ExponentialML
af6fe36f17
Adds the feature recommended from this issue: https://github.com/ExponentialML/ComfyUI_VisualStylePrompting/issues/9#issue-2199077769. Also fixes bugs that wasn't allowing style prompting to work correctly.
97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
import comfy
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import torch
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import nodes
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from .utils.attention_functions import VisualStyleProcessor, ATTENTION_LAYER_CANDIDATES
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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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"vae": ("VAE", ),
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"reference_image": ("IMAGE",),
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"conditioning_prompt": ("CONDITIONING",),
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"reference_image_prompt": ("CONDITIONING",),
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"negative_prompt": ("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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"middle_block": ("BOOLEAN", {"default": False}),
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"output_blocks": ("BOOLEAN", {"default": True}),
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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] == True:
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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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vae,
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reference_image,
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conditioning_prompt,
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reference_image_prompt,
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negative_prompt,
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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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):
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self.model = model
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reference_latent = vae.encode(reference_image[:,:,:,:3])
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block_choices = self.get_block_choices(input_blocks, middle_block, output_blocks)
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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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processor = VisualStyleProcessor(m, enabled=self.activate_block_choice(n, block_choices))
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setattr(m, 'forward', processor.visual_style_forward)
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conditioning_prompt[0][0] = torch.cat([reference_image_prompt[0][0], conditioning_prompt[0][0]])
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negative_prompt[0][0] = torch.cat([negative_prompt[0][0]] * 2)
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latents = torch.zeros_like(reference_latent)
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latents = torch.cat([latents] * 2)
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if denoise < 1.0:
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latents[::1] = reference_latent[:1]
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else:
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latents[::2] = reference_latent
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denoise_mask = torch.ones_like(latents)[:, :1, ...] * denoise
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denoise_mask[::2] = 0.
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return (model, conditioning_prompt, negative_prompt, {"samples": latents, "noise_mask": denoise_mask})
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NODE_CLASS_MAPPINGS = {"ApplyVisualStyle": ApplyVisualStyle}
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NODE_DISPLAY_NAME_MAPPINGS = {"ApplyVisualStyle": "Apply Visual Style Prompting"} |