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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 From 7bd8581e461623932ffbd5762ee931ee51f798db Mon Sep 17 00:00:00 2001 From: Sihan Wang <31711261+shwang95@users.noreply.github.com> Date: Wed, 26 Oct 2022 20:32:55 +0800 Subject: Fix error caused by EXIF transpose when using custom scripts Some custom scripts read image directly and no need to select image in UI, this will cause error. --- modules/img2img.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/img2img.py b/modules/img2img.py index 9c0cf23e..86a19f37 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -80,7 +80,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro mask = None # Use the EXIF orientation of photos taken by smartphones. - image = ImageOps.exif_transpose(image) + if image is not None: + image = ImageOps.exif_transpose(image) assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' -- cgit v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 From be27fd4690b1eb6c74da1e31c9696a0f1901fbba Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 17:01:01 +0800 Subject: fix broken progress api by previous rework --- modules/shared.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index e4f163c1..2c7d28a5 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -4,6 +4,7 @@ import json import os import sys from collections import OrderedDict +import time import gradio as gr import tqdm @@ -132,6 +133,7 @@ class State: current_image = None current_image_sampling_step = 0 textinfo = None + time_start = None def skip(self): self.skipped = True @@ -168,6 +170,7 @@ class State: self.skipped = False self.interrupted = False self.textinfo = None + self.time_start = time.time() devices.torch_gc() -- cgit v1.2.3 From 1a4ff2de6a835cd8cc1590bbc1a8dedb5ad37e5b Mon Sep 17 00:00:00 2001 From: evshiron Date: Sun, 30 Oct 2022 17:02:47 +0800 Subject: fix current image in progress api when parallel processing enabled --- modules/api/api.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 6c06d449..97497f3f 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -3,10 +3,9 @@ import uvicorn from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image from fastapi import APIRouter, Depends, HTTPException import modules.shared as shared -from modules import devices from modules.api.models import * from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images -from modules.sd_samplers import all_samplers +from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid from modules.extras import run_extras, run_pnginfo @@ -170,6 +169,16 @@ class Api: progress = min(progress, 1) + # copy from check_progress_call of ui.py + + if shared.parallel_processing_allowed: + if shared.state.sampling_step - shared.state.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.state.current_latent is not None: + if shared.opts.show_progress_grid: + shared.state.current_image = samples_to_image_grid(shared.state.current_latent) + else: + shared.state.current_image = sample_to_image(shared.state.current_latent) + shared.state.current_image_sampling_step = shared.state.sampling_step + current_image = None if shared.state.current_image and not req.skip_current_image: current_image = encode_pil_to_base64(shared.state.current_image) -- cgit v1.2.3 From 4b8a192f680101de247dca79e48974b53bf961fe Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Sat, 29 Oct 2022 16:36:43 +0900 Subject: add optimizer save option to shared.opts --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index e4f163c1..065b893d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -286,6 +286,7 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."), + "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), -- cgit v1.2.3 From 20194fd9752a280306fb66b57b258609b0918c46 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Sat, 29 Oct 2022 16:56:42 +0900 Subject: We have duplicate linear now --- modules/hypernetworks/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index aad09ffc..c2d4b51c 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared from modules.hypernetworks import hypernetwork not_available = ["hardswish", "multiheadattention"] -keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) +keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): # Remove illegal characters from name. -- cgit v1.2.3 From 9d96d7d0a0aa0a966a9aefd24342345eb65952ed Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Sun, 30 Oct 2022 20:39:04 +0900 Subject: resolve conflicts --- modules/hypernetworks/hypernetwork.py | 44 ++++++++++++++++++++++++++++++----- 1 file changed, 38 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index a11e01d6..8f74cdea 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -21,6 +21,7 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm from collections import defaultdict, deque from statistics import stdev, mean +optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} class HypernetworkModule(torch.nn.Module): multiplier = 1.0 @@ -139,6 +140,8 @@ class Hypernetwork: self.weight_init = weight_init self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout + self.optimizer_name = None + self.optimizer_state_dict = None for size in enable_sizes or []: self.layers[size] = ( @@ -171,6 +174,10 @@ 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 + if self.optimizer_name is not None: + state_dict['optimizer_name'] = self.optimizer_name + if self.optimizer_state_dict: + state_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(state_dict, filename) @@ -190,7 +197,14 @@ class Hypernetwork: 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) - print(f"Dropout usage is set to {self.use_dropout}" ) + print(f"Dropout usage is set to {self.use_dropout}") + self.optimizer_name = state_dict.get('optimizer_name', 'AdamW') + print(f"Optimizer name is {self.optimizer_name}") + self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None) + if self.optimizer_state_dict: + print("Loaded existing optimizer from checkpoint") + else: + print("No saved optimizer exists in checkpoint") for size, sd in state_dict.items(): if type(size) == int: @@ -392,8 +406,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True - # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + # Here we use optimizer from saved HN, or we can specify as UI option. + if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: + optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + else: + print(f"Optimizer type {optimizer_name} is not defined!") + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. + try: + optimizer.load_state_dict(hypernetwork.optimizer_state_dict) + except RuntimeError as e: + print("Cannot resume from saved optimizer!") + print(e) steps_without_grad = 0 @@ -455,8 +480,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) - + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", "learn_rate": scheduler.learn_rate @@ -514,14 +542,18 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) - + del optimizer + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. return hypernetwork, filename + def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): old_hypernetwork_name = hypernetwork.name old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None -- cgit v1.2.3 From c9bb33dd43dbb9479ff1b70351df14508c89ac60 Mon Sep 17 00:00:00 2001 From: victorca25 Date: Sun, 30 Oct 2022 12:52:50 +0100 Subject: add resrgan 8x, allow use 1x and up to 8x extra models, move BSRGAN model, add nearest --- modules/esrgan_model.py | 17 +++++++++++++---- modules/modelloader.py | 3 +++ modules/ui.py | 2 +- modules/upscaler.py | 17 ++++++++++++++++- 4 files changed, 33 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index a13cf6ac..c61669b4 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -50,6 +50,7 @@ def mod2normal(state_dict): def resrgan2normal(state_dict, nb=23): # this code is copied from https://github.com/victorca25/iNNfer if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict: + re8x = 0 crt_net = {} items = [] for k, v in state_dict.items(): @@ -75,10 +76,18 @@ def resrgan2normal(state_dict, nb=23): crt_net['model.3.bias'] = state_dict['conv_up1.bias'] crt_net['model.6.weight'] = state_dict['conv_up2.weight'] crt_net['model.6.bias'] = state_dict['conv_up2.bias'] - crt_net['model.8.weight'] = state_dict['conv_hr.weight'] - crt_net['model.8.bias'] = state_dict['conv_hr.bias'] - crt_net['model.10.weight'] = state_dict['conv_last.weight'] - crt_net['model.10.bias'] = state_dict['conv_last.bias'] + + if 'conv_up3.weight' in state_dict: + # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py + re8x = 3 + crt_net['model.9.weight'] = state_dict['conv_up3.weight'] + crt_net['model.9.bias'] = state_dict['conv_up3.bias'] + + crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight'] + crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias'] + crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight'] + crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias'] + state_dict = crt_net return state_dict diff --git a/modules/modelloader.py b/modules/modelloader.py index b0f2f33d..e4a6f8ac 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -85,6 +85,9 @@ def cleanup_models(): src_path = os.path.join(root_path, "ESRGAN") dest_path = os.path.join(models_path, "ESRGAN") move_files(src_path, dest_path) + src_path = os.path.join(models_path, "BSRGAN") + dest_path = os.path.join(models_path, "ESRGAN") + move_files(src_path, dest_path, ".pth") src_path = os.path.join(root_path, "gfpgan") dest_path = os.path.join(models_path, "GFPGAN") move_files(src_path, dest_path) diff --git a/modules/ui.py b/modules/ui.py index 5055ca64..47610f5c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Tabs(elem_id="extras_resize_mode"): with gr.TabItem('Scale by'): - upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2) + upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4) with gr.TabItem('Scale to'): with gr.Group(): with gr.Row(): diff --git a/modules/upscaler.py b/modules/upscaler.py index 6ab2fb40..83fde7ca 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -10,6 +10,7 @@ import modules.shared from modules import modelloader, shared LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST) from modules.paths import models_path @@ -57,7 +58,7 @@ class Upscaler: dest_w = img.width * scale dest_h = img.height * scale for i in range(3): - if img.width >= dest_w and img.height >= dest_h: + if img.width > dest_w and img.height > dest_h: break img = self.do_upscale(img, selected_model) if img.width != dest_w or img.height != dest_h: @@ -120,3 +121,17 @@ class UpscalerLanczos(Upscaler): self.name = "Lanczos" self.scalers = [UpscalerData("Lanczos", None, self)] + +class UpscalerNearest(Upscaler): + scalers = [] + + def do_upscale(self, img, selected_model=None): + return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST) + + def load_model(self, _): + pass + + def __init__(self, dirname=None): + super().__init__(False) + self.name = "Nearest" + self.scalers = [UpscalerData("Nearest", None, self)] \ No newline at end of file -- cgit v1.2.3 From cb31abcf58ea1f64266e6d821937eed058c35f4d Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sun, 30 Oct 2022 21:54:31 +0700 Subject: Settings to select VAE --- modules/sd_models.py | 31 +++++-------- modules/sd_vae.py | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++ modules/shared.py | 8 ++-- 3 files changed, 136 insertions(+), 24 deletions(-) create mode 100644 modules/sd_vae.py (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index f86dc3ed..91ad4b5e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -8,7 +8,7 @@ from omegaconf import OmegaConf from ldm.util import instantiate_from_config -from modules import shared, modelloader, devices, script_callbacks +from modules import shared, modelloader, devices, script_callbacks, sd_vae from modules.paths import models_path from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting @@ -160,12 +160,11 @@ def get_state_dict_from_checkpoint(pl_sd): vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} - -def load_model_weights(model, checkpoint_info): +def load_model_weights(model, checkpoint_info, force=False): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash - if checkpoint_info not in checkpoints_loaded: + if force or checkpoint_info not in checkpoints_loaded: print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) @@ -186,17 +185,7 @@ def load_model_weights(model, checkpoint_info): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 - vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" - - if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: - vae_file = shared.cmd_opts.vae_path - - if os.path.exists(vae_file): - print(f"Loading VAE weights from: {vae_file}") - vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) - vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} - model.first_stage_model.load_state_dict(vae_dict) - + sd_vae.load_vae(model, checkpoint_file) model.first_stage_model.to(devices.dtype_vae) if shared.opts.sd_checkpoint_cache > 0: @@ -213,7 +202,7 @@ def load_model_weights(model, checkpoint_info): model.sd_checkpoint_info = checkpoint_info -def load_model(checkpoint_info=None): +def load_model(checkpoint_info=None, force=False): from modules import lowvram, sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() @@ -234,7 +223,7 @@ def load_model(checkpoint_info=None): do_inpainting_hijack() sd_model = instantiate_from_config(sd_config.model) - load_model_weights(sd_model, checkpoint_info) + load_model_weights(sd_model, checkpoint_info, force=force) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram) @@ -252,16 +241,16 @@ def load_model(checkpoint_info=None): return sd_model -def reload_model_weights(sd_model, info=None): +def reload_model_weights(sd_model, info=None, force=False): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() - if sd_model.sd_model_checkpoint == checkpoint_info.filename: + if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force: return if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() - load_model(checkpoint_info) + load_model(checkpoint_info, force=force) return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: @@ -271,7 +260,7 @@ def reload_model_weights(sd_model, info=None): sd_hijack.model_hijack.undo_hijack(sd_model) - load_model_weights(sd_model, checkpoint_info) + load_model_weights(sd_model, checkpoint_info, force=force) sd_hijack.model_hijack.hijack(sd_model) script_callbacks.model_loaded_callback(sd_model) diff --git a/modules/sd_vae.py b/modules/sd_vae.py new file mode 100644 index 00000000..82764e55 --- /dev/null +++ b/modules/sd_vae.py @@ -0,0 +1,121 @@ +import torch +import os +from collections import namedtuple +from modules import shared, devices +from modules.paths import models_path +import glob + +model_dir = "Stable-diffusion" +model_path = os.path.abspath(os.path.join(models_path, model_dir)) +vae_dir = "VAE" +vae_path = os.path.abspath(os.path.join(models_path, vae_dir)) + +vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} +default_vae_dict = {"auto": "auto", "None": "None"} +default_vae_list = ["auto", "None"] +default_vae_values = [default_vae_dict[x] for x in default_vae_list] +vae_dict = dict(default_vae_dict) +vae_list = list(default_vae_list) +first_load = True + +def get_filename(filepath): + return os.path.splitext(os.path.basename(filepath))[0] + +def refresh_vae_list(vae_path=vae_path, model_path=model_path): + global vae_dict, vae_list + res = {} + candidates = [ + *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True), + *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True), + *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True), + *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True) + ] + if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path): + candidates.append(shared.cmd_opts.vae_path) + for filepath in candidates: + name = get_filename(filepath) + res[name] = filepath + vae_list.clear() + vae_list.extend(default_vae_list) + vae_list.extend(list(res.keys())) + vae_dict.clear() + vae_dict.update(default_vae_dict) + vae_dict.update(res) + return vae_list + +def load_vae(model, checkpoint_file, vae_file="auto"): + global first_load, vae_dict, vae_list + # save_settings = False + + # if vae_file argument is provided, it takes priority + if vae_file and vae_file not in default_vae_list: + if not os.path.isfile(vae_file): + vae_file = "auto" + # save_settings = True + print("VAE provided as function argument doesn't exist") + # for the first load, if vae-path is provided, it takes priority and failure is reported + if first_load and shared.cmd_opts.vae_path is not None: + if os.path.isfile(shared.cmd_opts.vae_path): + vae_file = shared.cmd_opts.vae_path + # save_settings = True + # print("Using VAE provided as command line argument") + else: + print("VAE provided as command line argument doesn't exist") + # else, we load from settings + if vae_file == "auto" and shared.opts.sd_vae is not None: + # if saved VAE settings isn't recognized, fallback to auto + vae_file = vae_dict.get(shared.opts.sd_vae, "auto") + # if VAE selected but not found, fallback to auto + if vae_file not in default_vae_values and not os.path.isfile(vae_file): + vae_file = "auto" + print("Selected VAE doesn't exist") + # vae-path cmd arg takes priority for auto + if vae_file == "auto" and shared.cmd_opts.vae_path is not None: + if os.path.isfile(shared.cmd_opts.vae_path): + vae_file = shared.cmd_opts.vae_path + print("Using VAE provided as command line argument") + # if still not found, try look for ".vae.pt" beside model + model_path = os.path.splitext(checkpoint_file)[0] + if vae_file == "auto": + vae_file_try = model_path + ".vae.pt" + if os.path.isfile(vae_file_try): + vae_file = vae_file_try + print("Using VAE found beside selected model") + # if still not found, try look for ".vae.ckpt" beside model + if vae_file == "auto": + vae_file_try = model_path + ".vae.ckpt" + if os.path.isfile(vae_file_try): + vae_file = vae_file_try + print("Using VAE found beside selected model") + # No more fallbacks for auto + if vae_file == "auto": + vae_file = None + # Last check, just because + if vae_file and not os.path.exists(vae_file): + vae_file = None + + if vae_file: + print(f"Loading VAE weights from: {vae_file}") + vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) + vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} + model.first_stage_model.load_state_dict(vae_dict_1) + + # If vae used is not in dict, update it + # It will be removed on refresh though + if vae_file is not None: + vae_opt = get_filename(vae_file) + if vae_opt not in vae_dict: + vae_dict[vae_opt] = vae_file + vae_list.append(vae_opt) + + """ + # Save current VAE to VAE settings, maybe? will it work? + if save_settings: + if vae_file is None: + vae_opt = "None" + + # shared.opts.sd_vae = vae_opt + """ + + first_load = False + model.first_stage_model.to(devices.dtype_vae) diff --git a/modules/shared.py b/modules/shared.py index e4f163c1..06440ac4 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -14,7 +14,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers, sd_models, localization +from modules import sd_samplers, sd_models, localization, sd_vae from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path @@ -295,6 +295,7 @@ options_templates.update(options_section(('training', "Training"), { options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), + "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), @@ -407,11 +408,12 @@ class Options: if bad_settings > 0: print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr) - def onchange(self, key, func): + def onchange(self, key, func, call=True): item = self.data_labels.get(key) item.onchange = func - func() + if call: + func() def dumpjson(self): d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()} -- cgit v1.2.3 From e1b2ea6e0012ecc988385fc523d8fb50ea5d6be5 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sun, 30 Oct 2022 22:11:45 +0700 Subject: Change VAE search order and thus priority --- modules/sd_vae.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 82764e55..0767b925 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -25,10 +25,10 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path): global vae_dict, vae_list res = {} candidates = [ - *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True), *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True), - *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True), + *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True), *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True) + *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True), ] if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path): candidates.append(shared.cmd_opts.vae_path) -- cgit v1.2.3 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 v1.2.3 From b96d0c4e9ecec3c856b9b4ec795dbd0d34fcac51 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 14:42:28 +0700 Subject: Fix typo from previous commit --- modules/sd_vae.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 0767b925..2ce44d5f 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -27,8 +27,8 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path): candidates = [ *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True), *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True), - *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True) - *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True), + *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True), + *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True) ] if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path): candidates.append(shared.cmd_opts.vae_path) -- cgit v1.2.3 From 726769da35970f4c100fa7edf11850f9dc059c41 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 15:19:34 +0700 Subject: Checkpoint cache by combination key of checkpoint and vae --- modules/sd_models.py | 27 ++++++++++++++++----------- modules/sd_vae.py | 8 +++++++- 2 files changed, 23 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 91ad4b5e..850f7b7b 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -160,11 +160,15 @@ def get_state_dict_from_checkpoint(pl_sd): vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} -def load_model_weights(model, checkpoint_info, force=False): +def load_model_weights(model, checkpoint_info, vae_file="auto"): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash - if force or checkpoint_info not in checkpoints_loaded: + vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file) + + checkpoint_key = (checkpoint_info, vae_file) + + if checkpoint_key not in checkpoints_loaded: print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) @@ -185,24 +189,25 @@ def load_model_weights(model, checkpoint_info, force=False): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 - sd_vae.load_vae(model, checkpoint_file) + sd_vae.load_vae(model, vae_file) model.first_stage_model.to(devices.dtype_vae) if shared.opts.sd_checkpoint_cache > 0: - checkpoints_loaded[checkpoint_info] = model.state_dict().copy() + checkpoints_loaded[checkpoint_key] = model.state_dict().copy() while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: checkpoints_loaded.popitem(last=False) # LRU else: - print(f"Loading weights [{sd_model_hash}] from cache") - checkpoints_loaded.move_to_end(checkpoint_info) - model.load_state_dict(checkpoints_loaded[checkpoint_info]) + vae_name = sd_vae.get_filename(vae_file) + print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache") + checkpoints_loaded.move_to_end(checkpoint_key) + model.load_state_dict(checkpoints_loaded[checkpoint_key]) model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info -def load_model(checkpoint_info=None, force=False): +def load_model(checkpoint_info=None): from modules import lowvram, sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() @@ -223,7 +228,7 @@ def load_model(checkpoint_info=None, force=False): do_inpainting_hijack() sd_model = instantiate_from_config(sd_config.model) - load_model_weights(sd_model, checkpoint_info, force=force) + load_model_weights(sd_model, checkpoint_info) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram) @@ -250,7 +255,7 @@ def reload_model_weights(sd_model, info=None, force=False): if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() - load_model(checkpoint_info, force=force) + load_model(checkpoint_info) return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: @@ -260,7 +265,7 @@ def reload_model_weights(sd_model, info=None, force=False): sd_hijack.model_hijack.undo_hijack(sd_model) - load_model_weights(sd_model, checkpoint_info, force=force) + load_model_weights(sd_model, checkpoint_info) sd_hijack.model_hijack.hijack(sd_model) script_callbacks.model_loaded_callback(sd_model) diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 2ce44d5f..e9239326 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -43,7 +43,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path): vae_dict.update(res) return vae_list -def load_vae(model, checkpoint_file, vae_file="auto"): +def resolve_vae(checkpoint_file, vae_file="auto"): global first_load, vae_dict, vae_list # save_settings = False @@ -94,6 +94,12 @@ def load_vae(model, checkpoint_file, vae_file="auto"): if vae_file and not os.path.exists(vae_file): vae_file = None + return vae_file + +def load_vae(model, vae_file): + global first_load, vae_dict, vae_list + # save_settings = False + if vae_file: print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) -- cgit v1.2.3 From 36966e3200943dbf890b5338cfa939df552d3c47 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 15:38:58 +0700 Subject: Fix #4035 --- modules/sd_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index f86dc3ed..a29c8c1a 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -201,7 +201,7 @@ def load_model_weights(model, checkpoint_info): if shared.opts.sd_checkpoint_cache > 0: checkpoints_loaded[checkpoint_info] = model.state_dict().copy() - while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: + while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: checkpoints_loaded.popitem(last=False) # LRU else: print(f"Loading weights [{sd_model_hash}] from cache") -- cgit v1.2.3 From bf7a699845675eefdabb9cfa40c55398976274ae Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 16:27:27 +0700 Subject: Fix #4035 for real now --- modules/sd_models.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index a29c8c1a..b2dd005a 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -165,6 +165,9 @@ def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash + if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"): + checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy() + if checkpoint_info not in checkpoints_loaded: print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") @@ -198,16 +201,14 @@ def load_model_weights(model, checkpoint_info): model.first_stage_model.load_state_dict(vae_dict) model.first_stage_model.to(devices.dtype_vae) - - if shared.opts.sd_checkpoint_cache > 0: - checkpoints_loaded[checkpoint_info] = model.state_dict().copy() - while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: - checkpoints_loaded.popitem(last=False) # LRU else: print(f"Loading weights [{sd_model_hash}] from cache") - checkpoints_loaded.move_to_end(checkpoint_info) model.load_state_dict(checkpoints_loaded[checkpoint_info]) + if shared.opts.sd_checkpoint_cache > 0: + while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: + checkpoints_loaded.popitem(last=False) # LRU + model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info -- cgit v1.2.3 From 056f06d3738c267b1014e6e8e1ef5bd97af1fb45 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Wed, 2 Nov 2022 12:51:46 +0700 Subject: Reload VAE without reloading sd checkpoint --- modules/sd_models.py | 15 ++++---- modules/sd_vae.py | 97 ++++++++++++++++++++++++++++++++++++++++++++++++---- 2 files changed, 97 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 6ab85b65..883639d1 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -159,15 +159,13 @@ def get_state_dict_from_checkpoint(pl_sd): return pl_sd -vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} - def load_model_weights(model, checkpoint_info, vae_file="auto"): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file) - checkpoint_key = (checkpoint_info, vae_file) + checkpoint_key = checkpoint_info if checkpoint_key not in checkpoints_loaded: print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") @@ -190,13 +188,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 - sd_vae.load_vae(model, vae_file) - model.first_stage_model.to(devices.dtype_vae) - if shared.opts.sd_checkpoint_cache > 0: + # if PR #4035 were to get merged, restore base VAE first before caching checkpoints_loaded[checkpoint_key] = model.state_dict().copy() while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: 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") @@ -207,6 +204,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info + sd_vae.load_vae(model, vae_file) + def load_model(checkpoint_info=None): from modules import lowvram, sd_hijack @@ -254,14 +253,14 @@ def load_model(checkpoint_info=None): return sd_model -def reload_model_weights(sd_model=None, info=None, force=False): +def reload_model_weights(sd_model=None, info=None): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() if not sd_model: sd_model = shared.sd_model - if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force: + if sd_model.sd_model_checkpoint == checkpoint_info.filename: return if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): diff --git a/modules/sd_vae.py b/modules/sd_vae.py index e9239326..78e14e8a 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -1,26 +1,65 @@ import torch import os from collections import namedtuple -from modules import shared, devices +from modules import shared, devices, script_callbacks from modules.paths import models_path import glob + model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) vae_dir = "VAE" vae_path = os.path.abspath(os.path.join(models_path, vae_dir)) + vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} + + default_vae_dict = {"auto": "auto", "None": "None"} default_vae_list = ["auto", "None"] + + default_vae_values = [default_vae_dict[x] for x in default_vae_list] vae_dict = dict(default_vae_dict) vae_list = list(default_vae_list) first_load = True + +base_vae = None +loaded_vae_file = None +checkpoint_info = None + + +def get_base_vae(model): + if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: + return base_vae + return None + + +def store_base_vae(model): + global base_vae, checkpoint_info + if checkpoint_info != model.sd_checkpoint_info: + base_vae = model.first_stage_model.state_dict().copy() + checkpoint_info = model.sd_checkpoint_info + + +def delete_base_vae(): + global base_vae, checkpoint_info + base_vae = None + checkpoint_info = None + + +def restore_base_vae(model): + global base_vae, checkpoint_info + if base_vae is not None and checkpoint_info == model.sd_checkpoint_info: + load_vae_dict(model, base_vae) + delete_base_vae() + + def get_filename(filepath): return os.path.splitext(os.path.basename(filepath))[0] + def refresh_vae_list(vae_path=vae_path, model_path=model_path): global vae_dict, vae_list res = {} @@ -43,6 +82,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path): vae_dict.update(res) return vae_list + def resolve_vae(checkpoint_file, vae_file="auto"): global first_load, vae_dict, vae_list # save_settings = False @@ -96,24 +136,26 @@ def resolve_vae(checkpoint_file, vae_file="auto"): return vae_file -def load_vae(model, vae_file): - global first_load, vae_dict, vae_list + +def load_vae(model, vae_file=None): + global first_load, vae_dict, vae_list, loaded_vae_file # save_settings = False if vae_file: print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} - model.first_stage_model.load_state_dict(vae_dict_1) + load_vae_dict(model, vae_dict_1) - # If vae used is not in dict, update it - # It will be removed on refresh though - if vae_file is not None: + # If vae used is not in dict, update it + # It will be removed on refresh though vae_opt = get_filename(vae_file) if vae_opt not in vae_dict: vae_dict[vae_opt] = vae_file vae_list.append(vae_opt) + loaded_vae_file = vae_file + """ # Save current VAE to VAE settings, maybe? will it work? if save_settings: @@ -124,4 +166,45 @@ def load_vae(model, vae_file): """ first_load = False + + +# don't call this from outside +def load_vae_dict(model, vae_dict_1=None): + if vae_dict_1: + store_base_vae(model) + model.first_stage_model.load_state_dict(vae_dict_1) + else: + restore_base_vae() model.first_stage_model.to(devices.dtype_vae) + + +def reload_vae_weights(sd_model=None, vae_file="auto"): + from modules import lowvram, devices, sd_hijack + + if not sd_model: + sd_model = shared.sd_model + + checkpoint_info = sd_model.sd_checkpoint_info + checkpoint_file = checkpoint_info.filename + vae_file = resolve_vae(checkpoint_file, vae_file=vae_file) + + if loaded_vae_file == vae_file: + return + + if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: + lowvram.send_everything_to_cpu() + else: + sd_model.to(devices.cpu) + + sd_hijack.model_hijack.undo_hijack(sd_model) + + load_vae(sd_model, vae_file) + + sd_hijack.model_hijack.hijack(sd_model) + script_callbacks.model_loaded_callback(sd_model) + + if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: + sd_model.to(devices.device) + + print(f"VAE Weights loaded.") + return sd_model -- cgit v1.2.3 From dd2108fdac2ebf943d4ac3563a49202222b88acf Mon Sep 17 00:00:00 2001 From: Maiko Tan Date: Wed, 2 Nov 2022 15:04:35 +0800 Subject: fix: should invoke callback as well in api only mode --- modules/script_callbacks.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index da88635b..c28e220e 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -2,6 +2,7 @@ import sys import traceback from collections import namedtuple import inspect +from typing import Optional from fastapi import FastAPI from gradio import Blocks @@ -62,7 +63,7 @@ def clear_callbacks(): callbacks_image_saved.clear() callbacks_cfg_denoiser.clear() -def app_started_callback(demo: Blocks, app: FastAPI): +def app_started_callback(demo: Optional[Blocks], app: FastAPI): for c in callbacks_app_started: try: c.callback(demo, app) -- cgit v1.2.3 From a5409a6e4bc3eaa9757a7505d4564ad8e0d899ea Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Wed, 2 Nov 2022 14:37:22 +0700 Subject: Save VAE provided by cmd_opts.vae_path --- modules/sd_vae.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 78e14e8a..71e7a6e6 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -78,27 +78,24 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path): vae_list.extend(default_vae_list) vae_list.extend(list(res.keys())) vae_dict.clear() - vae_dict.update(default_vae_dict) vae_dict.update(res) + vae_dict.update(default_vae_dict) return vae_list def resolve_vae(checkpoint_file, vae_file="auto"): global first_load, vae_dict, vae_list - # save_settings = False - # if vae_file argument is provided, it takes priority + # if vae_file argument is provided, it takes priority, but not saved if vae_file and vae_file not in default_vae_list: if not os.path.isfile(vae_file): vae_file = "auto" - # save_settings = True print("VAE provided as function argument doesn't exist") - # for the first load, if vae-path is provided, it takes priority and failure is reported + # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported if first_load and shared.cmd_opts.vae_path is not None: if os.path.isfile(shared.cmd_opts.vae_path): vae_file = shared.cmd_opts.vae_path - # save_settings = True - # print("Using VAE provided as command line argument") + shared.opts.data['sd_vae'] = get_filename(vae_file) else: print("VAE provided as command line argument doesn't exist") # else, we load from settings -- cgit v1.2.3 From 4a8cf01f6f7f072cc9c67d6b31662384b212dd9c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 2 Nov 2022 12:12:32 +0300 Subject: remove duplicate code from #3970 --- modules/api/api.py | 10 +--------- modules/shared.py | 14 ++++++++++++++ modules/ui.py | 10 +--------- 3 files changed, 16 insertions(+), 18 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index b3d85e46..71c9c160 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -178,15 +178,7 @@ class Api: progress = min(progress, 1) - # copy from check_progress_call of ui.py - - if shared.parallel_processing_allowed: - if shared.state.sampling_step - shared.state.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.state.current_latent is not None: - if shared.opts.show_progress_grid: - shared.state.current_image = samples_to_image_grid(shared.state.current_latent) - else: - shared.state.current_image = sample_to_image(shared.state.current_latent) - shared.state.current_image_sampling_step = shared.state.sampling_step + shared.state.set_current_image() current_image = None if shared.state.current_image and not req.skip_current_image: diff --git a/modules/shared.py b/modules/shared.py index 04aaa648..e65f6080 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -184,6 +184,20 @@ class State: devices.torch_gc() + """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this""" + def set_current_image(self): + if not parallel_processing_allowed: + return + + if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None: + if opts.show_progress_grid: + self.current_image = sd_samplers.samples_to_image_grid(self.current_latent) + else: + self.current_image = sd_samplers.sample_to_image(self.current_latent) + + self.current_image_sampling_step = self.sampling_step + + state = State() artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv')) diff --git a/modules/ui.py b/modules/ui.py index 45cd8c3f..784439ba 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -277,15 +277,7 @@ def check_progress_call(id_part): preview_visibility = gr_show(False) if opts.show_progress_every_n_steps > 0: - if shared.parallel_processing_allowed: - - if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None: - if opts.show_progress_grid: - shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent) - else: - shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent) - shared.state.current_image_sampling_step = shared.state.sampling_step - + shared.state.set_current_image() image = shared.state.current_image if image is None: -- cgit v1.2.3 From 9c67408004ed132637d10321bf44565f82055fd2 Mon Sep 17 00:00:00 2001 From: timntorres <116157310+timntorres@users.noreply.github.com> Date: Wed, 2 Nov 2022 02:18:21 -0700 Subject: Allow saving "before-highres-fix. (#4150) * Save image/s before doing highres fix. --- modules/processing.py | 17 +++++++++++++++-- modules/sd_samplers.py | 5 ++--- modules/shared.py | 1 + 3 files changed, 18 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index b541ee2b..2dcf4879 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -521,7 +521,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}" with devices.autocast(): - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) + # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. + if isinstance(p, StableDiffusionProcessingTxt2Img): + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n) + else: + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) @@ -649,7 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: @@ -685,6 +689,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) + # Save a copy of the image/s before doing highres fix, if applicable. + if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix: + for i in range(self.batch_size): + # This batch's ith image. + img = sd_samplers.sample_to_image(samples, i) + # Index that accounts for both batch size and batch count. + ind = i + self.batch_size*n + images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix") + shared.state.nextjob() self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 44d4c189..d7fa89a0 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -93,9 +93,8 @@ def single_sample_to_image(sample): return Image.fromarray(x_sample) -def sample_to_image(samples): - return single_sample_to_image(samples[0]) - +def sample_to_image(samples, index=0): + return single_sample_to_image(samples[index]) def samples_to_image_grid(samples): return images.image_grid([single_sample_to_image(sample) for sample in samples]) diff --git a/modules/shared.py b/modules/shared.py index e65f6080..ce991424 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -255,6 +255,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."), "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."), + "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"), -- cgit v1.2.3 From eb5e82c7ddf5e72fa13b83bd1f12d3a07a4de1a4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 2 Nov 2022 12:45:03 +0300 Subject: do not unnecessarily run VAE one more time when saving intermediate image with hires fix --- modules/processing.py | 39 ++++++++++++++++++++------------------- modules/sd_samplers.py | 1 + modules/shared.py | 2 +- 3 files changed, 22 insertions(+), 20 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 2dcf4879..3a364b5f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -199,7 +199,7 @@ class StableDiffusionProcessing(): def init(self, all_prompts, all_seeds, all_subseeds): pass - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): raise NotImplementedError() def close(self): @@ -521,11 +521,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}" with devices.autocast(): - # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. - if isinstance(p, StableDiffusionProcessingTxt2Img): - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n) - else: - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts) samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) @@ -653,7 +649,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: @@ -666,9 +662,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] + """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images""" + def save_intermediate(image, index): + if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix: + return + + if not isinstance(image, Image.Image): + image = sd_samplers.sample_to_image(image, index) + + images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") + 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") + for i in range(samples.shape[0]): + save_intermediate(samples, i) else: decoded_samples = decode_first_stage(self.sd_model, samples) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) @@ -678,6 +686,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) image = Image.fromarray(x_sample) + + save_intermediate(image, i) + image = images.resize_image(0, image, self.width, self.height) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) @@ -689,15 +700,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) - # Save a copy of the image/s before doing highres fix, if applicable. - if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix: - for i in range(self.batch_size): - # This batch's ith image. - img = sd_samplers.sample_to_image(samples, i) - # Index that accounts for both batch size and batch count. - ind = i + self.batch_size*n - images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix") - shared.state.nextjob() self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -844,8 +846,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask) - - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning) @@ -856,4 +857,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): del x devices.torch_gc() - return samples \ No newline at end of file + return samples diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d7fa89a0..c7c414ef 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -96,6 +96,7 @@ def single_sample_to_image(sample): def sample_to_image(samples, index=0): return single_sample_to_image(samples[index]) + def samples_to_image_grid(samples): return images.image_grid([single_sample_to_image(sample) for sample in samples]) diff --git a/modules/shared.py b/modules/shared.py index ce991424..01f47e38 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -256,6 +256,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."), "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."), "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."), + "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"), @@ -322,7 +323,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), - "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), -- cgit v1.2.3 From f2a5cbe6f55592c4c5527b8e0bf99ea8d658f057 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 2 Nov 2022 14:41:29 +0300 Subject: fix #3986 breaking --no-half-vae --- modules/sd_models.py | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 883639d1..5075fadb 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -183,11 +183,20 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): model.to(memory_format=torch.channels_last) if not shared.cmd_opts.no_half: + vae = model.first_stage_model + + # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16 + if shared.cmd_opts.no_half_vae: + model.first_stage_model = None + model.half() + model.first_stage_model = vae devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 + model.first_stage_model.to(devices.dtype_vae) + if shared.opts.sd_checkpoint_cache > 0: # if PR #4035 were to get merged, restore base VAE first before caching checkpoints_loaded[checkpoint_key] = model.state_dict().copy() -- cgit v1.2.3 From 3178c35224467893cf8dcedb1028c59c6c23db58 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Wed, 2 Nov 2022 22:16:32 +0900 Subject: resolve conflicts --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 065b893d..959937d7 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -285,7 +285,7 @@ options_templates.update(options_section(('system', "System"), { })) options_templates.update(options_section(('training', "Training"), { - "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."), + "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), -- cgit v1.2.3 From 9b5f85ac83f864310fe19c9deab6670bad695b0d Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Wed, 2 Nov 2022 22:18:04 +0900 Subject: first revert --- modules/shared.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 959937d7..7e8c552b 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -286,7 +286,6 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), - "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), -- cgit v1.2.3 From 7ea5956ad5fa925f92116e8a3bf78d7f6517b654 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Wed, 2 Nov 2022 22:18:55 +0900 Subject: now add --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index d8e99f85..7ecb40d8 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -309,6 +309,7 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), + "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), -- cgit v1.2.3 From e21fcd72fcf147904a1df060226c4df12acf251e Mon Sep 17 00:00:00 2001 From: evshiron Date: Wed, 2 Nov 2022 22:37:45 +0800 Subject: add back png info in image api --- modules/api/api.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 71c9c160..ceaf08b0 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -7,8 +7,9 @@ from fastapi import APIRouter, Depends, HTTPException import modules.shared as shared from modules.api.models import * from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images -from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid +from modules.sd_samplers import all_samplers from modules.extras import run_extras, run_pnginfo +from PIL import PngImagePlugin def upscaler_to_index(name: str): @@ -31,9 +32,21 @@ def setUpscalers(req: dict): def encode_pil_to_base64(image): - buffer = io.BytesIO() - image.save(buffer, format="png") - return base64.b64encode(buffer.getvalue()) + with io.BytesIO() as output_bytes: + + # Copy any text-only metadata + use_metadata = False + metadata = PngImagePlugin.PngInfo() + for key, value in image.info.items(): + if isinstance(key, str) and isinstance(value, str): + metadata.add_text(key, value) + use_metadata = True + + image.save( + output_bytes, "PNG", pnginfo=(metadata if use_metadata else None) + ) + bytes_data = output_bytes.getvalue() + return base64.b64encode(bytes_data) class Api: -- cgit v1.2.3 From a9e979977a8e3999b01b6a086bb1332ab7ab308b Mon Sep 17 00:00:00 2001 From: Artem Zagidulin Date: Wed, 2 Nov 2022 19:05:01 +0300 Subject: process_one --- modules/processing.py | 3 +++ modules/scripts.py | 16 ++++++++++++++++ 2 files changed, 19 insertions(+) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 3a364b5f..72a2ee4e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -509,6 +509,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if len(prompts) == 0: break + if p.scripts is not None: + p.scripts.process_one(p) + with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) diff --git a/modules/scripts.py b/modules/scripts.py index 533db45c..9f82efea 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -70,6 +70,13 @@ class Script: pass + def process_one(self, p, *args): + """ + Same as process(), but called for every iteration + """ + + pass + def postprocess(self, p, processed, *args): """ This function is called after processing ends for AlwaysVisible scripts. @@ -294,6 +301,15 @@ class ScriptRunner: print(f"Error running process: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) + def process_one(self, p): + for script in self.alwayson_scripts: + try: + script_args = p.script_args[script.args_from:script.args_to] + script.process_one(p, *script_args) + except Exception: + print(f"Error running process_one: {script.filename}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + def postprocess(self, p, processed): for script in self.alwayson_scripts: try: -- cgit v1.2.3 From f1b6ac64e451036fb4dfabe66d79488c56c06776 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kyu=E2=99=A5?= <3ad4gum@gmail.com> Date: Wed, 2 Nov 2022 17:24:42 +0100 Subject: Added option to preview Created images on batch completion. --- modules/shared.py | 25 ++++++++++++++++--------- modules/ui.py | 2 +- 2 files changed, 17 insertions(+), 10 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index d8e99f85..d4cf32a4 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -146,6 +146,9 @@ class State: self.interrupted = True def nextjob(self): + if opts.show_progress_every_n_steps == -1: + self.do_set_current_image() + self.job_no += 1 self.sampling_step = 0 self.current_image_sampling_step = 0 @@ -186,17 +189,21 @@ class State: """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this""" def set_current_image(self): + if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0: + self.do_set_current_image() + + def do_set_current_image(self): if not parallel_processing_allowed: return + if self.current_latent is None: + return + + if opts.show_progress_grid: + self.current_image = sd_samplers.samples_to_image_grid(self.current_latent) + else: + self.current_image = sd_samplers.sample_to_image(self.current_latent) - if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None: - if opts.show_progress_grid: - self.current_image = sd_samplers.samples_to_image_grid(self.current_latent) - else: - self.current_image = sd_samplers.sample_to_image(self.current_latent) - - self.current_image_sampling_step = self.sampling_step - + self.current_image_sampling_step = self.sampling_step state = State() @@ -351,7 +358,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), - "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), + "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}), "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"), "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), diff --git a/modules/ui.py b/modules/ui.py index 2609857e..29de1e10 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -276,7 +276,7 @@ def check_progress_call(id_part): image = gr_show(False) preview_visibility = gr_show(False) - if opts.show_progress_every_n_steps > 0: + if opts.show_progress_every_n_steps != 0: shared.state.set_current_image() image = shared.state.current_image -- cgit v1.2.3 From c07f1d0d7821f85b9ce1419992c118963d605bd7 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 2 Nov 2022 16:59:10 +0000 Subject: Convert callbacks into a private map, add utility functions for removing callbacks --- modules/script_callbacks.py | 68 +++++++++++++++++++++++++++------------------ 1 file changed, 41 insertions(+), 27 deletions(-) (limited to 'modules') diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index c28e220e..4a7fb944 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -46,25 +46,23 @@ class CFGDenoiserParams: ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"]) -callbacks_app_started = [] -callbacks_model_loaded = [] -callbacks_ui_tabs = [] -callbacks_ui_settings = [] -callbacks_before_image_saved = [] -callbacks_image_saved = [] -callbacks_cfg_denoiser = [] +__callback_map = dict( + callbacks_app_started=[], + callbacks_model_loaded=[], + callbacks_ui_tabs=[], + callbacks_ui_settings=[], + callbacks_before_image_saved=[], + callbacks_image_saved=[], + callbacks_cfg_denoiser=[] +) def clear_callbacks(): - callbacks_model_loaded.clear() - callbacks_ui_tabs.clear() - callbacks_ui_settings.clear() - callbacks_before_image_saved.clear() - callbacks_image_saved.clear() - callbacks_cfg_denoiser.clear() + for callback_list in __callback_map.values(): + callback_list.clear() def app_started_callback(demo: Optional[Blocks], app: FastAPI): - for c in callbacks_app_started: + for c in __callback_map['callbacks_app_started']: try: c.callback(demo, app) except Exception: @@ -72,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI): def model_loaded_callback(sd_model): - for c in callbacks_model_loaded: + for c in __callback_map['callbacks_model_loaded']: try: c.callback(sd_model) except Exception: @@ -82,7 +80,7 @@ def model_loaded_callback(sd_model): def ui_tabs_callback(): res = [] - for c in callbacks_ui_tabs: + for c in __callback_map['callbacks_ui_tabs']: try: res += c.callback() or [] except Exception: @@ -92,7 +90,7 @@ def ui_tabs_callback(): def ui_settings_callback(): - for c in callbacks_ui_settings: + for c in __callback_map['callbacks_ui_settings']: try: c.callback() except Exception: @@ -100,7 +98,7 @@ def ui_settings_callback(): def before_image_saved_callback(params: ImageSaveParams): - for c in callbacks_before_image_saved: + for c in __callback_map['callbacks_before_image_saved']: try: c.callback(params) except Exception: @@ -108,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams): def image_saved_callback(params: ImageSaveParams): - for c in callbacks_image_saved: + for c in __callback_map['callbacks_image_saved']: try: c.callback(params) except Exception: @@ -116,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams): def cfg_denoiser_callback(params: CFGDenoiserParams): - for c in callbacks_cfg_denoiser: + for c in __callback_map['callbacks_cfg_denoiser']: try: c.callback(params) except Exception: @@ -129,17 +127,33 @@ def add_callback(callbacks, fun): callbacks.append(ScriptCallback(filename, fun)) + +def remove_current_script_callbacks(): + stack = [x for x in inspect.stack() if x.filename != __file__] + filename = stack[0].filename if len(stack) > 0 else 'unknown file' + if filename == 'unknown file': + return + for callback_list in __callback_map.values(): + for callback_to_remove in [cb for cb in callback_list if cb.script == filename]: + callback_list.remove(callback_to_remove) + + +def remove_callbacks_for_function(callback_func): + for callback_list in __callback_map.values(): + for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]: + callback_list.remove(callback_to_remove) + def on_app_started(callback): """register a function to be called when the webui started, the gradio `Block` component and fastapi `FastAPI` object are passed as the arguments""" - add_callback(callbacks_app_started, callback) + add_callback(__callback_map['callbacks_app_started'], callback) def on_model_loaded(callback): """register a function to be called when the stable diffusion model is created; the model is passed as an argument""" - add_callback(callbacks_model_loaded, callback) + add_callback(__callback_map['callbacks_model_loaded'], callback) def on_ui_tabs(callback): @@ -152,13 +166,13 @@ def on_ui_tabs(callback): title is tab text displayed to user in the UI elem_id is HTML id for the tab """ - add_callback(callbacks_ui_tabs, callback) + add_callback(__callback_map['callbacks_ui_tabs'], callback) def on_ui_settings(callback): """register a function to be called before UI settings are populated; add your settings by using shared.opts.add_option(shared.OptionInfo(...)) """ - add_callback(callbacks_ui_settings, callback) + add_callback(__callback_map['callbacks_ui_settings'], callback) def on_before_image_saved(callback): @@ -166,7 +180,7 @@ def on_before_image_saved(callback): The callback is called with one argument: - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object. """ - add_callback(callbacks_before_image_saved, callback) + add_callback(__callback_map['callbacks_before_image_saved'], callback) def on_image_saved(callback): @@ -174,7 +188,7 @@ def on_image_saved(callback): The callback is called with one argument: - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing. """ - add_callback(callbacks_image_saved, callback) + add_callback(__callback_map['callbacks_image_saved'], callback) def on_cfg_denoiser(callback): @@ -182,5 +196,5 @@ def on_cfg_denoiser(callback): The callback is called with one argument: - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details. """ - add_callback(callbacks_cfg_denoiser, callback) + add_callback(__callback_map['callbacks_cfg_denoiser'], callback) -- cgit v1.2.3 From de64146ad2fc2030a4cd3545676f9e18c93b8b18 Mon Sep 17 00:00:00 2001 From: Artem Zagidulin Date: Wed, 2 Nov 2022 21:30:50 +0300 Subject: add number of itter --- modules/processing.py | 2 +- modules/scripts.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 72a2ee4e..17f4a5ec 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -510,7 +510,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: break if p.scripts is not None: - p.scripts.process_one(p) + p.scripts.process_one(p, n) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) diff --git a/modules/scripts.py b/modules/scripts.py index 9f82efea..7aa0d56a 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -70,7 +70,7 @@ class Script: pass - def process_one(self, p, *args): + def process_one(self, p, n, *args): """ Same as process(), but called for every iteration """ @@ -301,11 +301,11 @@ class ScriptRunner: print(f"Error running process: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) - def process_one(self, p): + def process_one(self, p, n): for script in self.alwayson_scripts: try: script_args = p.script_args[script.args_from:script.args_to] - script.process_one(p, *script_args) + script.process_one(p, n, *script_args) except Exception: print(f"Error running process_one: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) -- cgit v1.2.3 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 v1.2.3 From 313e14de04d9955c6ad077341feceb0fc7f2f1d3 Mon Sep 17 00:00:00 2001 From: Chris OBryan <13701027+cobryan05@users.noreply.github.com> Date: Wed, 2 Nov 2022 21:37:43 -0500 Subject: extras - skip unnecessary second hash of image There is no need to re-hash the input image each iteration of the loop. This also reverts PR #4026 as it was determined the cache hits it avoids were actually valid. --- modules/extras.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 8e2ab35c..71b93a06 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -136,12 +136,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]: blended_result: Image.Image = None + image_hash: str = hash(np.array(image.getdata()).tobytes()) for upscaler in params: upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) - cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()), + cache_key = LruCache.Key(image_hash=image_hash, info_hash=hash(info), - args_hash=hash((upscale_args, upscale_first))) + args_hash=hash(upscale_args)) cached_entry = cached_images.get(cache_key) if cached_entry is None: res = upscale(image, *upscale_args) -- cgit v1.2.3 From 7a2e36b583ef9eaefa44322e16faff6f9f1af169 Mon Sep 17 00:00:00 2001 From: Bruno Seoane Date: Thu, 3 Nov 2022 00:51:22 -0300 Subject: Add config and lists endpoints --- modules/api/api.py | 97 ++++++++++++++++++++++++++++++++++++++++++++++++--- modules/api/models.py | 70 +++++++++++++++++++++++++++++++++++-- 2 files changed, 159 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 71c9c160..ed2dce5d 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -2,14 +2,17 @@ import base64 import io import time import uvicorn -from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image -from fastapi import APIRouter, Depends, HTTPException +from threading import Lock +from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image +from fastapi import APIRouter, Depends, FastAPI, HTTPException import modules.shared as shared from modules.api.models import * from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images -from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid +from modules.sd_samplers import all_samplers from modules.extras import run_extras, run_pnginfo - +from modules.sd_models import checkpoints_list +from modules.realesrgan_model import get_realesrgan_models +from typing import List def upscaler_to_index(name: str): try: @@ -37,7 +40,7 @@ def encode_pil_to_base64(image): class Api: - def __init__(self, app, queue_lock): + def __init__(self, app: FastAPI, queue_lock: Lock): self.router = APIRouter() self.app = app self.queue_lock = queue_lock @@ -48,6 +51,19 @@ class Api: self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse) self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse) self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel) + self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel) + self.app.add_api_route("/sdapi/v1/info", self.get_info, methods=["GET"]) + self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem]) + self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem]) + self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem]) + self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem]) + self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem]) + self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem]) + self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem]) + self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str]) + self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem]) def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) @@ -190,6 +206,77 @@ class Api: shared.state.interrupt() return {} + + def get_config(self): + options = {} + for key in shared.opts.data.keys(): + metadata = shared.opts.data_labels.get(key) + if(metadata is not None): + options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)}) + else: + options.update({key: shared.opts.data.get(key, None)}) + + return options + + def set_config(self, req: OptionsModel): + reqDict = vars(req) + for o in reqDict: + setattr(shared.opts, o, reqDict[o]) + + shared.opts.save(shared.config_filename) + return + + def get_cmd_flags(self): + return vars(shared.cmd_opts) + + def get_info(self): + + return { + "hypernetworks": [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks], + "face_restorers": [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers], + "realesrgan_models":[{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)], + "promp_styles":[shared.prompt_styles.styles[k] for k in shared.prompt_styles.styles], + "artists_categories": shared.artist_db.cats, + # "artists": [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists] + } + + def get_samplers(self): + return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers] + + def get_upscalers(self): + upscalers = [] + + for upscaler in shared.sd_upscalers: + u = upscaler.scaler + upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url}) + + return upscalers + + def get_sd_models(self): + return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()] + + def get_hypernetworks(self): + return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] + + def get_face_restorers(self): + return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers] + + def get_realesrgan_models(self): + return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] + + def get_promp_styles(self): + styleList = [] + for k in shared.prompt_styles.styles: + style = shared.prompt_styles.styles[k] + styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]}) + + return styleList + + def get_artists_categories(self): + return shared.artist_db.cats + + def get_artists(self): + return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists] def launch(self, server_name, port): self.app.include_router(self.router) diff --git a/modules/api/models.py b/modules/api/models.py index 9ee42a17..b54b188a 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,11 +1,10 @@ import inspect -from click import prompt from pydantic import BaseModel, Field, create_model -from typing import Any, Optional +from typing import Any, Optional, Union from typing_extensions import Literal from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img -from modules.shared import sd_upscalers +from modules.shared import sd_upscalers, opts, parser API_NOT_ALLOWED = [ "self", @@ -165,3 +164,68 @@ class ProgressResponse(BaseModel): eta_relative: float = Field(title="ETA in secs") state: dict = Field(title="State", description="The current state snapshot") current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.") + +fields = {} +for key, value in opts.data.items(): + metadata = opts.data_labels.get(key) + optType = opts.typemap.get(type(value), type(value)) + + if (metadata is not None): + fields.update({key: (Optional[optType], Field( + default=metadata.default ,description=metadata.label))}) + else: + fields.update({key: (Optional[optType], Field())}) + +OptionsModel = create_model("Options", **fields) + +flags = {} +_options = vars(parser)['_option_string_actions'] +for key in _options: + if(_options[key].dest != 'help'): + flag = _options[key] + _type = str + if(_options[key].default != None): _type = type(_options[key].default) + flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))}) + +FlagsModel = create_model("Flags", **flags) + +class SamplerItem(BaseModel): + name: str = Field(title="Name") + aliases: list[str] = Field(title="Aliases") + options: dict[str, str] = Field(title="Options") + +class UpscalerItem(BaseModel): + name: str = Field(title="Name") + model_name: str | None = Field(title="Model Name") + model_path: str | None = Field(title="Path") + model_url: str | None = Field(title="URL") + +class SDModelItem(BaseModel): + title: str = Field(title="Title") + model_name: str = Field(title="Model Name") + hash: str = Field(title="Hash") + filename: str = Field(title="Filename") + config: str = Field(title="Config file") + +class HypernetworkItem(BaseModel): + name: str = Field(title="Name") + path: str | None = Field(title="Path") + +class FaceRestorerItem(BaseModel): + name: str = Field(title="Name") + cmd_dir: str | None = Field(title="Path") + +class RealesrganItem(BaseModel): + name: str = Field(title="Name") + path: str | None = Field(title="Path") + scale: int | None = Field(title="Scale") + +class PromptStyleItem(BaseModel): + name: str = Field(title="Name") + prompt: str | None = Field(title="Prompt") + negative_prompt: str | None = Field(title="Negative Prompt") + +class ArtistItem(BaseModel): + name: str = Field(title="Name") + score: float = Field(title="Score") + category: str = Field(title="Category") \ No newline at end of file -- cgit v1.2.3 From 743fffa3d6c2e9e6bb5f48093a4c88f3b53e001d Mon Sep 17 00:00:00 2001 From: Bruno Seoane Date: Thu, 3 Nov 2022 00:52:01 -0300 Subject: Remove unused endpoint --- modules/api/api.py | 12 ------------ 1 file changed, 12 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index ed2dce5d..a49f3755 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -54,7 +54,6 @@ class Api: self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel) self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel) - self.app.add_api_route("/sdapi/v1/info", self.get_info, methods=["GET"]) self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem]) self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem]) self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem]) @@ -229,17 +228,6 @@ class Api: def get_cmd_flags(self): return vars(shared.cmd_opts) - def get_info(self): - - return { - "hypernetworks": [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks], - "face_restorers": [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers], - "realesrgan_models":[{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)], - "promp_styles":[shared.prompt_styles.styles[k] for k in shared.prompt_styles.styles], - "artists_categories": shared.artist_db.cats, - # "artists": [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists] - } - def get_samplers(self): return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers] -- cgit v1.2.3 From e33d6cbddd08870e348d10a58af41fb677a39fd6 Mon Sep 17 00:00:00 2001 From: Ju1-js <40339350+Ju1-js@users.noreply.github.com> Date: Wed, 2 Nov 2022 21:04:49 -0700 Subject: Make extension manager Remote links open a new tab --- modules/ui_extensions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index ab807722..a81de9a7 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -86,7 +86,7 @@ def extension_table(): code += f""" - {html.escape(ext.remote or '')} + {html.escape(ext.remote or '')} {ext_status} """ -- cgit v1.2.3 From 0b143c1163a96b193a4e8512be9c5831c661a50d Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Thu, 3 Nov 2022 14:30:53 +0900 Subject: Separate .optim file from model --- modules/hypernetworks/hypernetwork.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8f74cdea..63c25de8 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -161,6 +161,7 @@ class Hypernetwork: def save(self, filename): state_dict = {} + optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) @@ -175,9 +176,10 @@ class Hypernetwork: state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name if self.optimizer_name is not None: - state_dict['optimizer_name'] = self.optimizer_name + optimizer_saved_dict['optimizer_name'] = self.optimizer_name if self.optimizer_state_dict: - state_dict['optimizer_state_dict'] = self.optimizer_state_dict + optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict + torch.save(optimizer_saved_dict, filename + '.optim') torch.save(state_dict, filename) @@ -198,9 +200,11 @@ class Hypernetwork: print(f"Layer norm is set to {self.add_layer_norm}") self.use_dropout = state_dict.get('use_dropout', False) print(f"Dropout usage is set to {self.use_dropout}") - self.optimizer_name = state_dict.get('optimizer_name', 'AdamW') + + optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} + self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') print(f"Optimizer name is {self.optimizer_name}") - self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None) + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) if self.optimizer_state_dict: print("Loaded existing optimizer from checkpoint") else: -- cgit v1.2.3 From 1764ac3c8bc482bd575987850e96630d9115e51a Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Thu, 3 Nov 2022 14:49:26 +0900 Subject: use hash to check valid optim --- modules/hypernetworks/hypernetwork.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 63c25de8..4230b8cf 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -177,12 +177,13 @@ class Hypernetwork: state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name if self.optimizer_name is not None: optimizer_saved_dict['optimizer_name'] = self.optimizer_name + + torch.save(state_dict, filename) if self.optimizer_state_dict: + optimizer_saved_dict['hash'] = sd_models.model_hash(filename) optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(optimizer_saved_dict, filename + '.optim') - torch.save(state_dict, filename) - def load(self, filename): self.filename = filename if self.name is None: @@ -204,7 +205,10 @@ class Hypernetwork: optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') print(f"Optimizer name is {self.optimizer_name}") - self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None): + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + else: + self.optimizer_state_dict = None if self.optimizer_state_dict: print("Loaded existing optimizer from checkpoint") else: @@ -229,7 +233,7 @@ def list_hypernetworks(path): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": - res[name] = filename + res[name + f"({sd_models.model_hash(filename)})"] = filename return res @@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) -- cgit v1.2.3 From b2c48091db394c2b7d375a33f18d90c924cd4363 Mon Sep 17 00:00:00 2001 From: Gur Date: Fri, 4 Nov 2022 06:55:03 +0800 Subject: fixed api compatibility with python 3.8 --- modules/api/models.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/api/models.py b/modules/api/models.py index 9ee42a17..29a934ba 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -6,6 +6,7 @@ from typing_extensions import Literal from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img from modules.shared import sd_upscalers +from typing import List API_NOT_ALLOWED = [ "self", @@ -109,12 +110,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( ).generate_model() class TextToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: dict info: str class ImageToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: dict info: str @@ -146,10 +147,10 @@ class FileData(BaseModel): name: str = Field(title="File name") class ExtrasBatchImagesRequest(ExtrasBaseRequest): - imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") + imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") class ExtrasBatchImagesResponse(ExtraBaseResponse): - images: list[str] = Field(title="Images", description="The generated images in base64 format.") + images: List[str] = Field(title="Images", description="The generated images in base64 format.") class PNGInfoRequest(BaseModel): image: str = Field(title="Image", description="The base64 encoded PNG image") -- cgit v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 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 v1.2.3 From 0abb39f461baa343ae7c23abffb261e57c3168d4 Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:47:19 +0900 Subject: resolve conflict - first revert --- modules/hypernetworks/hypernetwork.py | 123 ++++++++++++++-------------------- 1 file changed, 52 insertions(+), 71 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4230b8cf..674fcedd 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -21,7 +21,6 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm from collections import defaultdict, deque from statistics import stdev, mean -optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} class HypernetworkModule(torch.nn.Module): multiplier = 1.0 @@ -34,9 +33,12 @@ class HypernetworkModule(torch.nn.Module): "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, } - 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'}) + 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): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', + add_layer_norm=False, use_dropout=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -47,7 +49,7 @@ class HypernetworkModule(torch.nn.Module): for i in range(len(layer_structure) - 1): # Add a fully-connected layer - linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + 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: @@ -59,7 +61,7 @@ class HypernetworkModule(torch.nn.Module): # Add layer normalization if add_layer_norm: - linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1]))) # Add dropout expect last layer if use_dropout and i < len(layer_structure) - 3: @@ -128,7 +130,8 @@ 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): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, + add_layer_norm=False, use_dropout=False): self.filename = None self.name = name self.layers = {} @@ -140,13 +143,13 @@ class Hypernetwork: self.weight_init = weight_init self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout - self.optimizer_name = None - self.optimizer_state_dict = None 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), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), ) def weights(self): @@ -161,7 +164,6 @@ class Hypernetwork: def save(self, filename): state_dict = {} - optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) @@ -175,14 +177,8 @@ 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 - if self.optimizer_name is not None: - optimizer_saved_dict['optimizer_name'] = self.optimizer_name torch.save(state_dict, filename) - if self.optimizer_state_dict: - optimizer_saved_dict['hash'] = sd_models.model_hash(filename) - optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict - torch.save(optimizer_saved_dict, filename + '.optim') def load(self, filename): self.filename = filename @@ -202,23 +198,13 @@ class Hypernetwork: self.use_dropout = state_dict.get('use_dropout', False) print(f"Dropout usage is set to {self.use_dropout}") - optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} - self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') - print(f"Optimizer name is {self.optimizer_name}") - if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None): - self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) - else: - self.optimizer_state_dict = None - if self.optimizer_state_dict: - print("Loaded existing optimizer from checkpoint") - else: - print("No saved optimizer exists in checkpoint") - 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), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.use_dropout), ) self.name = state_dict.get('name', self.name) @@ -233,7 +219,7 @@ def list_hypernetworks(path): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": - res[name + f"({sd_models.model_hash(filename)})"] = filename + res[name] = filename return res @@ -330,7 +316,7 @@ def statistics(data): std = 0 else: std = stdev(data) - total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})" + total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})" recent_data = data[-32:] if len(recent_data) < 2: std = 0 @@ -340,7 +326,7 @@ def statistics(data): return total_information, recent_information -def report_statistics(loss_info:dict): +def report_statistics(loss_info: dict): keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) for key in keys: try: @@ -352,14 +338,18 @@ def report_statistics(loss_info:dict): print(e) - -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, + training_height, steps, create_image_every, save_hypernetwork_every, template_file, + preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, + preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images save_hypernetwork_every = save_hypernetwork_every or 0 create_image_every = create_image_every or 0 - textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, + save_hypernetwork_every, create_image_every, log_directory, + name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -379,7 +369,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None - hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) @@ -395,39 +384,34 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, + height=training_height, + repeats=shared.opts.training_image_repeats_per_epoch, + placeholder_token=hypernetwork_name, + model=shared.sd_model, device=devices.device, + template_file=template_file, include_cond=True, + batch_size=batch_size) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) size = len(ds.indexes) - loss_dict = defaultdict(lambda : deque(maxlen = 1024)) + loss_dict = defaultdict(lambda: deque(maxlen=1024)) losses = torch.zeros((size,)) previous_mean_losses = [0] previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - + weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True - # Here we use optimizer from saved HN, or we can specify as UI option. - if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: - optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) - else: - print(f"Optimizer type {optimizer_name} is not defined!") - optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) - optimizer_name = 'AdamW' - if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. - try: - optimizer.load_state_dict(hypernetwork.optimizer_state_dict) - except RuntimeError as e: - print("Cannot resume from saved optimizer!") - print(e) + # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... + optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) steps_without_grad = 0 @@ -441,7 +425,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if len(loss_dict) > 0: previous_mean_losses = [i[-1] for i in loss_dict.values()] previous_mean_loss = mean(previous_mean_losses) - + scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break @@ -460,7 +444,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() for entry in entries: loss_dict[entry.filename].append(loss.item()) - + optimizer.zero_grad() weights[0].grad = None loss.backward() @@ -475,9 +459,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log steps_done = hypernetwork.step + 1 - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): raise RuntimeError("Loss diverged.") - + if len(previous_mean_losses) > 1: std = stdev(previous_mean_losses) else: @@ -489,11 +473,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') - hypernetwork.optimizer_name = optimizer_name - if shared.opts.save_optimizer_state: - hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) - hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", "learn_rate": scheduler.learn_rate @@ -529,7 +510,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log preview_text = p.prompt processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + image = processed.images[0] if len(processed.images) > 0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -537,7 +518,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None: shared.state.current_image = image - last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, + shared.opts.samples_format, processed.infotexts[0], + p=p, forced_filename=forced_filename, + save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step @@ -551,15 +535,12 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" + report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') - hypernetwork.optimizer_name = optimizer_name - if shared.opts.save_optimizer_state: - hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) - del optimizer - hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + return hypernetwork, filename @@ -576,4 +557,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): hypernetwork.sd_checkpoint = old_sd_checkpoint hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name hypernetwork.name = old_hypernetwork_name - raise + raise \ No newline at end of file -- cgit v1.2.3 From 0d07cbfa15d34294a4fa22d74359cdd6fe2f799c Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:50:54 +0900 Subject: I blame code autocomplete --- modules/hypernetworks/hypernetwork.py | 76 +++++++++++++---------------------- 1 file changed, 27 insertions(+), 49 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 674fcedd..a11e01d6 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -33,12 +33,9 @@ class HypernetworkModule(torch.nn.Module): "tanh": torch.nn.Tanh, "sigmoid": torch.nn.Sigmoid, } - 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'}) + 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): + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" @@ -49,7 +46,7 @@ class HypernetworkModule(torch.nn.Module): for i in range(len(layer_structure) - 1): # Add a fully-connected layer - linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1]))) + 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: @@ -61,7 +58,7 @@ class HypernetworkModule(torch.nn.Module): # Add layer normalization if add_layer_norm: - linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1]))) + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Add dropout expect last layer if use_dropout and i < len(layer_structure) - 3: @@ -130,8 +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): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False): self.filename = None self.name = name self.layers = {} @@ -146,10 +142,8 @@ class Hypernetwork: 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), - HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, - self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), ) def weights(self): @@ -196,15 +190,13 @@ class Hypernetwork: 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) - print(f"Dropout usage is set to {self.use_dropout}") + print(f"Dropout usage is set to {self.use_dropout}" ) 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), - HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, - self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout), ) self.name = state_dict.get('name', self.name) @@ -316,7 +308,7 @@ def statistics(data): std = 0 else: std = stdev(data) - total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})" + total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})" recent_data = data[-32:] if len(recent_data) < 2: std = 0 @@ -326,7 +318,7 @@ def statistics(data): return total_information, recent_information -def report_statistics(loss_info: dict): +def report_statistics(loss_info:dict): keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) for key in keys: try: @@ -338,18 +330,14 @@ def report_statistics(loss_info: dict): print(e) -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, - training_height, steps, create_image_every, save_hypernetwork_every, template_file, - preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, - preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images save_hypernetwork_every = save_hypernetwork_every or 0 create_image_every = create_image_every or 0 - textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, - save_hypernetwork_every, create_image_every, log_directory, - name="hypernetwork") + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() @@ -384,29 +372,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, - height=training_height, - repeats=shared.opts.training_image_repeats_per_epoch, - placeholder_token=hypernetwork_name, - model=shared.sd_model, device=devices.device, - template_file=template_file, include_cond=True, - batch_size=batch_size) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) size = len(ds.indexes) - loss_dict = defaultdict(lambda: deque(maxlen=1024)) + loss_dict = defaultdict(lambda : deque(maxlen = 1024)) losses = torch.zeros((size,)) previous_mean_losses = [0] previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - + weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True @@ -425,7 +407,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if len(loss_dict) > 0: previous_mean_losses = [i[-1] for i in loss_dict.values()] previous_mean_loss = mean(previous_mean_losses) - + scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break @@ -444,7 +426,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() for entry in entries: loss_dict[entry.filename].append(loss.item()) - + optimizer.zero_grad() weights[0].grad = None loss.backward() @@ -459,9 +441,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log steps_done = hypernetwork.step + 1 - if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): raise RuntimeError("Loss diverged.") - + if len(previous_mean_losses) > 1: std = stdev(previous_mean_losses) else: @@ -510,7 +492,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log preview_text = p.prompt processed = processing.process_images(p) - image = processed.images[0] if len(processed.images) > 0 else None + image = processed.images[0] if len(processed.images)>0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) @@ -518,10 +500,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None: shared.state.current_image = image - last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, - shared.opts.samples_format, processed.infotexts[0], - p=p, forced_filename=forced_filename, - save_to_dirs=False) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step @@ -535,7 +514,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - + report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') @@ -543,7 +522,6 @@ Last saved image: {html.escape(last_saved_image)}
return hypernetwork, filename - def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): old_hypernetwork_name = hypernetwork.name old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None @@ -557,4 +535,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): hypernetwork.sd_checkpoint = old_sd_checkpoint hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name hypernetwork.name = old_hypernetwork_name - raise \ No newline at end of file + raise -- cgit v1.2.3 From 283249d2390f0f3a1c8a55d5d9aa551e3e9b2f9c Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 15:57:17 +0900 Subject: apply --- modules/hypernetworks/hypernetwork.py | 54 +++++++++++++++++++++++++++++++---- 1 file changed, 49 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6e1a10cf..de8688a9 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -22,6 +22,8 @@ from collections import defaultdict, deque from statistics import stdev, mean +optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} + class HypernetworkModule(torch.nn.Module): multiplier = 1.0 activation_dict = { @@ -142,6 +144,8 @@ class Hypernetwork: 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 + self.optimizer_name = None + self.optimizer_state_dict = None for size in enable_sizes or []: self.layers[size] = ( @@ -163,6 +167,7 @@ class Hypernetwork: def save(self, filename): state_dict = {} + optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) @@ -178,8 +183,15 @@ class Hypernetwork: 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 - + + if self.optimizer_name is not None: + optimizer_saved_dict['optimizer_name'] = self.optimizer_name + torch.save(state_dict, filename) + if self.optimizer_state_dict: + optimizer_saved_dict['hash'] = sd_models.model_hash(filename) + optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict + torch.save(optimizer_saved_dict, filename + '.optim') def load(self, filename): self.filename = filename @@ -202,6 +214,18 @@ class Hypernetwork: print(f"Activate last layer is set to {self.activate_output}") self.last_layer_dropout = state_dict.get('last_layer_dropout', False) + optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} + self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') + print(f"Optimizer name is {self.optimizer_name}") + if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None): + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + else: + self.optimizer_state_dict = None + if self.optimizer_state_dict: + print("Loaded existing optimizer from checkpoint") + else: + print("No saved optimizer exists in checkpoint") + for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( @@ -223,7 +247,7 @@ def list_hypernetworks(path): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": - res[name] = filename + res[name + f"({sd_models.model_hash(filename)})"] = filename return res @@ -369,6 +393,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) @@ -404,8 +429,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True - # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + # Here we use optimizer from saved HN, or we can specify as UI option. + if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: + optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + else: + print(f"Optimizer type {optimizer_name} is not defined!") + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. + try: + optimizer.load_state_dict(hypernetwork.optimizer_state_dict) + except RuntimeError as e: + print("Cannot resume from saved optimizer!") + print(e) steps_without_grad = 0 @@ -467,7 +503,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{previous_mean_loss:.7f}", @@ -530,8 +570,12 @@ Last saved image: {html.escape(last_saved_image)}
report_statistics(loss_dict) filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) - + del optimizer + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. return hypernetwork, filename def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): -- cgit v1.2.3 From f5d394214d6ee74a682d0a1016bcbebc4b43c13a Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 16:04:03 +0900 Subject: split before declaring file name --- 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 de8688a9..9b6a3e62 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -382,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) @@ -393,7 +394,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: hypernetwork_dir = None - hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) -- cgit v1.2.3 From 1ca0bcd3a7003dd2c1324de7d97fd2a6fc5ddc53 Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 16:09:19 +0900 Subject: only save if option is enabled --- 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 9b6a3e62..b1f308e2 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -188,7 +188,7 @@ class Hypernetwork: optimizer_saved_dict['optimizer_name'] = self.optimizer_name torch.save(state_dict, filename) - if self.optimizer_state_dict: + if shared.opts.save_optimizer_state and self.optimizer_state_dict: optimizer_saved_dict['hash'] = sd_models.model_hash(filename) optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(optimizer_saved_dict, filename + '.optim') -- cgit v1.2.3 From ccf1a15412ef6b518f9f54cc26a0ee5edf458108 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 10:16:19 +0300 Subject: add an option to enable installing extensions with --listen or --share --- modules/shared.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 024c771a..0a39cdf2 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -44,6 +44,7 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision", parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site") parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None) parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us") +parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options") parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) @@ -99,7 +100,7 @@ restricted_opts = { "outdir_save", } -cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen +cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer']) -- cgit v1.2.3 From 321e13ca176b256177c4a752d1f2bbee79b5532e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 10:35:30 +0300 Subject: produce a readable error message when setting an option fails on the settings screen --- modules/ui.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 633b56ef..3ac7540c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1439,8 +1439,7 @@ def create_ui(wrap_gradio_gpu_call): changed = 0 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() + assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}" for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp == dummy_component: @@ -1458,7 +1457,7 @@ def create_ui(wrap_gradio_gpu_call): opts.save(shared.config_filename) - return f'{changed} settings changed.', opts.dumpjson() + return opts.dumpjson(), f'{changed} settings changed.' def run_settings_single(value, key): if not opts.same_type(value, opts.data_labels[key].default): @@ -1622,9 +1621,9 @@ def create_ui(wrap_gradio_gpu_call): text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) settings_submit.click( - fn=run_settings, + fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]), inputs=components, - outputs=[result, text_settings], + outputs=[text_settings, result], ) for i, k, item in quicksettings_list: -- cgit v1.2.3 From f674c488d9701e577e2aaf25e331fb44ada4f1ef Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 10:45:34 +0300 Subject: bugfix: save image for hires fix BEFORE upscaling latent space --- modules/processing.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index a46e592d..7a2fc218 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -665,17 +665,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") if opts.use_scale_latent_for_hires_fix: + for i in range(samples.shape[0]): + save_intermediate(samples, i) + samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") - + # 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) - - for i in range(samples.shape[0]): - save_intermediate(samples, i) else: decoded_samples = decode_first_stage(self.sd_model, samples) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) -- cgit v1.2.3 From 7278897982bfb640ee95f144c97ed25fb3f77ea3 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Fri, 4 Nov 2022 17:12:28 +0900 Subject: Update shared.py --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 4d6e1c8b..6e7a02e0 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -309,7 +309,7 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), - "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."), + "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), -- cgit v1.2.3 From 99043f33606d3057f83ea52a403e10cd29d1f7e7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 11:20:42 +0300 Subject: fix one of previous merges breaking the program --- modules/sd_models.py | 2 ++ 1 file changed, 2 insertions(+) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 63e07a12..34c57bfa 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -167,6 +167,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): sd_vae.restore_base_vae(model) checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy() + vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file) + if checkpoint_info not in checkpoints_loaded: print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") -- cgit v1.2.3 From eeb07330131012c0294afb79165b90270679b9c7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 11:21:40 +0300 Subject: change process_one virtual function for script to process_batch, add extra args and docs --- modules/processing.py | 2 +- modules/scripts.py | 16 +++++++++++----- 2 files changed, 12 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index e20d8fc4..03c9143d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -502,7 +502,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: break if p.scripts is not None: - p.scripts.process_one(p, n) + p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) diff --git a/modules/scripts.py b/modules/scripts.py index 75e47cd2..366c90d7 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -73,9 +73,15 @@ class Script: pass - def process_one(self, p, n, *args): + def process_batch(self, p, *args, **kwargs): """ - Same as process(), but called for every iteration + Same as process(), but called for every batch. + + **kwargs will have those items: + - batch_number - index of current batch, from 0 to number of batches-1 + - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things + - seeds - list of seeds for current batch + - subseeds - list of subseeds for current batch """ pass @@ -303,13 +309,13 @@ class ScriptRunner: print(f"Error running process: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) - def process_one(self, p, n): + def process_batch(self, p, **kwargs): for script in self.alwayson_scripts: try: script_args = p.script_args[script.args_from:script.args_to] - script.process_one(p, n, *script_args) + script.process_batch(p, *script_args, **kwargs) except Exception: - print(f"Error running process_one: {script.filename}", file=sys.stderr) + print(f"Error running process_batch: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) def postprocess(self, p, processed): -- cgit v1.2.3 From fd62727893f9face287b0a9620251afaa38a627d Mon Sep 17 00:00:00 2001 From: Isaac Poulton Date: Fri, 4 Nov 2022 18:34:35 +0700 Subject: Sort hypernetworks --- 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 6e1a10cf..f1f04a70 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -224,7 +224,7 @@ def list_hypernetworks(path): # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name] = filename - return res + return dict(sorted(res.items())) def load_hypernetwork(filename): -- cgit v1.2.3 From c3cd0d7a86f35a5bfc58fdc3ecfaf203c0aee06f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 4 Nov 2022 12:19:16 +0000 Subject: Should be one underscore for module privates not two --- modules/script_callbacks.py | 37 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 19 deletions(-) (limited to 'modules') diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 4a7fb944..83da7ca4 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -46,7 +46,7 @@ class CFGDenoiserParams: ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"]) -__callback_map = dict( +_callback_map = dict( callbacks_app_started=[], callbacks_model_loaded=[], callbacks_ui_tabs=[], @@ -58,11 +58,11 @@ __callback_map = dict( def clear_callbacks(): - for callback_list in __callback_map.values(): + for callback_list in _callback_map.values(): callback_list.clear() def app_started_callback(demo: Optional[Blocks], app: FastAPI): - for c in __callback_map['callbacks_app_started']: + for c in _callback_map['callbacks_app_started']: try: c.callback(demo, app) except Exception: @@ -70,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI): def model_loaded_callback(sd_model): - for c in __callback_map['callbacks_model_loaded']: + for c in _callback_map['callbacks_model_loaded']: try: c.callback(sd_model) except Exception: @@ -80,7 +80,7 @@ def model_loaded_callback(sd_model): def ui_tabs_callback(): res = [] - for c in __callback_map['callbacks_ui_tabs']: + for c in _callback_map['callbacks_ui_tabs']: try: res += c.callback() or [] except Exception: @@ -90,7 +90,7 @@ def ui_tabs_callback(): def ui_settings_callback(): - for c in __callback_map['callbacks_ui_settings']: + for c in _callback_map['callbacks_ui_settings']: try: c.callback() except Exception: @@ -98,7 +98,7 @@ def ui_settings_callback(): def before_image_saved_callback(params: ImageSaveParams): - for c in __callback_map['callbacks_before_image_saved']: + for c in _callback_map['callbacks_before_image_saved']: try: c.callback(params) except Exception: @@ -106,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams): def image_saved_callback(params: ImageSaveParams): - for c in __callback_map['callbacks_image_saved']: + for c in _callback_map['callbacks_image_saved']: try: c.callback(params) except Exception: @@ -114,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams): def cfg_denoiser_callback(params: CFGDenoiserParams): - for c in __callback_map['callbacks_cfg_denoiser']: + for c in _callback_map['callbacks_cfg_denoiser']: try: c.callback(params) except Exception: @@ -133,13 +133,13 @@ def remove_current_script_callbacks(): filename = stack[0].filename if len(stack) > 0 else 'unknown file' if filename == 'unknown file': return - for callback_list in __callback_map.values(): + for callback_list in _callback_map.values(): for callback_to_remove in [cb for cb in callback_list if cb.script == filename]: callback_list.remove(callback_to_remove) def remove_callbacks_for_function(callback_func): - for callback_list in __callback_map.values(): + for callback_list in _callback_map.values(): for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]: callback_list.remove(callback_to_remove) @@ -147,13 +147,13 @@ def remove_callbacks_for_function(callback_func): def on_app_started(callback): """register a function to be called when the webui started, the gradio `Block` component and fastapi `FastAPI` object are passed as the arguments""" - add_callback(__callback_map['callbacks_app_started'], callback) + add_callback(_callback_map['callbacks_app_started'], callback) def on_model_loaded(callback): """register a function to be called when the stable diffusion model is created; the model is passed as an argument""" - add_callback(__callback_map['callbacks_model_loaded'], callback) + add_callback(_callback_map['callbacks_model_loaded'], callback) def on_ui_tabs(callback): @@ -166,13 +166,13 @@ def on_ui_tabs(callback): title is tab text displayed to user in the UI elem_id is HTML id for the tab """ - add_callback(__callback_map['callbacks_ui_tabs'], callback) + add_callback(_callback_map['callbacks_ui_tabs'], callback) def on_ui_settings(callback): """register a function to be called before UI settings are populated; add your settings by using shared.opts.add_option(shared.OptionInfo(...)) """ - add_callback(__callback_map['callbacks_ui_settings'], callback) + add_callback(_callback_map['callbacks_ui_settings'], callback) def on_before_image_saved(callback): @@ -180,7 +180,7 @@ def on_before_image_saved(callback): The callback is called with one argument: - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object. """ - add_callback(__callback_map['callbacks_before_image_saved'], callback) + add_callback(_callback_map['callbacks_before_image_saved'], callback) def on_image_saved(callback): @@ -188,7 +188,7 @@ def on_image_saved(callback): The callback is called with one argument: - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing. """ - add_callback(__callback_map['callbacks_image_saved'], callback) + add_callback(_callback_map['callbacks_image_saved'], callback) def on_cfg_denoiser(callback): @@ -196,5 +196,4 @@ def on_cfg_denoiser(callback): The callback is called with one argument: - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details. """ - add_callback(__callback_map['callbacks_cfg_denoiser'], callback) - + add_callback(_callback_map['callbacks_cfg_denoiser'], callback) -- cgit v1.2.3 From f316280ad3634a2343b086a6de0bfcd473e18599 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 16:48:40 +0300 Subject: fix the error that prevents from setting some options --- modules/shared.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index a9e28b9c..962115f6 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -406,7 +406,8 @@ class Options: 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 + info = opts.data_labels.get(key, None) + comp_args = info.component_args if info else None 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") -- cgit v1.2.3 From 116bcf730ade8d3ac5d76d04c5887b6bba000970 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 16:48:46 +0300 Subject: disable setting options via API until it is fixed by the author --- modules/api/api.py | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index a49f3755..8a7ab2f5 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -218,6 +218,10 @@ class Api: return options def set_config(self, req: OptionsModel): + # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will + # overwrite all options with default values. + raise RuntimeError('Setting options via API is not supported') + reqDict = vars(req) for o in reqDict: setattr(shared.opts, o, reqDict[o]) -- cgit v1.2.3 From 08feb4c364e8b2aed929fd7d22dfa21a93d78b2c Mon Sep 17 00:00:00 2001 From: Isaac Poulton Date: Fri, 4 Nov 2022 20:53:11 +0700 Subject: Sort straight out of the glob --- 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 f1f04a70..a441ab10 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -219,12 +219,12 @@ class Hypernetwork: def list_hypernetworks(path): res = {} - for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): + for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name] = filename - return dict(sorted(res.items())) + return res def load_hypernetwork(filename): -- cgit v1.2.3 From 5844ef8a9a165e0f456a4658bda830282cf5a55e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 4 Nov 2022 16:02:25 +0000 Subject: remove private underscore indicator --- modules/script_callbacks.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) (limited to 'modules') diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 83da7ca4..74dfb880 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -46,7 +46,7 @@ class CFGDenoiserParams: ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"]) -_callback_map = dict( +callback_map = dict( callbacks_app_started=[], callbacks_model_loaded=[], callbacks_ui_tabs=[], @@ -58,11 +58,11 @@ _callback_map = dict( def clear_callbacks(): - for callback_list in _callback_map.values(): + for callback_list in callback_map.values(): callback_list.clear() def app_started_callback(demo: Optional[Blocks], app: FastAPI): - for c in _callback_map['callbacks_app_started']: + for c in callback_map['callbacks_app_started']: try: c.callback(demo, app) except Exception: @@ -70,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI): def model_loaded_callback(sd_model): - for c in _callback_map['callbacks_model_loaded']: + for c in callback_map['callbacks_model_loaded']: try: c.callback(sd_model) except Exception: @@ -80,7 +80,7 @@ def model_loaded_callback(sd_model): def ui_tabs_callback(): res = [] - for c in _callback_map['callbacks_ui_tabs']: + for c in callback_map['callbacks_ui_tabs']: try: res += c.callback() or [] except Exception: @@ -90,7 +90,7 @@ def ui_tabs_callback(): def ui_settings_callback(): - for c in _callback_map['callbacks_ui_settings']: + for c in callback_map['callbacks_ui_settings']: try: c.callback() except Exception: @@ -98,7 +98,7 @@ def ui_settings_callback(): def before_image_saved_callback(params: ImageSaveParams): - for c in _callback_map['callbacks_before_image_saved']: + for c in callback_map['callbacks_before_image_saved']: try: c.callback(params) except Exception: @@ -106,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams): def image_saved_callback(params: ImageSaveParams): - for c in _callback_map['callbacks_image_saved']: + for c in callback_map['callbacks_image_saved']: try: c.callback(params) except Exception: @@ -114,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams): def cfg_denoiser_callback(params: CFGDenoiserParams): - for c in _callback_map['callbacks_cfg_denoiser']: + for c in callback_map['callbacks_cfg_denoiser']: try: c.callback(params) except Exception: @@ -133,13 +133,13 @@ def remove_current_script_callbacks(): filename = stack[0].filename if len(stack) > 0 else 'unknown file' if filename == 'unknown file': return - for callback_list in _callback_map.values(): + for callback_list in callback_map.values(): for callback_to_remove in [cb for cb in callback_list if cb.script == filename]: callback_list.remove(callback_to_remove) def remove_callbacks_for_function(callback_func): - for callback_list in _callback_map.values(): + for callback_list in callback_map.values(): for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]: callback_list.remove(callback_to_remove) @@ -147,13 +147,13 @@ def remove_callbacks_for_function(callback_func): def on_app_started(callback): """register a function to be called when the webui started, the gradio `Block` component and fastapi `FastAPI` object are passed as the arguments""" - add_callback(_callback_map['callbacks_app_started'], callback) + add_callback(callback_map['callbacks_app_started'], callback) def on_model_loaded(callback): """register a function to be called when the stable diffusion model is created; the model is passed as an argument""" - add_callback(_callback_map['callbacks_model_loaded'], callback) + add_callback(callback_map['callbacks_model_loaded'], callback) def on_ui_tabs(callback): @@ -166,13 +166,13 @@ def on_ui_tabs(callback): title is tab text displayed to user in the UI elem_id is HTML id for the tab """ - add_callback(_callback_map['callbacks_ui_tabs'], callback) + add_callback(callback_map['callbacks_ui_tabs'], callback) def on_ui_settings(callback): """register a function to be called before UI settings are populated; add your settings by using shared.opts.add_option(shared.OptionInfo(...)) """ - add_callback(_callback_map['callbacks_ui_settings'], callback) + add_callback(callback_map['callbacks_ui_settings'], callback) def on_before_image_saved(callback): @@ -180,7 +180,7 @@ def on_before_image_saved(callback): The callback is called with one argument: - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object. """ - add_callback(_callback_map['callbacks_before_image_saved'], callback) + add_callback(callback_map['callbacks_before_image_saved'], callback) def on_image_saved(callback): @@ -188,7 +188,7 @@ def on_image_saved(callback): The callback is called with one argument: - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing. """ - add_callback(_callback_map['callbacks_image_saved'], callback) + add_callback(callback_map['callbacks_image_saved'], callback) def on_cfg_denoiser(callback): @@ -196,4 +196,4 @@ def on_cfg_denoiser(callback): The callback is called with one argument: - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details. """ - add_callback(_callback_map['callbacks_cfg_denoiser'], callback) + add_callback(callback_map['callbacks_cfg_denoiser'], callback) -- cgit v1.2.3 From 0d7e01d9950e013784c4b77c05aa7583ea69edc8 Mon Sep 17 00:00:00 2001 From: innovaciones Date: Fri, 4 Nov 2022 12:14:32 -0600 Subject: Open extensions links in new tab Fixed for "Available" tab --- modules/ui_extensions.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index a81de9a7..8e0d41d5 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -188,7 +188,7 @@ def refresh_available_extensions_from_data(): code += f""" - {html.escape(name)} + {html.escape(name)} {html.escape(description)} {install_code} -- cgit v1.2.3 From b8435e632f7ba0da12a2c8e9c788dda519279d24 Mon Sep 17 00:00:00 2001 From: evshiron Date: Sat, 5 Nov 2022 02:36:47 +0800 Subject: add --cors-allow-origins cmd opt --- modules/shared.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index a9e28b9c..e83cbcdf 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -86,6 +86,7 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI") parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False) +parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None) cmd_opts = parser.parse_args() restricted_opts = { @@ -147,9 +148,9 @@ class State: self.interrupted = True def nextjob(self): - if opts.show_progress_every_n_steps == -1: + if opts.show_progress_every_n_steps == -1: self.do_set_current_image() - + self.job_no += 1 self.sampling_step = 0 self.current_image_sampling_step = 0 @@ -198,7 +199,7 @@ class State: return if self.current_latent is None: return - + if opts.show_progress_grid: self.current_image = sd_samplers.samples_to_image_grid(self.current_latent) else: -- cgit v1.2.3 From 467d8b967b5d1b1984ab113bec3fff217736e7ac Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Sat, 5 Nov 2022 04:24:42 +0900 Subject: Fix errors from commit f2b697 with --hide-ui-dir-config https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/f2b69709eaff88fc3a2bd49585556ec0883bf5ea --- modules/ui.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 4c2829af..76ca9b07 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1446,17 +1446,19 @@ def create_ui(wrap_gradio_gpu_call): continue oldval = opts.data.get(key, None) - - setattr(opts, key, value) - + try: + setattr(opts, key, value) + except RuntimeError: + continue if oldval != value: if opts.data_labels[key].onchange is not None: opts.data_labels[key].onchange() changed += 1 - - opts.save(shared.config_filename) - + try: + opts.save(shared.config_filename) + except RuntimeError: + return opts.dumpjson(), f'{changed} settings changed without save.' return opts.dumpjson(), f'{changed} settings changed.' def run_settings_single(value, key): -- cgit v1.2.3 From 30b1bcc64e67ad50c5d3af3a6fe1bd1e9553f34e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 4 Nov 2022 22:56:18 +0300 Subject: fix upscale loop erroneously applied multiple times --- modules/upscaler.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/upscaler.py b/modules/upscaler.py index 83fde7ca..c4e6e6bd 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -57,10 +57,18 @@ class Upscaler: self.scale = scale dest_w = img.width * scale dest_h = img.height * scale + for i in range(3): - if img.width > dest_w and img.height > dest_h: - break + shape = (img.width, img.height) + img = self.do_upscale(img, selected_model) + + if shape == (img.width, img.height): + break + + if img.width >= dest_w and img.height >= dest_h: + break + if img.width != dest_w or img.height != dest_h: img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS) -- cgit v1.2.3 From 6008c0773ea575353f9b87da8a58454e20cc7857 Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Fri, 4 Nov 2022 23:03:05 +0000 Subject: Add support for new DPM-Solver++ samplers --- modules/sd_samplers.py | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index c7c414ef..7ece6556 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -29,6 +29,10 @@ samplers_k_diffusion = [ ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}), ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), + ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), + ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), + ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), + ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ] samplers_data_k_diffusion = [ -- cgit v1.2.3 From f92dc505a013af9e385c7edbdf97539be62503d6 Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Fri, 4 Nov 2022 23:12:48 +0000 Subject: Fix name --- modules/sd_samplers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 7ece6556..b28a2e4c 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -31,7 +31,7 @@ samplers_k_diffusion = [ ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), - ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), + ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ] -- cgit v1.2.3 From 1b6c2fc749e12f12bbee4705e65f217d23fa9072 Mon Sep 17 00:00:00 2001 From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com> Date: Fri, 4 Nov 2022 23:28:13 +0000 Subject: Reorder samplers --- modules/sd_samplers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index b28a2e4c..1e88f7ee 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -24,13 +24,13 @@ samplers_k_diffusion = [ ('Heun', 'sample_heun', ['k_heun'], {}), ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}), ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}), + ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), + ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}), ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}), ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}), ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), - ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), - ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ] -- cgit v1.2.3 From ebce0c57c78a3f22178e3a38938d19ec0dfb703d Mon Sep 17 00:00:00 2001 From: Billy Cao Date: Sat, 5 Nov 2022 11:38:24 +0800 Subject: Use typing.Optional instead of | to add support for Python 3.9 and below. --- modules/api/models.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) (limited to 'modules') diff --git a/modules/api/models.py b/modules/api/models.py index 2ae75f43..a44c5ddd 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,6 +1,6 @@ import inspect from pydantic import BaseModel, Field, create_model -from typing import Any, Optional, Union +from typing import Any, Optional from typing_extensions import Literal from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img @@ -185,22 +185,22 @@ _options = vars(parser)['_option_string_actions'] for key in _options: if(_options[key].dest != 'help'): flag = _options[key] - _type = str - if(_options[key].default != None): _type = type(_options[key].default) + _type = str + if _options[key].default is not None: _type = type(_options[key].default) flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))}) FlagsModel = create_model("Flags", **flags) class SamplerItem(BaseModel): name: str = Field(title="Name") - aliases: list[str] = Field(title="Aliases") + aliases: list[str] = Field(title="Aliases") options: dict[str, str] = Field(title="Options") class UpscalerItem(BaseModel): name: str = Field(title="Name") - model_name: str | None = Field(title="Model Name") - model_path: str | None = Field(title="Path") - model_url: str | None = Field(title="URL") + model_name: Optional[str] = Field(title="Model Name") + model_path: Optional[str] = Field(title="Path") + model_url: Optional[str] = Field(title="URL") class SDModelItem(BaseModel): title: str = Field(title="Title") @@ -211,21 +211,21 @@ class SDModelItem(BaseModel): class HypernetworkItem(BaseModel): name: str = Field(title="Name") - path: str | None = Field(title="Path") + path: Optional[str] = Field(title="Path") class FaceRestorerItem(BaseModel): name: str = Field(title="Name") - cmd_dir: str | None = Field(title="Path") + cmd_dir: Optional[str] = Field(title="Path") class RealesrganItem(BaseModel): name: str = Field(title="Name") - path: str | None = Field(title="Path") - scale: int | None = Field(title="Scale") + path: Optional[str] = Field(title="Path") + scale: Optional[int] = Field(title="Scale") class PromptStyleItem(BaseModel): name: str = Field(title="Name") - prompt: str | None = Field(title="Prompt") - negative_prompt: str | None = Field(title="Negative Prompt") + prompt: Optional[str] = Field(title="Prompt") + negative_prompt: Optional[str] = Field(title="Negative Prompt") class ArtistItem(BaseModel): name: str = Field(title="Name") -- cgit v1.2.3 From e9a5562b9b27a1a4f9c282637b111cefd9727a41 Mon Sep 17 00:00:00 2001 From: papuSpartan Date: Sat, 5 Nov 2022 04:06:51 -0500 Subject: add support for tls (gradio tls options) --- modules/shared.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 962115f6..7a20c3af 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -86,6 +86,9 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI") parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False) +parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None) +parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None) +parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None) cmd_opts = parser.parse_args() restricted_opts = { -- cgit v1.2.3 From 03b08c4a6b0609f24ec789d40100529b92ef0612 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 5 Nov 2022 15:04:48 +0300 Subject: do not die when an extension's repo has no remote --- modules/extensions.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/extensions.py b/modules/extensions.py index 897af96e..8e0977fd 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -34,8 +34,11 @@ class Extension: if repo is None or repo.bare: self.remote = None else: - self.remote = next(repo.remote().urls, None) - self.status = 'unknown' + try: + self.remote = next(repo.remote().urls, None) + self.status = 'unknown' + except Exception: + self.remote = None def list_files(self, subdir, extension): from modules import scripts -- cgit v1.2.3 From a170e3d22231e145f42bb878a76ae5f76fdca230 Mon Sep 17 00:00:00 2001 From: Evgeniy Date: Sat, 5 Nov 2022 17:06:56 +0300 Subject: Python 3.8 typing compatibility Solves problems with ```Traceback (most recent call last): File "webui.py", line 201, in webui() File "webui.py", line 178, in webui create_api(app) File "webui.py", line 117, in create_api from modules.api.api import Api File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\api.py", line 9, in from modules.api.models import * File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 194, in class SamplerItem(BaseModel): File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 196, in SamplerItem aliases: list[str] = Field(title="Aliases") TypeError: 'type' object is not subscriptable``` and ```Traceback (most recent call last): File "webui.py", line 201, in webui() File "webui.py", line 178, in webui create_api(app) File "webui.py", line 117, in create_api from modules.api.api import Api File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\api.py", line 9, in from modules.api.models import * File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 194, in class SamplerItem(BaseModel): File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 197, in SamplerItem options: dict[str, str] = Field(title="Options") TypeError: 'type' object is not subscriptable``` --- modules/api/models.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/api/models.py b/modules/api/models.py index a44c5ddd..f89da1ff 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -5,7 +5,7 @@ from typing_extensions import Literal from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img from modules.shared import sd_upscalers, opts, parser -from typing import List +from typing import Dict, List API_NOT_ALLOWED = [ "self", @@ -193,8 +193,8 @@ FlagsModel = create_model("Flags", **flags) class SamplerItem(BaseModel): name: str = Field(title="Name") - aliases: list[str] = Field(title="Aliases") - options: dict[str, str] = Field(title="Options") + aliases: List[str] = Field(title="Aliases") + options: Dict[str, str] = Field(title="Options") class UpscalerItem(BaseModel): name: str = Field(title="Name") @@ -230,4 +230,4 @@ class PromptStyleItem(BaseModel): class ArtistItem(BaseModel): name: str = Field(title="Name") score: float = Field(title="Score") - category: str = Field(title="Category") \ No newline at end of file + category: str = Field(title="Category") -- cgit v1.2.3 From 62e3d71aa778928d63cab81d9d8cde33e55bebb3 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 5 Nov 2022 17:09:42 +0300 Subject: rework the code to not use the walrus operator because colab's 3.7 does not support it --- modules/hypernetworks/hypernetwork.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 5ceed6ee..7f182712 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -429,13 +429,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True + # Here we use optimizer from saved HN, or we can specify as UI option. - if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict: + if hypernetwork.optimizer_name in optimizer_dict: optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + optimizer_name = hypernetwork.optimizer_name else: - print(f"Optimizer type {optimizer_name} is not defined!") + print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!") optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) optimizer_name = 'AdamW' + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. try: optimizer.load_state_dict(hypernetwork.optimizer_state_dict) -- cgit v1.2.3 From 159475e072f2ed3db8235aab9c3fa18640b93b80 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 5 Nov 2022 18:32:22 +0300 Subject: tweak names a bit for new samplers --- modules/sd_samplers.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 1e88f7ee..783992d2 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -24,15 +24,15 @@ samplers_k_diffusion = [ ('Heun', 'sample_heun', ['k_heun'], {}), ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}), ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}), - ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), - ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), + ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}), + ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}), ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}), ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}), ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}), ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}), ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}), - ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), - ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), + ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), + ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}), ] samplers_data_k_diffusion = [ -- cgit v1.2.3