - 1 # [0, 1] -> [-1, 1] + mask = (mask > 120) * 255 + mask = norm_img(mask) + + image = torch.from_numpy(image).unsqueeze(0).to(self.device) + mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) + + erased_img = image * (1 - mask) + input_image = torch.cat([0.5 - mask, erased_img], dim=1) + + output = self.model(input_image) + output = ( + (output.permute(0, 2, 3, 1) * 127.5 + 127.5) + .round() + .clamp(0, 255) + .to(torch.uint8) + ) + output = output[0].cpu().numpy() + cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) + return cur_res diff --git a/lama_cleaner/model_manager.py b/lama_cleaner/model_manager.py index 43d9ced..8461c95 100644 --- a/lama_cleaner/model_manager.py +++ b/lama_cleaner/model_manager.py @@ -3,7 +3,11 @@ import gc from loguru import logger -from lama_cleaner.const import SD15_MODELS, MODELS_SUPPORT_FREEU, MODELS_SUPPORT_LCM_LORA +from lama_cleaner.const import ( + SD15_MODELS, + MODELS_SUPPORT_FREEU, + MODELS_SUPPORT_LCM_LORA, +) from lama_cleaner.helper import switch_mps_device from lama_cleaner.model.controlnet import ControlNet from lama_cleaner.model.fcf import FcF @@ -12,6 +16,7 @@ from lama_cleaner.model.lama import LaMa from lama_cleaner.model.ldm import LDM from lama_cleaner.model.manga import Manga from lama_cleaner.model.mat import MAT +from lama_cleaner.model.mi_gan import MIGAN from lama_cleaner.model.paint_by_example import PaintByExample from lama_cleaner.model.instruct_pix2pix import InstructPix2Pix from lama_cleaner.model.sd import SD15, SD2, Anything4, RealisticVision14 @@ -37,6 +42,7 @@ models = { "instruct_pix2pix": InstructPix2Pix, Kandinsky22.name: Kandinsky22, SDXL.name: SDXL, + MIGAN.name: MIGAN, } @@ -146,4 +152,3 @@ class ModelManager: self.model.model.load_lora_weights(self.model.lcm_lora_id) else: self.model.model.disable_lora() - diff --git a/lama_cleaner/tests/test_model.py b/lama_cleaner/tests/test_model.py index be253b5..f6931ce 100644 --- a/lama_cleaner/tests/test_model.py +++ b/lama_cleaner/tests/test_model.py @@ -170,7 +170,7 @@ def test_cv2(strategy, cv2_flag, cv2_radius): assert_equal( model, cfg, - f"sd_{strategy.capitalize()}_{cv2_flag}_{cv2_radius}.png", + f"cv2_{strategy.capitalize()}_{cv2_flag}_{cv2_radius}.png", img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", ) @@ -188,7 +188,23 @@ def test_manga(strategy): assert_equal( model, cfg, - f"sd_{strategy.capitalize()}.png", + f"manga_{strategy.capitalize()}.png", + img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", + mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", + ) + + +@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL]) +def test_mi_gan(strategy): + model = ModelManager( + name="migan", + device=torch.device(device), + ) + cfg = get_config(strategy) + assert_equal( + model, + cfg, + f"migan_{strategy.capitalize()}.png", img_p=current_dir / "overture-creations-5sI6fQgYIuo.png", mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png", ) From 04c5dfece888c55e09eab77d297f3dfc6eb8e0fd Mon Sep 17 00:00:00 2001 From: Qing Date: Wed, 22 Nov 2023 08:53:20 +0800 Subject: [PATCH 024/134] new web init --- web_app/.eslintrc.cjs | 18 + web_app/.gitignore | 24 + web_app/README.md | 30 + web_app/components.json | 16 + web_app/index.html | 13 + web_app/package-lock.json | 5775 +++++++++++++++++++++ web_app/package.json | 65 + web_app/postcss.config.js | 6 + web_app/src/App.tsx | 173 + web_app/src/components/Editor.tsx | 1704 ++++++ web_app/src/components/FileManager.tsx | 321 ++ web_app/src/components/FileSelect.tsx | 71 + web_app/src/components/Header.tsx | 173 + web_app/src/components/PromptInput.tsx | 66 + web_app/src/components/Shortcuts.tsx | 97 + web_app/src/components/Workspace.tsx | 114 + web_app/src/components/theme-provider.tsx | 73 + web_app/src/components/ui/accordion.tsx | 55 + web_app/src/components/ui/button.tsx | 123 + web_app/src/components/ui/dialog.tsx | 120 + web_app/src/components/ui/input.tsx | 25 + web_app/src/components/ui/label.tsx | 24 + web_app/src/components/ui/popover.tsx | 29 + web_app/src/components/ui/scroll-area.tsx | 46 + web_app/src/components/ui/select.tsx | 162 + web_app/src/components/ui/slider.tsx | 26 + web_app/src/components/ui/switch.tsx | 27 + web_app/src/components/ui/tabs.tsx | 53 + web_app/src/components/ui/textarea.tsx | 24 + web_app/src/components/ui/toast.tsx | 127 + web_app/src/components/ui/toaster.tsx | 33 + web_app/src/components/ui/toggle.tsx | 43 + web_app/src/components/ui/tooltip.tsx | 28 + web_app/src/components/ui/use-toast.ts | 192 + web_app/src/globals.css | 100 + web_app/src/hooks/useAsyncMemo.tsx | 33 + web_app/src/hooks/useHotkey.tsx | 22 + web_app/src/hooks/useImage.tsx | 24 + web_app/src/hooks/useInputImage.tsx | 34 + web_app/src/hooks/useResolution.tsx | 31 + web_app/src/lib/api.ts | 270 + web_app/src/lib/event.ts | 22 + web_app/src/lib/store.ts | 902 ++++ web_app/src/lib/types.ts | 9 + web_app/src/lib/utils.ts | 133 + web_app/src/main.tsx | 16 + web_app/src/vite-env.d.ts | 1 + web_app/tailwind.config.js | 76 + web_app/tsconfig.json | 32 + web_app/tsconfig.node.json | 10 + web_app/vite.config.ts | 12 + 51 files changed, 11603 insertions(+) create mode 100644 web_app/.eslintrc.cjs create mode 100644 web_app/.gitignore create mode 100644 web_app/README.md create mode 100644 web_app/components.json create mode 100644 web_app/index.html create mode 100644 web_app/package-lock.json create mode 100644 web_app/package.json create mode 100644 web_app/postcss.config.js create mode 100644 web_app/src/App.tsx create mode 100644 web_app/src/components/Editor.tsx create mode 100644 web_app/src/components/FileManager.tsx create mode 100644 web_app/src/components/FileSelect.tsx create mode 100644 web_app/src/components/Header.tsx create mode 100644 web_app/src/components/PromptInput.tsx create mode 100644 web_app/src/components/Shortcuts.tsx create mode 100644 web_app/src/components/Workspace.tsx create mode 100644 web_app/src/components/theme-provider.tsx create mode 100644 web_app/src/components/ui/accordion.tsx create mode 100644 web_app/src/components/ui/button.tsx create mode 100644 web_app/src/components/ui/dialog.tsx create mode 100644 web_app/src/components/ui/input.tsx create mode 100644 web_app/src/components/ui/label.tsx create mode 100644 web_app/src/components/ui/popover.tsx create mode 100644 web_app/src/components/ui/scroll-area.tsx create mode 100644 web_app/src/components/ui/select.tsx create mode 100644 web_app/src/components/ui/slider.tsx create mode 100644 web_app/src/components/ui/switch.tsx create mode 100644 web_app/src/components/ui/tabs.tsx create mode 100644 web_app/src/components/ui/textarea.tsx create mode 100644 web_app/src/components/ui/toast.tsx create mode 100644 web_app/src/components/ui/toaster.tsx create mode 100644 web_app/src/components/ui/toggle.tsx create mode 100644 web_app/src/components/ui/tooltip.tsx create mode 100644 web_app/src/components/ui/use-toast.ts create mode 100644 web_app/src/globals.css create mode 100644 web_app/src/hooks/useAsyncMemo.tsx create mode 100644 web_app/src/hooks/useHotkey.tsx create mode 100644 web_app/src/hooks/useImage.tsx create mode 100644 web_app/src/hooks/useInputImage.tsx create mode 100644 web_app/src/hooks/useResolution.tsx create mode 100644 web_app/src/lib/api.ts create mode 100644 web_app/src/lib/event.ts create mode 100644 web_app/src/lib/store.ts create mode 100644 web_app/src/lib/types.ts create mode 100644 web_app/src/lib/utils.ts create mode 100644 web_app/src/main.tsx create mode 100644 web_app/src/vite-env.d.ts create mode 100644 web_app/tailwind.config.js create mode 100644 web_app/tsconfig.json create mode 100644 web_app/tsconfig.node.json create mode 100644 web_app/vite.config.ts diff --git a/web_app/.eslintrc.cjs b/web_app/.eslintrc.cjs new file mode 100644 index 0000000..d6c9537 --- /dev/null +++ b/web_app/.eslintrc.cjs @@ -0,0 +1,18 @@ +module.exports = { + root: true, + env: { browser: true, es2020: true }, + extends: [ + 'eslint:recommended', + 'plugin:@typescript-eslint/recommended', + 'plugin:react-hooks/recommended', + ], + ignorePatterns: ['dist', '.eslintrc.cjs'], + parser: '@typescript-eslint/parser', + plugins: ['react-refresh'], + rules: { + 'react-refresh/only-export-components': [ + 'warn', + { allowConstantExport: true }, + ], + }, +} diff --git a/web_app/.gitignore b/web_app/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/web_app/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/web_app/README.md b/web_app/README.md new file mode 100644 index 0000000..0d6babe --- /dev/null +++ b/web_app/README.md @@ -0,0 +1,30 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type aware lint rules: + +- Configure the top-level `parserOptions` property like this: + +```js +export default { + // other rules... + parserOptions: { + ecmaVersion: 'latest', + sourceType: 'module', + project: ['./tsconfig.json', './tsconfig.node.json'], + tsconfigRootDir: __dirname, + }, +} +``` + +- Replace `plugin:@typescript-eslint/recommended` to `plugin:@typescript-eslint/recommended-type-checked` or `plugin:@typescript-eslint/strict-type-checked` +- Optionally add `plugin:@typescript-eslint/stylistic-type-checked` +- Install [eslint-plugin-react](https://github.com/jsx-eslint/eslint-plugin-react) and add `plugin:react/recommended` & `plugin:react/jsx-runtime` to the `extends` list diff --git a/web_app/components.json b/web_app/components.json new file mode 100644 index 0000000..0cc3425 --- /dev/null +++ b/web_app/components.json @@ -0,0 +1,16 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "tailwind.config.js", + "css": "app/globals.css", + "baseColor": "gray", + "cssVariables": true + }, + "aliases": { + "components": "@/components", + "utils": "@/lib/utils" + } +} \ No newline at end of file diff --git a/web_app/index.html b/web_app/index.html new file mode 100644 index 0000000..e4b78ea --- /dev/null +++ b/web_app/index.html @@ -0,0 +1,13 @@ + + + + + + + Vite + React + TS + + +
+ + + diff --git a/web_app/package-lock.json b/web_app/package-lock.json new file mode 100644 index 0000000..90627ca --- /dev/null +++ b/web_app/package-lock.json @@ -0,0 +1,5775 @@ +{ + "name": "web_app", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "web_app", + "version": "0.0.0", + "dependencies": { + "@heroicons/react": "^2.0.18", + "@radix-ui/react-accordion": "^1.1.2", + "@radix-ui/react-dialog": "^1.0.5", + "@radix-ui/react-icons": "^1.3.0", + "@radix-ui/react-label": "^2.0.2", + "@radix-ui/react-popover": "^1.0.7", + "@radix-ui/react-scroll-area": "^1.0.5", + "@radix-ui/react-select": "^2.0.0", + "@radix-ui/react-slider": "^1.1.2", + "@radix-ui/react-slot": "^1.0.2", + "@radix-ui/react-switch": "^1.0.3", + "@radix-ui/react-tabs": "^1.0.4", + "@radix-ui/react-toast": "^1.1.5", + "@radix-ui/react-toggle": "^1.0.3", + "@radix-ui/react-tooltip": "^1.0.7", + "@uidotdev/usehooks": "^2.4.1", + "class-variance-authority": "^0.7.0", 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0000000..9ef38e3 --- /dev/null +++ b/web_app/package.json @@ -0,0 +1,65 @@ +{ + "name": "web_app", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc && vite build", + "lint": "eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0", + "preview": "vite preview" + }, + "dependencies": { + "@heroicons/react": "^2.0.18", + "@radix-ui/react-accordion": "^1.1.2", + "@radix-ui/react-dialog": "^1.0.5", + "@radix-ui/react-icons": "^1.3.0", + "@radix-ui/react-label": "^2.0.2", + "@radix-ui/react-popover": "^1.0.7", + "@radix-ui/react-scroll-area": "^1.0.5", + "@radix-ui/react-select": "^2.0.0", + "@radix-ui/react-slider": "^1.1.2", + "@radix-ui/react-slot": "^1.0.2", + "@radix-ui/react-switch": "^1.0.3", + "@radix-ui/react-tabs": "^1.0.4", + "@radix-ui/react-toast": "^1.1.5", + "@radix-ui/react-toggle": "^1.0.3", + "@radix-ui/react-tooltip": "^1.0.7", + "@uidotdev/usehooks": "^2.4.1", + "class-variance-authority": "^0.7.0", + "clsx": "^2.0.0", + "flexsearch": "^0.7.21", + "lodash": "^4.17.21", + "lucide-react": "^0.292.0", + "mitt": "^3.0.1", + "next-themes": "^0.2.1", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "react-hotkeys-hook": "^4.4.1", + "react-photo-album": "^2.3.0", + "react-use": "^17.4.0", + "react-zoom-pan-pinch": "^3.3.0", + "recoil": "^0.7.7", + "tailwind-merge": "^2.0.0", + "tailwindcss-animate": "^1.0.7" + }, + "devDependencies": { + "@types/flexsearch": "^0.7.3", + "@types/lodash": "^4.14.201", + "@types/node": "^20.9.2", + "@types/react": "^18.2.37", + "@types/react-dom": "^18.2.15", + "@typescript-eslint/eslint-plugin": "^6.10.0", + "@typescript-eslint/parser": "^6.10.0", + "@vitejs/plugin-react": "^4.2.0", + "@vitejs/plugin-react-swc": "^3.5.0", + "autoprefixer": "^10.4.16", + "eslint": "^8.53.0", + "eslint-plugin-react-hooks": "^4.6.0", + "eslint-plugin-react-refresh": "^0.4.4", + "postcss": "^8.4.31", + "tailwindcss": "^3.3.5", + "typescript": "^5.2.2", + "vite": "^5.0.0" + } +} diff --git a/web_app/postcss.config.js b/web_app/postcss.config.js new file mode 100644 index 0000000..2e7af2b --- /dev/null +++ b/web_app/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/web_app/src/App.tsx b/web_app/src/App.tsx new file mode 100644 index 0000000..f845013 --- /dev/null +++ b/web_app/src/App.tsx @@ -0,0 +1,173 @@ +import { useCallback, useEffect, useMemo, useRef, useState } from "react" +import { nanoid } from "nanoid" +import { useRecoilState, useSetRecoilState } from "recoil" +import { fileState, serverConfigState } from "@/lib/store" +import useInputImage from "@/hooks/useInputImage" +import { keepGUIAlive } from "@/lib/utils" +import { getServerConfig, isDesktop } from "@/lib/api" +import Header from "@/components/Header" +import Workspace from "@/components/Workspace" +import FileSelect from "@/components/FileSelect" +import { Toaster } from "./components/ui/toaster" + +const SUPPORTED_FILE_TYPE = [ + "image/jpeg", + "image/png", + "image/webp", + "image/bmp", + "image/tiff", +] +function Home() { + const [file, setFile] = useRecoilState(fileState) + const userInputImage = useInputImage() + const setServerConfigState = useSetRecoilState(serverConfigState) + + // Set Input Image + useEffect(() => { + setFile(userInputImage) + }, [userInputImage, setFile]) + + // Keeping GUI Window Open + useEffect(() => { + const fetchData = async () => { + const isRunDesktop = await isDesktop().then((res) => res.text()) + if (isRunDesktop === "True") { + keepGUIAlive() + } + } + fetchData() + }, []) + + useEffect(() => { + const fetchServerConfig = async () => { + const serverConfig = await getServerConfig().then((res) => res.json()) + console.log(serverConfig) + setServerConfigState(serverConfig) + } + fetchServerConfig() + }, []) + + const workspaceId = useMemo(() => { + return nanoid() + }, [file]) + + const [isDragging, setIsDragging] = useState(false) + const dragCounter = useRef(0) + + const handleDrag = useCallback((event: any) => { + event.preventDefault() + event.stopPropagation() + }, []) + + const handleDragIn = useCallback((event: any) => { + event.preventDefault() + event.stopPropagation() + dragCounter.current += 1 + if (event.dataTransfer.items && event.dataTransfer.items.length > 0) { + setIsDragging(true) + } + }, []) + + const handleDragOut = useCallback((event: any) => { + event.preventDefault() + event.stopPropagation() + dragCounter.current -= 1 + if (dragCounter.current > 0) return + setIsDragging(false) + }, []) + + const handleDrop = useCallback((event: any) => { + event.preventDefault() + event.stopPropagation() + setIsDragging(false) + if (event.dataTransfer.files && event.dataTransfer.files.length > 0) { + if (event.dataTransfer.files.length > 1) { + // setToastState({ + // open: true, + // desc: "Please drag and drop only one file", + // state: "error", + // duration: 3000, + // }) + } else { + const dragFile = event.dataTransfer.files[0] + const fileType = dragFile.type + if (SUPPORTED_FILE_TYPE.includes(fileType)) { + setFile(dragFile) + } else { + // setToastState({ + // open: true, + // desc: "Please drag and drop an image file", + // state: "error", + // duration: 3000, + // }) + } + } + event.dataTransfer.clearData() + } + }, []) + + const onPaste = useCallback((event: any) => { + // TODO: when sd side panel open, ctrl+v not work + // https://htmldom.dev/paste-an-image-from-the-clipboard/ + if (!event.clipboardData) { + return + } + const clipboardItems = event.clipboardData.items + const items: DataTransferItem[] = [].slice + .call(clipboardItems) + .filter((item: DataTransferItem) => { + // Filter the image items only + return item.type.indexOf("image") !== -1 + }) + + if (items.length === 0) { + return + } + + event.preventDefault() + event.stopPropagation() + + // TODO: add confirm dialog + + const item = items[0] + // Get the blob of image + const blob = item.getAsFile() + if (blob) { + setFile(blob) + } + }, []) + + useEffect(() => { + window.addEventListener("dragenter", handleDragIn) + window.addEventListener("dragleave", handleDragOut) + window.addEventListener("dragover", handleDrag) + window.addEventListener("drop", handleDrop) + window.addEventListener("paste", onPaste) + return function cleanUp() { + window.removeEventListener("dragenter", handleDragIn) + window.removeEventListener("dragleave", handleDragOut) + window.removeEventListener("dragover", handleDrag) + window.removeEventListener("drop", handleDrop) + window.removeEventListener("paste", onPaste) + } + }) + + return ( +
+ +
+ + {!file ? ( + { + setFile(f) + }} + /> + ) : ( + <> + )} +
+ ) +} + +export default Home diff --git a/web_app/src/components/Editor.tsx b/web_app/src/components/Editor.tsx new file mode 100644 index 0000000..2de8d1a --- /dev/null +++ b/web_app/src/components/Editor.tsx @@ -0,0 +1,1704 @@ +import React, { + SyntheticEvent, + useCallback, + useEffect, + useRef, + useState, +} from "react" +import { + CursorArrowRaysIcon, + ArrowsPointingOutIcon, + ArrowDownTrayIcon, +} from "@heroicons/react/24/outline" +import { + ReactZoomPanPinchRef, + TransformComponent, + TransformWrapper, +} from "react-zoom-pan-pinch" +import { useRecoilState, useRecoilValue, useSetRecoilState } from "recoil" +import { useWindowSize } from "react-use" +// import { useWindowSize, useKey, useKeyPressEvent } from "@uidotdev/usehooks" +import inpaint, { downloadToOutput, runPlugin } from "@/lib/api" +import { Button, IconButton } from "@/components/ui/button" +import { + askWritePermission, + copyCanvasImage, + downloadImage, + isMidClick, + isRightClick, + loadImage, + srcToFile, +} from "@/lib/utils" +import { Eraser, Eye, Redo, Undo } from "lucide-react" +import { + appState, + brushSizeState, + croperState, + enableFileManagerState, + fileState, + imageHeightState, + imageWidthState, + interactiveSegClicksState, + isDiffusionModelsState, + isEnableAutoSavingState, + isInpaintingState, + isInteractiveSegRunningState, + isInteractiveSegState, + isPix2PixState, + isPluginRunningState, + isProcessingState, + negativePropmtState, + propmtState, + runManuallyState, + seedState, + settingState, + toastState, +} from "@/lib/store" +// import Croper from "../Croper/Croper" +import emitter, { + EVENT_PROMPT, + EVENT_CUSTOM_MASK, + EVENT_PAINT_BY_EXAMPLE, + RERUN_LAST_MASK, + DREAM_BUTTON_MOUSE_ENTER, + DREAM_BUTTON_MOUSE_LEAVE, +} from "@/lib/event" +import { useImage } from "@/hooks/useImage" +import { Slider } from "./ui/slider" +// import FileSelect from "../FileSelect/FileSelect" +// import InteractiveSeg from "../InteractiveSeg/InteractiveSeg" +// import InteractiveSegConfirmActions from "../InteractiveSeg/ConfirmActions" +// import InteractiveSegReplaceModal from "../InteractiveSeg/ReplaceModal" +import { PluginName } from "@/lib/types" +import { useHotkeys } from "react-hotkeys-hook" +// import MakeGIF from "./MakeGIF" + +const TOOLBAR_SIZE = 200 +const MIN_BRUSH_SIZE = 10 +const MAX_BRUSH_SIZE = 200 +const BRUSH_COLOR = "#ffcc00bb" + +interface Line { + size?: number + pts: { x: number; y: number }[] +} + +type LineGroup = Array + +function drawLines( + ctx: CanvasRenderingContext2D, + lines: LineGroup, + color = BRUSH_COLOR +) { + ctx.strokeStyle = color + ctx.lineCap = "round" + ctx.lineJoin = "round" + + lines.forEach((line) => { + if (!line?.pts.length || !line.size) { + return + } + ctx.lineWidth = line.size + ctx.beginPath() + ctx.moveTo(line.pts[0].x, line.pts[0].y) + line.pts.forEach((pt) => ctx.lineTo(pt.x, pt.y)) + ctx.stroke() + }) +} + +function mouseXY(ev: SyntheticEvent) { + const mouseEvent = ev.nativeEvent as MouseEvent + return { x: mouseEvent.offsetX, y: mouseEvent.offsetY } +} + +export default function Editor() { + const [file, setFile] = useRecoilState(fileState) + const promptVal = useRecoilValue(propmtState) + const negativePromptVal = useRecoilValue(negativePropmtState) + const settings = useRecoilValue(settingState) + const [seedVal, setSeed] = useRecoilState(seedState) + const croperRect = useRecoilValue(croperState) + const setToastState = useSetRecoilState(toastState) + const [isInpainting, setIsInpainting] = useRecoilState(isInpaintingState) + const setIsPluginRunning = useSetRecoilState(isPluginRunningState) + const isProcessing = useRecoilValue(isProcessingState) + const runMannually = useRecoilValue(runManuallyState) + const isDiffusionModels = useRecoilValue(isDiffusionModelsState) + const isPix2Pix = useRecoilValue(isPix2PixState) + const [isInteractiveSeg, setIsInteractiveSeg] = useRecoilState( + isInteractiveSegState + ) + const setIsInteractiveSegRunning = useSetRecoilState( + isInteractiveSegRunningState + ) + + const [showInteractiveSegModal, setShowInteractiveSegModal] = useState(false) + const [interactiveSegMask, setInteractiveSegMask] = useState< + HTMLImageElement | null | undefined + >(null) + // only used while interactive segmentation is on + const [tmpInteractiveSegMask, setTmpInteractiveSegMask] = useState< + HTMLImageElement | null | undefined + >(null) + const [prevInteractiveSegMask, setPrevInteractiveSegMask] = useState< + HTMLImageElement | null | undefined + >(null) + + // 仅用于在 dream button hover 时显示提示 + const [dreamButtonHoverSegMask, setDreamButtonHoverSegMask] = useState< + HTMLImageElement | null | undefined + >(null) + const [dreamButtonHoverLineGroup, setDreamButtonHoverLineGroup] = + useState([]) + + const [clicks, setClicks] = useRecoilState(interactiveSegClicksState) + + const [brushSize, setBrushSize] = useRecoilState(brushSizeState) + + const [original, isOriginalLoaded] = useImage(file) + const [renders, setRenders] = useState([]) + const [context, setContext] = useState() + const [maskCanvas] = useState(() => { + return document.createElement("canvas") + }) + const [lineGroups, setLineGroups] = useState([]) + const [lastLineGroup, setLastLineGroup] = useState([]) + const [curLineGroup, setCurLineGroup] = useState([]) + const [{ x, y }, setCoords] = useState({ x: -1, y: -1 }) + const [showBrush, setShowBrush] = useState(false) + const [showRefBrush, setShowRefBrush] = useState(false) + const [isPanning, setIsPanning] = useState(false) + const [isChangingBrushSizeByMouse, setIsChangingBrushSizeByMouse] = + useState(false) + const [changeBrushSizeByMouseInit, setChangeBrushSizeByMouseInit] = useState({ + x: -1, + y: -1, + brushSize: 20, + }) + + const [showOriginal, setShowOriginal] = useState(false) + const [scale, setScale] = useState(1) + const [panned, setPanned] = useState(false) + const [minScale, setMinScale] = useState(1.0) + const windowSize = useWindowSize() + const windowCenterX = windowSize.width / 2 + const windowCenterY = windowSize.height / 2 + const viewportRef = useRef(null) + // Indicates that the image has been loaded and is centered on first load + const [initialCentered, setInitialCentered] = useState(false) + + const [isDraging, setIsDraging] = useState(false) + const [isMultiStrokeKeyPressed, setIsMultiStrokeKeyPressed] = useState(false) + + const [sliderPos, setSliderPos] = useState(0) + + // redo 相关 + const [redoRenders, setRedoRenders] = useState([]) + const [redoCurLines, setRedoCurLines] = useState([]) + const [redoLineGroups, setRedoLineGroups] = useState([]) + const enableFileManager = useRecoilValue(enableFileManagerState) + const isEnableAutoSaving = useRecoilValue(isEnableAutoSavingState) + + const [imageWidth, setImageWidth] = useRecoilState(imageWidthState) + const [imageHeight, setImageHeight] = useRecoilState(imageHeightState) + const app = useRecoilValue(appState) + + const draw = useCallback( + (render: HTMLImageElement, lineGroup: LineGroup) => { + if (!context) { + return + } + console.log( + `[draw] render size: ${render.width}x${render.height} image size: ${imageWidth}x${imageHeight} canvas size: ${context.canvas.width}x${context.canvas.height}` + ) + + context.clearRect(0, 0, context.canvas.width, context.canvas.height) + context.drawImage(render, 0, 0, imageWidth, imageHeight) + if (isInteractiveSeg && tmpInteractiveSegMask) { + context.drawImage(tmpInteractiveSegMask, 0, 0, imageWidth, imageHeight) + } + if (!isInteractiveSeg && interactiveSegMask) { + context.drawImage(interactiveSegMask, 0, 0, imageWidth, imageHeight) + } + if (dreamButtonHoverSegMask) { + context.drawImage( + dreamButtonHoverSegMask, + 0, + 0, + imageWidth, + imageHeight + ) + } + drawLines(context, lineGroup) + drawLines(context, dreamButtonHoverLineGroup) + }, + [ + context, + isInteractiveSeg, + tmpInteractiveSegMask, + dreamButtonHoverSegMask, + interactiveSegMask, + imageHeight, + imageWidth, + dreamButtonHoverLineGroup, + ] + ) + + const drawLinesOnMask = useCallback( + (_lineGroups: LineGroup[], maskImage?: HTMLImageElement | null) => { + if (!context?.canvas.width || !context?.canvas.height) { + throw new Error("canvas has invalid size") + } + maskCanvas.width = context?.canvas.width + maskCanvas.height = context?.canvas.height + const ctx = maskCanvas.getContext("2d") + if (!ctx) { + throw new Error("could not retrieve mask canvas") + } + + if (maskImage !== undefined && maskImage !== null) { + // TODO: check whether draw yellow mask works on backend + ctx.drawImage(maskImage, 0, 0, imageWidth, imageHeight) + } + + _lineGroups.forEach((lineGroup) => { + drawLines(ctx, lineGroup, "white") + }) + + if ( + (maskImage === undefined || maskImage === null) && + _lineGroups.length === 1 && + _lineGroups[0].length === 0 && + isPix2Pix + ) { + // For InstructPix2Pix without mask + drawLines( + ctx, + [ + { + size: 9999999999, + pts: [ + { x: 0, y: 0 }, + { x: imageWidth, y: 0 }, + { x: imageWidth, y: imageHeight }, + { x: 0, y: imageHeight }, + ], + }, + ], + "white" + ) + } + }, + [context, maskCanvas, isPix2Pix, imageWidth, imageHeight] + ) + + const hadDrawSomething = useCallback(() => { + if (isPix2Pix) { + return true + } + return curLineGroup.length !== 0 + }, [curLineGroup, isPix2Pix]) + + const drawOnCurrentRender = useCallback( + (lineGroup: LineGroup) => { + console.log("[drawOnCurrentRender] draw on current render") + if (renders.length === 0) { + draw(original, lineGroup) + } else { + draw(renders[renders.length - 1], lineGroup) + } + }, + [original, renders, draw] + ) + + const runInpainting = useCallback( + async ( + useLastLineGroup?: boolean, + customMask?: File, + maskImage?: HTMLImageElement | null, + paintByExampleImage?: File + ) => { + // customMask: mask uploaded by user + // maskImage: mask from interactive segmentation + if (file === undefined) { + return + } + const useCustomMask = customMask !== undefined && customMask !== null + const useMaskImage = maskImage !== undefined && maskImage !== null + // useLastLineGroup 的影响 + // 1. 使用上一次的 mask + // 2. 结果替换当前 render + console.log("runInpainting") + console.log({ + useCustomMask, + useMaskImage, + }) + + let maskLineGroup: LineGroup = [] + if (useLastLineGroup === true) { + if (lastLineGroup.length === 0) { + return + } + maskLineGroup = lastLineGroup + } else if (!useCustomMask) { + if (!hadDrawSomething() && !useMaskImage) { + return + } + + setLastLineGroup(curLineGroup) + maskLineGroup = curLineGroup + } + + const newLineGroups = [...lineGroups, maskLineGroup] + + setCurLineGroup([]) + setIsDraging(false) + setIsInpainting(true) + if (settings.graduallyInpainting) { + drawLinesOnMask([maskLineGroup], maskImage) + } else { + drawLinesOnMask(newLineGroups) + } + + let targetFile = file + if (settings.graduallyInpainting === true) { + if (useLastLineGroup === true) { + // renders.length == 1 还是用原来的 + if (renders.length > 1) { + const lastRender = renders[renders.length - 2] + targetFile = await srcToFile( + lastRender.currentSrc, + file.name, + file.type + ) + } + } else if (renders.length > 0) { + console.info("gradually inpainting on last result") + + const lastRender = renders[renders.length - 1] + targetFile = await srcToFile( + lastRender.currentSrc, + file.name, + file.type + ) + } + } + + try { + console.log("before run inpaint") + const res = await inpaint( + targetFile, + settings, + croperRect, + promptVal, + negativePromptVal, + seedVal, + useCustomMask ? undefined : maskCanvas.toDataURL(), + useCustomMask ? customMask : undefined, + paintByExampleImage + ) + if (!res) { + throw new Error("Something went wrong on server side.") + } + const { blob, seed } = res + if (seed) { + setSeed(parseInt(seed, 10)) + } + const newRender = new Image() + await loadImage(newRender, blob) + + if (useLastLineGroup === true) { + const prevRenders = renders.slice(0, -1) + const newRenders = [...prevRenders, newRender] + setRenders(newRenders) + } else { + const newRenders = [...renders, newRender] + setRenders(newRenders) + } + + draw(newRender, []) + // Only append new LineGroup after inpainting success + setLineGroups(newLineGroups) + + // clear redo stack + resetRedoState() + } catch (e: any) { + setToastState({ + open: true, + desc: e.message ? e.message : e.toString(), + state: "error", + duration: 4000, + }) + drawOnCurrentRender([]) + } + setIsInpainting(false) + setPrevInteractiveSegMask(maskImage) + setTmpInteractiveSegMask(null) + setInteractiveSegMask(null) + }, + [ + lineGroups, + curLineGroup, + maskCanvas, + settings.graduallyInpainting, + settings, + croperRect, + promptVal, + negativePromptVal, + drawOnCurrentRender, + hadDrawSomething, + drawLinesOnMask, + seedVal, + ] + ) + + useEffect(() => { + emitter.on(EVENT_PROMPT, () => { + if (hadDrawSomething() || interactiveSegMask) { + runInpainting(false, undefined, interactiveSegMask) + } else if (lastLineGroup.length !== 0) { + // 使用上一次手绘的 mask 生成 + runInpainting(true, undefined, prevInteractiveSegMask) + } else if (prevInteractiveSegMask) { + // 使用上一次 IS 的 mask 生成 + runInpainting(false, undefined, prevInteractiveSegMask) + } else if (isPix2Pix) { + runInpainting(false, undefined, null) + } else { + setToastState({ + open: true, + desc: "Please draw mask on picture", + state: "error", + duration: 1500, + }) + } + emitter.emit(DREAM_BUTTON_MOUSE_LEAVE) + }) + + return () => { + emitter.off(EVENT_PROMPT) + } + }, [ + hadDrawSomething, + runInpainting, + promptVal, + interactiveSegMask, + prevInteractiveSegMask, + ]) + + useEffect(() => { + emitter.on(DREAM_BUTTON_MOUSE_ENTER, () => { + // 当前 canvas 上没有手绘 mask 或者 interactiveSegMask 时，显示上一次的 mask + if (!hadDrawSomething() && !interactiveSegMask) { + if (prevInteractiveSegMask) { + setDreamButtonHoverSegMask(prevInteractiveSegMask) + } + let lineGroup2Show: LineGroup = [] + if (redoLineGroups.length !== 0) { + lineGroup2Show = redoLineGroups[redoLineGroups.length - 1] + } else if (lineGroups.length !== 0) { + lineGroup2Show = lineGroups[lineGroups.length - 1] + } + console.log( + `[DREAM_BUTTON_MOUSE_ENTER], prevInteractiveSegMask: ${prevInteractiveSegMask} lineGroup2Show: ${lineGroup2Show.length}` + ) + if (lineGroup2Show) { + setDreamButtonHoverLineGroup(lineGroup2Show) + } + } + }) + return () => { + emitter.off(DREAM_BUTTON_MOUSE_ENTER) + } + }, [ + hadDrawSomething, + interactiveSegMask, + prevInteractiveSegMask, + drawOnCurrentRender, + lineGroups, + redoLineGroups, + ]) + + useEffect(() => { + emitter.on(DREAM_BUTTON_MOUSE_LEAVE, () => { + // 当前 canvas 上没有手绘 mask 或者 interactiveSegMask 时，显示上一次的 mask + if (!hadDrawSomething() && !interactiveSegMask) { + setDreamButtonHoverSegMask(null) + setDreamButtonHoverLineGroup([]) + drawOnCurrentRender([]) + } + }) + return () => { + emitter.off(DREAM_BUTTON_MOUSE_LEAVE) + } + }, [hadDrawSomething, interactiveSegMask, drawOnCurrentRender]) + + useEffect(() => { + emitter.on(EVENT_CUSTOM_MASK, (data: any) => { + // TODO: not work with paint by example + runInpainting(false, data.mask) + }) + + return () => { + emitter.off(EVENT_CUSTOM_MASK) + } + }, [runInpainting]) + + useEffect(() => { + emitter.on(EVENT_PAINT_BY_EXAMPLE, (data: any) => { + if (hadDrawSomething() || interactiveSegMask) { + runInpainting(false, undefined, interactiveSegMask, data.image) + } else if (lastLineGroup.length !== 0) { + // 使用上一次手绘的 mask 生成 + runInpainting(true, undefined, prevInteractiveSegMask, data.image) + } else if (prevInteractiveSegMask) { + // 使用上一次 IS 的 mask 生成 + runInpainting(false, undefined, prevInteractiveSegMask, data.image) + } else { + setToastState({ + open: true, + desc: "Please draw mask on picture", + state: "error", + duration: 1500, + }) + } + }) + + return () => { + emitter.off(EVENT_PAINT_BY_EXAMPLE) + } + }, [runInpainting]) + + useEffect(() => { + emitter.on(RERUN_LAST_MASK, () => { + if (lastLineGroup.length !== 0) { + // 使用上一次手绘的 mask 生成 + runInpainting(true, undefined, prevInteractiveSegMask) + } else if (prevInteractiveSegMask) { + // 使用上一次 IS 的 mask 生成 + runInpainting(false, undefined, prevInteractiveSegMask) + } else { + setToastState({ + open: true, + desc: "No mask to reuse", + state: "error", + duration: 1500, + }) + } + }) + return () => { + emitter.off(RERUN_LAST_MASK) + } + }, [runInpainting]) + + const getCurrentRender = useCallback(async () => { + let targetFile = file + if (renders.length > 0) { + const lastRender = renders[renders.length - 1] + targetFile = await srcToFile(lastRender.currentSrc, file.name, file.type) + } + return targetFile + }, [file, renders]) + + useEffect(() => { + emitter.on(PluginName.InteractiveSeg, () => { + setIsInteractiveSeg(true) + if (interactiveSegMask !== null) { + setShowInteractiveSegModal(true) + } + }) + return () => { + emitter.off(PluginName.InteractiveSeg) + } + }) + + const runRenderablePlugin = useCallback( + async (name: string, data?: any) => { + if (isProcessing) { + return + } + try { + // TODO 要不要加 undoCurrentLine？？ + const start = new Date() + setIsPluginRunning(true) + const targetFile = await getCurrentRender() + const res = await runPlugin(name, targetFile, data?.upscale) + if (!res) { + throw new Error("Something went wrong on server side.") + } + const { blob } = res + const newRender = new Image() + await loadImage(newRender, blob) + setImageHeight(newRender.height) + setImageWidth(newRender.width) + const newRenders = [...renders, newRender] + setRenders(newRenders) + const newLineGroups = [...lineGroups, []] + setLineGroups(newLineGroups) + + const end = new Date() + const time = end.getTime() - start.getTime() + + setToastState({ + open: true, + desc: `Run ${name} successfully in ${time / 1000}s`, + state: "success", + duration: 3000, + }) + + const rW = windowSize.width / newRender.width + const rH = (windowSize.height - TOOLBAR_SIZE) / newRender.height + let s = 1.0 + if (rW < 1 || rH < 1) { + s = Math.min(rW, rH) + } + setMinScale(s) + setScale(s) + viewportRef.current?.centerView(s, 1) + } catch (e: any) { + setToastState({ + open: true, + desc: e.message ? e.message : e.toString(), + state: "error", + duration: 3000, + }) + } finally { + setIsPluginRunning(false) + } + }, + [ + renders, + setRenders, + getCurrentRender, + setIsPluginRunning, + isProcessing, + setImageHeight, + setImageWidth, + lineGroups, + viewportRef, + windowSize, + setLineGroups, + ] + ) + + useEffect(() => { + emitter.on(PluginName.RemoveBG, () => { + runRenderablePlugin(PluginName.RemoveBG) + }) + return () => { + emitter.off(PluginName.RemoveBG) + } + }, [runRenderablePlugin]) + + useEffect(() => { + emitter.on(PluginName.AnimeSeg, () => { + runRenderablePlugin(PluginName.AnimeSeg) + }) + return () => { + emitter.off(PluginName.AnimeSeg) + } + }, [runRenderablePlugin]) + + useEffect(() => { + emitter.on(PluginName.GFPGAN, () => { + runRenderablePlugin(PluginName.GFPGAN) + }) + return () => { + emitter.off(PluginName.GFPGAN) + } + }, [runRenderablePlugin]) + + useEffect(() => { + emitter.on(PluginName.RestoreFormer, () => { + runRenderablePlugin(PluginName.RestoreFormer) + }) + return () => { + emitter.off(PluginName.RestoreFormer) + } + }, [runRenderablePlugin]) + + useEffect(() => { + emitter.on(PluginName.RealESRGAN, (data: any) => { + runRenderablePlugin(PluginName.RealESRGAN, data) + }) + return () => { + emitter.off(PluginName.RealESRGAN) + } + }, [runRenderablePlugin]) + + const hadRunInpainting = () => { + return renders.length !== 0 + } + + const getCurrentWidthHeight = useCallback(() => { + let width = 512 + let height = 512 + if (!isOriginalLoaded) { + return [width, height] + } + if (renders.length === 0) { + width = original.naturalWidth + height = original.naturalHeight + } else if (renders.length !== 0) { + width = renders[renders.length - 1].width + height = renders[renders.length - 1].height + } + + return [width, height] + }, [original, isOriginalLoaded, renders]) + + // Draw once the original image is loaded + useEffect(() => { + if (!isOriginalLoaded) { + return + } + + const [width, height] = getCurrentWidthHeight() + setImageWidth(width) + setImageHeight(height) + + const rW = windowSize.width / width + const rH = (windowSize.height - TOOLBAR_SIZE) / height + + let s = 1.0 + if (rW < 1 || rH < 1) { + s = Math.min(rW, rH) + } + setMinScale(s) + setScale(s) + + console.log( + `[on file load] image size: ${width}x${height}, canvas size: ${context?.canvas.width}x${context?.canvas.height} scale: ${s}, initialCentered: ${initialCentered}` + ) + + if (context?.canvas) { + context.canvas.width = width + context.canvas.height = height + console.log("[on file load] set canvas size && drawOnCurrentRender") + drawOnCurrentRender([]) + } + + if (!initialCentered) { + // 防止每次擦除以后图片 zoom 还原 + viewportRef.current?.centerView(s, 1) + console.log("[on file load] centerView") + setInitialCentered(true) + } + }, [ + // context?.canvas, + viewportRef, + original, + isOriginalLoaded, + windowSize, + initialCentered, + drawOnCurrentRender, + getCurrentWidthHeight, + ]) + + useEffect(() => { + console.log("[useEffect] centerView") + // render 改变尺寸以后，undo/redo 重新 center + viewportRef?.current?.centerView(minScale, 1) + }, [context?.canvas.height, context?.canvas.width, viewportRef, minScale]) + + // Zoom reset + const resetZoom = useCallback(() => { + if (!minScale || !windowSize) { + return + } + const viewport = viewportRef.current + if (!viewport) { + return + } + const offsetX = (windowSize.width - imageWidth * minScale) / 2 + const offsetY = (windowSize.height - imageHeight * minScale) / 2 + viewport.setTransform(offsetX, offsetY, minScale, 200, "easeOutQuad") + if (viewport.state) { + viewport.state.scale = minScale + } + + setScale(minScale) + setPanned(false) + }, [ + viewportRef, + windowSize, + imageHeight, + imageWidth, + windowSize.height, + minScale, + ]) + + const resetRedoState = () => { + setRedoCurLines([]) + setRedoLineGroups([]) + setRedoRenders([]) + } + + useEffect(() => { + window.addEventListener("resize", () => { + resetZoom() + }) + return () => { + window.removeEventListener("resize", () => { + resetZoom() + }) + } + }, [windowSize, resetZoom]) + + useEffect(() => { + window.addEventListener("blur", () => { + setIsChangingBrushSizeByMouse(false) + }) + return () => { + window.removeEventListener("blur", () => { + setIsChangingBrushSizeByMouse(false) + }) + } + }, []) + + const onInteractiveCancel = useCallback(() => { + setIsInteractiveSeg(false) + setIsInteractiveSegRunning(false) + setClicks([]) + setTmpInteractiveSegMask(null) + }, []) + + const handleEscPressed = () => { + if (isProcessing) { + return + } + + if (isInteractiveSeg) { + onInteractiveCancel() + return + } + + if (isDraging || isMultiStrokeKeyPressed) { + setIsDraging(false) + setCurLineGroup([]) + drawOnCurrentRender([]) + } else { + resetZoom() + } + } + + useHotkeys("Escape", handleEscPressed, [ + isDraging, + isInpainting, + isMultiStrokeKeyPressed, + isInteractiveSeg, + onInteractiveCancel, + resetZoom, + drawOnCurrentRender, + ]) + + const onMouseMove = (ev: SyntheticEvent) => { + const mouseEvent = ev.nativeEvent as MouseEvent + setCoords({ x: mouseEvent.pageX, y: mouseEvent.pageY }) + } + + const onMouseDrag = (ev: SyntheticEvent) => { + if (isChangingBrushSizeByMouse) { + const initX = changeBrushSizeByMouseInit.x + // move right: increase brush size + const newSize = changeBrushSizeByMouseInit.brushSize + (x - initX) + if (newSize <= MAX_BRUSH_SIZE && newSize >= MIN_BRUSH_SIZE) { + setBrushSize(newSize) + } + return + } + if (isInteractiveSeg) { + return + } + if (isPanning) { + return + } + if (!isDraging) { + return + } + if (curLineGroup.length === 0) { + return + } + const lineGroup = [...curLineGroup] + lineGroup[lineGroup.length - 1].pts.push(mouseXY(ev)) + setCurLineGroup(lineGroup) + drawOnCurrentRender(lineGroup) + } + + const runInteractiveSeg = async (newClicks: number[][]) => { + if (!file) { + return + } + + setIsInteractiveSegRunning(true) + const targetFile = await getCurrentRender() + const prevMask = null + try { + const res = await runPlugin( + PluginName.InteractiveSeg, + targetFile, + undefined, + prevMask, + newClicks + ) + if (!res) { + throw new Error("Something went wrong on server side.") + } + const { blob } = res + const img = new Image() + img.onload = () => { + setTmpInteractiveSegMask(img) + } + img.src = blob + } catch (e: any) { + setToastState({ + open: true, + desc: e.message ? e.message : e.toString(), + state: "error", + duration: 4000, + }) + } + setIsInteractiveSegRunning(false) + } + + const onPointerUp = (ev: SyntheticEvent) => { + if (isMidClick(ev)) { + setIsPanning(false) + } + if (isInteractiveSeg) { + return + } + + if (isPanning) { + return + } + if (!original.src) { + return + } + const canvas = context?.canvas + if (!canvas) { + return + } + if (isInpainting) { + return + } + if (!isDraging) { + return + } + + if (isMultiStrokeKeyPressed) { + setIsDraging(false) + return + } + + if (runMannually) { + setIsDraging(false) + } else { + runInpainting() + } + } + + const isOutsideCroper = (clickPnt: { x: number; y: number }) => { + if (clickPnt.x < croperRect.x) { + return true + } + if (clickPnt.y < croperRect.y) { + return true + } + if (clickPnt.x > croperRect.x + croperRect.width) { + return true + } + if (clickPnt.y > croperRect.y + croperRect.height) { + return true + } + return false + } + + const onCanvasMouseUp = (ev: SyntheticEvent) => { + if (isInteractiveSeg) { + const xy = mouseXY(ev) + const isX = xy.x + const isY = xy.y + const newClicks: number[][] = [...clicks] + if (isRightClick(ev)) { + newClicks.push([isX, isY, 0, newClicks.length]) + } else { + newClicks.push([isX, isY, 1, newClicks.length]) + } + // runInteractiveSeg(newClicks) + setClicks(newClicks) + } + } + + const onMouseDown = (ev: SyntheticEvent) => { + if (isProcessing) { + return + } + if (isInteractiveSeg) { + return + } + if (isChangingBrushSizeByMouse) { + return + } + if (isPanning) { + return + } + if (!original.src) { + return + } + const canvas = context?.canvas + if (!canvas) { + return + } + + if (isRightClick(ev)) { + return + } + + if (isMidClick(ev)) { + setIsPanning(true) + return + } + + if ( + isDiffusionModels && + settings.showCroper && + isOutsideCroper(mouseXY(ev)) + ) { + return + } + + setIsDraging(true) + + let lineGroup: LineGroup = [] + if (isMultiStrokeKeyPressed || runMannually) { + lineGroup = [...curLineGroup] + } + lineGroup.push({ size: brushSize, pts: [mouseXY(ev)] }) + setCurLineGroup(lineGroup) + drawOnCurrentRender(lineGroup) + } + + const undoStroke = useCallback(() => { + if (curLineGroup.length === 0) { + return + } + setLastLineGroup([]) + + const lastLine = curLineGroup.pop()! + const newRedoCurLines = [...redoCurLines, lastLine] + setRedoCurLines(newRedoCurLines) + + const newLineGroup = [...curLineGroup] + setCurLineGroup(newLineGroup) + drawOnCurrentRender(newLineGroup) + }, [curLineGroup, redoCurLines, drawOnCurrentRender]) + + const undoRender = useCallback(() => { + if (!renders.length) { + return + } + + // save line Group + const latestLineGroup = lineGroups.pop()! + setRedoLineGroups([...redoLineGroups, latestLineGroup]) + // If render is undo, clear strokes + setRedoCurLines([]) + + setLineGroups([...lineGroups]) + setCurLineGroup([]) + setIsDraging(false) + + // save render + const lastRender = renders.pop()! + setRedoRenders([...redoRenders, lastRender]) + + const newRenders = [...renders] + setRenders(newRenders) + // if (newRenders.length === 0) { + // draw(original, []) + // } else { + // draw(newRenders[newRenders.length - 1], []) + // } + }, [ + draw, + renders, + redoRenders, + redoLineGroups, + lineGroups, + original, + context, + ]) + + const undo = () => { + // TODO: prevent default event + console.log("undo") + if (runMannually && curLineGroup.length !== 0) { + undoStroke() + } else { + undoRender() + } + } + + useHotkeys("meta+z,ctrl+z", undo, undefined, [ + undoStroke, + undoRender, + runMannually, + curLineGroup, + context?.canvas, + renders, + ]) + + const disableUndo = () => { + if (isProcessing) { + return true + } + if (renders.length > 0) { + return false + } + + if (runMannually) { + if (curLineGroup.length === 0) { + return true + } + } else if (renders.length === 0) { + return true + } + + return false + } + + const redoStroke = useCallback(() => { + if (redoCurLines.length === 0) { + return + } + const line = redoCurLines.pop()! + setRedoCurLines([...redoCurLines]) + + const newLineGroup = [...curLineGroup, line] + setCurLineGroup(newLineGroup) + drawOnCurrentRender(newLineGroup) + }, [curLineGroup, redoCurLines, drawOnCurrentRender]) + + const redoRender = useCallback(() => { + if (redoRenders.length === 0) { + return + } + const lineGroup = redoLineGroups.pop()! + setRedoLineGroups([...redoLineGroups]) + + setLineGroups([...lineGroups, lineGroup]) + setCurLineGroup([]) + setIsDraging(false) + + const render = redoRenders.pop()! + const newRenders = [...renders, render] + setRenders(newRenders) + // draw(newRenders[newRenders.length - 1], []) + }, [draw, renders, redoRenders, redoLineGroups, lineGroups, original]) + + const redo = () => { + if (runMannually && redoCurLines.length !== 0) { + redoStroke() + } else { + redoRender() + } + } + + // Handle Cmd+shift+Z + const redoPredicate = (event: KeyboardEvent) => { + const isCmdZ = + (event.metaKey || event.ctrlKey) && + event.shiftKey && + event.key.toLowerCase() === "z" + // Handle tab switch + if (event.key === "Tab") { + event.preventDefault() + } + if (isCmdZ) { + event.preventDefault() + return true + } + return false + } + + // useKey(redoPredicate, redo, undefined, [ + // redoStroke, + // redoRender, + // runMannually, + // redoCurLines, + // ]) + + const disableRedo = () => { + if (isProcessing) { + return true + } + if (redoRenders.length > 0) { + return false + } + + if (runMannually) { + if (redoCurLines.length === 0) { + return true + } + } else if (redoRenders.length === 0) { + return true + } + + return false + } + + // useKeyPressEvent( + // "Tab", + // (ev) => { + // ev?.preventDefault() + // ev?.stopPropagation() + // if (hadRunInpainting()) { + // setShowOriginal(() => { + // window.setTimeout(() => { + // setSliderPos(100) + // }, 10) + // return true + // }) + // } + // }, + // (ev) => { + // ev?.preventDefault() + // ev?.stopPropagation() + // if (hadRunInpainting()) { + // setSliderPos(0) + // window.setTimeout(() => { + // setShowOriginal(false) + // }, 350) + // } + // } + // ) + + function download() { + if (file === undefined) { + return + } + if ((enableFileManager || isEnableAutoSaving) && renders.length > 0) { + try { + downloadToOutput(renders[renders.length - 1], file.name, file.type) + setToastState({ + open: true, + desc: `Save image success`, + state: "success", + duration: 2000, + }) + } catch (e: any) { + setToastState({ + open: true, + desc: e.message ? e.message : e.toString(), + state: "error", + duration: 2000, + }) + } + return + } + + // TODO: download to output directory + const name = file.name.replace(/(\.[\w\d_-]+)$/i, "_cleanup$1") + const curRender = renders[renders.length - 1] + downloadImage(curRender.currentSrc, name) + if (settings.downloadMask) { + let maskFileName = file.name.replace(/(\.[\w\d_-]+)$/i, "_mask$1") + maskFileName = maskFileName.replace(/\.[^/.]+$/, ".jpg") + + drawLinesOnMask(lineGroups) + // Create a link + const aDownloadLink = document.createElement("a") + // Add the name of the file to the link + aDownloadLink.download = maskFileName + // Attach the data to the link + aDownloadLink.href = maskCanvas.toDataURL("image/jpeg") + // Get the code to click the download link + aDownloadLink.click() + } + } + + const toggleShowBrush = (newState: boolean) => { + if (newState !== showBrush && !isPanning) { + setShowBrush(newState) + } + } + + const getCursor = useCallback(() => { + if (isPanning) { + return "grab" + } + if (showBrush) { + return "none" + } + return undefined + }, [showBrush, isPanning]) + + // Standard Hotkeys for Brush Size + // useHotKey("[", () => { + // setBrushSize((currentBrushSize: number) => { + // if (currentBrushSize > 10) { + // return currentBrushSize - 10 + // } + // if (currentBrushSize <= 10 && currentBrushSize > 0) { + // return currentBrushSize - 5 + // } + // return currentBrushSize + // }) + // }) + + // useHotKey("]", () => { + // setBrushSize((currentBrushSize: number) => { + // return currentBrushSize + 10 + // }) + // }) + + // // Manual Inpainting Hotkey + // useHotKey( + // "shift+r", + // () => { + // if (runMannually && hadDrawSomething()) { + // runInpainting() + // } + // }, + // {}, + // [runMannually, runInpainting, hadDrawSomething] + // ) + + // useHotKey( + // "ctrl+c, cmd+c", + // async () => { + // const hasPermission = await askWritePermission() + // if (hasPermission && renders.length > 0) { + // if (context?.canvas) { + // await copyCanvasImage(context?.canvas) + // setToastState({ + // open: true, + // desc: "Copy inpainting result to clipboard", + // state: "success", + // duration: 3000, + // }) + // } + // } + // }, + // {}, + // [renders, context] + // ) + + // Toggle clean/zoom tool on spacebar. + // useKeyPressEvent( + // " ", + // (ev) => { + // if (!app.disableShortCuts) { + // ev?.preventDefault() + // ev?.stopPropagation() + // setShowBrush(false) + // setIsPanning(true) + // } + // }, + // (ev) => { + // if (!app.disableShortCuts) { + // ev?.preventDefault() + // ev?.stopPropagation() + // setShowBrush(true) + // setIsPanning(false) + // } + // } + // ) + + // useKeyPressEvent( + // "Alt", + // (ev) => { + // ev?.preventDefault() + // ev?.stopPropagation() + // setIsChangingBrushSizeByMouse(true) + // setChangeBrushSizeByMouseInit({ x, y, brushSize }) + // }, + // (ev) => { + // ev?.preventDefault() + // ev?.stopPropagation() + // setIsChangingBrushSizeByMouse(false) + // } + // ) + + const getCurScale = (): number => { + let s = minScale + if (viewportRef.current?.state?.scale !== undefined) { + s = viewportRef.current?.state.scale + } + return s! + } + + const getBrushStyle = (_x: number, _y: number) => { + const curScale = getCurScale() + return { + width: `${brushSize * curScale}px`, + height: `${brushSize * curScale}px`, + left: `${_x}px`, + top: `${_y}px`, + transform: "translate(-50%, -50%)", + } + } + + const handleSliderChange = (value: number) => { + setBrushSize(value) + + if (!showRefBrush) { + setShowRefBrush(true) + window.setTimeout(() => { + setShowRefBrush(false) + }, 10000) + } + } + + const renderInteractiveSegCursor = () => { + return ( +
+ +
+ ) + } + + const renderCanvas = () => { + return ( + { + if (!panned) { + setPanned(true) + } + }} + onZoom={(ref) => { + setScale(ref.state.scale) + }} + > + +
+ { + e.preventDefault() + }} + onMouseOver={() => { + toggleShowBrush(true) + setShowRefBrush(false) + }} + onFocus={() => toggleShowBrush(true)} + onMouseLeave={() => toggleShowBrush(false)} + onMouseDown={onMouseDown} + onMouseUp={onCanvasMouseUp} + onMouseMove={onMouseDrag} + ref={(r) => { + if (r && !context) { + const ctx = r.getContext("2d") + if (ctx) { + setContext(ctx) + } + } + }} + /> +
+ {showOriginal && ( + <> +
+ original + + )} +
+
+ + {/* */} + + {/* {isInteractiveSeg ? : <>} */} + + + ) + } + + const onInteractiveAccept = () => { + setInteractiveSegMask(tmpInteractiveSegMask) + setTmpInteractiveSegMask(null) + + if (!runMannually && tmpInteractiveSegMask) { + runInpainting(false, undefined, tmpInteractiveSegMask) + } + } + + return ( +