mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
major refactor: remove backend original
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+9
-108
@@ -4,12 +4,9 @@ import inspect
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import hashlib
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from typing import Any, Dict, List
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from dataclasses import dataclass, field
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import torch
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import numpy as np
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import cv2
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from PIL import Image, ImageOps
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from modules import shared, devices, images, scripts_manager, masking, sd_samplers, sd_models, processing_helpers
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from modules.sd_hijack_hypertile import hypertile_set
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from modules import shared, images, scripts_manager, masking, sd_models, processing_helpers
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debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
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@@ -258,8 +255,6 @@ class StableDiffusionProcessing:
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self.all_subseeds = None
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# a1111 compatibility items
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if not shared.native:
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shared.opts.data['clip_skip'] = int(self.clip_skip) # for compatibility with a1111 sd_hijack_clip
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self.seed_enable_extras: bool = True
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self.is_using_inpainting_conditioning = False # a111 compatibility
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self.batch_index = 0
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@@ -268,8 +263,6 @@ class StableDiffusionProcessing:
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self.all_hr_prompts = []
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self.hr_negative_prompt = ''
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self.all_hr_negative_prompts = []
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self.truncate_x = 0
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self.truncate_y = 0
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self.comments = {}
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self.sampler = None
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self.nmask = None
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@@ -284,16 +277,6 @@ class StableDiffusionProcessing:
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self.script_args = script_args
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self.per_script_args = {}
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# settings to processing
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self.ddim_discretize = shared.opts.ddim_discretize
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self.s_min_uncond = shared.opts.s_min_uncond
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self.s_churn = shared.opts.s_churn
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self.s_noise = shared.opts.s_noise
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self.s_min = shared.opts.s_min
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self.s_max = shared.opts.s_max
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self.s_tmin = shared.opts.s_tmin
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self.s_tmax = float('inf') # not representable as a standard ui option
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# ip adapter
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self.ip_adapter_names = []
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self.ip_adapter_scales = [0.0]
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@@ -364,9 +347,6 @@ class StableDiffusionProcessing:
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def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
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pass
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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raise NotImplementedError
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def close(self):
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self.sampler = None
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self.scripts = None
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@@ -387,8 +367,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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super().__init__(**kwargs)
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def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
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if shared.native:
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shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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self.width = self.width or 1024
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self.height = self.height or 1024
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if all_prompts is not None:
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@@ -411,35 +390,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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elif self.hr_resize_x == 0:
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self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
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self.hr_upscale_to_y = self.hr_resize_y
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elif self.hr_resize_x > 0 and self.hr_resize_y > 0 and shared.native:
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elif self.hr_resize_x > 0 and self.hr_resize_y > 0:
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self.hr_upscale_to_x = self.hr_resize_x
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self.hr_upscale_to_y = self.hr_resize_y
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else:
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target_w = self.hr_resize_x
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target_h = self.hr_resize_y
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src_ratio = self.width / self.height
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dst_ratio = self.hr_resize_x / self.hr_resize_y
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if src_ratio < dst_ratio:
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self.hr_upscale_to_x = self.hr_resize_x
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self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
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else:
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self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
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self.hr_upscale_to_y = self.hr_resize_y
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self.truncate_x = (self.hr_upscale_to_x - target_w) // 8
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self.truncate_y = (self.hr_upscale_to_y - target_h) // 8
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if not shared.native: # diffusers are handled in processing_diffusers
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if (self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height) or upscaler is None or upscaler == 'None': # special case: the user has chosen to do nothing
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self.is_hr_pass = False
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return
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self.is_hr_pass = True
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hypertile_set(self, hr=True)
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shared.state.job_count = 2 * self.n_iter
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shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.hr_sampler_name}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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from modules import processing_original
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return processing_original.sample_txt2img(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts)
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class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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def __init__(self, **kwargs):
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@@ -452,9 +407,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.width = int(8 * (self.init_images[0].width * self.scale_by // 8))
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if self.height is None or self.height == 0:
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self.height = int(8 * (self.init_images[0].height * self.scale_by // 8))
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if shared.native and getattr(self, 'image_mask', None) is not None:
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if getattr(self, 'image_mask', None) is not None:
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shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
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elif shared.native and getattr(self, 'init_images', None) is not None:
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elif getattr(self, 'init_images', None) is not None:
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shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
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if all_prompts is not None:
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@@ -463,14 +418,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.all_seeds = all_seeds
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if all_subseeds is not None:
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self.all_subseeds = all_subseeds
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if self.sampler_name == 'PLMS':
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self.sampler_name = 'Default'
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if not shared.native:
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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if hasattr(self.sampler, "initialize"):
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self.sampler.initialize(self)
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if self.image_mask is not None:
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self.ops.append('inpaint')
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elif hasattr(self, 'init_images') and self.init_images is not None:
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@@ -480,21 +427,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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if self.image_mask is not None:
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if type(self.image_mask) == list:
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self.image_mask = self.image_mask[0]
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if not shared.native: # original way of processing mask
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self.image_mask = processing_helpers.create_binary_mask(self.image_mask)
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if self.inpainting_mask_invert:
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self.image_mask = ImageOps.invert(self.image_mask)
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if self.mask_blur > 0:
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np_mask = np.array(self.image_mask)
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kernel_size = 2 * int(2.5 * self.mask_blur + 0.5) + 1
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np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur)
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np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur)
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self.image_mask = Image.fromarray(np_mask)
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elif shared.native:
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if 'control' in self.ops:
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self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, return_type='Grayscale', invert=self.inpainting_mask_invert==1) # blur/padding are handled in masking module
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else:
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self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, return_type='Grayscale', invert=self.inpainting_mask_invert==1, mask_blur=self.mask_blur, mask_padding=self.inpaint_full_res_padding) # old img2img
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if 'control' in self.ops:
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self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, return_type='Grayscale', invert=self.inpainting_mask_invert==1) # blur/padding are handled in masking module
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else:
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self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, return_type='Grayscale', invert=self.inpainting_mask_invert==1, mask_blur=self.mask_blur, mask_padding=self.inpaint_full_res_padding) # old img2img
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if self.inpaint_full_res: # mask only inpaint
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self.mask_for_overlay = self.image_mask
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mask = self.image_mask.convert('L')
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@@ -558,38 +494,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.overlay_images = self.overlay_images * self.batch_size
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if self.color_corrections is not None and len(self.color_corrections) == 1:
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self.color_corrections = self.color_corrections * self.batch_size
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if shared.native:
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return # we've already set self.init_images and self.mask and we dont need any more processing
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elif not shared.native:
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self.init_images = [np.moveaxis((np.array(image).astype(np.float32) / 255.0), 2, 0) for image in self.init_images]
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if len(self.init_images) == 1:
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batch_images = np.expand_dims(self.init_images[0], axis=0).repeat(self.batch_size, axis=0)
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elif len(self.init_images) <= self.batch_size:
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batch_images = np.array(self.init_images)
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else:
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batch_images = np.array(self.init_images[:self.batch_size])
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image = torch.from_numpy(batch_images)
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image = 2. * image - 1.
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image = image.to(device=shared.device, dtype=devices.dtype_vae)
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self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
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if self.image_mask is not None:
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init_mask = latent_mask
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latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
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latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
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latmask = latmask[0]
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latmask = np.tile(latmask[None], (4, 1, 1))
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latmask = np.around(latmask)
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self.mask = torch.asarray(1.0 - latmask).to(device=shared.device, dtype=self.sd_model.dtype)
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self.nmask = torch.asarray(latmask).to(device=shared.device, dtype=self.sd_model.dtype)
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if self.inpainting_fill == 2:
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self.init_latent = self.init_latent * self.mask + processing_helpers.create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
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elif self.inpainting_fill == 3:
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self.init_latent = self.init_latent * self.mask
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self.image_conditioning = processing_helpers.img2img_image_conditioning(self, image, self.init_latent, self.image_mask)
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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from modules import processing_original
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return processing_original.sample_img2img(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts)
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class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
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@@ -597,9 +501,6 @@ class StableDiffusionProcessingControl(StableDiffusionProcessingImg2Img):
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debug(f'Process init: mode={self.__class__.__name__} kwargs={kwargs}') # pylint: disable=protected-access
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super().__init__(**kwargs)
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): # abstract
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pass
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def init_hr(self, scale = None, upscaler = None, force = False):
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scale = scale or self.scale_by
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upscaler = upscaler or self.resize_name
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