diff --git a/modules/devices.py b/modules/devices.py
index 919048d0..52c3e7cd 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,22 +1,17 @@
-import sys, os, shlex
+import sys
import contextlib
import torch
from modules import errors
-from modules.sd_hijack_utils import CondFunc
-from packaging import version
+
+if sys.platform == "darwin":
+ from modules import mac_specific
-# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
-# check `getattr` and try it for compatibility
def has_mps() -> bool:
- if not getattr(torch, 'has_mps', False):
+ if sys.platform != "darwin":
return False
- try:
- torch.zeros(1).to(torch.device("mps"))
- return True
- except Exception:
- return False
-
+ else:
+ return mac_specific.has_mps
def extract_device_id(args, name):
for x in range(len(args)):
@@ -155,36 +150,3 @@ def test_for_nans(x, where):
message += " Use --disable-nan-check commandline argument to disable this check."
raise NansException(message)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
-def cumsum_fix(input, cumsum_func, *args, **kwargs):
- if input.device.type == 'mps':
- output_dtype = kwargs.get('dtype', input.dtype)
- if output_dtype == torch.int64:
- return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
- elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
- return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
- return cumsum_func(input, *args, **kwargs)
-
-
-if has_mps():
- if version.parse(torch.__version__) < version.parse("1.13"):
- # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
-
- # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
- CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
- lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
- # MPS workaround for https://github.com/pytorch/pytorch/issues/80800
- CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
- lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
- # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
- CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
- elif version.parse(torch.__version__) > version.parse("1.13.1"):
- cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
- cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
- cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
- CondFunc('torch.cumsum', cumsum_fix_func, None)
- CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
- CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
-
diff --git a/modules/hashes.py b/modules/hashes.py
index 819362a3..83272a07 100644
--- a/modules/hashes.py
+++ b/modules/hashes.py
@@ -4,6 +4,7 @@ import os.path
import filelock
+from modules import shared
from modules.paths import data_path
@@ -68,6 +69,9 @@ def sha256(filename, title):
if sha256_value is not None:
return sha256_value
+ if shared.cmd_opts.no_hashing:
+ return None
+
print(f"Calculating sha256 for {filename}: ", end='')
sha256_value = calculate_sha256(filename)
print(f"{sha256_value}")
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 503534e2..825a93b2 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -307,7 +307,7 @@ class Hypernetwork:
def shorthash(self):
sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
- return sha256[0:10]
+ return sha256[0:10] if sha256 else None
def list_hypernetworks(path):
diff --git a/modules/images.py b/modules/images.py
index ae3cdaf4..4bdbd730 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -130,7 +130,7 @@ class GridAnnotation:
self.size = None
-def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
+def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
def wrap(drawing, text, font, line_length):
lines = ['']
for word in text.split():
@@ -194,25 +194,28 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
line.allowed_width = allowed_width
hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
- ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
- ver_texts]
+ ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
- result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
- result.paste(im, (pad_left, pad_top))
+ result = Image.new("RGB", (im.width + pad_left + margin * (rows-1), im.height + pad_top + margin * (cols-1)), "white")
+
+ for row in range(rows):
+ for col in range(cols):
+ cell = im.crop((width * col, height * row, width * (col+1), height * (row+1)))
+ result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row))
d = ImageDraw.Draw(result)
for col in range(cols):
- x = pad_left + width * col + width / 2
+ x = pad_left + (width + margin) * col + width / 2
y = pad_top / 2 - hor_text_heights[col] / 2
draw_texts(d, x, y, hor_texts[col], fnt, fontsize)
for row in range(rows):
x = pad_left / 2
- y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
+ y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2
draw_texts(d, x, y, ver_texts[row], fnt, fontsize)
diff --git a/modules/mac_specific.py b/modules/mac_specific.py
new file mode 100644
index 00000000..ddcea53b
--- /dev/null
+++ b/modules/mac_specific.py
@@ -0,0 +1,53 @@
+import torch
+from modules import paths
+from modules.sd_hijack_utils import CondFunc
+from packaging import version
+
+
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
+# check `getattr` and try it for compatibility
+def check_for_mps() -> bool:
+ if not getattr(torch, 'has_mps', False):
+ return False
+ try:
+ torch.zeros(1).to(torch.device("mps"))
+ return True
+ except Exception:
+ return False
+has_mps = check_for_mps()
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
+def cumsum_fix(input, cumsum_func, *args, **kwargs):
+ if input.device.type == 'mps':
+ output_dtype = kwargs.get('dtype', input.dtype)
+ if output_dtype == torch.int64:
+ return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
+ elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
+ return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
+ return cumsum_func(input, *args, **kwargs)
+
+
+if has_mps:
+ # MPS fix for randn in torchsde
+ CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
+
+ if version.parse(torch.__version__) < version.parse("1.13"):
+ # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+ CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
+ lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/80800
+ CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
+ lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+ CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
+ elif version.parse(torch.__version__) > version.parse("1.13.1"):
+ cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
+ cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
+ cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
+ CondFunc('torch.cumsum', cumsum_fix_func, None)
+ CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
+ CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
+
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 300387a9..af1731e5 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -59,13 +59,17 @@ class CheckpointInfo:
def calculate_shorthash(self):
self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
+ if self.sha256 is None:
+ return
+
self.shorthash = self.sha256[0:10]
if self.shorthash not in self.ids:
- self.ids += [self.shorthash, self.sha256]
- self.register()
+ self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
+ checkpoints_list.pop(self.title)
self.title = f'{self.name} [{self.shorthash}]'
+ self.register()
return self.shorthash
@@ -158,7 +162,7 @@ def select_checkpoint():
print(f" - directory {model_path}", file=sys.stderr)
if shared.cmd_opts.ckpt_dir is not None:
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
- print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
+ print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
exit(1)
checkpoint_info = next(iter(checkpoints_list.values()))
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 3c03d442..a1aac7cf 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -2,7 +2,6 @@ from collections import namedtuple
import numpy as np
import torch
from PIL import Image
-import torchsde._brownian.brownian_interval
from modules import devices, processing, images, sd_vae_approx
from modules.shared import opts, state
@@ -61,18 +60,3 @@ def store_latent(decoded):
class InterruptedException(BaseException):
pass
-
-
-# MPS fix for randn in torchsde
-# XXX move this to separate file for MPS
-def torchsde_randn(size, dtype, device, seed):
- if device.type == 'mps':
- generator = torch.Generator(devices.cpu).manual_seed(int(seed))
- return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
- else:
- generator = torch.Generator(device).manual_seed(int(seed))
- return torch.randn(size, dtype=dtype, device=device, generator=generator)
-
-
-torchsde._brownian.brownian_interval._randn = torchsde_randn
-
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 6c57fdec..f076fc55 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -41,90 +41,6 @@ sampler_extra_params = {
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
}
-class CFGDenoiserEdit(torch.nn.Module):
- """
- Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
- that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
- instead of one. Originally, the second prompt is just an empty string, but we use non-empty
- negative prompt.
- """
-
- def __init__(self, model):
- super().__init__()
- self.inner_model = model
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.step = 0
-
- def combine_denoised(self, x_out, conds_list, uncond, cond_scale, image_cfg_scale):
- denoised_uncond = x_out[-uncond.shape[0]:]
- denoised = torch.clone(denoised_uncond)
-
- for i, conds in enumerate(conds_list):
- for cond_index, weight in conds:
- out_cond, out_img_cond, out_uncond = x_out.chunk(3)
- denoised[i] = out_uncond[cond_index] + cond_scale * (out_cond[cond_index] - out_img_cond[cond_index]) + image_cfg_scale * (out_img_cond[cond_index] - out_uncond[cond_index])
-
- return denoised
-
- def forward(self, x, sigma, uncond, cond, cond_scale, image_cond, image_cfg_scale):
- if state.interrupted or state.skipped:
- raise sd_samplers_common.InterruptedException
-
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
- batch_size = len(conds_list)
- repeats = [len(conds_list[i]) for i in range(batch_size)]
-
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
-
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
- cfg_denoiser_callback(denoiser_params)
- x_in = denoiser_params.x
- image_cond_in = denoiser_params.image_cond
- sigma_in = denoiser_params.sigma
-
- if tensor.shape[1] == uncond.shape[1]:
- cond_in = torch.cat([tensor, uncond, uncond])
-
- if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
- else:
- x_out = torch.zeros_like(x_in)
- for batch_offset in range(0, x_out.shape[0], batch_size):
- a = batch_offset
- b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
- else:
- x_out = torch.zeros_like(x_in)
- batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
- for batch_offset in range(0, tensor.shape[0], batch_size):
- a = batch_offset
- b = min(a + batch_size, tensor.shape[0])
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": torch.cat([tensor[a:b]], uncond) , "c_concat": [image_cond_in[a:b]]})
-
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
-
- devices.test_for_nans(x_out, "unet")
-
- if opts.live_preview_content == "Prompt":
- sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
- elif opts.live_preview_content == "Negative prompt":
- sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale, image_cfg_scale)
-
- if self.mask is not None:
- denoised = self.init_latent * self.mask + self.nmask * denoised
-
- self.step += 1
-
- return denoised
-
class CFGDenoiser(torch.nn.Module):
"""
@@ -141,6 +57,7 @@ class CFGDenoiser(torch.nn.Module):
self.nmask = None
self.init_latent = None
self.step = 0
+ self.image_cfg_scale = None
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@@ -152,19 +69,36 @@ class CFGDenoiser(torch.nn.Module):
return denoised
+ def combine_denoised_for_edit_model(self, x_out, cond_scale):
+ out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+ denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+ return denoised
+
def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
+ # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+ # so is_edit_model is set to False to support AND composition.
+ is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+ assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ if not is_edit_model:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ else:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
cfg_denoiser_callback(denoiser_params)
@@ -173,7 +107,10 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = denoiser_params.sigma
if tensor.shape[1] == uncond.shape[1]:
- cond_in = torch.cat([tensor, uncond])
+ if not is_edit_model:
+ cond_in = torch.cat([tensor, uncond])
+ else:
+ cond_in = torch.cat([tensor, uncond, uncond])
if shared.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
@@ -189,7 +126,13 @@ class CFGDenoiser(torch.nn.Module):
for batch_offset in range(0, tensor.shape[0], batch_size):
a = batch_offset
b = min(a + batch_size, tensor.shape[0])
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
+
+ if not is_edit_model:
+ c_crossattn = [tensor[a:b]]
+ else:
+ c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
@@ -200,7 +143,10 @@ class CFGDenoiser(torch.nn.Module):
elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ if not is_edit_model:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ else:
+ denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
@@ -280,12 +226,10 @@ class KDiffusionSampler:
return p.steps
def initialize(self, p):
- if shared.sd_model.cond_stage_key == "edit" and getattr(p, 'image_cfg_scale', None) != 1:
- self.model_wrap_cfg = CFGDenoiserEdit(self.model_wrap)
-
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.step = 0
+ self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
self.eta = p.eta if p.eta is not None else opts.eta_ancestral
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
@@ -355,9 +299,6 @@ class KDiffusionSampler:
'cond_scale': p.cfg_scale,
}
- if hasattr(p, 'image_cfg_scale') and p.image_cfg_scale != 1 and p.image_cfg_scale != None:
- extra_args['image_cfg_scale'] = p.image_cfg_scale
-
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples
diff --git a/modules/shared.py b/modules/shared.py
index 5600d480..79fbf724 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -106,7 +106,7 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
-
+parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
script_loading.preload_extensions(extensions.extensions_dir, parser)
diff --git a/modules/ui.py b/modules/ui.py
index a68a4b19..7f1c4e5b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -866,8 +866,8 @@ def create_ui():
with gr.Row():
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
- image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale (for pix2pix models)', value=1.5, elem_id="img2img_image_cfg_scale")
-
+ image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+
with FormRow():
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
@@ -1669,6 +1669,12 @@ def create_ui():
outputs=[component, text_settings],
)
+ text_settings.change(
+ fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
+ inputs=[],
+ outputs=[image_cfg_scale],
+ )
+
button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
button_set_checkpoint.click(
fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 83367968..95b30f4a 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -29,8 +29,9 @@ def add_pages_to_demo(app):
if not any([Path(x).resolve() in Path(filename).resolve().parents for x in allowed_dirs]):
raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
- if os.path.splitext(filename)[1].lower() != ".png":
- raise ValueError(f"File cannot be fetched: {filename}. Only png.")
+ ext = os.path.splitext(filename)[1].lower()
+ if ext not in (".png", ".jpg"):
+ raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg.")
# would profit from returning 304
return FileResponse(filename, headers={"Accept-Ranges": "bytes"})