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option to let users select which samplers they want to hide

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AUTOMATIC
2022-10-06 12:08:48 +03:00
parent 6e7057b31b
commit 5f24b7bcf4
4 changed files with 35 additions and 16 deletions
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13
modules/processing.py
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@@ -11,9 +11,8 @@ import cv2
from skimage import exposure
import modules.sd_hijack
from modules import devices, prompt_parser, masking
from modules import devices, prompt_parser, masking, sd_samplers
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.face_restoration
@@ -110,7 +109,7 @@ class Processed:
self.width = p.width
self.height = p.height
self.sampler_index = p.sampler_index
self.sampler = samplers[p.sampler_index].name
self.sampler = sd_samplers.samplers[p.sampler_index].name
self.cfg_scale = p.cfg_scale
self.steps = p.steps
self.batch_size = p.batch_size
@@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
generation_params = {
"Steps": p.steps,
"Sampler": samplers[p.sampler_index].name,
"Sampler": sd_samplers.samplers[p.sampler_index].name,
"CFG scale": p.cfg_scale,
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
@@ -478,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.firstphase_height_truncated = int(scale * self.height)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
if not self.enable_hr:
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -521,7 +520,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
shared.state.nextjob()
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
# GC now before running the next img2img to prevent running out of memory
@@ -556,7 +555,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.nmask = None
def init(self, all_prompts, all_seeds, all_subseeds):
self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
crop_region = None
if self.image_mask is not None: