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import torch
from torch import nn, einsum
from einops import rearrange, repeat
import torch.nn.functional as F
import math
from comfy import model_management
import types
import os
def exists(val):
return val is not None
# better than a division by 0 hey
abs_mean = lambda x: torch.where(torch.isnan(x) | torch.isinf(x), torch.zeros_like(x), x).abs().mean()
class temperature_patcher():
def __init__(self, temperature, layer_name="None"):
self.temperature = temperature
self.layer_name = layer_name
# taken from comfy.ldm.modules
def attention_basic_with_temperature(self, q, k, v, extra_options, mask=None, attn_precision=None):
if isinstance(extra_options, int):
heads = extra_options
else:
heads = extra_options['n_heads']
b, _, dim_head = q.shape
dim_head //= heads
scale = dim_head ** -0.5
h = heads
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, -1, heads, dim_head)
.permute(0, 2, 1, 3)
.reshape(b * heads, -1, dim_head)
.contiguous(),
(q, k, v),
)
# force cast to fp32 to avoid overflowing
if attn_precision == torch.float32:
sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * scale
del q, k
if exists(mask):
if mask.dtype == torch.bool:
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
else:
if len(mask.shape) == 2:
bs = 1
else:
bs = mask.shape[0]
mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
sim.add_(mask)
# attention, what we cannot get enough of
sim = sim.div(self.temperature if self.temperature > 0 else abs_mean(sim)).softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
out = (
out.unsqueeze(0)
.reshape(b, heads, -1, dim_head)
.permute(0, 2, 1, 3)
.reshape(b, -1, heads * dim_head)
)
return out
layers_SD15 = {
"input":[1,2,4,5,7,8],
"middle":[0],
"output":[3,4,5,6,7,8,9,10,11],
}
layers_SDXL = {
"input":[4,5,7,8],
"middle":[0],
"output":[0,1,2,3,4,5],
}
class ExperimentalTemperaturePatch:
@classmethod
def INPUT_TYPES(s):
required_inputs = {f"{key}_{layer}": ("BOOLEAN", {"default": False}) for key, layers in s.TOGGLES.items() for layer in layers}
required_inputs["model"] = ("MODEL",)
required_inputs["Temperature"] = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": 0.01})
required_inputs["Attention"] = (["both","self","cross"],)
return {"required": required_inputs}
TOGGLES = {}
RETURN_TYPES = ("MODEL","STRING",)
RETURN_NAMES = ("Model","String",)
FUNCTION = "patch"
CATEGORY = "model_patches/Automatic_CFG/Standalone_temperature_patches"
def patch(self, model, Temperature, Attention, **kwargs):
m = model.clone()
levels = ["input","middle","output"]
parameters_output = {level:[] for level in levels}
for key, toggle_enabled in kwargs.items():
current_level = key.split("_")[0]
if current_level in levels and toggle_enabled:
b_number = int(key.split("_")[1])
parameters_output[current_level].append(b_number)
patcher = temperature_patcher(Temperature,key)
if Attention in ["both","self"]:
m.set_model_attn1_replace(patcher.attention_basic_with_temperature, current_level, b_number)
if Attention in ["both","cross"]:
m.set_model_attn2_replace(patcher.attention_basic_with_temperature, current_level, b_number)
parameters_as_string = "\n".join(f"{k}: {','.join(map(str, v))}" for k, v in parameters_output.items())
parameters_as_string = f"Temperature: {Temperature}\n{parameters_as_string}\nAttention: {Attention}"
return (m, parameters_as_string,)
ExperimentalTemperaturePatchSDXL = type("ExperimentalTemperaturePatch_SDXL", (ExperimentalTemperaturePatch,), {"TOGGLES": layers_SDXL})
ExperimentalTemperaturePatchSD15 = type("ExperimentalTemperaturePatch_SD15", (ExperimentalTemperaturePatch,), {"TOGGLES": layers_SD15})
class CLIPTemperaturePatch:
@classmethod
def INPUT_TYPES(cls):
return {"required": { "clip": ("CLIP",),
"Temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "patch"
CATEGORY = "model_patches/Automatic_CFG/Standalone_temperature_patches"
def patch(self, clip, Temperature):
def custom_optimized_attention(device, mask=None, small_input=True):
return temperature_patcher(Temperature).attention_basic_with_temperature
def new_forward(self, x, mask=None, intermediate_output=None):
optimized_attention = custom_optimized_attention(x.device, mask=mask is not None, small_input=True)
if intermediate_output is not None:
if intermediate_output < 0:
intermediate_output = len(self.layers) + intermediate_output
intermediate = None
for i, l in enumerate(self.layers):
x = l(x, mask, optimized_attention)
if i == intermediate_output:
intermediate = x.clone()
return x, intermediate
m = clip.clone()
clip_encoder_instance = m.cond_stage_model.clip_l.transformer.text_model.encoder
clip_encoder_instance.forward = types.MethodType(new_forward, clip_encoder_instance)
if getattr(m.cond_stage_model, f"clip_g", None) is not None:
clip_encoder_instance_g = m.cond_stage_model.clip_g.transformer.text_model.encoder
clip_encoder_instance_g.forward = types.MethodType(new_forward, clip_encoder_instance_g)
return (m,)
class CLIPTemperaturePatchDual:
@classmethod
def INPUT_TYPES(cls):
return {"required": { "clip": ("CLIP",),
"Temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"CLIP_Model": (["clip_g","clip_l","both"],),
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "patch"
CATEGORY = "model_patches/Automatic_CFG/Standalone_temperature_patches"
def patch(self, clip, Temperature, CLIP_Model):
def custom_optimized_attention(device, mask=None, small_input=True):
return temperature_patcher(Temperature, "CLIP").attention_basic_with_temperature
def new_forward(self, x, mask=None, intermediate_output=None):
optimized_attention = custom_optimized_attention(x.device, mask=mask is not None, small_input=True)
if intermediate_output is not None:
if intermediate_output < 0:
intermediate_output = len(self.layers) + intermediate_output
intermediate = None
for i, l in enumerate(self.layers):
x = l(x, mask, optimized_attention)
if i == intermediate_output:
intermediate = x.clone()
return x, intermediate
m = clip.clone()
if CLIP_Model in ["clip_l","both"]:
clip_encoder_instance = m.cond_stage_model.clip_l.transformer.text_model.encoder
clip_encoder_instance.forward = types.MethodType(new_forward, clip_encoder_instance)
if CLIP_Model in ["clip_g","both"]:
if getattr(m.cond_stage_model, f"clip_g", None) is not None:
clip_encoder_instance_g = m.cond_stage_model.clip_g.transformer.text_model.encoder
clip_encoder_instance_g.forward = types.MethodType(new_forward, clip_encoder_instance_g)
return (m,)
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