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major refactor: remove backend original

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-07-05 13:16:46 -04:00
parent f408e5f315
commit e8b5ea3847
317 changed files with 386 additions and 39149 deletions
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scripts/loopback.py
+2 -13
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@@ -21,11 +21,10 @@ class Script(scripts_manager.Script):
final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
with gr.Row():
denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
return [loops, final_denoising_strength, denoising_curve, append_interrogation]
return [loops, final_denoising_strength, denoising_curve]
def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation): # pylint: disable=arguments-differ
def run(self, p, loops, final_denoising_strength, denoising_curve): # pylint: disable=arguments-differ
processing.fix_seed(p)
batch_count = p.n_iter
p.extra_generation_params = {
@@ -44,7 +43,6 @@ class Script(scripts_manager.Script):
grids = []
all_images = []
original_init_image = p.init_images
original_prompt = p.prompt
original_inpainting_fill = p.inpainting_fill
state.job_count = loops * batch_count
@@ -86,15 +84,6 @@ class Script(scripts_manager.Script):
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
if append_interrogation != "None":
p.prompt = f"{original_prompt}, " if original_prompt else ""
if append_interrogation == "CLIP":
from modules.interrogate import openclip
p.prompt += openclip.interrogator.interrogate(p.init_images[0])
elif append_interrogation == "DeepBooru":
from modules.interrogate import deepbooru
p.prompt += deepbooru.model.tag(p.init_images[0])
state.job = f"loopback iteration {i+1}/{loops} batch {n+1}/{batch_count}"
processed = processing.process_images(p)