Spaces:
Runtime error
Runtime error
File size: 3,846 Bytes
2e82449 |
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 |
import gradio as gr
from modules import scripts
from backend.misc.image_resize import adaptive_resize
class PatchModelAddDownscale:
def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
sigma_start = model.model.predictor.percent_to_sigma(start_percent)
sigma_end = model.model.predictor.percent_to_sigma(end_percent)
def input_block_patch(h, transformer_options):
if transformer_options["block"][1] == block_number:
sigma = transformer_options["sigmas"][0].item()
if sigma <= sigma_start and sigma >= sigma_end:
h = adaptive_resize(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled")
return h
def output_block_patch(h, hsp, transformer_options):
if h.shape[2] != hsp.shape[2]:
h = adaptive_resize(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled")
return h, hsp
m = model.clone()
if downscale_after_skip:
m.set_model_input_block_patch_after_skip(input_block_patch)
else:
m.set_model_input_block_patch(input_block_patch)
m.set_model_output_block_patch(output_block_patch)
return (m,)
opPatchModelAddDownscale = PatchModelAddDownscale()
class KohyaHRFixForForge(scripts.Script):
sorting_priority = 14
def title(self):
return "Kohya HRFix Integrated"
def show(self, is_img2img):
return scripts.AlwaysVisible
def ui(self, *args, **kwargs):
upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
with gr.Accordion(open=False, label=self.title()):
enabled = gr.Checkbox(label='Enabled', value=False)
block_number = gr.Slider(label='Block Number', value=3, minimum=1, maximum=32, step=1)
downscale_factor = gr.Slider(label='Downscale Factor', value=2.0, minimum=0.1, maximum=9.0, step=0.001)
start_percent = gr.Slider(label='Start Percent', value=0.0, minimum=0.0, maximum=1.0, step=0.001)
end_percent = gr.Slider(label='End Percent', value=0.35, minimum=0.0, maximum=1.0, step=0.001)
downscale_after_skip = gr.Checkbox(label='Downscale After Skip', value=True)
downscale_method = gr.Radio(label='Downscale Method', choices=upscale_methods, value=upscale_methods[0])
upscale_method = gr.Radio(label='Upscale Method', choices=upscale_methods, value=upscale_methods[0])
return enabled, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method
def process_before_every_sampling(self, p, *script_args, **kwargs):
enabled, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method = script_args
block_number = int(block_number)
if not enabled:
return
unet = p.sd_model.forge_objects.unet
unet = opPatchModelAddDownscale.patch(unet, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method)[0]
p.sd_model.forge_objects.unet = unet
p.extra_generation_params.update(dict(
kohya_hrfix_enabled=enabled,
kohya_hrfix_block_number=block_number,
kohya_hrfix_downscale_factor=downscale_factor,
kohya_hrfix_start_percent=start_percent,
kohya_hrfix_end_percent=end_percent,
kohya_hrfix_downscale_after_skip=downscale_after_skip,
kohya_hrfix_downscale_method=downscale_method,
kohya_hrfix_upscale_method=upscale_method,
))
return
|