Merge remote-tracking branch 'origin/master' into multigpu_support

CODEOWNERS

@@ -15,6 +15,7 @@
 # Python web server
 /api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 /app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
+/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 
 # Frontend assets
 /web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang

README.md

@@ -154,9 +154,9 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2```
 
-This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
 
 ### Intel GPUs (Windows and Linux)
 

app/custom_node_manager.py

@@ -4,12 +4,93 @@ import os
 import folder_paths
 import glob
 from aiohttp import web
+import json
+import logging
+from functools import lru_cache
+
+from utils.json_util import merge_json_recursive
+
+
+# Extra locale files to load into main.json
+EXTRA_LOCALE_FILES = [
+    "nodeDefs.json",
+    "commands.json",
+    "settings.json",
+]
+
+
+def safe_load_json_file(file_path: str) -> dict:
+    if not os.path.exists(file_path):
+        return {}
+
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except json.JSONDecodeError:
+        logging.error(f"Error loading {file_path}")
+        return {}
+
 
 class CustomNodeManager:
-    """
-    Placeholder to refactor the custom node management features from ComfyUI-Manager.
-    Currently it only contains the custom workflow templates feature.
-    """
+    @lru_cache(maxsize=1)
+    def build_translations(self):
+        """Load all custom nodes translations during initialization. Translations are
+        expected to be loaded from `locales/` folder.
+
+        The folder structure is expected to be the following:
+        - custom_nodes/
+            - custom_node_1/
+                - locales/
+                    - en/
+                        - main.json
+                        - commands.json
+                        - settings.json
+
+        returned translations are expected to be in the following format:
+        {
+            "en": {
+                "nodeDefs": {...},
+                "commands": {...},
+                "settings": {...},
+                ...{other main.json keys}
+            }
+        }
+        """
+
+        translations = {}
+
+        for folder in folder_paths.get_folder_paths("custom_nodes"):
+            # Sort glob results for deterministic ordering
+            for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
+                locales_dir = os.path.join(custom_node_dir, "locales")
+                if not os.path.exists(locales_dir):
+                    continue
+
+                for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
+                    lang_code = os.path.basename(os.path.dirname(lang_dir))
+
+                    if lang_code not in translations:
+                        translations[lang_code] = {}
+
+                    # Load main.json
+                    main_file = os.path.join(lang_dir, "main.json")
+                    node_translations = safe_load_json_file(main_file)
+
+                    # Load extra locale files
+                    for extra_file in EXTRA_LOCALE_FILES:
+                        extra_file_path = os.path.join(lang_dir, extra_file)
+                        key = extra_file.split(".")[0]
+                        json_data = safe_load_json_file(extra_file_path)
+                        if json_data:
+                            node_translations[key] = json_data
+
+                    if node_translations:
+                        translations[lang_code] = merge_json_recursive(
+                            translations[lang_code], node_translations
+                        )
+
+        return translations
+
     def add_routes(self, routes, webapp, loadedModules):
 
         @routes.get("/workflow_templates")
@@ -18,17 +99,36 @@ class CustomNodeManager:
             files = [
                 file
                 for folder in folder_paths.get_folder_paths("custom_nodes")
-                for file in glob.glob(os.path.join(folder, '*/example_workflows/*.json'))
+                for file in glob.glob(
+                    os.path.join(folder, "*/example_workflows/*.json")
+                )
             ]
-            workflow_templates_dict = {} # custom_nodes folder name -> example workflow names
+            workflow_templates_dict = (
+                {}
+            )  # custom_nodes folder name -> example workflow names
             for file in files:
-                custom_nodes_name = os.path.basename(os.path.dirname(os.path.dirname(file)))
+                custom_nodes_name = os.path.basename(
+                    os.path.dirname(os.path.dirname(file))
+                )
                 workflow_name = os.path.splitext(os.path.basename(file))[0]
-                workflow_templates_dict.setdefault(custom_nodes_name, []).append(workflow_name)
+                workflow_templates_dict.setdefault(custom_nodes_name, []).append(
+                    workflow_name
+                )
             return web.json_response(workflow_templates_dict)
 
         # Serve workflow templates from custom nodes.
         for module_name, module_dir in loadedModules:
-            workflows_dir = os.path.join(module_dir, 'example_workflows')
+            workflows_dir = os.path.join(module_dir, "example_workflows")
             if os.path.exists(workflows_dir):
-                webapp.add_routes([web.static('/api/workflow_templates/' + module_name, workflows_dir)])
+                webapp.add_routes(
+                    [
+                        web.static(
+                            "/api/workflow_templates/" + module_name, workflows_dir
+                        )
+                    ]
+                )
+
+        @routes.get("/i18n")
+        async def get_i18n(request):
+            """Returns translations from all custom nodes' locales folders."""
+            return web.json_response(self.build_translations())

comfy/conds.py

@@ -3,9 +3,6 @@ import math
 import comfy.utils
 
 
-def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
-    return abs(a*b) // math.gcd(a, b)
-
 class CONDRegular:
     def __init__(self, cond):
         self.cond = cond
@@ -46,7 +43,7 @@ class CONDCrossAttn(CONDRegular):
             if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
                 return False
 
-            mult_min = lcm(s1[1], s2[1])
+            mult_min = math.lcm(s1[1], s2[1])
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
@@ -57,7 +54,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

comfy/diffusers_convert.py

@@ -4,105 +4,6 @@ import logging
 
 # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
 
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i < 3:
-            # no attention layers in down_blocks.3
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(3):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i > 0:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2 * j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
-    return new_state_dict
-
-
 # ================#
 # VAE Conversion #
 # ================#
@@ -213,6 +114,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
 code2idx = {"q": 0, "k": 1, "v": 2}
 
+
 # This function exists because at the time of writing torch.cat can't do fp8 with cuda
 def cat_tensors(tensors):
     x = 0
@@ -229,6 +131,7 @@ def cat_tensors(tensors):
 
     return out
 
+
 def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
     capture_qkv_weight = {}
@@ -284,5 +187,3 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
 
 def convert_text_enc_state_dict(text_enc_dict):
     return text_enc_dict
-
-

comfy/k_diffusion/sampling.py

@@ -1336,3 +1336,26 @@ def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disab
 @torch.no_grad()
 def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
     return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)
+
+@torch.no_grad()
+def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
+    """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    old_d = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        d = to_d(x, sigmas[i], denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        dt = sigmas[i + 1] - sigmas[i]
+        if i == 0:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Gradient estimation
+            d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
+            x = x + d_bar * dt
+        old_d = d
+    return x

comfy/ldm/flux/model.py

@@ -109,9 +109,8 @@ class Flux(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
         if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+            if guidance is not None:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
@@ -186,7 +185,7 @@ class Flux(nn.Module):
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))

comfy/ldm/hunyuan_video/model.py

@@ -240,9 +240,8 @@ class HunyuanVideo(nn.Module):
         vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
 
         if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+            if guidance is not None:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
@@ -314,7 +313,7 @@ class HunyuanVideo(nn.Module):
         img = img.reshape(initial_shape)
         return img
 
-    def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])

comfy/ldm/modules/diffusionmodules/model.py

@@ -702,9 +702,6 @@ class Decoder(nn.Module):
                                         padding=1)
 
     def forward(self, z, **kwargs):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
         # timestep embedding
         temb = None
 

comfy/model_base.py

@@ -148,7 +148,9 @@ class BaseModel(torch.nn.Module):
 
         xc = xc.to(dtype)
         t = self.model_sampling.timestep(t).float()
-        context = context.to(dtype)
+        if context is not None:
+            context = context.to(dtype)
+
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
@@ -549,6 +551,10 @@ class SD_X4Upscaler(BaseModel):
 
         out['c_concat'] = comfy.conds.CONDNoiseShape(image)
         out['y'] = comfy.conds.CONDRegular(noise_level)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
         return out
 
 class IP2P:
@@ -806,7 +812,10 @@ class Flux(BaseModel):
             (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
             attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
             out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+
+        guidance = kwargs.get("guidance", 3.5)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class GenmoMochi(BaseModel):
@@ -863,7 +872,10 @@ class HunyuanVideo(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)]))
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class CosmosVideo(BaseModel):

comfy/model_management.py

@@ -237,7 +237,7 @@ def is_amd():
 
 MIN_WEIGHT_MEMORY_RATIO = 0.4
 if is_nvidia():
-    MIN_WEIGHT_MEMORY_RATIO = 0.2
+    MIN_WEIGHT_MEMORY_RATIO = 0.1
 
 ENABLE_PYTORCH_ATTENTION = False
 if args.use_pytorch_cross_attention:
@@ -554,14 +554,11 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             vram_set_state = vram_state
         lowvram_model_memory = 0
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load:
-            model_size = loaded_model.model_memory_required(torch_dev)
             loaded_memory = loaded_model.model_loaded_memory()
             current_free_mem = get_free_memory(torch_dev) + loaded_memory
 
             lowvram_model_memory = max(64 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
             lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory)
-            if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
-                lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
             lowvram_model_memory = 0.1

comfy/sampler_helpers.py

@@ -60,7 +60,6 @@ def convert_cond(cond):
         temp = c[1].copy()
         model_conds = temp.get("model_conds", {})
         if c[0] is not None:
-            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
             temp["cross_attn"] = c[0]
         temp["model_conds"] = model_conds
         temp["uuid"] = uuid.uuid4()

comfy/samplers.py

@@ -879,7 +879,7 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
-                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "gradient_estimation"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy_extras/nodes_flux.py

@@ -38,7 +38,26 @@ class FluxGuidance:
         return (c, )
 
 
+class FluxDisableGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning": ("CONDITIONING", ),
+            }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "advanced/conditioning/flux"
+    DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
+
+    def append(self, conditioning):
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
+        return (c, )
+
+
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
     "FluxGuidance": FluxGuidance,
+    "FluxDisableGuidance": FluxDisableGuidance,
 }

comfy_extras/nodes_latent.py

@@ -2,10 +2,14 @@ import comfy.utils
 import comfy_extras.nodes_post_processing
 import torch
 
-def reshape_latent_to(target_shape, latent):
+
+def reshape_latent_to(target_shape, latent, repeat_batch=True):
     if latent.shape[1:] != target_shape[1:]:
-        latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
-    return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+        latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
+    if repeat_batch:
+        return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+    else:
+        return latent
 
 
 class LatentAdd:
@@ -116,8 +120,7 @@ class LatentBatch:
         s1 = samples1["samples"]
         s2 = samples2["samples"]
 
-        if s1.shape[1:] != s2.shape[1:]:
-            s2 = comfy.utils.common_upscale(s2, s1.shape[-1], s1.shape[-2], "bilinear", "center")
+        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
         s = torch.cat((s1, s2), dim=0)
         samples_out["samples"] = s
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])

comfy_extras/nodes_load_3d.py

@@ -19,9 +19,6 @@ class Load3D():
             "image": ("LOAD_3D", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
             "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
@@ -69,9 +66,6 @@ class Load3DAnimation():
             "image": ("LOAD_3D_ANIMATION", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
             "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
@@ -109,9 +103,6 @@ class Preview3D():
     def INPUT_TYPES(s):
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
             "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),

folder_paths.py

@@ -39,10 +39,10 @@ folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")]
 
 folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
 
-output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
-temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
-input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
-user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user")
+output_directory = os.path.join(base_path, "output")
+temp_directory = os.path.join(base_path, "temp")
+input_directory = os.path.join(base_path, "input")
+user_directory = os.path.join(base_path, "user")
 
 filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 

main.py

@@ -138,6 +138,8 @@ import server
 from server import BinaryEventTypes
 import nodes
 import comfy.model_management
+import comfyui_version
+
 
 def cuda_malloc_warning():
     device = comfy.model_management.get_torch_device()
@@ -292,6 +294,7 @@ def start_comfyui(asyncio_loop=None):
 
 if __name__ == "__main__":
     # Running directly, just start ComfyUI.
+    logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
     event_loop, _, start_all_func = start_comfyui()
     try:
         event_loop.run_until_complete(start_all_func())

nodes.py

@@ -63,6 +63,8 @@ class CLIPTextEncode(ComfyNodeABC):
     DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
 
     def encode(self, clip, text):
+        if clip is None:
+            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
         tokens = clip.tokenize(text)
         return (clip.encode_from_tokens_scheduled(tokens), )
 
@@ -937,6 +939,8 @@ class CLIPLoader:
             clip_type = comfy.sd.CLIPType.LTXV
         elif type == "pixart":
             clip_type = comfy.sd.CLIPType.PIXART
+        elif type == "cosmos":
+            clip_type = comfy.sd.CLIPType.COSMOS
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 

tests-unit/app_test/custom_node_manager_test.py

@@ -2,39 +2,146 @@ import pytest
 from aiohttp import web
 from unittest.mock import patch
 from app.custom_node_manager import CustomNodeManager
+import json
 
 pytestmark = (
     pytest.mark.asyncio
 )  # This applies the asyncio mark to all test functions in the module
 
+
 @pytest.fixture
 def custom_node_manager():
     return CustomNodeManager()
 
+
 @pytest.fixture
 def app(custom_node_manager):
     app = web.Application()
     routes = web.RouteTableDef()
-    custom_node_manager.add_routes(routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")])
+    custom_node_manager.add_routes(
+        routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")]
+    )
     app.add_routes(routes)
     return app
 
+
 async def test_get_workflow_templates(aiohttp_client, app, tmp_path):
     client = await aiohttp_client(app)
     # Setup temporary custom nodes file structure with 1 workflow file
     custom_nodes_dir = tmp_path / "custom_nodes"
-    example_workflows_dir = custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows"
+    example_workflows_dir = (
+        custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows"
+    )
     example_workflows_dir.mkdir(parents=True)
     template_file = example_workflows_dir / "workflow1.json"
-    template_file.write_text('')
+    template_file.write_text("")
 
-    with patch('folder_paths.folder_names_and_paths', {
-        'custom_nodes': ([str(custom_nodes_dir)], None)
-    }):
-        response = await client.get('/workflow_templates')
+    with patch(
+        "folder_paths.folder_names_and_paths",
+        {"custom_nodes": ([str(custom_nodes_dir)], None)},
+    ):
+        response = await client.get("/workflow_templates")
         assert response.status == 200
         workflows_dict = await response.json()
         assert isinstance(workflows_dict, dict)
         assert "ComfyUI-TestExtension1" in workflows_dict
         assert isinstance(workflows_dict["ComfyUI-TestExtension1"], list)
         assert workflows_dict["ComfyUI-TestExtension1"][0] == "workflow1"
+
+
+async def test_build_translations_empty_when_no_locales(custom_node_manager, tmp_path):
+    custom_nodes_dir = tmp_path / "custom_nodes"
+    custom_nodes_dir.mkdir(parents=True)
+
+    with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]):
+        translations = custom_node_manager.build_translations()
+        assert translations == {}
+
+
+async def test_build_translations_loads_all_files(custom_node_manager, tmp_path):
+    # Setup test directory structure
+    custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension"
+    locales_dir = custom_nodes_dir / "locales" / "en"
+    locales_dir.mkdir(parents=True)
+
+    # Create test translation files
+    main_content = {"title": "Test Extension"}
+    (locales_dir / "main.json").write_text(json.dumps(main_content))
+
+    node_defs = {"node1": "Node 1"}
+    (locales_dir / "nodeDefs.json").write_text(json.dumps(node_defs))
+
+    commands = {"cmd1": "Command 1"}
+    (locales_dir / "commands.json").write_text(json.dumps(commands))
+
+    settings = {"setting1": "Setting 1"}
+    (locales_dir / "settings.json").write_text(json.dumps(settings))
+
+    with patch(
+        "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"]
+    ):
+        translations = custom_node_manager.build_translations()
+
+        assert translations == {
+            "en": {
+                "title": "Test Extension",
+                "nodeDefs": {"node1": "Node 1"},
+                "commands": {"cmd1": "Command 1"},
+                "settings": {"setting1": "Setting 1"},
+            }
+        }
+
+
+async def test_build_translations_handles_invalid_json(custom_node_manager, tmp_path):
+    # Setup test directory structure
+    custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension"
+    locales_dir = custom_nodes_dir / "locales" / "en"
+    locales_dir.mkdir(parents=True)
+
+    # Create valid main.json
+    main_content = {"title": "Test Extension"}
+    (locales_dir / "main.json").write_text(json.dumps(main_content))
+
+    # Create invalid JSON file
+    (locales_dir / "nodeDefs.json").write_text("invalid json{")
+
+    with patch(
+        "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"]
+    ):
+        translations = custom_node_manager.build_translations()
+
+        assert translations == {
+            "en": {
+                "title": "Test Extension",
+            }
+        }
+
+
+async def test_build_translations_merges_multiple_extensions(
+    custom_node_manager, tmp_path
+):
+    # Setup test directory structure for two extensions
+    custom_nodes_dir = tmp_path / "custom_nodes"
+    ext1_dir = custom_nodes_dir / "extension1" / "locales" / "en"
+    ext2_dir = custom_nodes_dir / "extension2" / "locales" / "en"
+    ext1_dir.mkdir(parents=True)
+    ext2_dir.mkdir(parents=True)
+
+    # Create translation files for extension 1
+    ext1_main = {"title": "Extension 1", "shared": "Original"}
+    (ext1_dir / "main.json").write_text(json.dumps(ext1_main))
+
+    # Create translation files for extension 2
+    ext2_main = {"description": "Extension 2", "shared": "Override"}
+    (ext2_dir / "main.json").write_text(json.dumps(ext2_main))
+
+    with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]):
+        translations = custom_node_manager.build_translations()
+
+        assert translations == {
+            "en": {
+                "title": "Extension 1",
+                "description": "Extension 2",
+                "shared": "Override",  # Second extension should override first
+            }
+        }

tests-unit/utils/json_util_test.py

@@ -0,0 +1,71 @@
+from utils.json_util import merge_json_recursive
+
+
+def test_merge_simple_dicts():
+    base = {"a": 1, "b": 2}
+    update = {"b": 3, "c": 4}
+    expected = {"a": 1, "b": 3, "c": 4}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_nested_dicts():
+    base = {"a": {"x": 1, "y": 2}, "b": 3}
+    update = {"a": {"y": 4, "z": 5}}
+    expected = {"a": {"x": 1, "y": 4, "z": 5}, "b": 3}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_lists():
+    base = {"a": [1, 2], "b": 3}
+    update = {"a": [3, 4]}
+    expected = {"a": [1, 2, 3, 4], "b": 3}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_nested_lists():
+    base = {"a": {"x": [1, 2]}}
+    update = {"a": {"x": [3, 4]}}
+    expected = {"a": {"x": [1, 2, 3, 4]}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_mixed_types():
+    base = {"a": [1, 2], "b": {"x": 1}}
+    update = {"a": [3], "b": {"y": 2}}
+    expected = {"a": [1, 2, 3], "b": {"x": 1, "y": 2}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_overwrite_non_dict():
+    base = {"a": 1}
+    update = {"a": {"x": 2}}
+    expected = {"a": {"x": 2}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_empty_dicts():
+    base = {}
+    update = {"a": 1}
+    expected = {"a": 1}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_none_values():
+    base = {"a": None}
+    update = {"a": {"x": 1}}
+    expected = {"a": {"x": 1}}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_different_types():
+    base = {"a": [1, 2]}
+    update = {"a": "string"}
+    expected = {"a": "string"}
+    assert merge_json_recursive(base, update) == expected
+
+
+def test_merge_complex_nested():
+    base = {"a": [1, 2], "b": {"x": [3, 4], "y": {"p": 1}}}
+    update = {"a": [5], "b": {"x": [6], "y": {"q": 2}}}
+    expected = {"a": [1, 2, 5], "b": {"x": [3, 4, 6], "y": {"p": 1, "q": 2}}}
+    assert merge_json_recursive(base, update) == expected

utils/json_util.py

@@ -0,0 +1,26 @@
+def merge_json_recursive(base, update):
+    """Recursively merge two JSON-like objects.
+    - Dictionaries are merged recursively
+    - Lists are concatenated
+    - Other types are overwritten by the update value
+
+    Args:
+        base: Base JSON-like object
+        update: Update JSON-like object to merge into base
+
+    Returns:
+        Merged JSON-like object
+    """
+    if not isinstance(base, dict) or not isinstance(update, dict):
+        if isinstance(base, list) and isinstance(update, list):
+            return base + update
+        return update
+
+    merged = base.copy()
+    for key, value in update.items():
+        if key in merged:
+            merged[key] = merge_json_recursive(merged[key], value)
+        else:
+            merged[key] = value
+
+    return merged