diff options
| -rw-r--r-- | scripts/loopback.py | 14 |
1 files changed, 12 insertions, 2 deletions
diff --git a/scripts/loopback.py b/scripts/loopback.py index 1dab9476..ec1f85e5 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -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)
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