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dddetailer
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add prompt, neg prompt, cfg scale 

* add prompt, neg prompt, cfg scale UI
* saves correct EXIF to image
pull/2/head
Noahge 2023-03-21 15:54:09 +09:00
parent 22a17414bb
commit cb86fab8e0
1 changed files with 75 additions and 26 deletions
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101
scripts/ddetailer.py
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@ -79,35 +79,43 @@ class DetectionDetailerScript(scripts.Script):
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Use from inpaint tab, inpaint at full res ON, denoise <0.5</p>")
else:
info = gr.HTML("")
dd_prompt = None
with gr.Group():
if not is_img2img:
with gr.Row():
dd_prompt = gr.Textbox(label="dd_prompt", elem_id="t2i_dd_prompt", show_label=False, lines=3, placeholder="Ddetailer Prompt")
with gr.Row():
dd_neg_prompt = gr.Textbox(label="dd_neg_prompt", elem_id="t2i_dd_neg_prompt", show_label=False, lines=2, placeholder="Ddetailer Negative prompt")
with gr.Row():
dd_model_a = gr.Dropdown(label="Primary detection model (A)", choices=model_list,value = "None", visible=True, type="value")
with gr.Row():
dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=False)
dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=False)
dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=True)
dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=True)
with gr.Row():
dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=True)
dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=True)
with gr.Row():
dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=False)
dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=False)
dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=True)
dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=True)
br = gr.HTML("<br>")
with gr.Group():
with gr.Row():
dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =False, type="value")
dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =True, type="value")
with gr.Row():
dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=False)
dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=False)
dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=True)
dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=True)
with gr.Row():
dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=True)
dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=True)
with gr.Group():
with gr.Row():
@ -118,6 +126,9 @@ class DetectionDetailerScript(scripts.Script):
dd_inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution ', value=True, visible = (not is_img2img))
dd_inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels ', minimum=0, maximum=256, step=4, value=32, visible=(not is_img2img))
with gr.Row():
dd_cfg_scale = gr.Slider(label='CFG Scale', minimum=0, maximum=30, step=0.5, value=7, visible=True)
dd_model_a.change(
lambda modelname: {
dd_model_b:gr_show( modelname != "None" ),
@ -143,8 +154,30 @@ class DetectionDetailerScript(scripts.Script):
inputs= [dd_model_b],
outputs =[dd_preprocess_b, dd_bitwise_op, dd_conf_b, dd_dilation_factor_b, dd_offset_x_b, dd_offset_y_b]
)
return [info,
if dd_prompt:
self.infotext_fields = (
(dd_prompt, "DDetailer prompt"),
(dd_neg_prompt, "DDetailer neg prompt"),
(dd_model_a, "DDetailer model a"),
(dd_conf_a, "DDetailer conf a"),
(dd_dilation_factor_a, "DDetailer dilation a"),
(dd_offset_x_a, "DDetailer offset x a"),
(dd_offset_y_a, "DDetailer offset y a"),
(dd_preprocess_b, "DDetailer preprocess b"),
(dd_bitwise_op, "DDetailer bitwise"),
(dd_model_b, "DDetailer model b"),
(dd_conf_b, "DDetailer conf b"),
(dd_dilation_factor_b, "DDetailer dilation b"),
(dd_offset_x_b, "DDetailer offset x b"),
(dd_offset_y_b, "DDetailer offset y b"),
(dd_mask_blur, "DDetailer mask blur"),
(dd_denoising_strength, "DDetailer denoising"),
(dd_inpaint_full_res, "DDetailer inpaint full"),
(dd_inpaint_full_res_padding, "DDetailer inpaint padding"),
(dd_cfg_scale, "DDetailer cfg")
)
ret = [info,
dd_model_a,
dd_conf_a, dd_dilation_factor_a,
dd_offset_x_a, dd_offset_y_a,
@ -154,8 +187,12 @@ class DetectionDetailerScript(scripts.Script):
dd_conf_b, dd_dilation_factor_b,
dd_offset_x_b, dd_offset_y_b,
dd_mask_blur, dd_denoising_strength,
dd_inpaint_full_res, dd_inpaint_full_res_padding
dd_inpaint_full_res, dd_inpaint_full_res_padding,
dd_cfg_scale
]
if not is_img2img:
ret += [dd_prompt, dd_neg_prompt]
return ret
def run(self, p, info,
dd_model_a,
@ -167,10 +204,12 @@ class DetectionDetailerScript(scripts.Script):
dd_conf_b, dd_dilation_factor_b,
dd_offset_x_b, dd_offset_y_b,
dd_mask_blur, dd_denoising_strength,
dd_inpaint_full_res, dd_inpaint_full_res_padding):
dd_inpaint_full_res, dd_inpaint_full_res_padding,
dd_cfg_scale,
dd_prompt=None, dd_neg_prompt=None):
processing.fix_seed(p)
initial_info = None
dd_info = None
seed = p.seed
p.batch_size = 1
ddetail_count = p.n_iter
@ -182,6 +221,8 @@ class DetectionDetailerScript(scripts.Script):
orig_image = p.init_images[0]
else:
p_txt = p
p_txt_prompt = dd_prompt if dd_prompt else p_txt.prompt
p_txt_neg_prompt = dd_neg_prompt if dd_neg_prompt else p_txt.negative_prompt
p = StableDiffusionProcessingImg2Img(
init_images = None,
resize_mode = 0,
@ -195,8 +236,8 @@ class DetectionDetailerScript(scripts.Script):
sd_model=p_txt.sd_model,
outpath_samples=p_txt.outpath_samples,
outpath_grids=p_txt.outpath_grids,
prompt=p_txt.prompt,
negative_prompt=p_txt.negative_prompt,
prompt=p_txt_prompt,
negative_prompt=p_txt_neg_prompt,
styles=p_txt.styles,
seed=p_txt.seed,
subseed=p_txt.subseed,
@ -206,7 +247,7 @@ class DetectionDetailerScript(scripts.Script):
sampler_name=p_txt.sampler_name,
n_iter=p_txt.n_iter,
steps=p_txt.steps,
cfg_scale=p_txt.cfg_scale,
cfg_scale=dd_cfg_scale,
width=p_txt.width,
height=p_txt.height,
tiling=p_txt.tiling,
@ -222,7 +263,8 @@ class DetectionDetailerScript(scripts.Script):
print(f"Processing initial image for output generation {n + 1}.")
p_txt.seed = start_seed
processed = processing.process_images(p_txt)
init_image = processed.images[0]
init_image = processed.images[0]
info = processed.info
else:
init_image = orig_image
@ -314,23 +356,30 @@ class DetectionDetailerScript(scripts.Script):
images.save_image(masks_a[i], opts.outdir_ddetailer_masks, "", start_seed, p.prompt, opts.samples_format, p=p)
processed = processing.process_images(p)
if initial_info is None:
initial_info = processed.info
if dd_info is None:
dd_info = info + (f", DDetailer prompt: \"{dd_prompt}\", DDetailer neg prompt: \"{dd_neg_prompt}\", "
f"DDetailer model a: \"{dd_model_a}\", DDetailer conf a: {dd_conf_a}, "
f"DDetailer dilation a: {dd_dilation_factor_a}, DDetailer offset x a: {dd_offset_x_a}, DDetailer offset y a: {dd_offset_y_a}, "
f"DDetailer preprocess b: {dd_preprocess_b}, DDetailer bitwise: {dd_bitwise_op}, DDetailer model b: \"{dd_model_b}\", "
f"DDetailer conf b: {dd_conf_b}, DDetailer dilation b: {dd_dilation_factor_b}, DDetailer offset x b: {dd_offset_x_b}, "
f"DDetailer offset y b: {dd_offset_y_b}, DDetailer mask blur: {dd_mask_blur}, DDetailer denoising: {dd_denoising_strength}, "
f"DDetailer inpaint full: {dd_inpaint_full_res}, DDetailer inpaint padding: {dd_inpaint_full_res_padding}, "
f"DDetailer cfg: {dd_cfg_scale}").replace("\n", " ")
p.seed = processed.seed + 1
p.init_images = processed.images
if (gen_count > 0):
output_images[n] = processed.images[0]
if ( opts.samples_save ):
images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=dd_info, p=p)
else:
print(f"No model {label_a} detections for output generation {n} with current settings.")
state.job = f"Generation {n + 1} out of {state.job_count}"
if (initial_info is None):
initial_info = "No detections found."
if (dd_info is None):
dd_info = info + ", No detections found."
return Processed(p, output_images, seed, initial_info)
return Processed(p, output_images, seed, dd_info)
def modeldataset(model_shortname):
path = modelpath(model_shortname)