diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 95a17093d..92874a792 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -39,6 +39,7 @@ def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu + ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_hijack_optimizations.patched_unet_forward if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 98123fbf4..8cd4c9549 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -5,6 +5,7 @@ import importlib import torch from torch import einsum +import torch.nn.functional as F from ldm.util import default from einops import rearrange @@ -12,6 +13,8 @@ from einops import rearrange from modules import shared from modules.hypernetworks import hypernetwork +from ldm.modules.diffusionmodules.util import timestep_embedding + if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers: try: @@ -310,3 +313,31 @@ def xformers_attnblock_forward(self, x): return x + out except NotImplementedError: return cross_attention_attnblock_forward(self, x) + +def patched_unet_forward(self, x, timesteps=None, context=None, y=None,**kwargs): + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape == (x.shape[0],) + emb = emb + self.label_emb(y) + + h = x.type(self.dtype) + for module in self.input_blocks: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + for module in self.output_blocks: + if h.shape[-2:] != hs[-1].shape[-2:]: + h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest") + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/modules/ui.py b/modules/ui.py index 79bb3d1f5..38096aad9 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -302,8 +302,8 @@ def create_seed_inputs(): with gr.Row(visible=False) as seed_extra_row_2: seed_extras.append(seed_extra_row_2) - seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0) - seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0) + seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0) + seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0) random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed]) random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed]) @@ -635,8 +635,8 @@ def create_ui(): sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index") with gr.Group(): - width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512) + height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512) with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) @@ -644,8 +644,8 @@ def create_ui(): enable_hr = gr.Checkbox(label='Highres. fix', value=False) with gr.Row(visible=False) as hr_options: - firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0) - firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0) + firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0) + firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0) denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7) with gr.Row(equal_height=True): @@ -835,8 +835,8 @@ def create_ui(): sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index") with gr.Group(): - width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width") - height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height") + width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") + height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) @@ -1171,8 +1171,8 @@ def create_ui(): with gr.Tab(label="Preprocess images"): process_src = gr.Textbox(label='Source directory') process_dst = gr.Textbox(label='Destination directory') - process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512) + process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512) preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) with gr.Row(): @@ -1230,8 +1230,8 @@ def create_ui(): dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) - training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512) + training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)