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Merge pull request #7571 from mezotaken/int_loop

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Optionally append interrogated prompt in loopback script
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AUTOMATIC1111 2023-02-19 14:23:31 +03:00 committed by GitHub
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1 changed files with 12 additions and 2 deletions
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14
scripts/loopback.py
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@ -8,6 +8,7 @@ from modules import processing, shared, sd_samplers, images
from modules.processing import Processed
from modules.sd_samplers import samplers
from modules.shared import opts, cmd_opts, state
from modules import deepbooru
class Script(scripts.Script):
@ -20,10 +21,11 @@ class Script(scripts.Script):
def ui(self, is_img2img):
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
return [loops, denoising_strength_change_factor]
return [loops, denoising_strength_change_factor, append_interrogation]
def run(self, p, loops, denoising_strength_change_factor):
def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
processing.fix_seed(p)
batch_count = p.n_iter
p.extra_generation_params = {
@ -40,6 +42,7 @@ class Script(scripts.Script):
grids = []
all_images = []
original_init_image = p.init_images
original_prompt = p.prompt
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
@ -58,6 +61,13 @@ class Script(scripts.Script):
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
if append_interrogation != "None":
p.prompt = original_prompt + ", " if original_prompt != "" else ""
if append_interrogation == "CLIP":
p.prompt += shared.interrogator.interrogate(p.init_images[0])
elif append_interrogation == "DeepBooru":
p.prompt += deepbooru.model.tag(p.init_images[0])
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)