import torch import math import types EPSILON = 1e-4 SD_layer_dims = { "SD1" : {"input_1": 4096,"input_2": 4096,"input_4": 1024,"input_5": 1024,"input_7": 256,"input_8": 256,"middle_0": 64,"output_3": 256,"output_4": 256,"output_5": 256,"output_6": 1024,"output_7": 1024,"output_8": 1024,"output_9": 4096,"output_10": 4096,"output_11": 4096}, "SDXL": {"input_4": 4096,"input_5": 4096,"input_7": 1024,"input_8": 1024,"middle_0": 1024,"output_0": 1024,"output_1": 1024,"output_2": 1024,"output_3": 4096,"output_4": 4096,"output_5": 4096}, } 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], } revert_dim = lambda x: 8 * math.sqrt(x) printed_var = "" def cprint(var): global printed_var str_var = str(var) if printed_var != str_var: print(" ",str_var) printed_var = str_var def dynamic_scale_attention(layer_name, model_name, q_size_1, q_size_2, **kwargs): return 1 / (math.sqrt(q_size_1) * (SD_layer_dims[model_name][layer_name] ** 0.5 / q_size_2 ** 0.5) ** 0.5) def temp_non_zero_div(layer_name, model_name, q_size_1, q_size_2, **kwargs): return 1 / (math.sqrt(q_size_1) * EPSILON) auto_temp_methods = {"dsa": dynamic_scale_attention, "clip":temp_non_zero_div} class temperature_patcher(): def __init__(self, temperature, layer_name = "", model_name="", eval_string = "", method="medium", base_resolution=(512,512), target_resolution=(512,512)): self.temperature = temperature self.layer_name = layer_name self.model_name = model_name self.eval_string = eval_string self.method = auto_temp_methods[method] self.base_resolution = base_resolution self.target_resolution = target_resolution def pytorch_attention_with_temperature(self, q, k, v, extra_options, mask=None, attn_precision=None): heads = extra_options if isinstance(extra_options, int) else extra_options['n_heads'] b, _, dim_head = q.shape dim_head //= heads q, k, v = map( lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), (q, k, v), ) if self.eval_string != "": if self.layer_name != "": layer_dim = SD_layer_dims[self.model_name][self.layer_name] q_size_1 = q.size(-1) q_size_2 = q.size(-2) c = [] evals_strings = self.eval_string.split(";") if len(evals_strings) > 1: for i in range(len(evals_strings[:-1])): c.append(eval(evals_strings[i])) scale = eval(evals_strings[-1]) else: scale = 1 / (math.sqrt(q.size(-1)) * self.temperature) if self.temperature > 0 else \ self.method(layer_name=self.layer_name, model_name=self.model_name, q_size_1=q.size(-1), q_size_2=q.size(-2), base_resolution=self.base_resolution,target_resolution=self.target_resolution) out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False,scale=scale) out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) return out class UnetTemperaturePatch: @classmethod def INPUT_TYPES(s): if not s.ANY_MODEL: required_inputs = {f"{key}_{layer}": ("BOOLEAN", {"default": True}) for key, layers in s.TOGGLES.items() for layer in layers} else: required_inputs = {} 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"],) # required_inputs["Method"] = (["strong","medium","light"],) # required_inputs["eval_string"] = ("STRING", {"multiline": True}) return {"required": required_inputs} ANY_MODEL = False LAYER_NAME = None TOGGLES = {} RETURN_TYPES = ("MODEL","STRING",) RETURN_NAMES = ("Model","String",) FUNCTION = "patch" CATEGORY = "model_patches/Temperature" def patch(self, model, Temperature, Attention, Method="dsa", eval_string="", **kwargs): model_name = self.__class__.MODEL_NAME any_model = self.__class__.ANY_MODEL if not any_model: layer_names = kwargs else: layer_names = {f"{l}_{n}": True for l in ["input", "middle", "output"] for n in range(12)} m = model.clone() levels = ["input","middle","output"] parameters_output = {level:[] for level in levels} for key, toggle_enabled in layer_names.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,method=Method if model_name in ["SDXL","SD1"] else "clip",layer_name=key,model_name=model_name,eval_string=eval_string) if Attention in ["both","self"]: m.set_model_attn1_replace(patcher.pytorch_attention_with_temperature, current_level, b_number) if Attention in ["both","cross"]: m.set_model_attn2_replace(patcher.pytorch_attention_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,) 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/Temperature" def patch(self, clip, Temperature): print(f"\n\n\nThe CLIP patch ignores the connection. Set at 1 to get default behavior. Or reload the model without this node.\n\n\n") def custom_optimized_attention(device, mask=None, small_input=True): return temperature_patcher(Temperature,method="clip").pytorch_attention_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 clip_encoder_instance = clip.cond_stage_model.clip_l.transformer.text_model.encoder clip_encoder_instance.forward = types.MethodType(new_forward, clip_encoder_instance) if getattr(clip.cond_stage_model, f"clip_g", None) is not None: clip_encoder_instance_g = clip.cond_stage_model.clip_g.transformer.text_model.encoder clip_encoder_instance_g.forward = types.MethodType(new_forward, clip_encoder_instance_g) return (clip,) UnetTemperaturePatchSDXL = type("Unet Temperature SDXL", (UnetTemperaturePatch,), {"TOGGLES": layers_SDXL,"MODEL_NAME":"SDXL","ANY_MODEL": True}) UnetTemperaturePatchSD15 = type("Unet Temperature SD1", (UnetTemperaturePatch,), {"TOGGLES": layers_SD15,"MODEL_NAME":"SD1", "ANY_MODEL": True,}) UnetTemperaturePatchSDXLpl = type("Unet Temperature SDXL per layer", (UnetTemperaturePatch,), {"TOGGLES": layers_SDXL,"MODEL_NAME":"SDXL","ANY_MODEL": False}) UnetTemperaturePatchSD15pl = type("Unet Temperature SD1 per layer", (UnetTemperaturePatch,), {"TOGGLES": layers_SD15,"MODEL_NAME":"SD1", "ANY_MODEL": False}) UnetTemperaturePatchAny = type("Unet Temperature any model", (UnetTemperaturePatch,), {"MODEL_NAME":"SD1","ANY_MODEL": True})