File size: 7,815 Bytes
3dabe4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
try:
import pag_nodes
if pag_nodes.BACKEND in {"Forge", "reForge"}:
import gradio as gr
from modules import scripts
from modules.ui_components import InputAccordion
opPerturbedAttention = pag_nodes.PerturbedAttention()
class PerturbedAttentionScript(scripts.Script):
def title(self):
return "Perturbed-Attention Guidance"
def show(self, is_img2img):
return scripts.AlwaysVisible
def ui(self, *args, **kwargs):
with gr.Accordion(open=False, label=self.title()):
enabled = gr.Checkbox(label="Enabled", value=False)
scale = gr.Slider(label="PAG Scale", minimum=0.0, maximum=30.0, step=0.01, value=3.0)
with gr.Row():
rescale_pag = gr.Slider(label="Rescale PAG", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
rescale_mode = gr.Dropdown(choices=["full", "partial", "snf"], value="full", label="Rescale Mode")
adaptive_scale = gr.Slider(label="Adaptive Scale", minimum=0.0, maximum=1.0, step=0.001, value=0.0)
with InputAccordion(False, label="Override for Hires. fix") as hr_override:
hr_cfg = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label="CFG Scale", value=7.0)
hr_scale = gr.Slider(label="PAG Scale", minimum=0.0, maximum=30.0, step=0.01, value=3.0)
with gr.Row():
hr_rescale_pag = gr.Slider(label="Rescale PAG", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
hr_rescale_mode = gr.Dropdown(choices=["full", "partial", "snf"], value="full", label="Rescale Mode")
hr_adaptive_scale = gr.Slider(label="Adaptive Scale", minimum=0.0, maximum=1.0, step=0.001, value=0.0)
with gr.Row():
block = gr.Dropdown(choices=["input", "middle", "output"], value="middle", label="U-Net Block")
block_id = gr.Number(label="U-Net Block Id", value=0, precision=0, minimum=0)
block_list = gr.Text(label="U-Net Block List")
with gr.Row():
sigma_start = gr.Number(minimum=-1.0, label="Sigma Start", value=-1.0)
sigma_end = gr.Number(minimum=-1.0, label="Sigma End", value=-1.0)
self.infotext_fields = (
(enabled, lambda p: gr.Checkbox.update(value="pag_enabled" in p)),
(scale, "pag_scale"),
(rescale_pag, "pag_rescale"),
(rescale_mode, lambda p: gr.Dropdown.update(value=p.get("pag_rescale_mode", "full"))),
(adaptive_scale, "pag_adaptive_scale"),
(hr_override, lambda p: gr.Checkbox.update(value="hr_override" in p)),
(hr_cfg, "pag_hr_cfg"),
(hr_scale, "pag_hr_scale"),
(hr_rescale_pag, "pag_hr_rescale"),
(hr_rescale_mode, lambda p: gr.Dropdown.update(value=p.get("pag_hr_rescale_mode", "full"))),
(hr_adaptive_scale, "pag_hr_adaptive_scale"),
(block, lambda p: gr.Dropdown.update(value=p.get("pag_block", "middle"))),
(block_id, "pag_block_id"),
(block_list, lambda p: gr.Text.update(value=p.get("pag_block_list", ""))),
(sigma_start, "pag_sigma_start"),
(sigma_end, "pag_sigma_end"),
)
return enabled, scale, rescale_pag, rescale_mode, adaptive_scale, block, block_id, block_list, hr_override, hr_cfg, hr_scale, hr_rescale_pag, hr_rescale_mode, hr_adaptive_scale, sigma_start, sigma_end
def process_before_every_sampling(self, p, *script_args, **kwargs):
(
enabled,
scale,
rescale_pag,
rescale_mode,
adaptive_scale,
block,
block_id,
block_list,
hr_override,
hr_cfg,
hr_scale,
hr_rescale_pag,
hr_rescale_mode,
hr_adaptive_scale,
sigma_start,
sigma_end,
) = script_args
if not enabled:
return
unet = p.sd_model.forge_objects.unet
hr_enabled = getattr(p, "enable_hr", False)
if hr_enabled and p.is_hr_pass and hr_override:
p.cfg_scale_before_hr = p.cfg_scale
p.cfg_scale = hr_cfg
unet = opPerturbedAttention.patch(unet, hr_scale, hr_adaptive_scale, block, block_id, sigma_start, sigma_end, hr_rescale_pag, hr_rescale_mode, block_list)[0]
else:
unet = opPerturbedAttention.patch(unet, scale, adaptive_scale, block, block_id, sigma_start, sigma_end, rescale_pag, rescale_mode, block_list)[0]
p.sd_model.forge_objects.unet = unet
p.extra_generation_params.update(
dict(
pag_enabled=enabled,
pag_scale=scale,
pag_rescale=rescale_pag,
pag_rescale_mode=rescale_mode,
pag_adaptive_scale=adaptive_scale,
pag_block=block,
pag_block_id=block_id,
pag_block_list=block_list,
)
)
if hr_enabled:
p.extra_generation_params["pag_hr_override"] = hr_override
if hr_override:
p.extra_generation_params.update(
dict(
pag_hr_cfg=hr_cfg,
pag_hr_scale=hr_scale,
pag_hr_rescale=hr_rescale_pag,
pag_hr_rescale_mode=hr_rescale_mode,
pag_hr_adaptive_scale=hr_adaptive_scale,
)
)
if sigma_start >= 0 or sigma_end >= 0:
p.extra_generation_params.update(
dict(
pag_sigma_start=sigma_start,
pag_sigma_end=sigma_end,
)
)
return
def post_sample(self, p, ps, *script_args):
(
enabled,
scale,
rescale_pag,
rescale_mode,
adaptive_scale,
block,
block_id,
block_list,
hr_override,
hr_cfg,
hr_scale,
hr_rescale_pag,
hr_rescale_mode,
hr_adaptive_scale,
sigma_start,
sigma_end,
) = script_args
if not enabled:
return
hr_enabled = getattr(p, "enable_hr", False)
if hr_enabled and hr_override:
p.cfg_scale = p.cfg_scale_before_hr
return
except ImportError:
pass
|