From 68999d0b15d612965e7bc7feb62d6b4d55e112fa Mon Sep 17 00:00:00 2001 From: space-nuko <24979496+space-nuko@users.noreply.github.com> Date: Sat, 25 Mar 2023 12:52:14 -0400 Subject: Add upscale slider to img2img --- modules/processing.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 2e5a363f..fc4b166c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -929,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs): + def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -949,11 +949,27 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = None self.nmask = None self.image_conditioning = None + self.scale = scale + + def get_final_size(self): + if self.scale > 1: + img = self.init_images[0] + width = int(img.width * self.scale) + height = int(img.height * self.scale) + return width, height + else: + return self.width, self.height + def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) crop_region = None + if self.scale > 1: + self.extra_generation_params["Img2Img Upscale"] = self.scale + + self.width, self.height = self.get_final_size() + image_mask = self.image_mask if image_mask is not None: -- cgit v1.2.3 From 7ea5d395c44be208f654b07ec7993aa2952f2510 Mon Sep 17 00:00:00 2001 From: space-nuko <24979496+space-nuko@users.noreply.github.com> Date: Sun, 19 Feb 2023 03:45:43 -0800 Subject: Add upscaler to img2img --- modules/processing.py | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index fc4b166c..afb8cfd1 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -929,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs): + def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -950,6 +950,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None self.image_conditioning = None self.scale = scale + self.upscaler = upscaler def get_final_size(self): if self.scale > 1: @@ -966,7 +967,16 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): crop_region = None if self.scale > 1: - self.extra_generation_params["Img2Img Upscale"] = self.scale + self.extra_generation_params["Img2Img upscale"] = self.scale + + # Non-latent upscalers are run before sampling + # Latent upscalers are run during sampling + init_upscaler = None + if self.upscaler is not None: + self.extra_generation_params["Img2Img upscaler"] = self.upscaler + if self.upscaler not in shared.latent_upscale_modes: + assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}" + init_upscaler = self.upscaler self.width, self.height = self.get_final_size() @@ -992,7 +1002,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: - image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height) + image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler) np_mask = np.array(image_mask) np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8) self.mask_for_overlay = Image.fromarray(np_mask) @@ -1009,7 +1019,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: - image = images.resize_image(self.resize_mode, image, self.width, self.height) + image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler) if image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) @@ -1054,8 +1064,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) - if self.resize_mode == 3: - self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") + if latent_scale_mode is not None: + self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) if image_mask is not None: init_mask = latent_mask -- cgit v1.2.3 From 8a34671fe91e142bce9e5556cca2258b3be9dd6e Mon Sep 17 00:00:00 2001 From: MrCheeze Date: Fri, 24 Mar 2023 22:48:16 -0400 Subject: Add support for the Variations models (unclip-h and unclip-l) --- modules/processing.py | 41 +++++++++++++++++++++++++++++------------ 1 file changed, 29 insertions(+), 12 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 59717b4c..1451811c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -78,21 +78,27 @@ def apply_overlay(image, paste_loc, index, overlays): def txt2img_image_conditioning(sd_model, x, width, height): - if sd_model.model.conditioning_key not in {'hybrid', 'concat'}: - # Dummy zero conditioning if we're not using inpainting model. - # Still takes up a bit of memory, but no encoder call. - # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. - return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) + if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models - # The "masked-image" in this case will just be all zeros since the entire image is masked. - image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) - image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning)) + # The "masked-image" in this case will just be all zeros since the entire image is masked. + image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) + image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning)) - # Add the fake full 1s mask to the first dimension. - image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) - image_conditioning = image_conditioning.to(x.dtype) + # Add the fake full 1s mask to the first dimension. + image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) + image_conditioning = image_conditioning.to(x.dtype) - return image_conditioning + return image_conditioning + + elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models + + return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device) + + else: + # Dummy zero conditioning if we're not using inpainting or unclip models. + # Still takes up a bit of memory, but no encoder call. + # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. + return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) class StableDiffusionProcessing: @@ -190,6 +196,14 @@ class StableDiffusionProcessing: return conditioning_image + def unclip_image_conditioning(self, source_image): + c_adm = self.sd_model.embedder(source_image) + if self.sd_model.noise_augmentor is not None: + noise_level = 0 # TODO: Allow other noise levels? + c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0])) + c_adm = torch.cat((c_adm, noise_level_emb), 1) + return c_adm + def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None): self.is_using_inpainting_conditioning = True @@ -241,6 +255,9 @@ class StableDiffusionProcessing: if self.sampler.conditioning_key in {'hybrid', 'concat'}: return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask) + if self.sampler.conditioning_key == "crossattn-adm": + return self.unclip_image_conditioning(source_image) + # Dummy zero conditioning if we're not using inpainting or depth model. return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1) -- cgit v1.2.3 From 433b3ab7017556a19173a86d1215ed0a0b5b1396 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 28 Mar 2023 20:36:57 +0300 Subject: Revert "Merge pull request #7931 from space-nuko/img2img-enhance" This reverts commit 426875937048e21305ac24bea53df06523bdaa81, reversing changes made to 1b63afbedc7789c0eb9a4742b780ab304d7a9caf. --- modules/processing.py | 37 +++++-------------------------------- 1 file changed, 5 insertions(+), 32 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 509b80b9..6d9c6a8d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -946,7 +946,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs): + def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -966,37 +966,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = None self.nmask = None self.image_conditioning = None - self.scale = scale - self.upscaler = upscaler - - def get_final_size(self): - if self.scale > 1: - img = self.init_images[0] - width = int(img.width * self.scale) - height = int(img.height * self.scale) - return width, height - else: - return self.width, self.height - def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) crop_region = None - if self.scale > 1: - self.extra_generation_params["Img2Img upscale"] = self.scale - - # Non-latent upscalers are run before sampling - # Latent upscalers are run during sampling - init_upscaler = None - if self.upscaler is not None: - self.extra_generation_params["Img2Img upscaler"] = self.upscaler - if self.upscaler not in shared.latent_upscale_modes: - assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}" - init_upscaler = self.upscaler - - self.width, self.height = self.get_final_size() - image_mask = self.image_mask if image_mask is not None: @@ -1019,7 +993,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: - image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler) + image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height) np_mask = np.array(image_mask) np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8) self.mask_for_overlay = Image.fromarray(np_mask) @@ -1036,7 +1010,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: - image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler) + image = images.resize_image(self.resize_mode, image, self.width, self.height) if image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) @@ -1081,9 +1055,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) - latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") - if latent_scale_mode is not None: - self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) + if self.resize_mode == 3: + self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") if image_mask is not None: init_mask = latent_mask -- cgit v1.2.3 From 42082e8a3239c1c32cd9e2a03a20b610af857b51 Mon Sep 17 00:00:00 2001 From: devdn Date: Tue, 28 Mar 2023 18:18:28 -0400 Subject: performance increase --- modules/processing.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 6d9c6a8d..9f00ce3c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -105,7 +105,7 @@ class StableDiffusionProcessing: """ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing """ - def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): + def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): if sampler_index is not None: print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr) @@ -140,6 +140,7 @@ class StableDiffusionProcessing: self.denoising_strength: float = denoising_strength self.sampler_noise_scheduler_override = None self.ddim_discretize = ddim_discretize or opts.ddim_discretize + self.s_min_uncond = s_min_uncond or opts.s_min_uncond self.s_churn = s_churn or opts.s_churn self.s_tmin = s_tmin or opts.s_tmin self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option @@ -162,6 +163,7 @@ class StableDiffusionProcessing: self.all_seeds = None self.all_subseeds = None self.iteration = 0 + @property def sd_model(self): -- cgit v1.2.3 From b3593d0997bfdcca7f8aa01663e81720db50e494 Mon Sep 17 00:00:00 2001 From: For Sure Date: Thu, 6 Apr 2023 19:42:26 +0300 Subject: Add support for saving init images in img2img --- modules/processing.py | 8 ++++++++ 1 file changed, 8 insertions(+) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 6d9c6a8d..5556afc5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -3,6 +3,7 @@ import math import os import sys import warnings +import hashlib import torch import numpy as np @@ -476,6 +477,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, + "Init image hash": getattr(p, 'init_img_hash', None) } generation_params.update(p.extra_generation_params) @@ -1007,6 +1009,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.color_corrections = [] imgs = [] for img in self.init_images: + + # Save init image + if opts.save_init_img: + self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest() + images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False) + image = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: -- cgit v1.2.3 From d609f6030ec464b371a899ced366c62bbd9a4a91 Mon Sep 17 00:00:00 2001 From: gk Date: Fri, 7 Apr 2023 21:04:46 +0900 Subject: Add [batch_number] and [generation_number] filename patterns --- modules/processing.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 6d9c6a8d..0e6a60ba 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -670,6 +670,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n) for i, x_sample in enumerate(x_samples_ddim): + p.batch_index = i + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) @@ -718,7 +720,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if opts.return_mask: output_images.append(image_mask) - + if opts.return_mask_composite: output_images.append(image_mask_composite) -- cgit v1.2.3 From 3af152d488db0c521f6058676e1a65c7157ccc14 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Fri, 14 Apr 2023 17:17:14 -0400 Subject: Fix image mask composite for weird resolutions --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 6d9c6a8d..f49992d9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -708,7 +708,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay: image_mask = p.mask_for_overlay.convert('RGB') - image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA') + image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA') if opts.save_mask: images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask") -- cgit v1.2.3 From fbab3fc6d122fb4e6648dd82291a70fc348c0b4a Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Fri, 14 Apr 2023 17:24:55 -0400 Subject: Only handle image mask if any option enabled --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index f49992d9..5c6edc60 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -706,7 +706,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image.info["parameters"] = text output_images.append(image) - if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay: + if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]): image_mask = p.mask_for_overlay.convert('RGB') image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA') -- cgit v1.2.3 From 56f8a6b081e3dbfc93956e5c44e507e74a2ba040 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Sun, 16 Apr 2023 20:34:52 -0400 Subject: Fix sampler schedules with step multiplier --- modules/processing.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 6d9c6a8d..f6514637 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -639,8 +639,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) - uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc) - c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c) + try: + step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1 + except: + step_multiplier = 1 + uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc) + c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: -- cgit v1.2.3 From 4d0c8163036b42dd66aad922c22d3b3d19217bd9 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Sun, 16 Apr 2023 20:39:45 -0400 Subject: Modify step multiplier flow --- modules/processing.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index f6514637..15ad629f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -639,10 +639,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) - try: - step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1 - except: - step_multiplier = 1 + step_multiplier = 1 + if shared.opts.fix_second_order_samplers_schedule: + try: + step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1 + except: + pass uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc) c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c) -- cgit v1.2.3 From 9de7298898951d6038eeadee78f7be3b09d2cfc3 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Sun, 16 Apr 2023 21:06:37 -0400 Subject: Update processing.py --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 15ad629f..2f391ee4 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -640,7 +640,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: file.write(processed.infotext(p, 0)) step_multiplier = 1 - if shared.opts.fix_second_order_samplers_schedule: + if not shared.opts.dont_fix_second_order_samplers_schedule: try: step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1 except: -- cgit v1.2.3 From 5fe0dd79beaa5ef737ff85254ee9870f60ae9464 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 11:29:37 +0300 Subject: rename CPU RNG to RNG source in settings, add infotext and parameters copypaste support to RNG source --- modules/processing.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 5556afc5..7bac154d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -477,7 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, - "Init image hash": getattr(p, 'init_img_hash', None) + "Init image hash": getattr(p, 'init_img_hash', None), + "RNG": (opts.randn_source if opts.randn_source != "GPU" else None) } generation_params.update(p.extra_generation_params) -- cgit v1.2.3 From 1d11e896984c883f6a0debb3abaef945595cbc70 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 15:57:09 +0300 Subject: rework Negative Guidance minimum sigma to work with AND, add infotext and copypaste parameters support --- modules/processing.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 04a06290..c50784f4 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -480,7 +480,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, "Init image hash": getattr(p, 'init_img_hash', None), - "RNG": (opts.randn_source if opts.randn_source != "GPU" else None) + "RNG": opts.randn_source if opts.randn_source != "GPU" else None, + "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond, } generation_params.update(p.extra_generation_params) -- cgit v1.2.3 From 7428fb5176ccfd203bddcfa30d75c8df5a772cb4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 29 Apr 2023 16:28:51 +0300 Subject: add is_hr_pass field for processing --- modules/processing.py | 5 +++++ 1 file changed, 5 insertions(+) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index c50784f4..8d7b2462 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -164,6 +164,7 @@ class StableDiffusionProcessing: self.all_seeds = None self.all_subseeds = None self.iteration = 0 + self.is_hr_pass = False @property @@ -883,6 +884,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not self.enable_hr: return samples + self.is_hr_pass = True + target_width = self.hr_upscale_to_x target_height = self.hr_upscale_to_y @@ -952,6 +955,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) + self.is_hr_pass = False + return samples -- cgit v1.2.3