From 2f4c91894d4c0a055c1069b2fda0e4da8fcda188 Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 12:10:30 +0800 Subject: Remove activation from final layer of HNs --- modules/hypernetworks/hypernetwork.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index d647ea55..54346b64 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -41,8 +41,8 @@ class HypernetworkModule(torch.nn.Module): # Add a fully-connected layer linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) - # Add an activation func - if activation_func == "linear" or activation_func is None: + # Add an activation func except last layer + if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3: pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) @@ -53,7 +53,7 @@ class HypernetworkModule(torch.nn.Module): if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) - # Add dropout expect last layer + # Add dropout except last layer if use_dropout and i < len(layer_structure) - 3: linears.append(torch.nn.Dropout(p=0.3)) -- cgit v1.2.1 From c702d4d0df21790199d199818f25c449213ffe0f Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 13:43:04 +0800 Subject: Fix off-by-one --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 54346b64..3ce85bb5 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # Add an activation func except last layer - if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3: + if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2: pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) @@ -54,7 +54,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if use_dropout and i < len(layer_structure) - 3: + if use_dropout and i < len(layer_structure) - 2: linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) -- cgit v1.2.1 From 877d94f97ca5491d8779440769b191e0dcd32c8e Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 14:50:58 +0800 Subject: Back compatibility --- modules/hypernetworks/hypernetwork.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3ce85bb5..dd317085 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -28,7 +28,7 @@ class HypernetworkModule(torch.nn.Module): "swish": torch.nn.Hardswish, } - def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # Add an activation func except last layer - if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2: + if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output): pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) @@ -105,7 +105,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False): self.filename = None self.name = name self.layers = {} @@ -116,11 +116,12 @@ class Hypernetwork: self.activation_func = activation_func self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout + self.activate_output = activate_output for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), ) def weights(self): @@ -147,6 +148,7 @@ class Hypernetwork: state_dict['use_dropout'] = self.use_dropout state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name + state_dict['activate_output'] = self.activate_output torch.save(state_dict, filename) @@ -161,12 +163,13 @@ class Hypernetwork: self.activation_func = state_dict.get('activation_func', None) self.add_layer_norm = state_dict.get('is_layer_norm', False) self.use_dropout = state_dict.get('use_dropout', False) + self.activate_output = state_dict.get('activate_output', True) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output), ) self.name = state_dict.get('name', self.name) -- cgit v1.2.1 From 91bb35b1e6842b30ce7553009c8ecea3643de8d2 Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 15:00:03 +0800 Subject: Merge fix --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index eab8b32f..bd171793 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -190,7 +190,7 @@ class Hypernetwork: print(f"Weight initialization is {self.weight_init}") self.add_layer_norm = state_dict.get('is_layer_norm', False) print(f"Layer norm is set to {self.add_layer_norm}") - self.use_dropout = state_dict.get('use_dropout', False + self.use_dropout = state_dict.get('use_dropout', False) print(f"Dropout usage is set to {self.use_dropout}" ) self.activate_output = state_dict.get('activate_output', True) -- cgit v1.2.1 From b6a8bb123bd519736306417399f6441e504f1e8b Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 15:15:19 +0800 Subject: Fix merge --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index bd171793..2997cead 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -60,7 +60,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if use_dropout and i < len(layer_structure) - 2: + if use_dropout and i < len(layer_structure) - 3: linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) @@ -126,7 +126,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False) + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False): self.filename = None self.name = name self.layers = {} -- cgit v1.2.1 From 85fcccc105aa50f1d78de559233eaa9f384608b5 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Wed, 26 Oct 2022 22:24:33 +0900 Subject: Squashed commit of fixing dropout silently fix dropouts for future hypernetworks add kwargs for Hypernetwork class hypernet UI for gradio input add recommended options remove as options revert adding options in ui --- modules/hypernetworks/hypernetwork.py | 25 +++++++++++++++++-------- modules/ui.py | 4 ++-- 2 files changed, 19 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 2997cead..dd921153 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -34,7 +34,8 @@ class HypernetworkModule(torch.nn.Module): } activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) - def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', + add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -60,7 +61,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if use_dropout and i < len(layer_structure) - 3: + if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2: linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) @@ -126,7 +127,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): self.filename = None self.name = name self.layers = {} @@ -139,11 +140,14 @@ class Hypernetwork: self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout self.activate_output = activate_output + self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) def weights(self): @@ -172,7 +176,8 @@ class Hypernetwork: state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name state_dict['activate_output'] = self.activate_output - + state_dict['last_layer_dropout'] = self.last_layer_dropout + torch.save(state_dict, filename) def load(self, filename): @@ -193,12 +198,16 @@ class Hypernetwork: self.use_dropout = state_dict.get('use_dropout', False) print(f"Dropout usage is set to {self.use_dropout}" ) self.activate_output = state_dict.get('activate_output', True) + print(f"Activate last layer is set to {self.activate_output}") + self.last_layer_dropout = state_dict.get('last_layer_dropout', False) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) self.name = state_dict.get('name', self.name) diff --git a/modules/ui.py b/modules/ui.py index 0a63e357..55cbe859 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1238,8 +1238,8 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") - new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys) - new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) + new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys) + new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Normal is default, for experiments, relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") -- cgit v1.2.1 From cc56df996e95c2c82295ab7b9928da2544791220 Mon Sep 17 00:00:00 2001 From: guaneec Date: Wed, 26 Oct 2022 23:51:51 +0800 Subject: Fix dropout logic --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index dd921153..b17598fe 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -35,7 +35,7 @@ class HypernetworkModule(torch.nn.Module): activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', - add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): + add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -61,7 +61,7 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout except last layer - if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2: + if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2): linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) -- cgit v1.2.1 From 029d7c75436558f1e884bb127caed73caaecb83a Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Thu, 27 Oct 2022 14:44:53 +0900 Subject: Revert unresolved changes in Bias initialization it should be zeros_ or parameterized in future properly. --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b17598fe..25427a37 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -75,7 +75,7 @@ class HypernetworkModule(torch.nn.Module): w, b = layer.weight.data, layer.bias.data if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm: normal_(w, mean=0.0, std=0.01) - normal_(b, mean=0.0, std=0.005) + normal_(b, mean=0.0, std=0) elif weight_init == 'XavierUniform': xavier_uniform_(w) zeros_(b) -- cgit v1.2.1 From 44ab954fabb9c1273366ebdca47f8da394d61aab Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Sat, 29 Oct 2022 10:02:56 -0700 Subject: Fix latent upscale highres fix #3888 --- modules/processing.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 548eec29..f18b7db2 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -653,6 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if opts.use_scale_latent_for_hires_fix: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + image_conditioning = self.txt2img_image_conditioning(samples) else: decoded_samples = decode_first_stage(self.sd_model, samples) @@ -674,6 +675,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) + image_conditioning = self.img2img_image_conditioning( + decoded_samples, + samples, + decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3]) + ) + shared.state.nextjob() self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -684,11 +691,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - image_conditioning = self.img2img_image_conditioning( - decoded_samples, - samples, - decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3]) - ) samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning) return samples -- cgit v1.2.1 From 6e2ce4e735db64afcd0fe637327ca4ec78335706 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Sat, 29 Oct 2022 10:35:51 -0700 Subject: Added image conditioning to latent upscale. Only comuted if the mask weight is not 1.0 to avoid extra memory. Also includes some code cleanup. --- modules/processing.py | 29 +++++++++++------------------ 1 file changed, 11 insertions(+), 18 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index f18b7db2..ee0e9e34 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -134,11 +134,7 @@ class StableDiffusionProcessing(): # Dummy zero conditioning if we're not using inpainting model. # Still takes up a bit of memory, but no encoder call. # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. - return torch.zeros( - x.shape[0], 5, 1, 1, - dtype=x.dtype, - device=x.device - ) + return x.new_zeros(x.shape[0], 5, 1, 1) height = height or self.height width = width or self.width @@ -156,11 +152,7 @@ class StableDiffusionProcessing(): def img2img_image_conditioning(self, source_image, latent_image, image_mask = None): if self.sampler.conditioning_key not in {'hybrid', 'concat'}: # Dummy zero conditioning if we're not using inpainting model. - return torch.zeros( - latent_image.shape[0], 5, 1, 1, - dtype=latent_image.dtype, - device=latent_image.device - ) + return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1) # Handle the different mask inputs if image_mask is not None: @@ -174,11 +166,10 @@ class StableDiffusionProcessing(): # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 conditioning_mask = torch.round(conditioning_mask) else: - conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:]) + conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:]) # Create another latent image, this time with a masked version of the original input. # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter. - conditioning_mask = conditioning_mask.to(source_image.device) conditioning_image = torch.lerp( source_image, source_image * (1.0 - conditioning_mask), @@ -653,7 +644,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if opts.use_scale_latent_for_hires_fix: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") - image_conditioning = self.txt2img_image_conditioning(samples) + + # Avoid making the inpainting conditioning unless necessary as + # this does need some extra compute to decode / encode the image again. + if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0: + image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples) + else: + image_conditioning = self.txt2img_image_conditioning(samples) else: decoded_samples = decode_first_stage(self.sd_model, samples) @@ -675,11 +672,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) - image_conditioning = self.img2img_image_conditioning( - decoded_samples, - samples, - decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3]) - ) + image_conditioning = self.img2img_image_conditioning(decoded_samples, samples) shared.state.nextjob() -- cgit v1.2.1 From 39f55c3c35873bc7dd9792cb2155746a1c3d4292 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Sat, 29 Oct 2022 14:13:02 -0700 Subject: Re-add explicit device move --- modules/processing.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index ee0e9e34..d07e3db9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -170,6 +170,7 @@ class StableDiffusionProcessing(): # Create another latent image, this time with a masked version of the original input. # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter. + conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype) conditioning_image = torch.lerp( source_image, source_image * (1.0 - conditioning_mask), -- cgit v1.2.1 From 71571e3f055237d71ba2d47756846ad1d73be00c Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Sun, 30 Oct 2022 00:35:40 -0700 Subject: Replaced master branch fix with updated fix. --- modules/processing.py | 2 -- 1 file changed, 2 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 3dd44d3a..512c484f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -688,8 +688,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - image_conditioning = self.txt2img_image_conditioning(x) - # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() -- cgit v1.2.1 From d9e4e4d7a09d4aee8ce249a3c8e91ce165b10fa5 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Sun, 30 Oct 2022 15:33:02 -0700 Subject: Fix non-square full resolution inpainting. --- modules/masking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/masking.py b/modules/masking.py index fd8d9241..a5c4d2da 100644 --- a/modules/masking.py +++ b/modules/masking.py @@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w ratio_processing = processing_width / processing_height if ratio_crop_region > ratio_processing: - desired_height = (x2 - x1) * ratio_processing + desired_height = (x2 - x1) / ratio_processing desired_height_diff = int(desired_height - (y2-y1)) y1 -= desired_height_diff//2 y2 += desired_height_diff - desired_height_diff//2 -- cgit v1.2.1 From 2ac25ea64f31fd0e7dea35d27a52f3646618c3b6 Mon Sep 17 00:00:00 2001 From: digburn Date: Wed, 2 Nov 2022 21:52:23 +0000 Subject: fix: Add required parameter to API extras route --- modules/api/models.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/api/models.py b/modules/api/models.py index 9ee42a17..9069c0ac 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -131,6 +131,7 @@ class ExtrasBaseRequest(BaseModel): upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") + upscale_first: bool = Field(default=True, title="Upscale first", description="Should the upscaler run before restoring faces?") class ExtraBaseResponse(BaseModel): html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") -- cgit v1.2.1 From 8eb64dab3e9e40531f6a3fa606a1c23a62987249 Mon Sep 17 00:00:00 2001 From: digburn <115176097+digburn@users.noreply.github.com> Date: Fri, 4 Nov 2022 00:35:18 +0000 Subject: fix: correct default val of upscale_first to False --- modules/api/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/api/models.py b/modules/api/models.py index 9069c0ac..68fb45c6 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -131,7 +131,7 @@ class ExtrasBaseRequest(BaseModel): upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") - upscale_first: bool = Field(default=True, title="Upscale first", description="Should the upscaler run before restoring faces?") + upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?") class ExtraBaseResponse(BaseModel): html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") -- cgit v1.2.1 From 3780ad3ad837dd406da39eebd5d91009b5a58445 Mon Sep 17 00:00:00 2001 From: digburn Date: Fri, 4 Nov 2022 00:40:21 +0000 Subject: fix: loading models without vae from cache --- modules/sd_models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 5075fadb..ae427a5c 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -204,8 +204,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): checkpoints_loaded.popitem(last=False) # LRU else: - vae_name = sd_vae.get_filename(vae_file) - print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache") + vae_name = sd_vae.get_filename(vae_file) if vae_file else None + vae_message = f" with {vae_name} VAE" if vae_name else "" + print(f"Loading weights [{sd_model_hash}]{vae_message} from cache") checkpoints_loaded.move_to_end(checkpoint_key) model.load_state_dict(checkpoints_loaded[checkpoint_key]) -- cgit v1.2.1 From e533ff61c1baa4ad047f9c8dc05c17b64ee89ddf Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 3 Nov 2022 22:28:22 -0700 Subject: Lift extras generate button a la #4246. --- modules/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 2609857e..6461002a 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1052,6 +1052,8 @@ def create_ui(wrap_gradio_gpu_call): extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.") show_extras_results = gr.Checkbox(label='Show result images', value=True) + submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') + with gr.Tabs(elem_id="extras_resize_mode"): with gr.TabItem('Scale by'): upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4) @@ -1079,8 +1081,6 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False) - submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') - result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples) submit.click( -- cgit v1.2.1 From 4dd898b8c15e342f817d3fb1c8dc9f2d5d111022 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 08:38:11 +0300 Subject: do not mess with components' visibility for scripts; instead create group components and show/hide those; this will break scripts that create invisible components and rely on UI but the earlier i make this change the better --- modules/scripts.py | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) (limited to 'modules') diff --git a/modules/scripts.py b/modules/scripts.py index 533db45c..28ce07f4 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -18,6 +18,9 @@ class Script: args_to = None alwayson = False + """A gr.Group component that has all script's UI inside it""" + group = None + infotext_fields = None """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example @@ -218,8 +221,6 @@ class ScriptRunner: for control in controls: control.custom_script_source = os.path.basename(script.filename) - if not script.alwayson: - control.visible = False if script.infotext_fields is not None: self.infotext_fields += script.infotext_fields @@ -229,40 +230,41 @@ class ScriptRunner: script.args_to = len(inputs) for script in self.alwayson_scripts: - with gr.Group(): + with gr.Group() as group: create_script_ui(script, inputs, inputs_alwayson) + script.group = group + dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index") dropdown.save_to_config = True inputs[0] = dropdown for script in self.selectable_scripts: - create_script_ui(script, inputs, inputs_alwayson) + with gr.Group(visible=False) as group: + create_script_ui(script, inputs, inputs_alwayson) + + script.group = group def select_script(script_index): - if 0 < script_index <= len(self.selectable_scripts): - script = self.selectable_scripts[script_index-1] - args_from = script.args_from - args_to = script.args_to - else: - args_from = 0 - args_to = 0 + selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None - return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)] + return [gr.update(visible=selected_script == s) for s in self.selectable_scripts] def init_field(title): + """called when an initial value is set from ui-config.json to show script's UI components""" + if title == 'None': return + script_index = self.titles.index(title) - script = self.selectable_scripts[script_index] - for i in range(script.args_from, script.args_to): - inputs[i].visible = True + self.selectable_scripts[script_index].group.visible = True dropdown.init_field = init_field + dropdown.change( fn=select_script, inputs=[dropdown], - outputs=inputs + outputs=[script.group for script in self.selectable_scripts] ) return inputs -- cgit v1.2.1 From f2b69709eaff88fc3a2bd49585556ec0883bf5ea Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 09:42:25 +0300 Subject: move option access checking to options class out of various places scattered through code --- modules/processing.py | 4 ++-- modules/shared.py | 11 +++++++++++ modules/ui.py | 20 +++++--------------- 3 files changed, 18 insertions(+), 17 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 2168208c..a46e592d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -418,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: try: for k, v in p.override_settings.items(): - opts.data[k] = v # we don't call onchange for simplicity which makes changing model, hypernet impossible + setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible res = process_images_inner(p) finally: for k, v in stored_opts.items(): - opts.data[k] = v + setattr(opts, k, v) return res diff --git a/modules/shared.py b/modules/shared.py index d8e99f85..024c771a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -396,6 +396,15 @@ class Options: def __setattr__(self, key, value): if self.data is not None: if key in self.data or key in self.data_labels: + assert not cmd_opts.freeze_settings, "changing settings is disabled" + + comp_args = opts.data_labels[key].component_args + if isinstance(comp_args, dict) and comp_args.get('visible', True) is False: + raise RuntimeError(f"not possible to set {key} because it is restricted") + + if cmd_opts.hide_ui_dir_config and key in restricted_opts: + raise RuntimeError(f"not possible to set {key} because it is restricted") + self.data[key] = value return @@ -412,6 +421,8 @@ class Options: return super(Options, self).__getattribute__(item) def save(self, filename): + assert not cmd_opts.freeze_settings, "saving settings is disabled" + with open(filename, "w", encoding="utf8") as file: json.dump(self.data, file, indent=4) diff --git a/modules/ui.py b/modules/ui.py index b2b1c854..633b56ef 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1438,8 +1438,6 @@ def create_ui(wrap_gradio_gpu_call): def run_settings(*args): changed = 0 - assert not shared.cmd_opts.freeze_settings, "changing settings is disabled" - for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default): return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson() @@ -1448,15 +1446,9 @@ def create_ui(wrap_gradio_gpu_call): if comp == dummy_component: continue - comp_args = opts.data_labels[key].component_args - if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False: - continue - - if cmd_opts.hide_ui_dir_config and key in restricted_opts: - continue - oldval = opts.data.get(key, None) - opts.data[key] = value + + setattr(opts, key, value) if oldval != value: if opts.data_labels[key].onchange is not None: @@ -1469,17 +1461,15 @@ def create_ui(wrap_gradio_gpu_call): return f'{changed} settings changed.', opts.dumpjson() def run_settings_single(value, key): - assert not shared.cmd_opts.freeze_settings, "changing settings is disabled" - if not opts.same_type(value, opts.data_labels[key].default): return gr.update(visible=True), opts.dumpjson() oldval = opts.data.get(key, None) - if cmd_opts.hide_ui_dir_config and key in restricted_opts: + try: + setattr(opts, key, value) + except Exception: return gr.update(value=oldval), opts.dumpjson() - opts.data[key] = value - if oldval != value: if opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() -- cgit v1.2.1