219 lines
12 KiB
Python
219 lines
12 KiB
Python
# components/normalize.py
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import torch
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from typing import Dict, Tuple
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from comfy.model_patcher import ModelPatcher
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from ..ddare.util import cuda_memory_profiler, get_device
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from ..ddare.tensor import relative_norm
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from ..ddare.const import UTIL_CATEGORY
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"""
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These are the layers that we are going to normalize, and how we are going to normalize them:
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.0.in_layers.{0,2}.{weight, bias} scale by weight
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.0.emb_layers.{1}.{weight, bias} scale by weight
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.0.out_layers.{0,3}.{weight, bias} scale by weight
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.1.{norm, proj_in, proj_out}.{weight, bias} scale by weight
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.1.transformer_blocks.0.{attn1, attn2}.{to_q, to_k, to_v}.weight q, v scaled by q weight, k inverse scaled
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.1.transformer_blocks.0.{attn1, attn2}.to_out.0.{weight, bias} scale by weight
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.1.transformer_blocks.0.ff.net.{0,2}.proj.{weight, bias} scale by weight
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diffusion_model.{{input_blocks, output_blocks}.{n}, {middle_block}}.1.transformer_blocks.0.{norm1, norm2, norm3}.{weight, bias} scale by weight
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"""
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class NormalizeUnet:
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"""
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A class to normalize the blocks from one model to the other, bringing them into the same scale.
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, tuple]:
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"""
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Defines the input types for the merging process.
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Returns:
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Dict[str, tuple]: A dictionary specifying the required model types and parameters.
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"""
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return {
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"required": {
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"model_a": ("MODEL",),
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"model_b": ("MODEL",),
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"method": (["q_norm", "all", "none", "attn_only"], {"default": "attn_only"}),
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}
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}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "normalize"
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CATEGORY = UTIL_CATEGORY
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def generate_key_groups_sd15(self):
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# Lets build our key selection strategy, and pick out the keys we want to patch from our key list
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# This is currently pretty manual, but we can make it more automatic later. Right now we are just
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# making sure it matches sd15.
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blocks = []
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for i in range(12):
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blocks.append(f"input_blocks.{i}")
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blocks.append(f"output_blocks.{i}")
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blocks.append("middle_block")
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key_groups = {}
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prefix = "diffusion_model"
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layers = [("0.in_layers", (0,2)), ("0.emb_layers", (1,)), ("0.out_layers", (0,3))]
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layers += [("2.in_layers", (0,2)), ("2.emb_layers", (1,)), ("2.out_layers", (0,3))]
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layers += [("1.norm", ()), ("1.proj_in", ()), ("1.proj_out", ())]
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for b in blocks:
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for lk, z in layers:
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if len(z) == 0:
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key_groups[f"{prefix}.{b}.{lk}.weight"] = (f"{prefix}.{b}.{lk}.weight", f"{prefix}.{b}.{lk}.bias")
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else:
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for i in z:
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key_groups[f"{prefix}.{b}.{lk}.{i}.weight"] = (f"{prefix}.{b}.{lk}.{i}.weight", f"{prefix}.{b}.{lk}.{i}.bias")
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key_groups[f"{prefix}.{b}.0.skip_connection.weight"] = (f"{prefix}.{b}.0.skip_connection.weight", f"{prefix}.{b}.0.skip_connection.bias")
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.norm1.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.norm1.weight", f"{prefix}.{b}.1.transformer_blocks.0.norm1.bias")
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.norm2.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.norm2.weight", f"{prefix}.{b}.1.transformer_blocks.0.norm2.bias")
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.norm3.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.norm3.weight", f"{prefix}.{b}.1.transformer_blocks.0.norm3.bias")
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.ff.net.0.proj.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.ff.net.0.proj.weight", f"{prefix}.{b}.1.transformer_blocks.0.ff.net.0.proj.bias")
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.ff.net.2.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.ff.net.2.weight", f"{prefix}.{b}.1.transformer_blocks.0.ff.net.2.bias")
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# Get our two attention blocks into our 5-tuple
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.attn1.to_q.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.attn1.to_q.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn1.to_k.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn1.to_v.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn1.to_out.0.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn1.to_out.0.bias")
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key_groups[f"{prefix}.{b}.1.transformer_blocks.0.attn2.to_q.weight"] = (f"{prefix}.{b}.1.transformer_blocks.0.attn2.to_q.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn2.to_k.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn2.to_v.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn2.to_out.0.weight", f"{prefix}.{b}.1.transformer_blocks.0.attn2.to_out.0.bias")
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key_groups[f"{prefix}.{b}.2.conv.weight"] = (f"{prefix}.{b}.2.conv.weight", f"{prefix}.{b}.2.conv.bias")
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key_groups[f"{prefix}.input_blocks.0.weight"] = (f"{prefix}.input_blocks.0.weight", f"{prefix}.input_blocks.0.bias")
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key_groups[f"{prefix}.input_blocks.0.0.weight"] = (f"{prefix}.input_blocks.0.0.weight", f"{prefix}.input_blocks.0.0.bias")
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key_groups[f"{prefix}.input_blocks.3.0.op.weight"] = (f"{prefix}.input_blocks.3.0.op.weight", f"{prefix}.input_blocks.3.0.op.bias")
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key_groups[f"{prefix}.input_blocks.6.0.op.weight"] = (f"{prefix}.input_blocks.6.0.op.weight", f"{prefix}.input_blocks.6.0.op.bias")
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key_groups[f"{prefix}.input_blocks.9.0.op.weight"] = (f"{prefix}.input_blocks.9.0.op.weight", f"{prefix}.input_blocks.9.0.op.bias")
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key_groups[f"{prefix}.out.0.weight"] = (f"{prefix}.out.0.weight", f"{prefix}.out.0.bias")
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key_groups[f"{prefix}.out.2.weight"] = (f"{prefix}.out.2.weight", f"{prefix}.out.2.bias")
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key_groups[f"{prefix}.output_blocks.2.1.conv.weight"] = (f"{prefix}.output_blocks.2.1.conv.weight", f"{prefix}.output_blocks.2.1.conv.bias")
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key_groups[f"{prefix}.time_embed.0.weight"] = (f"{prefix}.time_embed.0.weight", f"{prefix}.time_embed.0.bias")
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key_groups[f"{prefix}.time_embed.2.weight"] = (f"{prefix}.time_embed.2.weight", f"{prefix}.time_embed.2.bias")
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return key_groups
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def normalize(self, model_a: ModelPatcher, model_b: ModelPatcher, method : str, **kwargs) -> Tuple[ModelPatcher]:
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"""
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Scales model A by the scaling factor calculated from model B.
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Args:
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model_a (ModelPatcher): Model to be scaled.
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model_b (ModelPatcher): Model to be used as reference.
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method (str): Method to be used for merging.
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**kwargs: Additional arguments specifying the merge ratios for different layers and sparsity.
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Returns:
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Tuple[ModelPatcher]: A tuple containing the merged ModelPatcher instance.
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"""
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device = get_device()
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with cuda_memory_profiler():
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m = model_a.clone() # Clone model_a to keep its structure
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if method == "none":
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if len(m.patches) > 0:
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print(f"Model A has patches: {m.patches.keys()}")
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return (m,)
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if len(model_a.patches) > 0:
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print("Model A has patches, applying them")
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m.patch_model(None, True)
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model_a_sd = m.model_state_dict() # State dict of model_a
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m.unpatch_model() # Unpatch model_a
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else:
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model_a_sd = m.model_state_dict() # State dict of model_a
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if len(model_b.patches) > 0:
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print("Model B has patches, applying them")
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model_b.patch_model(None, True)
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model_b_sd = model_b.model_state_dict()
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model_b.unpatch_model()
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else:
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model_b_sd = model_b.model_state_dict()
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strength_patch = 1.0
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strength_model = 0.0
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processed_keys = {}
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for k in sorted(model_a_sd.keys()):
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processed_keys[k] = False
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for key, group in self.generate_key_groups_sd15().items():
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if key not in model_a_sd or key not in model_b_sd:
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#print("could not patch. key doesn't exist in model:", key)
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continue
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elif len(group) == 2 and (method != "attn_only"):
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# Normalize our weight and bias
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weight_key, bias_key = group
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weight_a : torch.Tensor = model_a_sd[weight_key].to(device)
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weight_b : torch.Tensor = model_b_sd[weight_key].to(device)
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bias_a : torch.Tensor = model_a_sd[bias_key].to(device)
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scale = relative_norm(weight_a, weight_b).to(device)
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na = torch.empty_like(weight_a, device=device)
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na = weight_a * scale
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nb = torch.empty_like(weight_b, device=device)
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nb = weight_b / scale
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#print("normalized:", weight_key, scale)
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del scale
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m.add_patches({weight_key: (na.to('cpu'),)}, strength_patch, strength_model)
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m.add_patches({bias_key: (nb.to('cpu'),)}, strength_patch, strength_model)
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weight_a.to("cpu")
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weight_b.to("cpu")
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bias_a.to("cpu")
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processed_keys[weight_key] = True
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processed_keys[bias_key] = True
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elif len(group) == 5 and (method == "q_norm" or method == "attn_only"):
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# Scaled attention, we determine the scaling factor from the q weight
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q, k, v, out_w, out_b = group
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q_a : torch.Tensor = model_a_sd[q].to(device)
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q_b : torch.Tensor = model_b_sd[q].to(device)
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k_a : torch.Tensor = model_a_sd[k].to(device)
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#v_a : torch.Tensor = model_a_sd[v].to(device)
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out_w_a : torch.Tensor = model_a_sd[out_w].to(device)
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out_w_b : torch.Tensor = model_b_sd[out_w].to(device)
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out_b_a : torch.Tensor = model_a_sd[out_b].to(device)
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scale_a = relative_norm(q_a, q_b).to(device)
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# allocate q, k, v, out_w, out_b
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nq = torch.empty_like(q_a, device=device)
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nq = q_a.to(device) * scale_a
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nk = torch.empty_like(k_a, device=device)
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nk = k_a.to(device) / scale_a
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#v_a = v_a.copy_(v_a * scale).to("cpu")
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scale_o = relative_norm(out_w_a, out_w_b)
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nout_w = torch.empty_like(out_w_a, device=device)
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nout_w = out_w_a.to(device) * scale_o
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nout_b = torch.empty_like(out_b_a, device=device)
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nout_b = out_b_a.to(device) * scale_o
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#print("normalized:", q, scale_a, scale_o)
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del scale_a, scale_o
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m.add_patches({q: (nq.to('cpu'),)}, strength_patch, strength_model)
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m.add_patches({k: (nk.to('cpu'),)}, strength_patch, strength_model)
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#m.add_patches({v: (v_a,)}, strength_patch, strength_model)
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m.add_patches({out_w: (nout_w.to('cpu'),)}, strength_patch, strength_model)
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m.add_patches({out_b: (nout_b.to('cpu'),)}, strength_patch, strength_model)
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q_a.to("cpu")
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q_b.to("cpu")
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k_a.to("cpu")
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#v_a.to("cpu")
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out_w_a.to("cpu")
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out_w_b.to("cpu")
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out_b_a.to("cpu")
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processed_keys[q] = True
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processed_keys[k] = True
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processed_keys[v] = True
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processed_keys[out_w] = True
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processed_keys[out_b] = True
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for k, v in processed_keys.items():
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if not v and method == "q_norm":
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#print("key not processed:", k)
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pass
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return (m,)
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