126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
import comfy
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import torch
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from .utils.attention_functions import VisualStyleProcessor
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from .utils.cond_functions import cat_cond
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class ApplyVisualStyle:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"reference_latent": ("LATENT",),
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"reference_cond": ("CONDITIONING",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"enabled": ("BOOLEAN", {"default": True}),
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"denoise": ("FLOAT", {"default": 1., "min": 0., "max": 1., "step": 1e-2}),
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"input_blocks": ("BOOLEAN", {"default": False}),
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"skip_input_layers": ("INT", {"default": 24, "min": 0, "max": 48, "step": 1}),
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"middle_block": ("BOOLEAN", {"default": False}),
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"skip_middle_layers": ("INT", {"default": 1, "min": 0, "max": 2, "step": 1}),
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"output_blocks": ("BOOLEAN", {"default": True}),
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"skip_output_layers": ("INT", {"default": 24, "min": 0, "max": 72, "step": 1}),
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},
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"optional": {
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"init_image": ("IMAGE",),
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}
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}
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CATEGORY = "VisualStylePrompting/apply"
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RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("model", "positive", "negative", "latents")
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FUNCTION = "apply_visual_style_prompt"
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def get_block_choices(self, input_blocks, middle_block, output_blocks):
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block_choices_map = (
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[input_blocks, "input"],
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[middle_block, "middle"],
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[output_blocks, "output"]
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)
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block_choices = []
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for block_choice in block_choices_map:
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if block_choice[0]:
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block_choices.append(block_choice[1])
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return block_choices
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def activate_block_choice(self, key, block_choices):
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return any([block in key for block in block_choices])
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def apply_visual_style_prompt(
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self,
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model: comfy.model_patcher.ModelPatcher,
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clip,
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reference_latent,
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reference_cond,
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positive,
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negative,
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enabled,
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denoise,
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input_blocks,
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middle_block,
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output_blocks,
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init_image = None,
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skip_input_layers=0,
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skip_middle_layers=0,
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skip_output_layers=0
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):
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reference_samples = reference_latent["samples"]
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block_choices = self.get_block_choices(input_blocks, middle_block, output_blocks)
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layer_indexes = {
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"input": 0,
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"middle": 0,
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"output": 0
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}
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n_skip_per_block = {
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"input": skip_input_layers,
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"middle": skip_middle_layers,
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"output": skip_output_layers
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}
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class_names = set()
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for n, m in model.model.diffusion_model.named_modules():
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if m.__class__.__name__ == "CrossAttention":
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is_enabled = self.activate_block_choice(n, block_choices)
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if is_enabled:
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block_name = n.split("_")[0]
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if layer_indexes[block_name] < n_skip_per_block[block_name]:
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is_enabled = False
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layer_indexes[block_name] += 1
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if hasattr(m.forward, 'module_self'):
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m.forward.enabled = is_enabled and enabled
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else:
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processor = VisualStyleProcessor(m, enabled=is_enabled)
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setattr(m, 'forward', processor)
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positive_cat = cat_cond(clip, reference_cond, positive)
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negative_cat = cat_cond(clip, negative, negative)
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latents = torch.zeros_like(reference_samples)
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latents = torch.cat([latents] * 2)
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latents[0] = reference_samples
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denoise_mask = torch.ones_like(latents)[:, :1, ...] * denoise
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denoise_mask[0] = 0.
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return (model, positive_cat, negative_cat, {"samples": latents, "noise_mask": denoise_mask})
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NODE_CLASS_MAPPINGS = {
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"ApplyVisualStyle": ApplyVisualStyle,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ApplyVisualStyle": "Apply Visual Style Prompting",
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} |