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import gradio
import torch
import modules.devices as devices
import modules.scripts as scripts
import modules.script_callbacks as script_callbacks
import modules.sd_unet as sd_unet
import modules.shared as shared
from ldm.modules.diffusionmodules.util import timestep_embedding as timestep_embedding
class DeepShrinkHiresFixAction():
def __init__(self, enable: bool, timestep: float, depth: int, scale: float):
self.enable = enable
self.timestep = timestep
self.depth = depth
self.scale = scale
pass
pass
class DeepShrinkHiresFix(scripts.Script):
deepShrinkHiresFixActions: list[DeepShrinkHiresFixAction] = []
enableExperimental: bool = False
experimentalTimestep: float = 900
experimentalScales: list[float] = []
def __init__(self):
pass
def title(self):
return "Deep Shrink Hires.fix"
pass
def show(self, is_img2img):
return scripts.AlwaysVisible
pass
def ui(self, is_img2img):
with gradio.Accordion(label="Deep Shrink Hires.fix", open=False):
with gradio.Row():
Enable_1 = gradio.Checkbox(value=True, label="Enable 1")
Timestep_1 = gradio.Number(value=900, label="Timestep 1")
Depth_1 = gradio.Number(value=3, label="Block Depth 1", precision=0)
Scale_1 = gradio.Number(value=2, label="Scale factor 1")
pass
with gradio.Row():
Enable_2 = gradio.Checkbox(value=True, label="Enable 2")
Timestep_2 = gradio.Number(value=650, label="Timestep 2")
Depth_2 = gradio.Number(value=3, label="Block Depth 2", precision=0)
Scale_2 = gradio.Number(value=2, label="Scale factor 2")
pass
with gradio.Accordion(label="Advanced Settings", open=False):
with gradio.Row():
Enable_3 = gradio.Checkbox(value=False, label="Enable 3")
Timestep_3 = gradio.Number(value=900, label="Timestep 3")
Depth_3 = gradio.Number(value=3, label="Block Depth 3", precision=0)
Scale_3 = gradio.Number(value=2, label="Scale factor 3")
pass
with gradio.Row():
Enable_4 = gradio.Checkbox(value=False, label="Enable 4")
Timestep_4 = gradio.Number(value=650, label="Timestep 4")
Depth_4 = gradio.Number(value=3, label="Block Depth 4", precision=0)
Scale_4 = gradio.Number(value=2, label="Scale factor 4")
pass
with gradio.Row():
Enable_5 = gradio.Checkbox(value=False, label="Enable 5")
Timestep_5 = gradio.Number(value=900, label="Timestep 5")
Depth_5 = gradio.Number(value=3, label="Block Depth 5", precision=0)
Scale_5 = gradio.Number(value=2, label="Scale factor 5")
pass
with gradio.Row():
Enable_6 = gradio.Checkbox(value=False, label="Enable 6")
Timestep_6 = gradio.Number(value=650, label="Timestep 6")
Depth_6 = gradio.Number(value=3, label="Block Depth 6", precision=0)
Scale_6 = gradio.Number(value=2, label="Scale factor 6")
pass
with gradio.Row():
Enable_7 = gradio.Checkbox(value=False, label="Enable 7")
Timestep_7 = gradio.Number(value=900, label="Timestep 7")
Depth_7 = gradio.Number(value=3, label="Block Depth 7", precision=0)
Scale_7 = gradio.Number(value=2, label="Scale factor 7")
pass
with gradio.Row():
Enable_8 = gradio.Checkbox(value=False, label="Enable 8")
Timestep_8 = gradio.Number(value=650, label="Timestep 8")
Depth_8 = gradio.Number(value=3, label="Block Depth 8", precision=0)
Scale_8 = gradio.Number(value=2, label="Scale factor 8")
pass
pass
with gradio.Accordion(label="Experimental Settings", open=False):
with gradio.Row():
Enable_Experimental = gradio.Checkbox(value=False, label="Enable Experimental Mode")
Timestep_Experimental = gradio.Number(value=900, label="Timestep")
Scale_Experimental = gradio.Textbox(value="1,1,1, 1,1,1, 1,1,1, 1,1,1, 2, 1,1,1, 1,1,1, 1,1,1, 1,1,1", label="Scale Factor List")
pass
pass
pass
return [Enable_1, Timestep_1, Depth_1, Scale_1, Enable_2, Timestep_2, Depth_2, Scale_2, Enable_3, Timestep_3, Depth_3, Scale_3, Enable_4, Timestep_4, Depth_4, Scale_4,
Enable_5, Timestep_5, Depth_5, Scale_5, Enable_6, Timestep_6, Depth_6, Scale_6, Enable_7, Timestep_7, Depth_7, Scale_7, Enable_8, Timestep_8, Depth_8, Scale_8,
Enable_Experimental, Timestep_Experimental, Scale_Experimental]
pass
def process(self, p, *args):
del DeepShrinkHiresFix.deepShrinkHiresFixActions[:]
for i in range(8):
DeepShrinkHiresFix.deepShrinkHiresFixActions.append(DeepShrinkHiresFixAction(args[i*4], args[i*4+1], args[i*4+2], args[i*4+3]))
pass
del DeepShrinkHiresFix.experimentalScales[:]
DeepShrinkHiresFix.enableExperimental = args[8*4]
DeepShrinkHiresFix.experimentalTimestep = args[8*4+1]
scaleFactorsTexts: str = args[8*4+2]
scaleFactorsTextsList = scaleFactorsTexts.split(",")
for scaleFactorsText in scaleFactorsTextsList:
DeepShrinkHiresFix.experimentalScales.append(float(scaleFactorsText))
pass
pass
class DeepShrinkHiresFixUNet(sd_unet.SdUnet):
def __init__(self, _model):
super().__init__()
self.model = _model.to(devices.device)
pass
def forward(self, x, timesteps, context, y=None, **kwargs):
assert (y is not None) == (
self.model.num_classes is not None
), "must specify y if and only if the model is class-conditional"
hs = []
ss = []
t_emb = timestep_embedding(timesteps, self.model.model_channels, repeat_only=False)
emb = self.model.time_embed(t_emb)
if self.model.num_classes is not None:
assert y.shape[0] == x.shape[0]
emb = emb + self.model.label_emb(y)
h = x.type(self.model.dtype)
depth = 0
block = 0
scale = 1
for module in self.model.input_blocks:
for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
h = torch.nn.functional.interpolate(h.float(), scale_factor=1/action.scale, mode="bicubic", align_corners=False).to(h.dtype)
break
pass
pass
if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
h = torch.nn.functional.interpolate(h.float(), scale_factor=scale/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype)
scale = DeepShrinkHiresFix.experimentalScales[block]
ss.append(scale)
pass
h = module(h, emb, context)
hs.append(h)
depth += 1
block += 1
pass
for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
h = torch.nn.functional.interpolate(h.float(), scale_factor=1/action.scale, mode="bicubic", align_corners=False).to(h.dtype)
break
pass
pass
if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
h = torch.nn.functional.interpolate(h.float(), scale_factor=scale/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype)
scale = DeepShrinkHiresFix.experimentalScales[block]
pass
h = self.model.middle_block(h, emb, context)
for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
h = torch.nn.functional.interpolate(h.float(), scale_factor=action.scale, mode="bicubic", align_corners=False).to(h.dtype)
break
pass
pass
block += 1
for module in self.model.output_blocks:
depth -= 1
if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
h = torch.cat([torch.nn.functional.interpolate(h.float(), scale_factor=scale/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype),
torch.nn.functional.interpolate(hs.pop().float(), scale_factor=ss.pop()/DeepShrinkHiresFix.experimentalScales[block], mode="bicubic", align_corners=False).to(h.dtype)], dim=1)
scale = DeepShrinkHiresFix.experimentalScales[block]
pass
else:
h = torch.cat([h, hs.pop()], dim=1)
pass
h = module(h, emb, context)
for action in DeepShrinkHiresFix.deepShrinkHiresFixActions:
if action.enable == True and action.depth == depth and action.timestep < timesteps[0]:
h = torch.nn.functional.interpolate(h.float(), scale_factor=action.scale, mode="bicubic", align_corners=False).to(h.dtype)
break
pass
pass
block += 1
pass
if DeepShrinkHiresFix.enableExperimental and timesteps[0] >= DeepShrinkHiresFix.experimentalTimestep:
h = torch.nn.functional.interpolate(h.float(), scale_factor=scale, mode="bicubic", align_corners=False).to(h.dtype)
pass
h = h.type(x.dtype)
if self.model.predict_codebook_ids:
return self.model.id_predictor(h)
else:
return self.model.out(h)
pass
pass
DeepShrinkHiresFixUNetOption = sd_unet.SdUnetOption()
DeepShrinkHiresFixUNetOption.label = "Deep Shrink Hires.fix"
DeepShrinkHiresFixUNetOption.create_unet = lambda: DeepShrinkHiresFix.DeepShrinkHiresFixUNet(shared.sd_model.model.diffusion_model)
pass
script_callbacks.on_list_unets(lambda unets: unets.append(DeepShrinkHiresFix.DeepShrinkHiresFixUNetOption)) |