Merge branch 'noahge4/master'
Bingsu
commit
a80df3ba03
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@ -79,35 +79,43 @@ class DetectionDetailerScript(scripts.Script):
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info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Use from inpaint tab, inpaint at full res ON, denoise <0.5</p>")
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else:
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info = gr.HTML("")
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dd_prompt = None
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with gr.Group():
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if not is_img2img:
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with gr.Row():
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dd_prompt = gr.Textbox(label="dd_prompt", elem_id="t2i_dd_prompt", show_label=False, lines=3, placeholder="Ddetailer Prompt")
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with gr.Row():
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dd_neg_prompt = gr.Textbox(label="dd_neg_prompt", elem_id="t2i_dd_neg_prompt", show_label=False, lines=2, placeholder="Ddetailer Negative prompt")
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with gr.Row():
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dd_model_a = gr.Dropdown(label="Primary detection model (A)", choices=model_list,value = "None", visible=True, type="value")
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with gr.Row():
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dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=False)
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dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=False)
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dd_conf_a = gr.Slider(label='Detection confidence threshold % (A)', minimum=0, maximum=100, step=1, value=30, visible=True)
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dd_dilation_factor_a = gr.Slider(label='Dilation factor (A)', minimum=0, maximum=255, step=1, value=4, visible=True)
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with gr.Row():
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dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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dd_offset_x_a = gr.Slider(label='X offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=True)
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dd_offset_y_a = gr.Slider(label='Y offset (A)', minimum=-200, maximum=200, step=1, value=0, visible=True)
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with gr.Row():
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dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=False)
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dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=False)
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dd_preprocess_b = gr.Checkbox(label='Inpaint model B detections before model A runs', value=False, visible=True)
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dd_bitwise_op = gr.Radio(label='Bitwise operation', choices=['None', 'A&B', 'A-B'], value="None", visible=True)
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br = gr.HTML("<br>")
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with gr.Group():
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with gr.Row():
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dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =False, type="value")
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dd_model_b = gr.Dropdown(label="Secondary detection model (B) (optional)", choices=model_list,value = "None", visible =True, type="value")
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with gr.Row():
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dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=False)
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dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=False)
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dd_conf_b = gr.Slider(label='Detection confidence threshold % (B)', minimum=0, maximum=100, step=1, value=30, visible=True)
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dd_dilation_factor_b = gr.Slider(label='Dilation factor (B)', minimum=0, maximum=255, step=1, value=4, visible=True)
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with gr.Row():
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dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=False)
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dd_offset_x_b = gr.Slider(label='X offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=True)
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dd_offset_y_b = gr.Slider(label='Y offset (B)', minimum=-200, maximum=200, step=1, value=0, visible=True)
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with gr.Group():
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with gr.Row():
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@ -118,6 +126,9 @@ class DetectionDetailerScript(scripts.Script):
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dd_inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution ', value=True, visible = (not is_img2img))
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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))
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with gr.Row():
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dd_cfg_scale = gr.Slider(label='CFG Scale', minimum=0, maximum=30, step=0.5, value=7, visible=True)
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dd_model_a.change(
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lambda modelname: {
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dd_model_b:gr_show( modelname != "None" ),
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@ -143,8 +154,30 @@ class DetectionDetailerScript(scripts.Script):
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inputs= [dd_model_b],
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outputs =[dd_preprocess_b, dd_bitwise_op, dd_conf_b, dd_dilation_factor_b, dd_offset_x_b, dd_offset_y_b]
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)
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return [info,
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if dd_prompt:
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self.infotext_fields = (
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(dd_prompt, "DDetailer prompt"),
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(dd_neg_prompt, "DDetailer neg prompt"),
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(dd_model_a, "DDetailer model a"),
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(dd_conf_a, "DDetailer conf a"),
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(dd_dilation_factor_a, "DDetailer dilation a"),
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(dd_offset_x_a, "DDetailer offset x a"),
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(dd_offset_y_a, "DDetailer offset y a"),
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(dd_preprocess_b, "DDetailer preprocess b"),
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(dd_bitwise_op, "DDetailer bitwise"),
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(dd_model_b, "DDetailer model b"),
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(dd_conf_b, "DDetailer conf b"),
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(dd_dilation_factor_b, "DDetailer dilation b"),
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(dd_offset_x_b, "DDetailer offset x b"),
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(dd_offset_y_b, "DDetailer offset y b"),
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(dd_mask_blur, "DDetailer mask blur"),
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(dd_denoising_strength, "DDetailer denoising"),
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(dd_inpaint_full_res, "DDetailer inpaint full"),
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(dd_inpaint_full_res_padding, "DDetailer inpaint padding"),
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(dd_cfg_scale, "DDetailer cfg")
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)
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ret = [info,
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dd_model_a,
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dd_conf_a, dd_dilation_factor_a,
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dd_offset_x_a, dd_offset_y_a,
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@ -154,8 +187,12 @@ class DetectionDetailerScript(scripts.Script):
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dd_conf_b, dd_dilation_factor_b,
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dd_offset_x_b, dd_offset_y_b,
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dd_mask_blur, dd_denoising_strength,
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dd_inpaint_full_res, dd_inpaint_full_res_padding
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dd_inpaint_full_res, dd_inpaint_full_res_padding,
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dd_cfg_scale
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]
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if not is_img2img:
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ret += [dd_prompt, dd_neg_prompt]
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return ret
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def run(self, p, info,
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dd_model_a,
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@ -167,10 +204,12 @@ class DetectionDetailerScript(scripts.Script):
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dd_conf_b, dd_dilation_factor_b,
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dd_offset_x_b, dd_offset_y_b,
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dd_mask_blur, dd_denoising_strength,
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dd_inpaint_full_res, dd_inpaint_full_res_padding):
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dd_inpaint_full_res, dd_inpaint_full_res_padding,
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dd_cfg_scale,
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dd_prompt=None, dd_neg_prompt=None):
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processing.fix_seed(p)
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initial_info = None
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dd_info = None
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seed = p.seed
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p.batch_size = 1
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ddetail_count = p.n_iter
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@ -182,6 +221,11 @@ class DetectionDetailerScript(scripts.Script):
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orig_image = p.init_images[0]
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else:
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p_txt = p
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img2img_sampler_name = p_txt.sampler_name
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if p_txt.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
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img2img_sampler_name = 'DDIM'
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p_txt_prompt = dd_prompt if dd_prompt else p_txt.prompt
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p_txt_neg_prompt = dd_neg_prompt if dd_neg_prompt else p_txt.negative_prompt
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p = StableDiffusionProcessingImg2Img(
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init_images = None,
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resize_mode = 0,
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@ -195,18 +239,18 @@ class DetectionDetailerScript(scripts.Script):
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sd_model=p_txt.sd_model,
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outpath_samples=p_txt.outpath_samples,
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outpath_grids=p_txt.outpath_grids,
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prompt=p_txt.prompt,
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negative_prompt=p_txt.negative_prompt,
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prompt=p_txt_prompt,
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negative_prompt=p_txt_neg_prompt,
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styles=p_txt.styles,
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seed=p_txt.seed,
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subseed=p_txt.subseed,
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subseed_strength=p_txt.subseed_strength,
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seed_resize_from_h=p_txt.seed_resize_from_h,
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seed_resize_from_w=p_txt.seed_resize_from_w,
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sampler_name=p_txt.sampler_name,
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sampler_name=img2img_sampler_name,
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n_iter=p_txt.n_iter,
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steps=p_txt.steps,
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cfg_scale=p_txt.cfg_scale,
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cfg_scale=dd_cfg_scale,
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width=p_txt.width,
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height=p_txt.height,
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tiling=p_txt.tiling,
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@ -222,7 +266,8 @@ class DetectionDetailerScript(scripts.Script):
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print(f"Processing initial image for output generation {n + 1}.")
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p_txt.seed = start_seed
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processed = processing.process_images(p_txt)
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init_image = processed.images[0]
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init_image = processed.images[0]
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info = processed.info
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else:
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init_image = orig_image
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@ -314,23 +359,30 @@ class DetectionDetailerScript(scripts.Script):
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images.save_image(masks_a[i], opts.outdir_ddetailer_masks, "", start_seed, p.prompt, opts.samples_format, p=p)
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processed = processing.process_images(p)
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if initial_info is None:
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initial_info = processed.info
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if dd_info is None:
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dd_info = info + (f", DDetailer prompt: \"{dd_prompt}\", DDetailer neg prompt: \"{dd_neg_prompt}\", "
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f"DDetailer model a: \"{dd_model_a}\", DDetailer conf a: {dd_conf_a}, "
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f"DDetailer dilation a: {dd_dilation_factor_a}, DDetailer offset x a: {dd_offset_x_a}, DDetailer offset y a: {dd_offset_y_a}, "
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f"DDetailer preprocess b: {dd_preprocess_b}, DDetailer bitwise: {dd_bitwise_op}, DDetailer model b: \"{dd_model_b}\", "
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f"DDetailer conf b: {dd_conf_b}, DDetailer dilation b: {dd_dilation_factor_b}, DDetailer offset x b: {dd_offset_x_b}, "
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f"DDetailer offset y b: {dd_offset_y_b}, DDetailer mask blur: {dd_mask_blur}, DDetailer denoising: {dd_denoising_strength}, "
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f"DDetailer inpaint full: {dd_inpaint_full_res}, DDetailer inpaint padding: {dd_inpaint_full_res_padding}, "
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f"DDetailer cfg: {dd_cfg_scale}").replace("\n", " ")
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p.seed = processed.seed + 1
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p.init_images = processed.images
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if (gen_count > 0):
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output_images[n] = processed.images[0]
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if ( opts.samples_save ):
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images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
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images.save_image(processed.images[0], p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=dd_info, p=p)
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else:
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print(f"No model {label_a} detections for output generation {n} with current settings.")
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state.job = f"Generation {n + 1} out of {state.job_count}"
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if (initial_info is None):
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initial_info = "No detections found."
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if (dd_info is None):
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dd_info = info + ", No detections found."
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return Processed(p, output_images, seed, initial_info)
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return Processed(p, output_images, seed, dd_info)
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def modeldataset(model_shortname):
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path = modelpath(model_shortname)
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