Merge remote-tracking branch 'origin/master' into group-nodes

comfy/conds.py

@@ -62,3 +62,18 @@ class CONDCrossAttn(CONDRegular):
                 c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
             out.append(c)
         return torch.cat(out)
+
+class CONDConstant(CONDRegular):
+    def __init__(self, cond):
+        self.cond = cond
+
+    def process_cond(self, batch_size, device, **kwargs):
+        return self._copy_with(self.cond)
+
+    def can_concat(self, other):
+        if self.cond != other.cond:
+            return False
+        return True
+
+    def concat(self, others):
+        return self.cond

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -251,6 +251,12 @@ class Timestep(nn.Module):
     def forward(self, t):
         return timestep_embedding(t, self.dim)
 
+def apply_control(h, control, name):
+    if control is not None and name in control and len(control[name]) > 0:
+        ctrl = control[name].pop()
+        if ctrl is not None:
+            h += ctrl
+    return h
 
 class UNetModel(nn.Module):
     """
@@ -617,25 +623,17 @@ class UNetModel(nn.Module):
         for id, module in enumerate(self.input_blocks):
             transformer_options["block"] = ("input", id)
             h = forward_timestep_embed(module, h, emb, context, transformer_options)
-            if control is not None and 'input' in control and len(control['input']) > 0:
+            h = apply_control(h, control, 'input')
-                ctrl = control['input'].pop()
-                if ctrl is not None:
-                    h += ctrl
             hs.append(h)
+
         transformer_options["block"] = ("middle", 0)
         h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
-        if control is not None and 'middle' in control and len(control['middle']) > 0:
+        h = apply_control(h, control, 'middle')
-            ctrl = control['middle'].pop()
-            if ctrl is not None:
-                h += ctrl
 
         for id, module in enumerate(self.output_blocks):
             transformer_options["block"] = ("output", id)
             hsp = hs.pop()
-            if control is not None and 'output' in control and len(control['output']) > 0:
+            hsp = apply_control(hsp, control, 'output')
-                ctrl = control['output'].pop()
-                if ctrl is not None:
-                    hsp += ctrl
 
             if "output_block_patch" in transformer_patches:
                 patch = transformer_patches["output_block_patch"]

comfy/ldm/modules/diffusionmodules/util.py

@@ -170,8 +170,8 @@ def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
     if not repeat_only:
         half = dim // 2
         freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
-        ).to(device=timesteps.device)
+        )
         args = timesteps[:, None].float() * freqs[None]
         embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
         if dim % 2:

comfy/model_base.py

@@ -1,11 +1,9 @@
 import torch
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
-from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep
 import comfy.model_management
 import comfy.conds
-import numpy as np
 from enum import Enum
 from . import utils
 
@@ -14,79 +12,7 @@ class ModelType(Enum):
     V_PREDICTION = 2
 
 
-#NOTE: all this sampling stuff will be moved
+from comfy.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete
-class EPS:
-    def calculate_input(self, sigma, noise):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
-        return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input - model_output * sigma
-
-
-class V_PREDICTION(EPS):
-    def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
-        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
-
-
-class ModelSamplingDiscrete(torch.nn.Module):
-    def __init__(self, model_config=None):
-        super().__init__()
-        beta_schedule = "linear"
-        if model_config is not None:
-            beta_schedule = model_config.beta_schedule
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
-        self.sigma_data = 1.0
-
-    def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if given_betas is not None:
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
-        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-
-        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
-        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
-        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
-
-        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
-
-        self.register_buffer('sigmas', sigmas)
-        self.register_buffer('log_sigmas', sigmas.log())
-
-    @property
-    def sigma_min(self):
-        return self.sigmas[0]
-
-    @property
-    def sigma_max(self):
-        return self.sigmas[-1]
-
-    def timestep(self, sigma):
-        log_sigma = sigma.log()
-        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
-        return dists.abs().argmin(dim=0).view(sigma.shape)
-
-    def sigma(self, timestep):
-        t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1))
-        low_idx = t.floor().long()
-        high_idx = t.ceil().long()
-        w = t.frac()
-        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
-        return log_sigma.exp()
-
-    def percent_to_sigma(self, percent):
-        return self.sigma(torch.tensor(percent * 999.0))
 
 def model_sampling(model_config, model_type):
     if model_type == ModelType.EPS:
@@ -102,7 +28,6 @@ def model_sampling(model_config, model_type):
     return ModelSampling(model_config)
 
 
-
 class BaseModel(torch.nn.Module):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__()
@@ -136,7 +61,10 @@ class BaseModel(torch.nn.Module):
         context = context.to(dtype)
         extra_conds = {}
         for o in kwargs:
-            extra_conds[o] = kwargs[o].to(dtype)
+            extra = kwargs[o]
+            if hasattr(extra, "to"):
+                extra = extra.to(dtype)
+            extra_conds[o] = extra
         model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
         return self.model_sampling.calculate_denoised(sigma, model_output, x)
 

comfy/model_patcher.py

@@ -11,6 +11,8 @@ class ModelPatcher:
         self.model = model
         self.patches = {}
         self.backup = {}
+        self.object_patches = {}
+        self.object_patches_backup = {}
         self.model_options = {"transformer_options":{}}
         self.model_size()
         self.load_device = load_device
@@ -38,6 +40,7 @@ class ModelPatcher:
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
 
+        n.object_patches = self.object_patches.copy()
         n.model_options = copy.deepcopy(self.model_options)
         n.model_keys = self.model_keys
         return n
@@ -91,6 +94,9 @@ class ModelPatcher:
     def set_model_output_block_patch(self, patch):
         self.set_model_patch(patch, "output_block_patch")
 
+    def add_object_patch(self, name, obj):
+        self.object_patches[name] = obj
+
     def model_patches_to(self, device):
         to = self.model_options["transformer_options"]
         if "patches" in to:
@@ -107,10 +113,10 @@ class ModelPatcher:
                 for k in patch_list:
                     if hasattr(patch_list[k], "to"):
                         patch_list[k] = patch_list[k].to(device)
-        if "unet_wrapper_function" in self.model_options:
+        if "model_function_wrapper" in self.model_options:
-            wrap_func = self.model_options["unet_wrapper_function"]
+            wrap_func = self.model_options["model_function_wrapper"]
             if hasattr(wrap_func, "to"):
-                self.model_options["unet_wrapper_function"] = wrap_func.to(device)
+                self.model_options["model_function_wrapper"] = wrap_func.to(device)
 
     def model_dtype(self):
         if hasattr(self.model, "get_dtype"):
@@ -150,6 +156,12 @@ class ModelPatcher:
         return sd
 
     def patch_model(self, device_to=None):
+        for k in self.object_patches:
+            old = getattr(self.model, k)
+            if k not in self.object_patches_backup:
+                self.object_patches_backup[k] = old
+            setattr(self.model, k, self.object_patches[k])
+
         model_sd = self.model_state_dict()
         for key in self.patches:
             if key not in model_sd:
@@ -290,3 +302,9 @@ class ModelPatcher:
         if device_to is not None:
             self.model.to(device_to)
             self.current_device = device_to
+
+        keys = list(self.object_patches_backup.keys())
+        for k in keys:
+            setattr(self.model, k, self.object_patches_backup[k])
+
+        self.object_patches_backup = {}

comfy/model_sampling.py

@@ -0,0 +1,80 @@
+import torch
+import numpy as np
+from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
+
+
+class EPS:
+    def calculate_input(self, sigma, noise):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input - model_output * sigma
+
+
+class V_PREDICTION(EPS):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
+
+class ModelSamplingDiscrete(torch.nn.Module):
+    def __init__(self, model_config=None):
+        super().__init__()
+        beta_schedule = "linear"
+        if model_config is not None:
+            beta_schedule = model_config.beta_schedule
+        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
+        self.sigma_data = 1.0
+
+    def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if given_betas is not None:
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
+        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+
+        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
+        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
+        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
+
+        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        self.set_sigmas(sigmas)
+
+    def set_sigmas(self, sigmas):
+        self.register_buffer('sigmas', sigmas)
+        self.register_buffer('log_sigmas', sigmas.log())
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        log_sigma = sigma.log()
+        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
+        return dists.abs().argmin(dim=0).view(sigma.shape)
+
+    def sigma(self, timestep):
+        t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1))
+        low_idx = t.floor().long()
+        high_idx = t.ceil().long()
+        w = t.frac()
+        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
+        return log_sigma.exp()
+
+    def percent_to_sigma(self, percent):
+        return self.sigma(torch.tensor(percent * 999.0))
+

comfy/sd.py

@@ -496,6 +496,9 @@ def load_unet(unet_path): #load unet in diffusers format
     model = model_config.get_model(new_sd, "")
     model = model.to(offload_device)
     model.load_model_weights(new_sd, "")
+    left_over = sd.keys()
+    if len(left_over) > 0:
+        print("left over keys in unet:", left_over)
     return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device)
 
 def save_checkpoint(output_path, model, clip, vae, metadata=None):

comfy/sd1_clip.py

@@ -8,32 +8,54 @@ import zipfile
 from . import model_management
 import contextlib
 
+def gen_empty_tokens(special_tokens, length):
+    start_token = special_tokens.get("start", None)
+    end_token = special_tokens.get("end", None)
+    pad_token = special_tokens.get("pad")
+    output = []
+    if start_token is not None:
+        output.append(start_token)
+    if end_token is not None:
+        output.append(end_token)
+    output += [pad_token] * (length - len(output))
+    return output
+
 class ClipTokenWeightEncoder:
     def encode_token_weights(self, token_weight_pairs):
-        to_encode = list(self.empty_tokens)
+        to_encode = list()
+        max_token_len = 0
+        has_weights = False
         for x in token_weight_pairs:
             tokens = list(map(lambda a: a[0], x))
+            max_token_len = max(len(tokens), max_token_len)
+            has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
             to_encode.append(tokens)
 
+        sections = len(to_encode)
+        if has_weights or sections == 0:
+            to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
+
         out, pooled = self.encode(to_encode)
-        z_empty = out[0:1]
+        if pooled is not None:
-        if pooled.shape[0] > 1:
+            first_pooled = pooled[0:1].cpu()
-            first_pooled = pooled[1:2]
         else:
-            first_pooled = pooled[0:1]
+            first_pooled = pooled
 
         output = []
-        for k in range(1, out.shape[0]):
+        for k in range(0, sections):
             z = out[k:k+1]
+            if has_weights:
+                z_empty = out[-1]
                 for i in range(len(z)):
                     for j in range(len(z[i])):
-                    weight = token_weight_pairs[k - 1][j][1]
+                        weight = token_weight_pairs[k][j][1]
-                    z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
+                        if weight != 1.0:
+                            z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
             output.append(z)
 
         if (len(output) == 0):
-            return z_empty.cpu(), first_pooled.cpu()
+            return out[-1:].cpu(), first_pooled
-        return torch.cat(output, dim=-2).cpu(), first_pooled.cpu()
+        return torch.cat(output, dim=-2).cpu(), first_pooled
 
 class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
@@ -43,37 +65,43 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         "hidden"
     ]
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
-                 freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None, dtype=None):  # clip-vit-base-patch32
+                 freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None, dtype=None,
+                 special_tokens={"start": 49406, "end": 49407, "pad": 49407},layer_norm_hidden_state=True, config_class=CLIPTextConfig,
+                 model_class=CLIPTextModel, inner_name="text_model"):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
         self.num_layers = 12
         if textmodel_path is not None:
-            self.transformer = CLIPTextModel.from_pretrained(textmodel_path)
+            self.transformer = model_class.from_pretrained(textmodel_path)
         else:
             if textmodel_json_config is None:
                 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
-            config = CLIPTextConfig.from_json_file(textmodel_json_config)
+            config = config_class.from_json_file(textmodel_json_config)
             self.num_layers = config.num_hidden_layers
             with comfy.ops.use_comfy_ops(device, dtype):
                 with modeling_utils.no_init_weights():
-                    self.transformer = CLIPTextModel(config)
+                    self.transformer = model_class(config)
 
+        self.inner_name = inner_name
         if dtype is not None:
             self.transformer.to(dtype)
-            self.transformer.text_model.embeddings.token_embedding.to(torch.float32)
+            inner_model = getattr(self.transformer, self.inner_name)
-            self.transformer.text_model.embeddings.position_embedding.to(torch.float32)
+            if hasattr(inner_model, "embeddings"):
+                inner_model.embeddings.to(torch.float32)
+            else:
+                self.transformer.set_input_embeddings(self.transformer.get_input_embeddings().to(torch.float32))
 
         self.max_length = max_length
         if freeze:
             self.freeze()
         self.layer = layer
         self.layer_idx = None
-        self.empty_tokens = [[49406] + [49407] * 76]
+        self.special_tokens = special_tokens
         self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
         self.enable_attention_masks = False
 
-        self.layer_norm_hidden_state = True
+        self.layer_norm_hidden_state = layer_norm_hidden_state
         if layer == "hidden":
             assert layer_idx is not None
             assert abs(layer_idx) <= self.num_layers
@@ -117,7 +145,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
                     else:
                         print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1])
             while len(tokens_temp) < len(x):
-                tokens_temp += [self.empty_tokens[0][-1]]
+                tokens_temp += [self.special_tokens["pad"]]
             out_tokens += [tokens_temp]
 
         n = token_dict_size
@@ -142,7 +170,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
         tokens = torch.LongTensor(tokens).to(device)
 
-        if self.transformer.text_model.final_layer_norm.weight.dtype != torch.float32:
+        if getattr(self.transformer, self.inner_name).final_layer_norm.weight.dtype != torch.float32:
             precision_scope = torch.autocast
         else:
             precision_scope = lambda a, b: contextlib.nullcontext(a)
@@ -168,12 +196,16 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             else:
                 z = outputs.hidden_states[self.layer_idx]
                 if self.layer_norm_hidden_state:
-                    z = self.transformer.text_model.final_layer_norm(z)
+                    z = getattr(self.transformer, self.inner_name).final_layer_norm(z)
 
-            pooled_output = outputs.pooler_output
+            if hasattr(outputs, "pooler_output"):
-            if self.text_projection is not None:
+                pooled_output = outputs.pooler_output.float()
+            else:
+                pooled_output = None
+
+            if self.text_projection is not None and pooled_output is not None:
                 pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
-        return z.float(), pooled_output.float()
+        return z.float(), pooled_output
 
     def encode(self, tokens):
         return self(tokens)
@@ -343,17 +375,24 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l'):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
-        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
         self.max_length = max_length
-        self.max_tokens_per_section = self.max_length - 2
 
         empty = self.tokenizer('')["input_ids"]
+        if has_start_token:
+            self.tokens_start = 1
             self.start_token = empty[0]
             self.end_token = empty[1]
+        else:
+            self.tokens_start = 0
+            self.start_token = None
+            self.end_token = empty[0]
         self.pad_with_end = pad_with_end
+        self.pad_to_max_length = pad_to_max_length
+
         vocab = self.tokenizer.get_vocab()
         self.inv_vocab = {v: k for k, v in vocab.items()}
         self.embedding_directory = embedding_directory
@@ -414,11 +453,13 @@ class SDTokenizer:
                     else:
                         continue
                 #parse word
-                tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][1:-1]])
+                tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]])
 
         #reshape token array to CLIP input size
         batched_tokens = []
-        batch = [(self.start_token, 1.0, 0)]
+        batch = []
+        if self.start_token is not None:
+            batch.append((self.start_token, 1.0, 0))
         batched_tokens.append(batch)
         for i, t_group in enumerate(tokens):
             #determine if we're going to try and keep the tokens in a single batch
@@ -435,16 +476,21 @@ class SDTokenizer:
                     #add end token and pad
                     else:
                         batch.append((self.end_token, 1.0, 0))
+                        if self.pad_to_max_length:
                             batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
                     #start new batch
-                    batch = [(self.start_token, 1.0, 0)]
+                    batch = []
+                    if self.start_token is not None:
+                        batch.append((self.start_token, 1.0, 0))
                     batched_tokens.append(batch)
                 else:
                     batch.extend([(t,w,i+1) for t,w in t_group])
                     t_group = []
 
         #fill last batch
-        batch.extend([(self.end_token, 1.0, 0)] + [(pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1))
+        batch.append((self.end_token, 1.0, 0))
+        if self.pad_to_max_length:
+            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]

comfy/sd2_clip.py

@@ -9,8 +9,7 @@ class SD2ClipHModel(sd1_clip.SDClipModel):
             layer_idx=23
 
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype)
+        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0})
-        self.empty_tokens = [[49406] + [49407] + [0] * 75]
 
 class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None):

comfy/sdxl_clip.py

@@ -9,9 +9,8 @@ class SDXLClipG(sd1_clip.SDClipModel):
             layer_idx=-2
 
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
-        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype)
+        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype,
-        self.empty_tokens = [[49406] + [49407] + [0] * 75]
+                         special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False)
-        self.layer_norm_hidden_state = False
 
     def load_sd(self, sd):
         return super().load_sd(sd)
@@ -38,8 +37,7 @@ class SDXLTokenizer:
 class SDXLClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):
         super().__init__()
-        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype)
+        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype, layer_norm_hidden_state=False)
-        self.clip_l.layer_norm_hidden_state = False
         self.clip_g = SDXLClipG(device=device, dtype=dtype)
 
     def clip_layer(self, layer_idx):

comfy_extras/nodes_model_advanced.py

@@ -0,0 +1,57 @@
+import folder_paths
+import comfy.sd
+import comfy.model_sampling
+
+
+def rescale_zero_terminal_snr_sigmas(sigmas):
+    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas_bar[-1] = 4.8973451890853435e-08
+    return ((1 - alphas_bar) / alphas_bar) ** 0.5
+
+class ModelSamplingDiscrete:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "sampling": (["eps", "v_prediction"],),
+                              "zsnr": ("BOOLEAN", {"default": False}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, sampling, zsnr):
+        m = model.clone()
+
+        if sampling == "eps":
+            sampling_type = comfy.model_sampling.EPS
+        elif sampling == "v_prediction":
+            sampling_type = comfy.model_sampling.V_PREDICTION
+
+        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced()
+        if zsnr:
+            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "ModelSamplingDiscrete": ModelSamplingDiscrete,
+}

comfy_extras/nodes_post_processing.py

@@ -23,7 +23,7 @@ class Blend:
                     "max": 1.0,
                     "step": 0.01
                 }),
-                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light"],),
+                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
             },
         }
 
@@ -54,6 +54,8 @@ class Blend:
             return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
         elif mode == "soft_light":
             return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+        elif mode == "difference":
+            return img1 - img2
         else:
             raise ValueError(f"Unsupported blend mode: {mode}")
 

nodes.py

@@ -1798,6 +1798,7 @@ def init_custom_nodes():
         "nodes_freelunch.py",
         "nodes_custom_sampler.py",
         "nodes_hypertile.py",
+        "nodes_model_advanced.py",
     ]
 
     for node_file in extras_files:

web/scripts/app.js

@@ -5,6 +5,22 @@ import { api } from "./api.js";
 import { defaultGraph } from "./defaultGraph.js";
 import { getPngMetadata, getWebpMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
 
+
+function sanitizeNodeName(string) {
+	let entityMap = {
+	'&': '',
+	'<': '',
+	'>': '',
+	'"': '',
+	"'": '',
+	'`': '',
+	'=': ''
+	};
+	return String(string).replace(/[&<>"'`=\/]/g, function fromEntityMap (s) {
+		return entityMap[s];
+	});
+}
+
 /**
  * @typedef {import("types/comfy").ComfyExtension} ComfyExtension
  */
@@ -1495,6 +1511,7 @@ export class ComfyApp {
 
 			// Find missing node types
 			if (!(n.type in LiteGraph.registered_node_types)) {
+				n.type = sanitizeNodeName(n.type);
 				missingNodeTypes.push(n.type);
 			}
 		}