ComfyUI version 0.3.40

Simplifies node organization by consolidating all Ideogram nodes under a single category instead of version-specific subcategories.

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -15,6 +15,14 @@ body:
         steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
 
         If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+  - type: checkboxes
+    id: custom-nodes-test
+    attributes:
+      label: Custom Node Testing
+      description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
+      options:
+        - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
+          required: true
   - type: textarea
     attributes:
       label: Expected Behavior

.github/ISSUE_TEMPLATE/user-support.yml

@@ -11,6 +11,14 @@ body:
             **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
 
                 If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: checkboxes
+      id: custom-nodes-test
+      attributes:
+        label: Custom Node Testing
+        description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
+        options:
+          - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
+            required: true
     - type: textarea
       attributes:
             label: Your question

CODEOWNERS

@@ -5,20 +5,20 @@
 # Inlined the team members for now.
 
 # Maintainers
-*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
 
 # Python web server
-/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
 
 # Node developers
-/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
-/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

README.md

@@ -95,7 +95,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Starts up very fast.
-- Works fully offline: will never download anything.
+- Works fully offline: core will never download anything unless you want to.
+- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
 - [Config file](extra_model_paths.yaml.example) to set the search paths for models.
 
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)

app/frontend_management.py

@@ -205,6 +205,19 @@ comfyui-workflow-templates is not installed.
 """.strip()
             )
 
+    @classmethod
+    def embedded_docs_path(cls) -> str:
+        """Get the path to embedded documentation"""
+        try:
+            import comfyui_embedded_docs
+
+            return str(
+                importlib.resources.files(comfyui_embedded_docs) / "docs"
+            )
+        except ImportError:
+            logging.info("comfyui-embedded-docs package not found")
+            return None
+
     @classmethod
     def parse_version_string(cls, value: str) -> tuple[str, str, str]:
         """

comfy/cli_args.py

@@ -88,6 +88,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
+parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"

comfy/conds.py

@@ -24,6 +24,10 @@ class CONDRegular:
             conds.append(x.cond)
         return torch.cat(conds)
 
+    def size(self):
+        return list(self.cond.size())
+
+
 class CONDNoiseShape(CONDRegular):
     def process_cond(self, batch_size, device, area, **kwargs):
         data = self.cond
@@ -64,6 +68,7 @@ class CONDCrossAttn(CONDRegular):
             out.append(c)
         return torch.cat(out)
 
+
 class CONDConstant(CONDRegular):
     def __init__(self, cond):
         self.cond = cond
@@ -78,3 +83,48 @@ class CONDConstant(CONDRegular):
 
     def concat(self, others):
         return self.cond
+
+    def size(self):
+        return [1]
+
+
+class CONDList(CONDRegular):
+    def __init__(self, cond):
+        self.cond = cond
+
+    def process_cond(self, batch_size, device, **kwargs):
+        out = []
+        for c in self.cond:
+            out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
+
+        return self._copy_with(out)
+
+    def can_concat(self, other):
+        if len(self.cond) != len(other.cond):
+            return False
+        for i in range(len(self.cond)):
+            if self.cond[i].shape != other.cond[i].shape:
+                return False
+
+        return True
+
+    def concat(self, others):
+        out = []
+        for i in range(len(self.cond)):
+            o = [self.cond[i]]
+            for x in others:
+                o.append(x.cond[i])
+            out.append(torch.cat(o))
+
+        return out
+
+    def size(self):  # hackish implementation to make the mem estimation work
+        o = 0
+        c = 1
+        for c in self.cond:
+            size = c.size()
+            o += math.prod(size)
+            if len(size) > 1:
+                c = size[1]
+
+        return [1, c, o // c]

comfy/ldm/chroma/layers.py

@@ -80,15 +80,13 @@ class DoubleStreamBlock(nn.Module):
         (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
 
         # prepare image for attention
-        img_modulated = self.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
         img_qkv = self.img_attn.qkv(img_modulated)
         img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
 
         # prepare txt for attention
-        txt_modulated = self.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
         txt_qkv = self.txt_attn.qkv(txt_modulated)
         txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
@@ -102,12 +100,12 @@ class DoubleStreamBlock(nn.Module):
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
-        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+        img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
+        img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
 
         # calculate the txt bloks
-        txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
+        txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
 
         if txt.dtype == torch.float16:
             txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
@@ -152,7 +150,7 @@ class SingleStreamBlock(nn.Module):
 
     def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
         mod = vec
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
         qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
@@ -162,7 +160,7 @@ class SingleStreamBlock(nn.Module):
         attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        x += mod.gate * output
+        x.addcmul_(mod.gate, output)
         if x.dtype == torch.float16:
             x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
         return x
@@ -178,6 +176,6 @@ class LastLayer(nn.Module):
         shift, scale = vec
         shift = shift.squeeze(1)
         scale = scale.squeeze(1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
         x = self.linear(x)
         return x

comfy/ldm/chroma/model.py

@@ -163,7 +163,7 @@ class Chroma(nn.Module):
         distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
 
         # get all modulation index
-        modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype)
+        modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)
         # we need to broadcast the modulation index here so each batch has all of the index
         modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
         # and we need to broadcast timestep and guidance along too

comfy/ldm/modules/attention.py

@@ -20,8 +20,11 @@ if model_management.xformers_enabled():
 if model_management.sage_attention_enabled():
     try:
         from sageattention import sageattn
-    except ModuleNotFoundError:
-        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+    except ModuleNotFoundError as e:
+        if e.name == "sageattention":
+            logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+        else:
+            raise e
         exit(-1)
 
 if model_management.flash_attention_enabled():

comfy/ldm/wan/model.py

@@ -539,13 +539,20 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def forward(self, x, timestep, context, clip_fea=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
         h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
         w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+
+        if time_dim_concat is not None:
+            time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
+            x = torch.cat([x, time_dim_concat], dim=2)
+            t_len = ((x.shape[2] + (patch_size[0] // 2)) // patch_size[0])
+
         img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
         img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
@@ -635,7 +642,7 @@ class VaceWanModel(WanModel):
         t,
         context,
         vace_context,
-        vace_strength=1.0,
+        vace_strength,
         clip_fea=None,
         freqs=None,
         transformer_options={},
@@ -661,8 +668,11 @@ class VaceWanModel(WanModel):
                 context = torch.concat([context_clip, context], dim=1)
             context_img_len = clip_fea.shape[-2]
 
+        orig_shape = list(vace_context.shape)
+        vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:])
         c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype)
         c = c.flatten(2).transpose(1, 2)
+        c = list(c.split(orig_shape[0], dim=0))
 
         # arguments
         x_orig = x
@@ -682,8 +692,9 @@ class VaceWanModel(WanModel):
 
             ii = self.vace_layers_mapping.get(i, None)
             if ii is not None:
-                c_skip, c = self.vace_blocks[ii](c, x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
-                x += c_skip * vace_strength
+                for iii in range(len(c)):
+                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                    x += c_skip * vace_strength[iii]
                 del c_skip
         # head
         x = self.head(x, e)

comfy/lora.py

@@ -283,8 +283,9 @@ def model_lora_keys_unet(model, key_map={}):
         for k in sdk:
             if k.startswith("diffusion_model."):
                 if k.endswith(".weight"):
-                    key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
-                    key_map["lycoris_{}".format(key_lora)] = k #SimpleTuner lycoris format
+                    key_lora = k[len("diffusion_model."):-len(".weight")]
+                    key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format
+                    key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format
 
     if isinstance(model, comfy.model_base.ACEStep):
         for k in sdk:

comfy/model_base.py

@@ -102,6 +102,13 @@ def model_sampling(model_config, model_type):
     return ModelSampling(model_config)
 
 
+def convert_tensor(extra, dtype):
+    if hasattr(extra, "dtype"):
+        if extra.dtype != torch.int and extra.dtype != torch.long:
+            extra = extra.to(dtype)
+    return extra
+
+
 class BaseModel(torch.nn.Module):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
         super().__init__()
@@ -135,6 +142,7 @@ class BaseModel(torch.nn.Module):
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
         self.memory_usage_factor = model_config.memory_usage_factor
+        self.memory_usage_factor_conds = ()
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         return comfy.patcher_extension.WrapperExecutor.new_class_executor(
@@ -164,9 +172,14 @@ class BaseModel(torch.nn.Module):
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
+
             if hasattr(extra, "dtype"):
-                if extra.dtype != torch.int and extra.dtype != torch.long:
-                    extra = extra.to(dtype)
+                extra = convert_tensor(extra, dtype)
+            elif isinstance(extra, list):
+                ex = []
+                for ext in extra:
+                    ex.append(convert_tensor(ext, dtype))
+                extra = ex
             extra_conds[o] = extra
 
         t = self.process_timestep(t, x=x, **extra_conds)
@@ -325,19 +338,28 @@ class BaseModel(torch.nn.Module):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
 
-    def memory_required(self, input_shape):
+    def memory_required(self, input_shape, cond_shapes={}):
+        input_shapes = [input_shape]
+        for c in self.memory_usage_factor_conds:
+            shape = cond_shapes.get(c, None)
+            if shape is not None and len(shape) > 0:
+                input_shapes += shape
+
         if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
             dtype = self.get_dtype()
             if self.manual_cast_dtype is not None:
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
-            area = input_shape[0] * math.prod(input_shape[2:])
+            area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))
             return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
-            area = input_shape[0] * math.prod(input_shape[2:])
+            area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))
             return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)
 
+    def extra_conds_shapes(self, **kwargs):
+        return {}
+
 
 def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
     adm_inputs = []
@@ -1047,6 +1069,11 @@ class WAN21(BaseModel):
         clip_vision_output = kwargs.get("clip_vision_output", None)
         if clip_vision_output is not None:
             out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states)
+
+        time_dim_concat = kwargs.get("time_dim_concat", None)
+        if time_dim_concat is not None:
+            out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat))
+
         return out
 
 
@@ -1062,20 +1089,25 @@ class WAN21_Vace(WAN21):
         vace_frames = kwargs.get("vace_frames", None)
         if vace_frames is None:
             noise_shape[1] = 32
-            vace_frames = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
-
-        for i in range(0, vace_frames.shape[1], 16):
-            vace_frames = vace_frames.clone()
-            vace_frames[:, i:i + 16] = self.process_latent_in(vace_frames[:, i:i + 16])
+            vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)]
 
         mask = kwargs.get("vace_mask", None)
         if mask is None:
             noise_shape[1] = 64
-            mask = torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)
+            mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames)
 
-        out['vace_context'] = comfy.conds.CONDRegular(torch.cat([vace_frames.to(noise), mask.to(noise)], dim=1))
+        vace_frames_out = []
+        for j in range(len(vace_frames)):
+            vf = vace_frames[j].clone()
+            for i in range(0, vf.shape[1], 16):
+                vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16])
+            vf = torch.cat([vf, mask[j]], dim=1)
+            vace_frames_out.append(vf)
 
-        vace_strength = kwargs.get("vace_strength", 1.0)
+        vace_frames = torch.stack(vace_frames_out, dim=1)
+        out['vace_context'] = comfy.conds.CONDRegular(vace_frames)
+
+        vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out))
         out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
         return out
 

comfy/model_detection.py

@@ -620,6 +620,9 @@ def convert_config(unet_config):
 
 
 def unet_config_from_diffusers_unet(state_dict, dtype=None):
+    if "conv_in.weight" not in state_dict:
+        return None
+
     match = {}
     transformer_depth = []
 

comfy/model_management.py

@@ -297,11 +297,16 @@ except:
 
 try:
     if is_amd():
+        try:
+            rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
+        except:
+            rocm_version = (6, -1)
         arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
         logging.info("AMD arch: {}".format(arch))
+        logging.info("ROCm version: {}".format(rocm_version))
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7:  # works on 2.6 but doesn't actually seem to improve much
-                if any((a in arch) for a in ["gfx1100", "gfx1101"]):  # TODO: more arches
+                if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches
                     ENABLE_PYTORCH_ATTENTION = True
 except:
     pass
@@ -695,7 +700,7 @@ def unet_inital_load_device(parameters, dtype):
         return torch_dev
 
     cpu_dev = torch.device("cpu")
-    if DISABLE_SMART_MEMORY:
+    if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM:
         return cpu_dev
 
     model_size = dtype_size(dtype) * parameters
@@ -1257,6 +1262,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     return False
 
 def supports_fp8_compute(device=None):
+    if args.supports_fp8_compute:
+        return True
+
     if not is_nvidia():
         return False
 

comfy/sampler_helpers.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 import uuid
+import math
+import collections
 import comfy.model_management
 import comfy.conds
 import comfy.utils
@@ -104,6 +106,21 @@ def cleanup_additional_models(models):
         if hasattr(m, 'cleanup'):
             m.cleanup()
 
+def estimate_memory(model, noise_shape, conds):
+    cond_shapes = collections.defaultdict(list)
+    cond_shapes_min = {}
+    for _, cs in conds.items():
+        for cond in cs:
+            for k, v in model.model.extra_conds_shapes(**cond).items():
+                cond_shapes[k].append(v)
+                if cond_shapes_min.get(k, None) is None:
+                    cond_shapes_min[k] = [v]
+                elif math.prod(v) > math.prod(cond_shapes_min[k][0]):
+                    cond_shapes_min[k] = [v]
+
+    memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes)
+    minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min)
+    return memory_required, minimum_memory_required
 
 def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
     executor = comfy.patcher_extension.WrapperExecutor.new_executor(
@@ -117,9 +134,8 @@ def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=Non
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
-    memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
-    minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
-    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
+    memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds)
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory)
     real_model = model.model
 
     return real_model, conds, models

comfy/samplers.py

@@ -256,7 +256,13 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
             for i in range(1, len(to_batch_temp) + 1):
                 batch_amount = to_batch_temp[:len(to_batch_temp)//i]
                 input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
-                if model.memory_required(input_shape) * 1.5 < free_memory:
+                cond_shapes = collections.defaultdict(list)
+                for tt in batch_amount:
+                    cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()}
+                    for k, v in to_run[tt][0].conditioning.items():
+                        cond_shapes[k].append(v.size())
+
+                if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory:
                     to_batch = batch_amount
                     break
 

comfy/text_encoders/long_clipl.json

@@ -1,25 +0,0 @@
-{
-  "_name_or_path": "openai/clip-vit-large-patch14",
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 49407,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 768,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 248,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "projection_dim": 768,
-  "torch_dtype": "float32",
-  "transformers_version": "4.24.0",
-  "vocab_size": 49408
-}

comfy_api/torch_helpers/__init__.py

@@ -0,0 +1,5 @@
+from .torch_compile import set_torch_compile_wrapper
+
+__all__ = [
+    "set_torch_compile_wrapper",
+]

comfy_api/torch_helpers/torch_compile.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+import torch
+
+import comfy.utils
+from comfy.patcher_extension import WrappersMP
+from typing import TYPE_CHECKING, Callable, Optional
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+    from comfy.patcher_extension import WrapperExecutor
+
+
+COMPILE_KEY = "torch.compile"
+TORCH_COMPILE_KWARGS = "torch_compile_kwargs"
+
+
+def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable:
+    '''
+    Create a wrapper that will refer to the compiled_diffusion_model.
+    '''
+    def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs):
+        try:
+            orig_modules = {}
+            for key, value in compiled_module_dict.items():
+                orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key)
+                comfy.utils.set_attr(executor.class_obj, key, value)
+            return executor(*args, **kwargs)
+        finally:
+            for key, value in orig_modules.items():
+                comfy.utils.set_attr(executor.class_obj, key, value)
+    return apply_torch_compile_wrapper
+
+
+def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None,
+                              mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None,
+                              keys: list[str]=["diffusion_model"], *args, **kwargs):
+    '''
+    Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance.
+
+    When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model.
+    When a list of keys is provided, it will perform torch.compile on only the selected modules.
+    '''
+    # clear out any other torch.compile wrappers
+    model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY)
+    # if no keys, default to 'diffusion_model'
+    if not keys:
+        keys = ["diffusion_model"]
+    # create kwargs dict that can be referenced later
+    compile_kwargs = {
+        "backend": backend,
+        "options": options,
+        "mode": mode,
+        "fullgraph": fullgraph,
+        "dynamic": dynamic,
+    }
+    # get a dict of compiled keys
+    compiled_modules = {}
+    for key in keys:
+        compiled_modules[key] = torch.compile(
+                model=model.get_model_object(key),
+                **compile_kwargs,
+            )
+    # add torch.compile wrapper
+    wrapper_func = apply_torch_compile_factory(
+        compiled_module_dict=compiled_modules,
+    )
+    # store wrapper to run on BaseModel's apply_model function
+    model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func)
+    # keep compile kwargs for reference
+    model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs

comfy_api_nodes/README.md

@@ -18,6 +18,8 @@ Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to
 python run main.py --comfy-api-base https://stagingapi.comfy.org
 ```
 
+To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging.
+
 API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes.
 
 ### Redocly Instructions 
@@ -28,7 +30,7 @@ When developing locally, use the `redocly-dev.yaml` file to generate pydantic mo
 Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging.
 
 ```bash
-# Download the OpenAPI file from prod server.
+# Download the OpenAPI file from staging server.
 curl -o openapi.yaml https://stagingapi.comfy.org/openapi
 
 # Filter out unneeded API definitions.
@@ -39,3 +41,25 @@ redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_no
 datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
 
 ```
+
+
+# Merging to Master
+
+Before merging to comfyanonymous/ComfyUI master, follow these steps:
+
+1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. 
+1. Make sure the ComfyUI API is deployed to prod with your changes.
+1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file.
+
+```bash
+# Download the OpenAPI file from prod server.
+curl -o openapi.yaml https://api.comfy.org/openapi
+
+# Filter out unneeded API definitions.
+npm install -g @redocly/cli
+redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components
+
+# Generate the pydantic datamodels for validation.
+datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
+
+```

comfy_api_nodes/apinode_utils.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 import io
 import logging
+import mimetypes
 from typing import Optional, Union
 from comfy.utils import common_upscale
 from comfy_api.input_impl import VideoFromFile
@@ -214,6 +215,7 @@ def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor:
     image_bytesio = download_url_to_bytesio(url, timeout)
     return bytesio_to_image_tensor(image_bytesio)
 
+
 def process_image_response(response: requests.Response) -> torch.Tensor:
     """Uses content from a Response object and converts it to a torch.Tensor"""
     return bytesio_to_image_tensor(BytesIO(response.content))
@@ -318,11 +320,27 @@ def tensor_to_data_uri(
     return f"data:{mime_type};base64,{base64_string}"
 
 
+def text_filepath_to_base64_string(filepath: str) -> str:
+    """Converts a text file to a base64 string."""
+    with open(filepath, "rb") as f:
+        file_content = f.read()
+    return base64.b64encode(file_content).decode("utf-8")
+
+
+def text_filepath_to_data_uri(filepath: str) -> str:
+    """Converts a text file to a data URI."""
+    base64_string = text_filepath_to_base64_string(filepath)
+    mime_type, _ = mimetypes.guess_type(filepath)
+    if mime_type is None:
+        mime_type = "application/octet-stream"
+    return f"data:{mime_type};base64,{base64_string}"
+
+
 def upload_file_to_comfyapi(
     file_bytes_io: BytesIO,
     filename: str,
     upload_mime_type: str,
-    auth_kwargs: Optional[dict[str,str]] = None,
+    auth_kwargs: Optional[dict[str, str]] = None,
 ) -> str:
     """
     Uploads a single file to ComfyUI API and returns its download URL.
@@ -357,9 +375,33 @@ def upload_file_to_comfyapi(
     return response.download_url
 
 
+def video_to_base64_string(
+    video: VideoInput,
+    container_format: VideoContainer = None,
+    codec: VideoCodec = None
+) -> str:
+    """
+    Converts a video input to a base64 string.
+
+    Args:
+        video: The video input to convert
+        container_format: Optional container format to use (defaults to video.container if available)
+        codec: Optional codec to use (defaults to video.codec if available)
+    """
+    video_bytes_io = io.BytesIO()
+
+    # Use provided format/codec if specified, otherwise use video's own if available
+    format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4)
+    codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264)
+
+    video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use)
+    video_bytes_io.seek(0)
+    return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
+
+
 def upload_video_to_comfyapi(
     video: VideoInput,
-    auth_kwargs: Optional[dict[str,str]] = None,
+    auth_kwargs: Optional[dict[str, str]] = None,
     container: VideoContainer = VideoContainer.MP4,
     codec: VideoCodec = VideoCodec.H264,
     max_duration: Optional[int] = None,
@@ -461,7 +503,7 @@ def audio_ndarray_to_bytesio(
 
 def upload_audio_to_comfyapi(
     audio: AudioInput,
-    auth_kwargs: Optional[dict[str,str]] = None,
+    auth_kwargs: Optional[dict[str, str]] = None,
     container_format: str = "mp4",
     codec_name: str = "aac",
     mime_type: str = "audio/mp4",
@@ -488,8 +530,25 @@ def upload_audio_to_comfyapi(
     return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs)
 
 
+def audio_to_base64_string(
+    audio: AudioInput, container_format: str = "mp4", codec_name: str = "aac"
+) -> str:
+    """Converts an audio input to a base64 string."""
+    sample_rate: int = audio["sample_rate"]
+    waveform: torch.Tensor = audio["waveform"]
+    audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+    audio_bytes_io = audio_ndarray_to_bytesio(
+        audio_data_np, sample_rate, container_format, codec_name
+    )
+    audio_bytes = audio_bytes_io.getvalue()
+    return base64.b64encode(audio_bytes).decode("utf-8")
+
+
 def upload_images_to_comfyapi(
-    image: torch.Tensor, max_images=8, auth_kwargs: Optional[dict[str,str]] = None, mime_type: Optional[str] = None
+    image: torch.Tensor,
+    max_images=8,
+    auth_kwargs: Optional[dict[str, str]] = None,
+    mime_type: Optional[str] = None,
 ) -> list[str]:
     """
     Uploads images to ComfyUI API and returns download URLs.
@@ -554,17 +613,24 @@ def upload_images_to_comfyapi(
     return download_urls
 
 
-def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor,
-                         upscale_method="nearest-exact", crop="disabled",
-                         allow_gradient=True, add_channel_dim=False):
+def resize_mask_to_image(
+    mask: torch.Tensor,
+    image: torch.Tensor,
+    upscale_method="nearest-exact",
+    crop="disabled",
+    allow_gradient=True,
+    add_channel_dim=False,
+):
     """
     Resize mask to be the same dimensions as an image, while maintaining proper format for API calls.
     """
     _, H, W, _ = image.shape
     mask = mask.unsqueeze(-1)
-    mask = mask.movedim(-1,1)
-    mask = common_upscale(mask, width=W, height=H, upscale_method=upscale_method, crop=crop)
-    mask = mask.movedim(1,-1)
+    mask = mask.movedim(-1, 1)
+    mask = common_upscale(
+        mask, width=W, height=H, upscale_method=upscale_method, crop=crop
+    )
+    mask = mask.movedim(1, -1)
     if not add_channel_dim:
         mask = mask.squeeze(-1)
     if not allow_gradient:
@@ -572,12 +638,41 @@ def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor,
     return mask
 
 
-def validate_string(string: str, strip_whitespace=True, field_name="prompt", min_length=None, max_length=None):
+def validate_string(
+    string: str,
+    strip_whitespace=True,
+    field_name="prompt",
+    min_length=None,
+    max_length=None,
+):
+    if string is None:
+        raise Exception(f"Field '{field_name}' cannot be empty.")
     if strip_whitespace:
         string = string.strip()
     if min_length and len(string) < min_length:
-        raise Exception(f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long.")
+        raise Exception(
+            f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long."
+        )
     if max_length and len(string) > max_length:
-        raise Exception(f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long.")
-    if not string:
-        raise Exception(f"Field '{field_name}' cannot be empty.")
+        raise Exception(
+            f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long."
+        )
+
+
+def image_tensor_pair_to_batch(
+    image1: torch.Tensor, image2: torch.Tensor
+) -> torch.Tensor:
+    """
+    Converts a pair of image tensors to a batch tensor.
+    If the images are not the same size, the smaller image is resized to
+    match the larger image.
+    """
+    if image1.shape[1:] != image2.shape[1:]:
+        image2 = common_upscale(
+            image2.movedim(-1, 1),
+            image1.shape[2],
+            image1.shape[1],
+            "bilinear",
+            "center",
+        ).movedim(1, -1)
+    return torch.cat((image1, image2), dim=0)

comfy_api_nodes/apis/bfl_api.py

@@ -108,6 +108,24 @@ class BFLFluxProGenerateRequest(BaseModel):
     # )
 
 
+class BFLFluxKontextProGenerateRequest(BaseModel):
+    prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
+    input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
+    seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
+    guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
+    steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
+    safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
+        2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
+    )
+    output_format: Optional[BFLOutputFormat] = Field(
+        BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
+    )
+    aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
+    prompt_upsampling: Optional[bool] = Field(
+        None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
+    )
+
+
 class BFLFluxProUltraGenerateRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt for image generation.')
     prompt_upsampling: Optional[bool] = Field(

comfy_api_nodes/apis/client.py

@@ -139,7 +139,7 @@ class EmptyRequest(BaseModel):
 
 class UploadRequest(BaseModel):
     file_name: str = Field(..., description="Filename to upload")
-    content_type: str | None = Field(
+    content_type: Optional[str] = Field(
         None,
         description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
     )
@@ -327,7 +327,9 @@ class ApiClient:
             ApiServerError: If the API server is unreachable but internet is working
             Exception: For other request failures
         """
-        url = urljoin(self.base_url, path)
+        # Use urljoin but ensure path is relative to avoid absolute path behavior
+        relative_path = path.lstrip('/')
+        url = urljoin(self.base_url, relative_path)
         self.check_auth(self.auth_token, self.comfy_api_key)
         # Combine default headers with any provided headers
         request_headers = self.get_headers()

comfy_api_nodes/apis/rodin_api.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from enum import Enum
+from typing import Optional, List
+from pydantic import BaseModel, Field
+
+
+class Rodin3DGenerateRequest(BaseModel):
+    seed: int = Field(..., description="seed_")
+    tier: str = Field(..., description="Tier of generation.")
+    material: str = Field(..., description="The material type.")
+    quality: str = Field(..., description="The generation quality of the mesh.")
+    mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
+
+class GenerateJobsData(BaseModel):
+    uuids: List[str] = Field(..., description="str LIST")
+    subscription_key: str = Field(..., description="subscription key")
+
+class Rodin3DGenerateResponse(BaseModel):
+    message: Optional[str] = Field(None, description="Return message.")
+    prompt: Optional[str] = Field(None, description="Generated Prompt from image.")
+    submit_time: Optional[str] = Field(None, description="Submit Time")
+    uuid: Optional[str] = Field(None, description="Task str")
+    jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs")
+
+class JobStatus(str, Enum):
+    """
+    Status for jobs
+    """
+    Done = "Done"
+    Failed = "Failed"
+    Generating = "Generating"
+    Waiting = "Waiting"
+
+class Rodin3DCheckStatusRequest(BaseModel):
+    subscription_key: str = Field(..., description="subscription from generate endpoint")
+
+class JobItem(BaseModel):
+    uuid: str = Field(..., description="uuid")
+    status: JobStatus = Field(...,description="Status Currently")
+
+class Rodin3DCheckStatusResponse(BaseModel):
+    jobs: List[JobItem] = Field(..., description="Job status List")
+
+class Rodin3DDownloadRequest(BaseModel):
+    task_uuid: str = Field(..., description="Task str")
+
+class RodinResourceItem(BaseModel):
+    url: str = Field(..., description="Download Url")
+    name: str = Field(..., description="File name with ext")
+
+class Rodin3DDownloadResponse(BaseModel):
+    list: List[RodinResourceItem] = Field(..., description="Source List")
+
+
+
+

comfy_api_nodes/apis/tripo_api.py

@@ -0,0 +1,275 @@
+from __future__ import annotations
+from comfy_api_nodes.apis import (
+    TripoModelVersion,
+    TripoTextureQuality,
+)
+from enum import Enum
+from typing import Optional, List, Dict, Any, Union
+
+from pydantic import BaseModel, Field, RootModel
+
+class TripoStyle(str, Enum):
+    PERSON_TO_CARTOON = "person:person2cartoon"
+    ANIMAL_VENOM = "animal:venom"
+    OBJECT_CLAY = "object:clay"
+    OBJECT_STEAMPUNK = "object:steampunk"
+    OBJECT_CHRISTMAS = "object:christmas"
+    OBJECT_BARBIE = "object:barbie"
+    GOLD = "gold"
+    ANCIENT_BRONZE = "ancient_bronze"
+    NONE = "None"
+
+class TripoTaskType(str, Enum):
+    TEXT_TO_MODEL = "text_to_model"
+    IMAGE_TO_MODEL = "image_to_model"
+    MULTIVIEW_TO_MODEL = "multiview_to_model"
+    TEXTURE_MODEL = "texture_model"
+    REFINE_MODEL = "refine_model"
+    ANIMATE_PRERIGCHECK = "animate_prerigcheck"
+    ANIMATE_RIG = "animate_rig"
+    ANIMATE_RETARGET = "animate_retarget"
+    STYLIZE_MODEL = "stylize_model"
+    CONVERT_MODEL = "convert_model"
+
+class TripoTextureAlignment(str, Enum):
+    ORIGINAL_IMAGE = "original_image"
+    GEOMETRY = "geometry"
+
+class TripoOrientation(str, Enum):
+    ALIGN_IMAGE = "align_image"
+    DEFAULT = "default"
+
+class TripoOutFormat(str, Enum):
+    GLB = "glb"
+    FBX = "fbx"
+
+class TripoTopology(str, Enum):
+    BIP = "bip"
+    QUAD = "quad"
+
+class TripoSpec(str, Enum):
+    MIXAMO = "mixamo"
+    TRIPO = "tripo"
+
+class TripoAnimation(str, Enum):
+    IDLE = "preset:idle"
+    WALK = "preset:walk"
+    CLIMB = "preset:climb"
+    JUMP = "preset:jump"
+    RUN = "preset:run"
+    SLASH = "preset:slash"
+    SHOOT = "preset:shoot"
+    HURT = "preset:hurt"
+    FALL = "preset:fall"
+    TURN = "preset:turn"
+
+class TripoStylizeStyle(str, Enum):
+    LEGO = "lego"
+    VOXEL = "voxel"
+    VORONOI = "voronoi"
+    MINECRAFT = "minecraft"
+
+class TripoConvertFormat(str, Enum):
+    GLTF = "GLTF"
+    USDZ = "USDZ"
+    FBX = "FBX"
+    OBJ = "OBJ"
+    STL = "STL"
+    _3MF = "3MF"
+
+class TripoTextureFormat(str, Enum):
+    BMP = "BMP"
+    DPX = "DPX"
+    HDR = "HDR"
+    JPEG = "JPEG"
+    OPEN_EXR = "OPEN_EXR"
+    PNG = "PNG"
+    TARGA = "TARGA"
+    TIFF = "TIFF"
+    WEBP = "WEBP"
+
+class TripoTaskStatus(str, Enum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    SUCCESS = "success"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+    UNKNOWN = "unknown"
+    BANNED = "banned"
+    EXPIRED = "expired"
+
+class TripoFileTokenReference(BaseModel):
+    type: Optional[str] = Field(None, description='The type of the reference')
+    file_token: str
+
+class TripoUrlReference(BaseModel):
+    type: Optional[str] = Field(None, description='The type of the reference')
+    url: str
+
+class TripoObjectStorage(BaseModel):
+    bucket: str
+    key: str
+
+class TripoObjectReference(BaseModel):
+    type: str
+    object: TripoObjectStorage
+
+class TripoFileEmptyReference(BaseModel):
+    pass
+
+class TripoFileReference(RootModel):
+    root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference]
+
+class TripoGetStsTokenRequest(BaseModel):
+    format: str = Field(..., description='The format of the image')
+
+class TripoTextToModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description='Type of task')
+    prompt: str = Field(..., description='The text prompt describing the model to generate', max_length=1024)
+    negative_prompt: Optional[str] = Field(None, description='The negative text prompt', max_length=1024)
+    model_version: Optional[TripoModelVersion] = TripoModelVersion.V2_5
+    face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model')
+    image_seed: Optional[int] = Field(None, description='The seed for the text')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard
+    style: Optional[TripoStyle] = None
+    auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model')
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model')
+
+class TripoImageToModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description='Type of task')
+    file: TripoFileReference = Field(..., description='The file reference to convert to a model')
+    model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation')
+    face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard
+    texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method')
+    style: Optional[TripoStyle] = Field(None, description='The style to apply to the generated model')
+    auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model')
+    orientation: Optional[TripoOrientation] = TripoOrientation.DEFAULT
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model')
+
+class TripoMultiviewToModelRequest(BaseModel):
+    type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL
+    files: List[TripoFileReference] = Field(..., description='The file references to convert to a model')
+    model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation')
+    orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection')
+    face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard
+    texture_alignment: Optional[TripoTextureAlignment] = TripoTextureAlignment.ORIGINAL_IMAGE
+    auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model')
+    orientation: Optional[TripoOrientation] = Field(TripoOrientation.DEFAULT, description='The orientation for the model')
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model')
+
+class TripoTextureModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    texture: Optional[bool] = Field(True, description='Whether to apply texture to the model')
+    pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the model')
+    model_seed: Optional[int] = Field(None, description='The seed for the model')
+    texture_seed: Optional[int] = Field(None, description='The seed for the texture')
+    texture_quality: Optional[TripoTextureQuality] = Field(None, description='The quality of the texture')
+    texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method')
+
+class TripoRefineModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.REFINE_MODEL, description='Type of task')
+    draft_model_task_id: str = Field(..., description='The task ID of the draft model')
+
+class TripoAnimatePrerigcheckRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_PRERIGCHECK, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+
+class TripoAnimateRigRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format')
+    spec: Optional[TripoSpec] = Field(TripoSpec.TRIPO, description='The specification for rigging')
+
+class TripoAnimateRetargetRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description='Type of task')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    animation: TripoAnimation = Field(..., description='The animation to apply')
+    out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format')
+    bake_animation: Optional[bool] = Field(True, description='Whether to bake the animation')
+
+class TripoStylizeModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.STYLIZE_MODEL, description='Type of task')
+    style: TripoStylizeStyle = Field(..., description='The style to apply to the model')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    block_size: Optional[int] = Field(80, description='The block size for stylization')
+
+class TripoConvertModelRequest(BaseModel):
+    type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description='Type of task')
+    format: TripoConvertFormat = Field(..., description='The format to convert to')
+    original_model_task_id: str = Field(..., description='The task ID of the original model')
+    quad: Optional[bool] = Field(False, description='Whether to apply quad to the model')
+    force_symmetry: Optional[bool] = Field(False, description='Whether to force symmetry')
+    face_limit: Optional[int] = Field(10000, description='The number of faces to limit the conversion to')
+    flatten_bottom: Optional[bool] = Field(False, description='Whether to flatten the bottom of the model')
+    flatten_bottom_threshold: Optional[float] = Field(0.01, description='The threshold for flattening the bottom')
+    texture_size: Optional[int] = Field(4096, description='The size of the texture')
+    texture_format: Optional[TripoTextureFormat] = Field(TripoTextureFormat.JPEG, description='The format of the texture')
+    pivot_to_center_bottom: Optional[bool] = Field(False, description='Whether to pivot to the center bottom')
+
+class TripoTaskRequest(RootModel):
+    root: Union[
+        TripoTextToModelRequest,
+        TripoImageToModelRequest,
+        TripoMultiviewToModelRequest,
+        TripoTextureModelRequest,
+        TripoRefineModelRequest,
+        TripoAnimatePrerigcheckRequest,
+        TripoAnimateRigRequest,
+        TripoAnimateRetargetRequest,
+        TripoStylizeModelRequest,
+        TripoConvertModelRequest
+    ]
+
+class TripoTaskOutput(BaseModel):
+    model: Optional[str] = Field(None, description='URL to the model')
+    base_model: Optional[str] = Field(None, description='URL to the base model')
+    pbr_model: Optional[str] = Field(None, description='URL to the PBR model')
+    rendered_image: Optional[str] = Field(None, description='URL to the rendered image')
+    riggable: Optional[bool] = Field(None, description='Whether the model is riggable')
+
+class TripoTask(BaseModel):
+    task_id: str = Field(..., description='The task ID')
+    type: Optional[str] = Field(None, description='The type of task')
+    status: Optional[TripoTaskStatus] = Field(None, description='The status of the task')
+    input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task')
+    output: Optional[TripoTaskOutput] = Field(None, description='The output of the task')
+    progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100)
+    create_time: Optional[int] = Field(None, description='The creation time of the task')
+    running_left_time: Optional[int] = Field(None, description='The estimated time left for the task')
+    queue_position: Optional[int] = Field(None, description='The position in the queue')
+
+class TripoTaskResponse(BaseModel):
+    code: int = Field(0, description='The response code')
+    data: TripoTask = Field(..., description='The task data')
+
+class TripoGeneralResponse(BaseModel):
+    code: int = Field(0, description='The response code')
+    data: Dict[str, str] = Field(..., description='The task ID data')
+
+class TripoBalanceData(BaseModel):
+    balance: float = Field(..., description='The account balance')
+    frozen: float = Field(..., description='The frozen balance')
+
+class TripoBalanceResponse(BaseModel):
+    code: int = Field(0, description='The response code')
+    data: TripoBalanceData = Field(..., description='The balance data')
+
+class TripoErrorResponse(BaseModel):
+    code: int = Field(..., description='The error code')
+    message: str = Field(..., description='The error message')
+    suggestion: str = Field(..., description='The suggestion for fixing the error')

comfy_api_nodes/nodes_bfl.py

@@ -1,6 +1,6 @@
 import io
 from inspect import cleandoc
-from typing import Union
+from typing import Union, Optional
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api_nodes.apis.bfl_api import (
     BFLStatus,
@@ -9,6 +9,7 @@ from comfy_api_nodes.apis.bfl_api import (
     BFLFluxCannyImageRequest,
     BFLFluxDepthImageRequest,
     BFLFluxProGenerateRequest,
+    BFLFluxKontextProGenerateRequest,
     BFLFluxProUltraGenerateRequest,
     BFLFluxProGenerateResponse,
 )
@@ -269,6 +270,158 @@ class FluxProUltraImageNode(ComfyNodeABC):
         return (output_image,)
 
 
+class FluxKontextProImageNode(ComfyNodeABC):
+    """
+    Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.
+    """
+
+    MINIMUM_RATIO = 1 / 4
+    MAXIMUM_RATIO = 4 / 1
+    MINIMUM_RATIO_STR = "1:4"
+    MAXIMUM_RATIO_STR = "4:1"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Prompt for the image generation - specify what and how to edit.",
+                    },
+                ),
+                "aspect_ratio": (
+                    IO.STRING,
+                    {
+                        "default": "16:9",
+                        "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
+                    },
+                ),
+                "guidance": (
+                    IO.FLOAT,
+                    {
+                        "default": 3.0,
+                        "min": 0.1,
+                        "max": 99.0,
+                        "step": 0.1,
+                        "tooltip": "Guidance strength for the image generation process"
+                    },
+                ),
+                "steps": (
+                    IO.INT,
+                    {
+                        "default": 50,
+                        "min": 1,
+                        "max": 150,
+                        "tooltip": "Number of steps for the image generation process"
+                    },
+                ),
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 1234,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": True,
+                        "tooltip": "The random seed used for creating the noise.",
+                    },
+                ),
+                "prompt_upsampling": (
+                    IO.BOOLEAN,
+                    {
+                        "default": False,
+                        "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+                    },
+                ),
+            },
+            "optional": {
+                "input_image": (IO.IMAGE,),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, aspect_ratio: str):
+        try:
+            validate_aspect_ratio(
+                aspect_ratio,
+                minimum_ratio=cls.MINIMUM_RATIO,
+                maximum_ratio=cls.MAXIMUM_RATIO,
+                minimum_ratio_str=cls.MINIMUM_RATIO_STR,
+                maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
+            )
+        except Exception as e:
+            return str(e)
+        return True
+
+    RETURN_TYPES = (IO.IMAGE,)
+    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    FUNCTION = "api_call"
+    API_NODE = True
+    CATEGORY = "api node/image/BFL"
+
+    BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate"
+
+    def api_call(
+        self,
+        prompt: str,
+        aspect_ratio: str,
+        guidance: float,
+        steps: int,
+        input_image: Optional[torch.Tensor]=None,
+        seed=0,
+        prompt_upsampling=False,
+        unique_id: Union[str, None] = None,
+        **kwargs,
+    ):
+        if input_image is None:
+            validate_string(prompt, strip_whitespace=False)
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=self.BFL_PATH,
+                method=HttpMethod.POST,
+                request_model=BFLFluxKontextProGenerateRequest,
+                response_model=BFLFluxProGenerateResponse,
+            ),
+            request=BFLFluxKontextProGenerateRequest(
+                prompt=prompt,
+                prompt_upsampling=prompt_upsampling,
+                guidance=round(guidance, 1),
+                steps=steps,
+                seed=seed,
+                aspect_ratio=validate_aspect_ratio(
+                    aspect_ratio,
+                    minimum_ratio=self.MINIMUM_RATIO,
+                    maximum_ratio=self.MAXIMUM_RATIO,
+                    minimum_ratio_str=self.MINIMUM_RATIO_STR,
+                    maximum_ratio_str=self.MAXIMUM_RATIO_STR,
+                ),
+                input_image=(
+                    input_image
+                    if input_image is None
+                    else convert_image_to_base64(input_image)
+                )
+            ),
+            auth_kwargs=kwargs,
+        )
+        output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+        return (output_image,)
+
+
+class FluxKontextMaxImageNode(FluxKontextProImageNode):
+    """
+    Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio.
+    """
+
+    DESCRIPTION = cleandoc(__doc__ or "")
+    BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
+
 
 class FluxProImageNode(ComfyNodeABC):
     """
@@ -914,6 +1067,8 @@ class FluxProDepthNode(ComfyNodeABC):
 NODE_CLASS_MAPPINGS = {
     "FluxProUltraImageNode": FluxProUltraImageNode,
     # "FluxProImageNode": FluxProImageNode,
+    "FluxKontextProImageNode": FluxKontextProImageNode,
+    "FluxKontextMaxImageNode": FluxKontextMaxImageNode,
     "FluxProExpandNode": FluxProExpandNode,
     "FluxProFillNode": FluxProFillNode,
     "FluxProCannyNode": FluxProCannyNode,
@@ -924,6 +1079,8 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
     # "FluxProImageNode": "Flux 1.1 [pro] Image",
+    "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image",
+    "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image",
     "FluxProExpandNode": "Flux.1 Expand Image",
     "FluxProFillNode": "Flux.1 Fill Image",
     "FluxProCannyNode": "Flux.1 Canny Control Image",

comfy_api_nodes/nodes_gemini.py

@@ -0,0 +1,446 @@
+"""
+API Nodes for Gemini Multimodal LLM Usage via Remote API
+See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+"""
+
+import os
+from enum import Enum
+from typing import Optional, Literal
+
+import torch
+
+import folder_paths
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from server import PromptServer
+from comfy_api_nodes.apis import (
+    GeminiContent,
+    GeminiGenerateContentRequest,
+    GeminiGenerateContentResponse,
+    GeminiInlineData,
+    GeminiPart,
+    GeminiMimeType,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+)
+from comfy_api_nodes.apinode_utils import (
+    validate_string,
+    audio_to_base64_string,
+    video_to_base64_string,
+    tensor_to_base64_string,
+)
+
+
+GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
+GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024  # 20 MB
+
+
+class GeminiModel(str, Enum):
+    """
+    Gemini Model Names allowed by comfy-api
+    """
+
+    gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06"
+    gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
+
+
+def get_gemini_endpoint(
+    model: GeminiModel,
+) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]:
+    """
+    Get the API endpoint for a given Gemini model.
+
+    Args:
+        model: The Gemini model to use, either as enum or string value.
+
+    Returns:
+        ApiEndpoint configured for the specific Gemini model.
+    """
+    if isinstance(model, str):
+        model = GeminiModel(model)
+    return ApiEndpoint(
+        path=f"{GEMINI_BASE_ENDPOINT}/{model.value}",
+        method=HttpMethod.POST,
+        request_model=GeminiGenerateContentRequest,
+        response_model=GeminiGenerateContentResponse,
+    )
+
+
+class GeminiNode(ComfyNodeABC):
+    """
+    Node to generate text responses from a Gemini model.
+
+    This node allows users to interact with Google's Gemini AI models, providing
+    multimodal inputs (text, images, audio, video, files) to generate coherent
+    text responses. The node works with the latest Gemini models, handling the
+    API communication and response parsing.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Text inputs to the model, used to generate a response. You can include detailed instructions, questions, or context for the model.",
+                    },
+                ),
+                "model": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "The Gemini model to use for generating responses.",
+                        "options": [model.value for model in GeminiModel],
+                        "default": GeminiModel.gemini_2_5_pro_preview_05_06.value,
+                    },
+                ),
+                "seed": (
+                    IO.INT,
+                    {
+                        "default": 42,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": True,
+                        "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.",
+                    },
+                ),
+            },
+            "optional": {
+                "images": (
+                    IO.IMAGE,
+                    {
+                        "default": None,
+                        "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+                    },
+                ),
+                "audio": (
+                    IO.AUDIO,
+                    {
+                        "tooltip": "Optional audio to use as context for the model.",
+                        "default": None,
+                    },
+                ),
+                "video": (
+                    IO.VIDEO,
+                    {
+                        "tooltip": "Optional video to use as context for the model.",
+                        "default": None,
+                    },
+                ),
+                "files": (
+                    "GEMINI_INPUT_FILES",
+                    {
+                        "default": None,
+                        "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.",
+                    },
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    DESCRIPTION = "Generate text responses with Google's Gemini AI model. You can provide multiple types of inputs (text, images, audio, video) as context for generating more relevant and meaningful responses."
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/text/Gemini"
+    API_NODE = True
+
+    def get_parts_from_response(
+        self, response: GeminiGenerateContentResponse
+    ) -> list[GeminiPart]:
+        """
+        Extract all parts from the Gemini API response.
+
+        Args:
+            response: The API response from Gemini.
+
+        Returns:
+            List of response parts from the first candidate.
+        """
+        return response.candidates[0].content.parts
+
+    def get_parts_by_type(
+        self, response: GeminiGenerateContentResponse, part_type: Literal["text"] | str
+    ) -> list[GeminiPart]:
+        """
+        Filter response parts by their type.
+
+        Args:
+            response: The API response from Gemini.
+            part_type: Type of parts to extract ("text" or a MIME type).
+
+        Returns:
+            List of response parts matching the requested type.
+        """
+        parts = []
+        for part in self.get_parts_from_response(response):
+            if part_type == "text" and hasattr(part, "text") and part.text:
+                parts.append(part)
+            elif (
+                hasattr(part, "inlineData")
+                and part.inlineData
+                and part.inlineData.mimeType == part_type
+            ):
+                parts.append(part)
+            # Skip parts that don't match the requested type
+        return parts
+
+    def get_text_from_response(self, response: GeminiGenerateContentResponse) -> str:
+        """
+        Extract and concatenate all text parts from the response.
+
+        Args:
+            response: The API response from Gemini.
+
+        Returns:
+            Combined text from all text parts in the response.
+        """
+        parts = self.get_parts_by_type(response, "text")
+        return "\n".join([part.text for part in parts])
+
+    def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]:
+        """
+        Convert video input to Gemini API compatible parts.
+
+        Args:
+            video_input: Video tensor from ComfyUI.
+            **kwargs: Additional arguments to pass to the conversion function.
+
+        Returns:
+            List of GeminiPart objects containing the encoded video.
+        """
+        from comfy_api.util import VideoContainer, VideoCodec
+        base_64_string = video_to_base64_string(
+            video_input,
+            container_format=VideoContainer.MP4,
+            codec=VideoCodec.H264
+        )
+        return [
+            GeminiPart(
+                inlineData=GeminiInlineData(
+                    mimeType=GeminiMimeType.video_mp4,
+                    data=base_64_string,
+                )
+            )
+        ]
+
+    def create_audio_parts(self, audio_input: IO.AUDIO) -> list[GeminiPart]:
+        """
+        Convert audio input to Gemini API compatible parts.
+
+        Args:
+            audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate.
+
+        Returns:
+            List of GeminiPart objects containing the encoded audio.
+        """
+        audio_parts: list[GeminiPart] = []
+        for batch_index in range(audio_input["waveform"].shape[0]):
+            # Recreate an IO.AUDIO object for the given batch dimension index
+            audio_at_index = {
+                "waveform": audio_input["waveform"][batch_index].unsqueeze(0),
+                "sample_rate": audio_input["sample_rate"],
+            }
+            # Convert to MP3 format for compatibility with Gemini API
+            audio_bytes = audio_to_base64_string(
+                audio_at_index,
+                container_format="mp3",
+                codec_name="libmp3lame",
+            )
+            audio_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.audio_mp3,
+                        data=audio_bytes,
+                    )
+                )
+            )
+        return audio_parts
+
+    def create_image_parts(self, image_input: torch.Tensor) -> list[GeminiPart]:
+        """
+        Convert image tensor input to Gemini API compatible parts.
+
+        Args:
+            image_input: Batch of image tensors from ComfyUI.
+
+        Returns:
+            List of GeminiPart objects containing the encoded images.
+        """
+        image_parts: list[GeminiPart] = []
+        for image_index in range(image_input.shape[0]):
+            image_as_b64 = tensor_to_base64_string(
+                image_input[image_index].unsqueeze(0)
+            )
+            image_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.image_png,
+                        data=image_as_b64,
+                    )
+                )
+            )
+        return image_parts
+
+    def create_text_part(self, text: str) -> GeminiPart:
+        """
+        Create a text part for the Gemini API request.
+
+        Args:
+            text: The text content to include in the request.
+
+        Returns:
+            A GeminiPart object with the text content.
+        """
+        return GeminiPart(text=text)
+
+    def api_call(
+        self,
+        prompt: str,
+        model: GeminiModel,
+        images: Optional[IO.IMAGE] = None,
+        audio: Optional[IO.AUDIO] = None,
+        video: Optional[IO.VIDEO] = None,
+        files: Optional[list[GeminiPart]] = None,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[str]:
+        # Validate inputs
+        validate_string(prompt, strip_whitespace=False)
+
+        # Create parts list with text prompt as the first part
+        parts: list[GeminiPart] = [self.create_text_part(prompt)]
+
+        # Add other modal parts
+        if images is not None:
+            image_parts = self.create_image_parts(images)
+            parts.extend(image_parts)
+        if audio is not None:
+            parts.extend(self.create_audio_parts(audio))
+        if video is not None:
+            parts.extend(self.create_video_parts(video))
+        if files is not None:
+            parts.extend(files)
+
+        # Create response
+        response = SynchronousOperation(
+            endpoint=get_gemini_endpoint(model),
+            request=GeminiGenerateContentRequest(
+                contents=[
+                    GeminiContent(
+                        role="user",
+                        parts=parts,
+                    )
+                ]
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+
+        # Get result output
+        output_text = self.get_text_from_response(response)
+        if unique_id and output_text:
+            PromptServer.instance.send_progress_text(output_text, node_id=unique_id)
+
+        return (output_text or "Empty response from Gemini model...",)
+
+
+class GeminiInputFiles(ComfyNodeABC):
+    """
+    Loads and formats input files for use with the Gemini API.
+
+    This node allows users to include text (.txt) and PDF (.pdf) files as input
+    context for the Gemini model. Files are converted to the appropriate format
+    required by the API and can be chained together to include multiple files
+    in a single request.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        """
+        For details about the supported file input types, see:
+        https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+        """
+        input_dir = folder_paths.get_input_directory()
+        input_files = [
+            f
+            for f in os.scandir(input_dir)
+            if f.is_file()
+            and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+            and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE
+        ]
+        input_files = sorted(input_files, key=lambda x: x.name)
+        input_files = [f.name for f in input_files]
+        return {
+            "required": {
+                "file": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+                        "options": input_files,
+                        "default": input_files[0] if input_files else None,
+                    },
+                ),
+            },
+            "optional": {
+                "GEMINI_INPUT_FILES": (
+                    "GEMINI_INPUT_FILES",
+                    {
+                        "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+                        "default": None,
+                    },
+                ),
+            },
+        }
+
+    DESCRIPTION = "Loads and prepares input files to include as inputs for Gemini LLM nodes. The files will be read by the Gemini model when generating a response. The contents of the text file count toward the token limit. 🛈 TIP: Can be chained together with other Gemini Input File nodes."
+    RETURN_TYPES = ("GEMINI_INPUT_FILES",)
+    FUNCTION = "prepare_files"
+    CATEGORY = "api node/text/Gemini"
+
+    def create_file_part(self, file_path: str) -> GeminiPart:
+        mime_type = (
+            GeminiMimeType.pdf
+            if file_path.endswith(".pdf")
+            else GeminiMimeType.text_plain
+        )
+        # Use base64 string directly, not the data URI
+        with open(file_path, "rb") as f:
+            file_content = f.read()
+        import base64
+        base64_str = base64.b64encode(file_content).decode("utf-8")
+
+        return GeminiPart(
+            inlineData=GeminiInlineData(
+                mimeType=mime_type,
+                data=base64_str,
+            )
+        )
+
+    def prepare_files(
+        self, file: str, GEMINI_INPUT_FILES: list[GeminiPart] = []
+    ) -> tuple[list[GeminiPart]]:
+        """
+        Loads and formats input files for Gemini API.
+        """
+        file_path = folder_paths.get_annotated_filepath(file)
+        input_file_content = self.create_file_part(file_path)
+        files = [input_file_content] + GEMINI_INPUT_FILES
+        return (files,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "GeminiNode": GeminiNode,
+    "GeminiInputFiles": GeminiInputFiles,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "GeminiNode": "Google Gemini",
+    "GeminiInputFiles": "Gemini Input Files",
+}

comfy_api_nodes/nodes_ideogram.py

@@ -324,7 +324,7 @@ class IdeogramV1(ComfyNodeABC):
 
     RETURN_TYPES = (IO.IMAGE,)
     FUNCTION = "api_call"
-    CATEGORY = "api node/image/Ideogram/v1"
+    CATEGORY = "api node/image/Ideogram"
     DESCRIPTION = cleandoc(__doc__ or "")
     API_NODE = True
 
@@ -483,7 +483,7 @@ class IdeogramV2(ComfyNodeABC):
 
     RETURN_TYPES = (IO.IMAGE,)
     FUNCTION = "api_call"
-    CATEGORY = "api node/image/Ideogram/v2"
+    CATEGORY = "api node/image/Ideogram"
     DESCRIPTION = cleandoc(__doc__ or "")
     API_NODE = True
 
@@ -649,7 +649,7 @@ class IdeogramV3(ComfyNodeABC):
 
     RETURN_TYPES = (IO.IMAGE,)
     FUNCTION = "api_call"
-    CATEGORY = "api node/image/Ideogram/v3"
+    CATEGORY = "api node/image/Ideogram"
     DESCRIPTION = cleandoc(__doc__ or "")
     API_NODE = True
 

comfy_api_nodes/nodes_openai.py

@@ -1,29 +1,86 @@
 import io
+from typing import TypedDict, Optional
+import json
+import os
+import time
+import re
+import uuid
+from enum import Enum
 from inspect import cleandoc
 import numpy as np
 import torch
 from PIL import Image
-
 from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from server import PromptServer
+import folder_paths
 
 
 from comfy_api_nodes.apis import (
     OpenAIImageGenerationRequest,
     OpenAIImageEditRequest,
     OpenAIImageGenerationResponse,
+    OpenAICreateResponse,
+    OpenAIResponse,
+    CreateModelResponseProperties,
+    Item,
+    Includable,
+    OutputContent,
+    InputImageContent,
+    Detail,
+    InputTextContent,
+    InputMessage,
+    InputMessageContentList,
+    InputContent,
+    InputFileContent,
 )
 
 from comfy_api_nodes.apis.client import (
     ApiEndpoint,
     HttpMethod,
     SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
 )
 
 from comfy_api_nodes.apinode_utils import (
     downscale_image_tensor,
     validate_and_cast_response,
     validate_string,
+    tensor_to_base64_string,
+    text_filepath_to_data_uri,
 )
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+
+
+RESPONSES_ENDPOINT = "/proxy/openai/v1/responses"
+STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
+
+
+class HistoryEntry(TypedDict):
+    """Type definition for a single history entry in the chat."""
+
+    prompt: str
+    response: str
+    response_id: str
+    timestamp: float
+
+
+class ChatHistory(TypedDict):
+    """Type definition for the chat history dictionary."""
+
+    __annotations__: dict[str, list[HistoryEntry]]
+
+
+class SupportedOpenAIModel(str, Enum):
+    o4_mini = "o4-mini"
+    o1 = "o1"
+    o3 = "o3"
+    o1_pro = "o1-pro"
+    gpt_4o = "gpt-4o"
+    gpt_4_1 = "gpt-4.1"
+    gpt_4_1_mini = "gpt-4.1-mini"
+    gpt_4_1_nano = "gpt-4.1-nano"
+
 
 class OpenAIDalle2(ComfyNodeABC):
     """
@@ -115,7 +172,7 @@ class OpenAIDalle2(ComfyNodeABC):
         n=1,
         size="1024x1024",
         unique_id=None,
-        **kwargs
+        **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
         model = "dall-e-2"
@@ -262,7 +319,7 @@ class OpenAIDalle3(ComfyNodeABC):
         quality="standard",
         size="1024x1024",
         unique_id=None,
-        **kwargs
+        **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
         model = "dall-e-3"
@@ -400,12 +457,12 @@ class OpenAIGPTImage1(ComfyNodeABC):
         n=1,
         size="1024x1024",
         unique_id=None,
-        **kwargs
+        **kwargs,
     ):
         validate_string(prompt, strip_whitespace=False)
         model = "gpt-image-1"
         path = "/proxy/openai/images/generations"
-        content_type="application/json"
+        content_type = "application/json"
         request_class = OpenAIImageGenerationRequest
         img_binaries = []
         mask_binary = None
@@ -414,7 +471,7 @@ class OpenAIGPTImage1(ComfyNodeABC):
         if image is not None:
             path = "/proxy/openai/images/edits"
             request_class = OpenAIImageEditRequest
-            content_type ="multipart/form-data"
+            content_type = "multipart/form-data"
 
             batch_size = image.shape[0]
 
@@ -486,17 +543,466 @@ class OpenAIGPTImage1(ComfyNodeABC):
         return (img_tensor,)
 
 
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
+class OpenAITextNode(ComfyNodeABC):
+    """
+    Base class for OpenAI text generation nodes.
+    """
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/text/OpenAI"
+    API_NODE = True
+
+
+class OpenAIChatNode(OpenAITextNode):
+    """
+    Node to generate text responses from an OpenAI model.
+    """
+
+    def __init__(self) -> None:
+        """Initialize the chat node with a new session ID and empty history."""
+        self.current_session_id: str = str(uuid.uuid4())
+        self.history: dict[str, list[HistoryEntry]] = {}
+        self.previous_response_id: Optional[str] = None
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "prompt": (
+                    IO.STRING,
+                    {
+                        "multiline": True,
+                        "default": "",
+                        "tooltip": "Text inputs to the model, used to generate a response.",
+                    },
+                ),
+                "persist_context": (
+                    IO.BOOLEAN,
+                    {
+                        "default": True,
+                        "tooltip": "Persist chat context between calls (multi-turn conversation)",
+                    },
+                ),
+                "model": model_field_to_node_input(
+                    IO.COMBO,
+                    OpenAICreateResponse,
+                    "model",
+                    enum_type=SupportedOpenAIModel,
+                ),
+            },
+            "optional": {
+                "images": (
+                    IO.IMAGE,
+                    {
+                        "default": None,
+                        "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+                    },
+                ),
+                "files": (
+                    "OPENAI_INPUT_FILES",
+                    {
+                        "default": None,
+                        "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.",
+                    },
+                ),
+                "advanced_options": (
+                    "OPENAI_CHAT_CONFIG",
+                    {
+                        "default": None,
+                        "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.",
+                    },
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    DESCRIPTION = "Generate text responses from an OpenAI model."
+
+    def get_result_response(
+        self,
+        response_id: str,
+        include: Optional[list[Includable]] = None,
+        auth_kwargs: Optional[dict[str, str]] = None,
+    ) -> OpenAIResponse:
+        """
+        Retrieve a model response with the given ID from the OpenAI API.
+
+        Args:
+            response_id (str): The ID of the response to retrieve.
+            include (Optional[List[Includable]]): Additional fields to include
+                in the response. See the `include` parameter for Response
+                creation above for more information.
+
+        """
+        return PollingOperation(
+            poll_endpoint=ApiEndpoint(
+                path=f"{RESPONSES_ENDPOINT}/{response_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=OpenAIResponse,
+                query_params={"include": include},
+            ),
+            completed_statuses=["completed"],
+            failed_statuses=["failed"],
+            status_extractor=lambda response: response.status,
+            auth_kwargs=auth_kwargs,
+        ).execute()
+
+    def get_message_content_from_response(
+        self, response: OpenAIResponse
+    ) -> list[OutputContent]:
+        """Extract message content from the API response."""
+        for output in response.output:
+            if output.root.type == "message":
+                return output.root.content
+        raise TypeError("No output message found in response")
+
+    def get_text_from_message_content(
+        self, message_content: list[OutputContent]
+    ) -> str:
+        """Extract text content from message content."""
+        for content_item in message_content:
+            if content_item.root.type == "output_text":
+                return str(content_item.root.text)
+        return "No text output found in response"
+
+    def get_history_text(self, session_id: str) -> str:
+        """Convert the entire history for a given session to JSON string."""
+        return json.dumps(self.history[session_id])
+
+    def display_history_on_node(self, session_id: str, node_id: str) -> None:
+        """Display formatted chat history on the node UI."""
+        render_spec = {
+            "node_id": node_id,
+            "component": "ChatHistoryWidget",
+            "props": {
+                "history": self.get_history_text(session_id),
+            },
+        }
+        PromptServer.instance.send_sync(
+            "display_component",
+            render_spec,
+        )
+
+    def add_to_history(
+        self, session_id: str, prompt: str, output_text: str, response_id: str
+    ) -> None:
+        """Add a new entry to the chat history."""
+        if session_id not in self.history:
+            self.history[session_id] = []
+        self.history[session_id].append(
+            {
+                "prompt": prompt,
+                "response": output_text,
+                "response_id": response_id,
+                "timestamp": time.time(),
+            }
+        )
+
+    def parse_output_text_from_response(self, response: OpenAIResponse) -> str:
+        """Extract text output from the API response."""
+        message_contents = self.get_message_content_from_response(response)
+        return self.get_text_from_message_content(message_contents)
+
+    def generate_new_session_id(self) -> str:
+        """Generate a new unique session ID."""
+        return str(uuid.uuid4())
+
+    def get_session_id(self, persist_context: bool) -> str:
+        """Get the current or generate a new session ID based on context persistence."""
+        return (
+            self.current_session_id
+            if persist_context
+            else self.generate_new_session_id()
+        )
+
+    def tensor_to_input_image_content(
+        self, image: torch.Tensor, detail_level: Detail = "auto"
+    ) -> InputImageContent:
+        """Convert a tensor to an input image content object."""
+        return InputImageContent(
+            detail=detail_level,
+            image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}",
+            type="input_image",
+        )
+
+    def create_input_message_contents(
+        self,
+        prompt: str,
+        image: Optional[torch.Tensor] = None,
+        files: Optional[list[InputFileContent]] = None,
+    ) -> InputMessageContentList:
+        """Create a list of input message contents from prompt and optional image."""
+        content_list: list[InputContent] = [
+            InputTextContent(text=prompt, type="input_text"),
+        ]
+        if image is not None:
+            for i in range(image.shape[0]):
+                content_list.append(
+                    self.tensor_to_input_image_content(image[i].unsqueeze(0))
+                )
+        if files is not None:
+            content_list.extend(files)
+
+        return InputMessageContentList(
+            root=content_list,
+        )
+
+    def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]:
+        """Extract response ID from prompt if it exists."""
+        parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt)
+        return parsed_id.group(1) if parsed_id else None
+
+    def strip_response_tag_from_prompt(self, prompt: str) -> str:
+        """Remove the response ID tag from the prompt."""
+        return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip())
+
+    def delete_history_after_response_id(
+        self, new_start_id: str, session_id: str
+    ) -> None:
+        """Delete history entries after a specific response ID."""
+        if session_id not in self.history:
+            return
+
+        new_history = []
+        i = 0
+        while (
+            i < len(self.history[session_id])
+            and self.history[session_id][i]["response_id"] != new_start_id
+        ):
+            new_history.append(self.history[session_id][i])
+            i += 1
+
+        # Since it's the new starting point (not the response being edited), we include it as well
+        if i < len(self.history[session_id]):
+            new_history.append(self.history[session_id][i])
+
+        self.history[session_id] = new_history
+
+    def api_call(
+        self,
+        prompt: str,
+        persist_context: bool,
+        model: SupportedOpenAIModel,
+        unique_id: Optional[str] = None,
+        images: Optional[torch.Tensor] = None,
+        files: Optional[list[InputFileContent]] = None,
+        advanced_options: Optional[CreateModelResponseProperties] = None,
+        **kwargs,
+    ) -> tuple[str]:
+        # Validate inputs
+        validate_string(prompt, strip_whitespace=False)
+
+        session_id = self.get_session_id(persist_context)
+        response_id_override = self.parse_response_id_from_prompt(prompt)
+        if response_id_override:
+            is_starting_from_beginning = response_id_override == "start"
+            if is_starting_from_beginning:
+                self.history[session_id] = []
+                previous_response_id = None
+            else:
+                previous_response_id = response_id_override
+                self.delete_history_after_response_id(response_id_override, session_id)
+            prompt = self.strip_response_tag_from_prompt(prompt)
+        elif persist_context:
+            previous_response_id = self.previous_response_id
+        else:
+            previous_response_id = None
+
+        # Create response
+        create_response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=RESPONSES_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=OpenAICreateResponse,
+                response_model=OpenAIResponse,
+            ),
+            request=OpenAICreateResponse(
+                input=[
+                    Item(
+                        root=InputMessage(
+                            content=self.create_input_message_contents(
+                                prompt, images, files
+                            ),
+                            role="user",
+                        )
+                    ),
+                ],
+                store=True,
+                stream=False,
+                model=model,
+                previous_response_id=previous_response_id,
+                **(
+                    advanced_options.model_dump(exclude_none=True)
+                    if advanced_options
+                    else {}
+                ),
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        response_id = create_response.id
+
+        # Get result output
+        result_response = self.get_result_response(response_id, auth_kwargs=kwargs)
+        output_text = self.parse_output_text_from_response(result_response)
+
+        # Update history
+        self.add_to_history(session_id, prompt, output_text, response_id)
+        self.display_history_on_node(session_id, unique_id)
+        self.previous_response_id = response_id
+
+        return (output_text,)
+
+
+class OpenAIInputFiles(ComfyNodeABC):
+    """
+    Loads and formats input files for OpenAI API.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        """
+        For details about the supported file input types, see:
+        https://platform.openai.com/docs/guides/pdf-files?api-mode=responses
+        """
+        input_dir = folder_paths.get_input_directory()
+        input_files = [
+            f
+            for f in os.scandir(input_dir)
+            if f.is_file()
+            and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+            and f.stat().st_size < 32 * 1024 * 1024
+        ]
+        input_files = sorted(input_files, key=lambda x: x.name)
+        input_files = [f.name for f in input_files]
+        return {
+            "required": {
+                "file": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+                        "options": input_files,
+                        "default": input_files[0] if input_files else None,
+                    },
+                ),
+            },
+            "optional": {
+                "OPENAI_INPUT_FILES": (
+                    "OPENAI_INPUT_FILES",
+                    {
+                        "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+                        "default": None,
+                    },
+                ),
+            },
+        }
+
+    DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes."
+    RETURN_TYPES = ("OPENAI_INPUT_FILES",)
+    FUNCTION = "prepare_files"
+    CATEGORY = "api node/text/OpenAI"
+
+    def create_input_file_content(self, file_path: str) -> InputFileContent:
+        return InputFileContent(
+            file_data=text_filepath_to_data_uri(file_path),
+            filename=os.path.basename(file_path),
+            type="input_file",
+        )
+
+    def prepare_files(
+        self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = []
+    ) -> tuple[list[InputFileContent]]:
+        """
+        Loads and formats input files for OpenAI API.
+        """
+        file_path = folder_paths.get_annotated_filepath(file)
+        input_file_content = self.create_input_file_content(file_path)
+        files = [input_file_content] + OPENAI_INPUT_FILES
+        return (files,)
+
+
+class OpenAIChatConfig(ComfyNodeABC):
+    """Allows setting additional configuration for the OpenAI Chat Node."""
+
+    RETURN_TYPES = ("OPENAI_CHAT_CONFIG",)
+    FUNCTION = "configure"
+    DESCRIPTION = (
+        "Allows specifying advanced configuration options for the OpenAI Chat Nodes."
+    )
+    CATEGORY = "api node/text/OpenAI"
+
+    @classmethod
+    def INPUT_TYPES(cls) -> InputTypeDict:
+        return {
+            "required": {
+                "truncation": (
+                    IO.COMBO,
+                    {
+                        "options": ["auto", "disabled"],
+                        "default": "auto",
+                        "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+                    },
+                ),
+            },
+            "optional": {
+                "max_output_tokens": model_field_to_node_input(
+                    IO.INT,
+                    OpenAICreateResponse,
+                    "max_output_tokens",
+                    min=16,
+                    default=4096,
+                    max=16384,
+                    tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
+                ),
+                "instructions": model_field_to_node_input(
+                    IO.STRING, OpenAICreateResponse, "instructions", multiline=True
+                ),
+            },
+        }
+
+    def configure(
+        self,
+        truncation: bool,
+        instructions: Optional[str] = None,
+        max_output_tokens: Optional[int] = None,
+    ) -> tuple[CreateModelResponseProperties]:
+        """
+        Configure advanced options for the OpenAI Chat Node.
+
+        Note:
+            While `top_p` and `temperature` are listed as properties in the
+            spec, they are not supported for all models (e.g., o4-mini).
+            They are not exposed as inputs at all to avoid having to manually
+            remove depending on model choice.
+        """
+        return (
+            CreateModelResponseProperties(
+                instructions=instructions,
+                truncation=truncation,
+                max_output_tokens=max_output_tokens,
+            ),
+        )
+
+
 NODE_CLASS_MAPPINGS = {
     "OpenAIDalle2": OpenAIDalle2,
     "OpenAIDalle3": OpenAIDalle3,
     "OpenAIGPTImage1": OpenAIGPTImage1,
+    "OpenAIChatNode": OpenAIChatNode,
+    "OpenAIInputFiles": OpenAIInputFiles,
+    "OpenAIChatConfig": OpenAIChatConfig,
 }
 
-# A dictionary that contains the friendly/humanly readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
     "OpenAIDalle2": "OpenAI DALL·E 2",
     "OpenAIDalle3": "OpenAI DALL·E 3",
     "OpenAIGPTImage1": "OpenAI GPT Image 1",
+    "OpenAIChatNode": "OpenAI Chat",
+    "OpenAIInputFiles": "OpenAI Chat Input Files",
+    "OpenAIChatConfig": "OpenAI Chat Advanced Options",
 }

comfy_api_nodes/nodes_pika.py

@@ -6,40 +6,42 @@ Pika API docs: https://pika-827374fb.mintlify.app/api-reference
 from __future__ import annotations
 
 import io
-from typing import Optional, TypeVar
 import logging
-import torch
+from typing import Optional, TypeVar
+
 import numpy as np
+import torch
+
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput
+from comfy_api_nodes.apinode_utils import (
+    download_url_to_video_output,
+    tensor_to_bytesio,
+)
 from comfy_api_nodes.apis import (
-    PikaBodyGenerate22T2vGenerate22T2vPost,
-    PikaGenerateResponse,
-    PikaBodyGenerate22I2vGenerate22I2vPost,
-    PikaVideoResponse,
-    PikaBodyGenerate22C2vGenerate22PikascenesPost,
     IngredientsMode,
-    PikaDurationEnum,
-    PikaResolutionEnum,
-    PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
-    PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
-    PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+    PikaBodyGenerate22C2vGenerate22PikascenesPost,
+    PikaBodyGenerate22I2vGenerate22I2vPost,
     PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
+    PikaBodyGenerate22T2vGenerate22T2vPost,
+    PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+    PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+    PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+    PikaDurationEnum,
     Pikaffect,
+    PikaGenerateResponse,
+    PikaResolutionEnum,
+    PikaVideoResponse,
 )
 from comfy_api_nodes.apis.client import (
     ApiEndpoint,
-    HttpMethod,
-    SynchronousOperation,
-    PollingOperation,
     EmptyRequest,
-)
-from comfy_api_nodes.apinode_utils import (
-    tensor_to_bytesio,
-    download_url_to_video_output,
+    HttpMethod,
+    PollingOperation,
+    SynchronousOperation,
 )
 from comfy_api_nodes.mapper_utils import model_field_to_node_input
-from comfy_api.input_impl.video_types import VideoInput, VideoContainer, VideoCodec
-from comfy_api.input_impl import VideoFromFile
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
 
 R = TypeVar("R")
 
@@ -204,6 +206,7 @@ class PikaImageToVideoV2_2(PikaNodeBase):
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
                 "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
             },
         }
 
@@ -457,7 +460,7 @@ class PikAdditionsNode(PikaNodeBase):
             },
         }
 
-    DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you’d like to add to create a seamlessly integrated result."
+    DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result."
 
     def api_call(
         self,

comfy_api_nodes/nodes_rodin.py

@@ -0,0 +1,462 @@
+"""
+ComfyUI X Rodin3D(Deemos) API Nodes
+
+Rodin API docs: https://developer.hyper3d.ai/
+
+"""
+
+from __future__ import annotations
+from inspect import cleandoc
+from comfy.comfy_types.node_typing import IO
+import folder_paths as comfy_paths
+import requests
+import os
+import datetime
+import shutil
+import time
+import io
+import logging
+import math
+from PIL import Image
+from comfy_api_nodes.apis.rodin_api import (
+    Rodin3DGenerateRequest,
+    Rodin3DGenerateResponse,
+    Rodin3DCheckStatusRequest,
+    Rodin3DCheckStatusResponse,
+    Rodin3DDownloadRequest,
+    Rodin3DDownloadResponse,
+    JobStatus,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+)
+
+
+COMMON_PARAMETERS = {
+    "Seed": (
+        IO.INT,
+        {
+            "default":0,
+            "min":0,
+            "max":65535,
+            "display":"number"
+        }
+    ),
+    "Material_Type": (
+        IO.COMBO,
+        {
+            "options": ["PBR", "Shaded"],
+            "default": "PBR"
+        }
+    ),
+    "Polygon_count": (
+        IO.COMBO,
+        {
+            "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
+            "default": "18K-Quad"
+        }
+    )
+}
+
+def create_task_error(response: Rodin3DGenerateResponse):
+    """Check if the response has error"""
+    return hasattr(response, "error")
+
+
+
+class Rodin3DAPI:
+    """
+    Generate 3D Assets using Rodin API
+    """
+    RETURN_TYPES = (IO.STRING,)
+    RETURN_NAMES = ("3D Model Path",)
+    CATEGORY = "api node/3d/Rodin"
+    DESCRIPTION = cleandoc(__doc__ or "")
+    FUNCTION = "api_call"
+    API_NODE = True
+
+    def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048):
+        """
+        Converts a PyTorch tensor to a file-like object.
+
+        Args:
+        - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
+          where C is the number of channels (3 for RGB), H is height, and W is width.
+
+        Returns:
+        - io.BytesIO: A file-like object containing the image data.
+        """
+        array = tensor.cpu().numpy()
+        array = (array * 255).astype('uint8')
+        image = Image.fromarray(array, 'RGB')
+
+        original_width, original_height = image.size
+        original_pixels = original_width * original_height
+        if original_pixels > max_pixels:
+            scale = math.sqrt(max_pixels / original_pixels)
+            new_width = int(original_width * scale)
+            new_height = int(original_height * scale)
+        else:
+            new_width, new_height = original_width, original_height
+
+        if new_width != original_width or new_height != original_height:
+            image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+        img_byte_arr = io.BytesIO()
+        image.save(img_byte_arr, format='PNG')  # PNG is used for lossless compression
+        img_byte_arr.seek(0)
+        return img_byte_arr
+
+    def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str:
+        has_failed = any(job.status == JobStatus.Failed for job in response.jobs)
+        all_done = all(job.status == JobStatus.Done for job in response.jobs)
+        status_list = [str(job.status) for job in response.jobs]
+        logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
+        if has_failed:
+            logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
+            raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
+        elif all_done:
+            return "DONE"
+        else:
+            return "Generating"
+
+    def CreateGenerateTask(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs):
+        if images == None:
+            raise Exception("Rodin 3D generate requires at least 1 image.")
+        if len(images) >= 5:
+            raise Exception("Rodin 3D generate requires up to 5 image.")
+
+        path = "/proxy/rodin/api/v2/rodin"
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=path,
+                method=HttpMethod.POST,
+                request_model=Rodin3DGenerateRequest,
+                response_model=Rodin3DGenerateResponse,
+            ),
+            request=Rodin3DGenerateRequest(
+                seed=seed,
+                tier=tier,
+                material=material,
+                quality=quality,
+                mesh_mode=mesh_mode
+            ),
+            files=[
+                (
+                    "images",
+                    open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image)
+                )
+                for image in images if image is not None
+            ],
+            content_type = "multipart/form-data",
+            auth_kwargs=kwargs,
+        )
+
+        response = operation.execute()
+
+        if create_task_error(response):
+            error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
+            logging.error(error_message)
+            raise Exception(error_message)
+
+        logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
+        subscription_key = response.jobs.subscription_key
+        task_uuid = response.uuid
+        logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
+        return task_uuid, subscription_key
+
+    def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse:
+
+        path = "/proxy/rodin/api/v2/status"
+
+        poll_operation = PollingOperation(
+            poll_endpoint=ApiEndpoint(
+                path = path,
+                method=HttpMethod.POST,
+                request_model=Rodin3DCheckStatusRequest,
+                response_model=Rodin3DCheckStatusResponse,
+            ),
+            request=Rodin3DCheckStatusRequest(
+                subscription_key = subscription_key
+            ),
+            completed_statuses=["DONE"],
+            failed_statuses=["FAILED"],
+            status_extractor=self.check_rodin_status,
+            poll_interval=3.0,
+            auth_kwargs=kwargs,
+        )
+
+        logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
+
+        return poll_operation.execute()
+
+
+
+    def GetRodinDownloadList(self, uuid, **kwargs) -> Rodin3DDownloadResponse:
+        logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
+
+        path = "/proxy/rodin/api/v2/download"
+        operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=path,
+                method=HttpMethod.POST,
+                request_model=Rodin3DDownloadRequest,
+                response_model=Rodin3DDownloadResponse,
+            ),
+            request=Rodin3DDownloadRequest(
+                task_uuid=uuid
+            ),
+            auth_kwargs=kwargs
+        )
+
+        return operation.execute()
+
+    def GetQualityAndMode(self, PolyCount):
+        if PolyCount == "200K-Triangle":
+            mesh_mode = "Raw"
+            quality = "medium"
+        else:
+            mesh_mode = "Quad"
+            if PolyCount == "4K-Quad":
+                quality = "extra-low"
+            elif PolyCount == "8K-Quad":
+                quality = "low"
+            elif PolyCount == "18K-Quad":
+                quality = "medium"
+            elif PolyCount == "50K-Quad":
+                quality = "high"
+            else:
+                quality = "medium"
+
+        return mesh_mode, quality
+
+    def DownLoadFiles(self, Url_List):
+        Save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
+        os.makedirs(Save_path, exist_ok=True)
+        model_file_path = None
+        for Item in Url_List.list:
+            url = Item.url
+            file_name = Item.name
+            file_path = os.path.join(Save_path, file_name)
+            if file_path.endswith(".glb"):
+                model_file_path = file_path
+            logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}")
+            max_retries = 5
+            for attempt in range(max_retries):
+                try:
+                    with requests.get(url, stream=True) as r:
+                        r.raise_for_status()
+                        with open(file_path, "wb") as f:
+                            shutil.copyfileobj(r.raw, f)
+                    break
+                except Exception as e:
+                    logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}")
+                    if attempt < max_retries - 1:
+                        logging.info("Retrying...")
+                        time.sleep(2)
+                    else:
+                        logging.info(f"[ Rodin3D API - download_files ] Failed to download {file_path} after {max_retries} attempts.")
+
+        return model_file_path
+
+
+class Rodin3D_Regular(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                **COMMON_PARAMETERS
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        **kwargs
+    ):
+        tier = "Regular"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+class Rodin3D_Detail(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                **COMMON_PARAMETERS
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        **kwargs
+    ):
+        tier = "Detail"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+class Rodin3D_Smooth(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                **COMMON_PARAMETERS
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        **kwargs
+    ):
+        tier = "Smooth"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+class Rodin3D_Sketch(Rodin3DAPI):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "Images":
+                (
+                    IO.IMAGE,
+                    {
+                        "forceInput":True,
+                    }
+                )
+            },
+            "optional": {
+                "Seed":
+                (
+                    IO.INT,
+                    {
+                        "default":0,
+                        "min":0,
+                        "max":65535,
+                        "display":"number"
+                    }
+                )
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        Images,
+        Seed,
+        **kwargs
+    ):
+        tier = "Sketch"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        material_type = "PBR"
+        quality = "medium"
+        mesh_mode = "Quad"
+        task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+        self.poll_for_task_status(subscription_key, **kwargs)
+        Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+        model = self.DownLoadFiles(Download_List)
+
+        return (model,)
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "Rodin3D_Regular": Rodin3D_Regular,
+    "Rodin3D_Detail": Rodin3D_Detail,
+    "Rodin3D_Smooth": Rodin3D_Smooth,
+    "Rodin3D_Sketch": Rodin3D_Sketch,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate",
+    "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate",
+    "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate",
+    "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate",
+}

comfy_api_nodes/nodes_runway.py

@@ -0,0 +1,635 @@
+"""Runway API Nodes
+
+API Docs:
+  - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete
+
+User Guides:
+  - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha
+  - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video
+  - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo
+  - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3
+
+"""
+
+from typing import Union, Optional, Any
+from enum import Enum
+
+import torch
+
+from comfy_api_nodes.apis import (
+    RunwayImageToVideoRequest,
+    RunwayImageToVideoResponse,
+    RunwayTaskStatusResponse as TaskStatusResponse,
+    RunwayTaskStatusEnum as TaskStatus,
+    RunwayModelEnum as Model,
+    RunwayDurationEnum as Duration,
+    RunwayAspectRatioEnum as AspectRatio,
+    RunwayPromptImageObject,
+    RunwayPromptImageDetailedObject,
+    RunwayTextToImageRequest,
+    RunwayTextToImageResponse,
+    Model4,
+    ReferenceImage,
+    RunwayTextToImageAspectRatioEnum,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+    upload_images_to_comfyapi,
+    download_url_to_video_output,
+    image_tensor_pair_to_batch,
+    validate_string,
+    download_url_to_image_tensor,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy_api.input_impl import VideoFromFile
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+
+PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
+PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
+PATH_GET_TASK_STATUS = "/proxy/runway/tasks"
+
+AVERAGE_DURATION_I2V_SECONDS = 64
+AVERAGE_DURATION_FLF_SECONDS = 256
+AVERAGE_DURATION_T2I_SECONDS = 41
+
+
+class RunwayApiError(Exception):
+    """Base exception for Runway API errors."""
+
+    pass
+
+
+class RunwayGen4TurboAspectRatio(str, Enum):
+    """Aspect ratios supported for Image to Video API when using gen4_turbo model."""
+
+    field_1280_720 = "1280:720"
+    field_720_1280 = "720:1280"
+    field_1104_832 = "1104:832"
+    field_832_1104 = "832:1104"
+    field_960_960 = "960:960"
+    field_1584_672 = "1584:672"
+
+
+class RunwayGen3aAspectRatio(str, Enum):
+    """Aspect ratios supported for Image to Video API when using gen3a_turbo model."""
+
+    field_768_1280 = "768:1280"
+    field_1280_768 = "1280:768"
+
+
+def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+    """Returns the video URL from the task status response if it exists."""
+    if response.output and len(response.output) > 0:
+        return response.output[0]
+    return None
+
+
+# TODO: replace with updated image validation utils (upstream)
+def validate_input_image(image: torch.Tensor) -> bool:
+    """
+    Validate the input image is within the size limits for the Runway API.
+    See: https://docs.dev.runwayml.com/assets/inputs/#common-error-reasons
+    """
+    return image.shape[2] < 8000 and image.shape[1] < 8000
+
+
+def poll_until_finished(
+    auth_kwargs: dict[str, str],
+    api_endpoint: ApiEndpoint[Any, TaskStatusResponse],
+    estimated_duration: Optional[int] = None,
+    node_id: Optional[str] = None,
+) -> TaskStatusResponse:
+    """Polls the Runway API endpoint until the task reaches a terminal state, then returns the response."""
+    return PollingOperation(
+        poll_endpoint=api_endpoint,
+        completed_statuses=[
+            TaskStatus.SUCCEEDED.value,
+        ],
+        failed_statuses=[
+            TaskStatus.FAILED.value,
+            TaskStatus.CANCELLED.value,
+        ],
+        status_extractor=lambda response: (response.status.value),
+        auth_kwargs=auth_kwargs,
+        result_url_extractor=get_video_url_from_task_status,
+        estimated_duration=estimated_duration,
+        node_id=node_id,
+        progress_extractor=extract_progress_from_task_status,
+    ).execute()
+
+
+def extract_progress_from_task_status(
+    response: TaskStatusResponse,
+) -> Union[float, None]:
+    if hasattr(response, "progress") and response.progress is not None:
+        return response.progress * 100
+    return None
+
+
+def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+    """Returns the image URL from the task status response if it exists."""
+    if response.output and len(response.output) > 0:
+        return response.output[0]
+    return None
+
+
+class RunwayVideoGenNode(ComfyNodeABC):
+    """Runway Video Node Base."""
+
+    RETURN_TYPES = ("VIDEO",)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/video/Runway"
+    API_NODE = True
+
+    def validate_task_created(self, response: RunwayImageToVideoResponse) -> bool:
+        """
+        Validate the task creation response from the Runway API matches
+        expected format.
+        """
+        if not bool(response.id):
+            raise RunwayApiError("Invalid initial response from Runway API.")
+        return True
+
+    def validate_response(self, response: RunwayImageToVideoResponse) -> bool:
+        """
+        Validate the successful task status response from the Runway API
+        matches expected format.
+        """
+        if not response.output or len(response.output) == 0:
+            raise RunwayApiError(
+                "Runway task succeeded but no video data found in response."
+            )
+        return True
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> RunwayImageToVideoResponse:
+        """Poll the task status until it is finished then get the response."""
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=TaskStatusResponse,
+            ),
+            estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            node_id=node_id,
+        )
+
+    def generate_video(
+        self,
+        request: RunwayImageToVideoRequest,
+        auth_kwargs: dict[str, str],
+        node_id: Optional[str] = None,
+    ) -> tuple[VideoFromFile]:
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=PATH_IMAGE_TO_VIDEO,
+                method=HttpMethod.POST,
+                request_model=RunwayImageToVideoRequest,
+                response_model=RunwayImageToVideoResponse,
+            ),
+            request=request,
+            auth_kwargs=auth_kwargs,
+        )
+
+        initial_response = initial_operation.execute()
+        self.validate_task_created(initial_response)
+        task_id = initial_response.id
+
+        final_response = self.get_response(task_id, auth_kwargs, node_id)
+        self.validate_response(final_response)
+
+        video_url = get_video_url_from_task_status(final_response)
+        return (download_url_to_video_output(video_url),)
+
+
+class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
+    """Runway Image to Video Node using Gen3a Turbo model."""
+
+    DESCRIPTION = "Generate a video from a single starting frame using Gen3a Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo."
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+                ),
+                "start_frame": (
+                    IO.IMAGE,
+                    {"tooltip": "Start frame to be used for the video"},
+                ),
+                "duration": model_field_to_node_input(
+                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayImageToVideoRequest,
+                    "ratio",
+                    enum_type=RunwayGen3aAspectRatio,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    RunwayImageToVideoRequest,
+                    "seed",
+                    control_after_generate=True,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    def api_call(
+        self,
+        prompt: str,
+        start_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[VideoFromFile]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+        validate_input_image(start_frame)
+
+        # Upload image
+        download_urls = upload_images_to_comfyapi(
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
+            auth_kwargs=kwargs,
+        )
+        if len(download_urls) != 1:
+            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+        return self.generate_video(
+            RunwayImageToVideoRequest(
+                promptText=prompt,
+                seed=seed,
+                model=Model("gen3a_turbo"),
+                duration=Duration(duration),
+                ratio=AspectRatio(ratio),
+                promptImage=RunwayPromptImageObject(
+                    root=[
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[0]), position="first"
+                        )
+                    ]
+                ),
+            ),
+            auth_kwargs=kwargs,
+            node_id=unique_id,
+        )
+
+
+class RunwayImageToVideoNodeGen4(RunwayVideoGenNode):
+    """Runway Image to Video Node using Gen4 Turbo model."""
+
+    DESCRIPTION = "Generate a video from a single starting frame using Gen4 Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video."
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+                ),
+                "start_frame": (
+                    IO.IMAGE,
+                    {"tooltip": "Start frame to be used for the video"},
+                ),
+                "duration": model_field_to_node_input(
+                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayImageToVideoRequest,
+                    "ratio",
+                    enum_type=RunwayGen4TurboAspectRatio,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    RunwayImageToVideoRequest,
+                    "seed",
+                    control_after_generate=True,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    def api_call(
+        self,
+        prompt: str,
+        start_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[VideoFromFile]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+        validate_input_image(start_frame)
+
+        # Upload image
+        download_urls = upload_images_to_comfyapi(
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
+            auth_kwargs=kwargs,
+        )
+        if len(download_urls) != 1:
+            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+        return self.generate_video(
+            RunwayImageToVideoRequest(
+                promptText=prompt,
+                seed=seed,
+                model=Model("gen4_turbo"),
+                duration=Duration(duration),
+                ratio=AspectRatio(ratio),
+                promptImage=RunwayPromptImageObject(
+                    root=[
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[0]), position="first"
+                        )
+                    ]
+                ),
+            ),
+            auth_kwargs=kwargs,
+            node_id=unique_id,
+        )
+
+
+class RunwayFirstLastFrameNode(RunwayVideoGenNode):
+    """Runway First-Last Frame Node."""
+
+    DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3."
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> RunwayImageToVideoResponse:
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=TaskStatusResponse,
+            ),
+            estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            node_id=node_id,
+        )
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+                ),
+                "start_frame": (
+                    IO.IMAGE,
+                    {"tooltip": "Start frame to be used for the video"},
+                ),
+                "end_frame": (
+                    IO.IMAGE,
+                    {
+                        "tooltip": "End frame to be used for the video. Supported for gen3a_turbo only."
+                    },
+                ),
+                "duration": model_field_to_node_input(
+                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayImageToVideoRequest,
+                    "ratio",
+                    enum_type=RunwayGen3aAspectRatio,
+                ),
+                "seed": model_field_to_node_input(
+                    IO.INT,
+                    RunwayImageToVideoRequest,
+                    "seed",
+                    control_after_generate=True,
+                ),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+            },
+        }
+
+    def api_call(
+        self,
+        prompt: str,
+        start_frame: torch.Tensor,
+        end_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[VideoFromFile]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+        validate_input_image(start_frame)
+        validate_input_image(end_frame)
+
+        # Upload images
+        stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
+        download_urls = upload_images_to_comfyapi(
+            stacked_input_images,
+            max_images=2,
+            mime_type="image/png",
+            auth_kwargs=kwargs,
+        )
+        if len(download_urls) != 2:
+            raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+        return self.generate_video(
+            RunwayImageToVideoRequest(
+                promptText=prompt,
+                seed=seed,
+                model=Model("gen3a_turbo"),
+                duration=Duration(duration),
+                ratio=AspectRatio(ratio),
+                promptImage=RunwayPromptImageObject(
+                    root=[
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[0]), position="first"
+                        ),
+                        RunwayPromptImageDetailedObject(
+                            uri=str(download_urls[1]), position="last"
+                        ),
+                    ]
+                ),
+            ),
+            auth_kwargs=kwargs,
+            node_id=unique_id,
+        )
+
+
+class RunwayTextToImageNode(ComfyNodeABC):
+    """Runway Text to Image Node."""
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "api_call"
+    CATEGORY = "api node/image/Runway"
+    API_NODE = True
+    DESCRIPTION = "Generate an image from a text prompt using Runway's Gen 4 model. You can also include reference images to guide the generation."
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": model_field_to_node_input(
+                    IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True
+                ),
+                "ratio": model_field_to_node_input(
+                    IO.COMBO,
+                    RunwayTextToImageRequest,
+                    "ratio",
+                    enum_type=RunwayTextToImageAspectRatioEnum,
+                ),
+            },
+            "optional": {
+                "reference_image": (
+                    IO.IMAGE,
+                    {"tooltip": "Optional reference image to guide the generation"},
+                )
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    def validate_task_created(self, response: RunwayTextToImageResponse) -> bool:
+        """
+        Validate the task creation response from the Runway API matches
+        expected format.
+        """
+        if not bool(response.id):
+            raise RunwayApiError("Invalid initial response from Runway API.")
+        return True
+
+    def validate_response(self, response: TaskStatusResponse) -> bool:
+        """
+        Validate the successful task status response from the Runway API
+        matches expected format.
+        """
+        if not response.output or len(response.output) == 0:
+            raise RunwayApiError(
+                "Runway task succeeded but no image data found in response."
+            )
+        return True
+
+    def get_response(
+        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+    ) -> TaskStatusResponse:
+        """Poll the task status until it is finished then get the response."""
+        return poll_until_finished(
+            auth_kwargs,
+            ApiEndpoint(
+                path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=TaskStatusResponse,
+            ),
+            estimated_duration=AVERAGE_DURATION_T2I_SECONDS,
+            node_id=node_id,
+        )
+
+    def api_call(
+        self,
+        prompt: str,
+        ratio: str,
+        reference_image: Optional[torch.Tensor] = None,
+        unique_id: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor]:
+        # Validate inputs
+        validate_string(prompt, min_length=1)
+
+        # Prepare reference images if provided
+        reference_images = None
+        if reference_image is not None:
+            validate_input_image(reference_image)
+            download_urls = upload_images_to_comfyapi(
+                reference_image,
+                max_images=1,
+                mime_type="image/png",
+                auth_kwargs=kwargs,
+            )
+            if len(download_urls) != 1:
+                raise RunwayApiError("Failed to upload reference image to comfy api.")
+
+            reference_images = [ReferenceImage(uri=str(download_urls[0]))]
+
+        # Create request
+        request = RunwayTextToImageRequest(
+            promptText=prompt,
+            model=Model4.gen4_image,
+            ratio=ratio,
+            referenceImages=reference_images,
+        )
+
+        # Execute initial request
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=PATH_TEXT_TO_IMAGE,
+                method=HttpMethod.POST,
+                request_model=RunwayTextToImageRequest,
+                response_model=RunwayTextToImageResponse,
+            ),
+            request=request,
+            auth_kwargs=kwargs,
+        )
+
+        initial_response = initial_operation.execute()
+        self.validate_task_created(initial_response)
+        task_id = initial_response.id
+
+        # Poll for completion
+        final_response = self.get_response(
+            task_id, auth_kwargs=kwargs, node_id=unique_id
+        )
+        self.validate_response(final_response)
+
+        # Download and return image
+        image_url = get_image_url_from_task_status(final_response)
+        return (download_url_to_image_tensor(image_url),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode,
+    "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a,
+    "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4,
+    "RunwayTextToImageNode": RunwayTextToImageNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "RunwayFirstLastFrameNode": "Runway First-Last-Frame to Video",
+    "RunwayImageToVideoNodeGen3a": "Runway Image to Video (Gen3a Turbo)",
+    "RunwayImageToVideoNodeGen4": "Runway Image to Video (Gen4 Turbo)",
+    "RunwayTextToImageNode": "Runway Text to Image",
+}

comfy_api_nodes/nodes_tripo.py

@@ -0,0 +1,574 @@
+import os
+from folder_paths import get_output_directory
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy.comfy_types.node_typing import IO
+from comfy_api_nodes.apis import (
+    TripoOrientation,
+    TripoModelVersion,
+)
+from comfy_api_nodes.apis.tripo_api import (
+    TripoTaskType,
+    TripoStyle,
+    TripoFileReference,
+    TripoFileEmptyReference,
+    TripoUrlReference,
+    TripoTaskResponse,
+    TripoTaskStatus,
+    TripoTextToModelRequest,
+    TripoImageToModelRequest,
+    TripoMultiviewToModelRequest,
+    TripoTextureModelRequest,
+    TripoRefineModelRequest,
+    TripoAnimateRigRequest,
+    TripoAnimateRetargetRequest,
+    TripoConvertModelRequest,
+)
+
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+    upload_images_to_comfyapi,
+    download_url_to_bytesio,
+)
+
+
+def upload_image_to_tripo(image, **kwargs):
+    urls = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs)
+    return TripoFileReference(TripoUrlReference(url=urls[0], type="jpeg"))
+
+def get_model_url_from_response(response: TripoTaskResponse) -> str:
+    if response.data is not None:
+        for key in ["pbr_model", "model", "base_model"]:
+            if getattr(response.data.output, key, None) is not None:
+                return getattr(response.data.output, key)
+    raise RuntimeError(f"Failed to get model url from response: {response}")
+
+
+def poll_until_finished(
+    kwargs: dict[str, str],
+    response: TripoTaskResponse,
+) -> tuple[str, str]:
+    """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response."""
+    if response.code != 0:
+        raise RuntimeError(f"Failed to generate mesh: {response.error}")
+    task_id = response.data.task_id
+    response_poll = PollingOperation(
+        poll_endpoint=ApiEndpoint(
+            path=f"/proxy/tripo/v2/openapi/task/{task_id}",
+            method=HttpMethod.GET,
+            request_model=EmptyRequest,
+            response_model=TripoTaskResponse,
+        ),
+        completed_statuses=[TripoTaskStatus.SUCCESS],
+        failed_statuses=[
+            TripoTaskStatus.FAILED,
+            TripoTaskStatus.CANCELLED,
+            TripoTaskStatus.UNKNOWN,
+            TripoTaskStatus.BANNED,
+            TripoTaskStatus.EXPIRED,
+        ],
+        status_extractor=lambda x: x.data.status,
+        auth_kwargs=kwargs,
+        node_id=kwargs["unique_id"],
+        result_url_extractor=get_model_url_from_response,
+        progress_extractor=lambda x: x.data.progress,
+    ).execute()
+    if response_poll.data.status == TripoTaskStatus.SUCCESS:
+        url = get_model_url_from_response(response_poll)
+        bytesio = download_url_to_bytesio(url)
+        # Save the downloaded model file
+        model_file = f"tripo_model_{task_id}.glb"
+        with open(os.path.join(get_output_directory(), model_file), "wb") as f:
+            f.write(bytesio.getvalue())
+        return model_file, task_id
+    raise RuntimeError(f"Failed to generate mesh: {response_poll}")
+
+class TripoTextToModelNode:
+    """
+    Generates 3D models synchronously based on a text prompt using Tripo's API.
+    """
+    AVERAGE_DURATION = 80
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": ("STRING", {"multiline": True}),
+            },
+            "optional": {
+                "negative_prompt": ("STRING", {"multiline": True}),
+                "model_version": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "model_version", enum_type=TripoModelVersion),
+                "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"),
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "image_seed": ("INT", {"default": 42}),
+                "model_seed": ("INT", {"default": 42}),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "quad": ("BOOLEAN", {"default": False})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+
+    def generate_mesh(self, prompt, negative_prompt=None, model_version=None, style=None, texture=None, pbr=None, image_seed=None, model_seed=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs):
+        style_enum = None if style == "None" else style
+        if not prompt:
+            raise RuntimeError("Prompt is required")
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoTextToModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoTextToModelRequest(
+                type=TripoTaskType.TEXT_TO_MODEL,
+                prompt=prompt,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                model_version=model_version,
+                style=style_enum,
+                texture=texture,
+                pbr=pbr,
+                image_seed=image_seed,
+                model_seed=model_seed,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                face_limit=face_limit,
+                auto_size=True,
+                quad=quad
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoImageToModelNode:
+    """
+    Generates 3D models synchronously based on a single image using Tripo's API.
+    """
+    AVERAGE_DURATION = 80
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+                "model_version": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "model_version", enum_type=TripoModelVersion),
+                "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"),
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "model_seed": ("INT", {"default": 42}),
+                "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "quad": ("BOOLEAN", {"default": False})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+
+    def generate_mesh(self, image, model_version=None, style=None, texture=None, pbr=None, model_seed=None, orientation=None, texture_alignment=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs):
+        style_enum = None if style == "None" else style
+        if image is None:
+            raise RuntimeError("Image is required")
+        tripo_file = upload_image_to_tripo(image, **kwargs)
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoImageToModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoImageToModelRequest(
+                type=TripoTaskType.IMAGE_TO_MODEL,
+                file=tripo_file,
+                model_version=model_version,
+                style=style_enum,
+                texture=texture,
+                pbr=pbr,
+                model_seed=model_seed,
+                orientation=orientation,
+                texture_alignment=texture_alignment,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                face_limit=face_limit,
+                auto_size=True,
+                quad=quad
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoMultiviewToModelNode:
+    """
+    Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API.
+    """
+    AVERAGE_DURATION = 80
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+                "image_left": ("IMAGE",),
+                "image_back": ("IMAGE",),
+                "image_right": ("IMAGE",),
+                "model_version": model_field_to_node_input(IO.COMBO, TripoMultiviewToModelRequest, "model_version", enum_type=TripoModelVersion),
+                "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation),
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "model_seed": ("INT", {"default": 42}),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "quad": ("BOOLEAN", {"default": False})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+
+    def generate_mesh(self, image, image_left=None, image_back=None, image_right=None, model_version=None, orientation=None, texture=None, pbr=None, model_seed=None, texture_seed=None, texture_quality=None, texture_alignment=None, face_limit=None, quad=None, **kwargs):
+        if image is None:
+            raise RuntimeError("front image for multiview is required")
+        images = []
+        image_dict = {
+            "image": image,
+            "image_left": image_left,
+            "image_back": image_back,
+            "image_right": image_right
+        }
+        if image_left is None and image_back is None and image_right is None:
+            raise RuntimeError("At least one of left, back, or right image must be provided for multiview")
+        for image_name in ["image", "image_left", "image_back", "image_right"]:
+            image_ = image_dict[image_name]
+            if image_ is not None:
+                tripo_file = upload_image_to_tripo(image_, **kwargs)
+                images.append(tripo_file)
+            else:
+                images.append(TripoFileEmptyReference())
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoMultiviewToModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoMultiviewToModelRequest(
+                type=TripoTaskType.MULTIVIEW_TO_MODEL,
+                files=images,
+                model_version=model_version,
+                orientation=orientation,
+                texture=texture,
+                pbr=pbr,
+                model_seed=model_seed,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                texture_alignment=texture_alignment,
+                face_limit=face_limit,
+                quad=quad,
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoTextureNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_task_id": ("MODEL_TASK_ID",),
+            },
+            "optional": {
+                "texture": ("BOOLEAN", {"default": True}),
+                "pbr": ("BOOLEAN", {"default": True}),
+                "texture_seed": ("INT", {"default": 42}),
+                "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+                "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 80
+
+    def generate_mesh(self, model_task_id, texture=None, pbr=None, texture_seed=None, texture_quality=None, texture_alignment=None, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoTextureModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoTextureModelRequest(
+                original_model_task_id=model_task_id,
+                texture=texture,
+                pbr=pbr,
+                texture_seed=texture_seed,
+                texture_quality=texture_quality,
+                texture_alignment=texture_alignment
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+
+class TripoRefineNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_task_id": ("MODEL_TASK_ID", {
+                    "tooltip": "Must be a v1.4 Tripo model"
+                }),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    DESCRIPTION = "Refine a draft model created by v1.4 Tripo models only."
+
+    RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+    RETURN_NAMES = ("model_file", "model task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 240
+
+    def generate_mesh(self, model_task_id, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoRefineModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoRefineModelRequest(
+                draft_model_task_id=model_task_id
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+
+class TripoRigNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_model_task_id": ("MODEL_TASK_ID",),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "RIG_TASK_ID")
+    RETURN_NAMES = ("model_file", "rig task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 180
+
+    def generate_mesh(self, original_model_task_id, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoAnimateRigRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoAnimateRigRequest(
+                original_model_task_id=original_model_task_id,
+                out_format="glb",
+                spec="tripo"
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoRetargetNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_model_task_id": ("RIG_TASK_ID",),
+                "animation": ([
+                    "preset:idle",
+                    "preset:walk",
+                    "preset:climb",
+                    "preset:jump",
+                    "preset:slash",
+                    "preset:shoot",
+                    "preset:hurt",
+                    "preset:fall",
+                    "preset:turn",
+                ],),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "RETARGET_TASK_ID")
+    RETURN_NAMES = ("model_file", "retarget task_id")
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 30
+
+    def generate_mesh(self, animation, original_model_task_id, **kwargs):
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoAnimateRetargetRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoAnimateRetargetRequest(
+                original_model_task_id=original_model_task_id,
+                animation=animation,
+                out_format="glb",
+                bake_animation=True
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+class TripoConversionNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "original_model_task_id": ("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID",),
+                "format": (["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"],),
+            },
+            "optional": {
+                "quad": ("BOOLEAN", {"default": False}),
+                "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+                "texture_size": ("INT", {"min": 128, "max": 4096, "default": 4096}),
+                "texture_format": (["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], {"default": "JPEG"})
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, input_types):
+        # The min and max of input1 and input2 are still validated because
+        # we didn't take `input1` or `input2` as arguments
+        if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"):
+            return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type"
+        return True
+
+    RETURN_TYPES = ()
+    FUNCTION = "generate_mesh"
+    CATEGORY = "api node/3d/Tripo"
+    API_NODE = True
+    OUTPUT_NODE = True
+    AVERAGE_DURATION = 30
+
+    def generate_mesh(self, original_model_task_id, format, quad, face_limit, texture_size, texture_format, **kwargs):
+        if not original_model_task_id:
+            raise RuntimeError("original_model_task_id is required")
+        response = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/tripo/v2/openapi/task",
+                method=HttpMethod.POST,
+                request_model=TripoConvertModelRequest,
+                response_model=TripoTaskResponse,
+            ),
+            request=TripoConvertModelRequest(
+                original_model_task_id=original_model_task_id,
+                format=format,
+                quad=quad if quad else None,
+                face_limit=face_limit if face_limit != -1 else None,
+                texture_size=texture_size if texture_size != 4096 else None,
+                texture_format=texture_format if texture_format != "JPEG" else None
+            ),
+            auth_kwargs=kwargs,
+        ).execute()
+        return poll_until_finished(kwargs, response)
+
+NODE_CLASS_MAPPINGS = {
+    "TripoTextToModelNode": TripoTextToModelNode,
+    "TripoImageToModelNode": TripoImageToModelNode,
+    "TripoMultiviewToModelNode": TripoMultiviewToModelNode,
+    "TripoTextureNode": TripoTextureNode,
+    "TripoRefineNode": TripoRefineNode,
+    "TripoRigNode": TripoRigNode,
+    "TripoRetargetNode": TripoRetargetNode,
+    "TripoConversionNode": TripoConversionNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "TripoTextToModelNode": "Tripo: Text to Model",
+    "TripoImageToModelNode": "Tripo: Image to Model",
+    "TripoMultiviewToModelNode": "Tripo: Multiview to Model",
+    "TripoTextureNode": "Tripo: Texture model",
+    "TripoRefineNode": "Tripo: Refine Draft model",
+    "TripoRigNode": "Tripo: Rig model",
+    "TripoRetargetNode": "Tripo: Retarget rigged model",
+    "TripoConversionNode": "Tripo: Convert model",
+}

comfy_config/config_parser.py

@@ -0,0 +1,97 @@
+import os
+from pathlib import Path
+from typing import Optional
+
+from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource
+
+from comfy_config.types import (
+    ComfyConfig,
+    ProjectConfig,
+    PyProjectConfig,
+    PyProjectSettings
+)
+
+"""
+Extract configuration from a custom node directory's pyproject.toml file or a Python file.
+
+This function reads and parses the pyproject.toml file in the specified directory
+to extract project and ComfyUI-specific configuration information. If no
+pyproject.toml file is found, it creates a minimal configuration using the
+folder name as the project name. If a Python file is provided, it uses the
+file name (without extension) as the project name.
+
+Args:
+    path (str): Path to the directory containing the pyproject.toml file, or
+               path to a .py file. If pyproject.toml doesn't exist in a directory,
+               the folder name will be used as the default project name. If a .py
+               file is provided, the filename (without .py extension) will be used
+               as the project name.
+
+Returns:
+    Optional[PyProjectConfig]: A PyProjectConfig object containing:
+        - project: Basic project information (name, version, dependencies, etc.)
+        - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.)
+        Returns None if configuration extraction fails or if the provided file
+        is not a Python file.
+
+Notes:
+    - If pyproject.toml is missing in a directory, creates a default config with folder name
+    - If a .py file is provided, creates a default config with filename (without extension)
+    - Returns None for non-Python files
+
+Example:
+    >>> from comfy_config import config_parser
+    >>> # For directory
+    >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__))
+    >>> project_config = config_parser.extract_node_configuration(custom_node_dir)
+    >>> print(project_config.project.name)  # "my_custom_node" or name from pyproject.toml
+    >>>
+    >>> # For single-file Python node file
+    >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py"
+    >>> project_config = config_parser.extract_node_configuration(py_file_path)
+    >>> print(project_config.project.name)  # "my_node"
+"""
+def extract_node_configuration(path) -> Optional[PyProjectConfig]:
+    if os.path.isfile(path):
+        file_path = Path(path)
+
+        if file_path.suffix.lower() != '.py':
+            return None
+
+        project_name = file_path.stem
+        project = ProjectConfig(name=project_name)
+        comfy = ComfyConfig()
+        return PyProjectConfig(project=project, tool_comfy=comfy)
+
+    folder_name = os.path.basename(path)
+    toml_path = Path(path) / "pyproject.toml"
+
+    if not toml_path.exists():
+        project = ProjectConfig(name=folder_name)
+        comfy = ComfyConfig()
+        return PyProjectConfig(project=project, tool_comfy=comfy)
+
+    raw_settings = load_pyproject_settings(toml_path)
+
+    project_data = raw_settings.project
+
+    tool_data = raw_settings.tool
+    comfy_data = tool_data.get("comfy", {}) if tool_data else {}
+
+    return PyProjectConfig(project=project_data, tool_comfy=comfy_data)
+
+
+def load_pyproject_settings(toml_path: Path) -> PyProjectSettings:
+    class PyProjectLoader(PyProjectSettings):
+        @classmethod
+        def settings_customise_sources(
+            cls,
+            settings_cls,
+            init_settings: PydanticBaseSettingsSource,
+            env_settings: PydanticBaseSettingsSource,
+            dotenv_settings: PydanticBaseSettingsSource,
+            file_secret_settings: PydanticBaseSettingsSource,
+        ):
+            return (TomlConfigSettingsSource(settings_cls, toml_path),)
+
+    return PyProjectLoader()

comfy_config/types.py

@@ -0,0 +1,80 @@
+from pydantic import BaseModel, Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import List, Optional
+
+# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes
+# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py.
+# Any changes to one must be reflected in the other to maintain consistency.
+
+class NodeVersion(BaseModel):
+    changelog: str
+    dependencies: List[str]
+    deprecated: bool
+    id: str
+    version: str
+    download_url: str
+
+
+class Node(BaseModel):
+    id: str
+    name: str
+    description: str
+    author: Optional[str] = None
+    license: Optional[str] = None
+    icon: Optional[str] = None
+    repository: Optional[str] = None
+    tags: List[str] = Field(default_factory=list)
+    latest_version: Optional[NodeVersion] = None
+
+
+class PublishNodeVersionResponse(BaseModel):
+    node_version: NodeVersion
+    signedUrl: str
+
+
+class URLs(BaseModel):
+    homepage: str = Field(default="", alias="Homepage")
+    documentation: str = Field(default="", alias="Documentation")
+    repository: str = Field(default="", alias="Repository")
+    issues: str = Field(default="", alias="Issues")
+
+
+class Model(BaseModel):
+    location: str
+    model_url: str
+
+
+class ComfyConfig(BaseModel):
+    publisher_id: str = Field(default="", alias="PublisherId")
+    display_name: str = Field(default="", alias="DisplayName")
+    icon: str = Field(default="", alias="Icon")
+    models: List[Model] = Field(default_factory=list, alias="Models")
+    includes: List[str] = Field(default_factory=list)
+
+
+class License(BaseModel):
+    file: str = ""
+    text: str = ""
+
+
+class ProjectConfig(BaseModel):
+    name: str = ""
+    description: str = ""
+    version: str = "1.0.0"
+    requires_python: str = Field(default=">= 3.9", alias="requires-python")
+    dependencies: List[str] = Field(default_factory=list)
+    license: License = Field(default_factory=License)
+    urls: URLs = Field(default_factory=URLs)
+
+
+class PyProjectConfig(BaseModel):
+    project: ProjectConfig = Field(default_factory=ProjectConfig)
+    tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig)
+
+
+class PyProjectSettings(BaseSettings):
+    project: dict = Field(default_factory=dict)
+
+    tool: dict = Field(default_factory=dict)
+
+    model_config = SettingsConfigDict()

comfy_extras/nodes_images.py

@@ -14,8 +14,10 @@ import re
 from io import BytesIO
 from inspect import cleandoc
 import torch
+import comfy.utils
 
-from comfy.comfy_types import FileLocator
+from comfy.comfy_types import FileLocator, IO
+from server import PromptServer
 
 MAX_RESOLUTION = nodes.MAX_RESOLUTION
 
@@ -229,6 +231,186 @@ class SVG:
             all_svgs_list.extend(svg_item.data)
         return SVG(all_svgs_list)
 
+
+class ImageStitch:
+    """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image1": ("IMAGE",),
+                "direction": (["right", "down", "left", "up"], {"default": "right"}),
+                "match_image_size": ("BOOLEAN", {"default": True}),
+                "spacing_width": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": 1024, "step": 2},
+                ),
+                "spacing_color": (
+                    ["white", "black", "red", "green", "blue"],
+                    {"default": "white"},
+                ),
+            },
+            "optional": {
+                "image2": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stitch"
+    CATEGORY = "image/transform"
+    DESCRIPTION = """
+Stitches image2 to image1 in the specified direction.
+If image2 is not provided, returns image1 unchanged.
+Optional spacing can be added between images.
+"""
+
+    def stitch(
+        self,
+        image1,
+        direction,
+        match_image_size,
+        spacing_width,
+        spacing_color,
+        image2=None,
+    ):
+        if image2 is None:
+            return (image1,)
+
+        # Handle batch size differences
+        if image1.shape[0] != image2.shape[0]:
+            max_batch = max(image1.shape[0], image2.shape[0])
+            if image1.shape[0] < max_batch:
+                image1 = torch.cat(
+                    [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)]
+                )
+            if image2.shape[0] < max_batch:
+                image2 = torch.cat(
+                    [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)]
+                )
+
+        # Match image sizes if requested
+        if match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+            aspect_ratio = w2 / h2
+
+            if direction in ["left", "right"]:
+                target_h, target_w = h1, int(h1 * aspect_ratio)
+            else:  # up, down
+                target_w, target_h = w1, int(w1 / aspect_ratio)
+
+            image2 = comfy.utils.common_upscale(
+                image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
+            ).movedim(1, -1)
+
+        # When not matching sizes, pad to align non-concat dimensions
+        if not match_image_size:
+            h1, w1 = image1.shape[1:3]
+            h2, w2 = image2.shape[1:3]
+
+            if direction in ["left", "right"]:
+                # For horizontal concat, pad heights to match
+                if h1 != h2:
+                    target_h = max(h1, h2)
+                    if h1 < target_h:
+                        pad_h = target_h - h1
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
+                    if h2 < target_h:
+                        pad_h = target_h - h2
+                        pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
+            else:  # up, down
+                # For vertical concat, pad widths to match
+                if w1 != w2:
+                    target_w = max(w1, w2)
+                    if w1 < target_w:
+                        pad_w = target_w - w1
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
+                    if w2 < target_w:
+                        pad_w = target_w - w2
+                        pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
+                        image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
+
+        # Ensure same number of channels
+        if image1.shape[-1] != image2.shape[-1]:
+            max_channels = max(image1.shape[-1], image2.shape[-1])
+            if image1.shape[-1] < max_channels:
+                image1 = torch.cat(
+                    [
+                        image1,
+                        torch.ones(
+                            *image1.shape[:-1],
+                            max_channels - image1.shape[-1],
+                            device=image1.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+            if image2.shape[-1] < max_channels:
+                image2 = torch.cat(
+                    [
+                        image2,
+                        torch.ones(
+                            *image2.shape[:-1],
+                            max_channels - image2.shape[-1],
+                            device=image2.device,
+                        ),
+                    ],
+                    dim=-1,
+                )
+
+        # Add spacing if specified
+        if spacing_width > 0:
+            spacing_width = spacing_width + (spacing_width % 2)  # Ensure even
+
+            color_map = {
+                "white": 1.0,
+                "black": 0.0,
+                "red": (1.0, 0.0, 0.0),
+                "green": (0.0, 1.0, 0.0),
+                "blue": (0.0, 0.0, 1.0),
+            }
+            color_val = color_map[spacing_color]
+
+            if direction in ["left", "right"]:
+                spacing_shape = (
+                    image1.shape[0],
+                    max(image1.shape[1], image2.shape[1]),
+                    spacing_width,
+                    image1.shape[-1],
+                )
+            else:
+                spacing_shape = (
+                    image1.shape[0],
+                    spacing_width,
+                    max(image1.shape[2], image2.shape[2]),
+                    image1.shape[-1],
+                )
+
+            spacing = torch.full(spacing_shape, 0.0, device=image1.device)
+            if isinstance(color_val, tuple):
+                for i, c in enumerate(color_val):
+                    if i < spacing.shape[-1]:
+                        spacing[..., i] = c
+                if spacing.shape[-1] == 4:  # Add alpha
+                    spacing[..., 3] = 1.0
+            else:
+                spacing[..., : min(3, spacing.shape[-1])] = color_val
+                if spacing.shape[-1] == 4:
+                    spacing[..., 3] = 1.0
+
+        # Concatenate images
+        images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
+        if spacing_width > 0:
+            images.insert(1, spacing)
+
+        concat_dim = 2 if direction in ["left", "right"] else 1
+        return (torch.cat(images, dim=concat_dim),)
+
+
 class SaveSVGNode:
     """
     Save SVG files on disk.
@@ -310,6 +492,37 @@ class SaveSVGNode:
             counter += 1
         return { "ui": { "images": results } }
 
+class GetImageSize:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": (IO.IMAGE,),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+            }
+        }
+
+    RETURN_TYPES = (IO.INT, IO.INT, IO.INT)
+    RETURN_NAMES = ("width", "height", "batch_size")
+    FUNCTION = "get_size"
+
+    CATEGORY = "image"
+    DESCRIPTION = """Returns width and height of the image, and passes it through unchanged."""
+
+    def get_size(self, image, unique_id=None) -> tuple[int, int]:
+        height = image.shape[1]
+        width = image.shape[2]
+        batch_size = image.shape[0]
+
+        # Send progress text to display size on the node
+        if unique_id:
+            PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id)
+
+        return width, height, batch_size
+
 NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
     "RepeatImageBatch": RepeatImageBatch,
@@ -318,4 +531,6 @@ NODE_CLASS_MAPPINGS = {
     "SaveAnimatedWEBP": SaveAnimatedWEBP,
     "SaveAnimatedPNG": SaveAnimatedPNG,
     "SaveSVGNode": SaveSVGNode,
+    "ImageStitch": ImageStitch,
+    "GetImageSize": GetImageSize,
 }

comfy_extras/nodes_load_3d.py

@@ -16,7 +16,7 @@ class Load3D():
 
         os.makedirs(input_dir, exist_ok=True)
 
-        files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.mtl', '.fbx', '.stl'))]
+        files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.fbx', '.stl'))]
 
         return {"required": {
             "model_file": (sorted(files), {"file_upload": True}),

comfy_extras/nodes_string.py

@@ -296,6 +296,41 @@ class RegexExtract():
 
         return result,
 
+
+class RegexReplace():
+    DESCRIPTION = "Find and replace text using regex patterns."
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "string": (IO.STRING, {"multiline": True}),
+                "regex_pattern": (IO.STRING, {"multiline": True}),
+                "replace": (IO.STRING, {"multiline": True}),
+            },
+            "optional": {
+                "case_insensitive": (IO.BOOLEAN, {"default": True}),
+                "multiline": (IO.BOOLEAN, {"default": False}),
+                "dotall": (IO.BOOLEAN, {"default": False, "tooltip": "When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."}),
+                "count": (IO.INT, {"default": 0, "min": 0, "max": 100, "tooltip": "Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."}),
+            }
+        }
+
+    RETURN_TYPES = (IO.STRING,)
+    FUNCTION = "execute"
+    CATEGORY = "utils/string"
+
+    def execute(self, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0, **kwargs):
+        flags = 0
+
+        if case_insensitive:
+            flags |= re.IGNORECASE
+        if multiline:
+            flags |= re.MULTILINE
+        if dotall:
+            flags |= re.DOTALL
+        result = re.sub(regex_pattern, replace, string, count=count, flags=flags)
+        return result,
+
 NODE_CLASS_MAPPINGS = {
     "StringConcatenate": StringConcatenate,
     "StringSubstring": StringSubstring,
@@ -306,7 +341,8 @@ NODE_CLASS_MAPPINGS = {
     "StringContains": StringContains,
     "StringCompare": StringCompare,
     "RegexMatch": RegexMatch,
-    "RegexExtract": RegexExtract
+    "RegexExtract": RegexExtract,
+    "RegexReplace": RegexReplace,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -319,5 +355,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StringContains": "Contains",
     "StringCompare": "Compare",
     "RegexMatch": "Regex Match",
-    "RegexExtract": "Regex Extract"
+    "RegexExtract": "Regex Extract",
+    "RegexReplace": "Regex Replace",
 }

comfy_extras/nodes_torch_compile.py

@@ -1,4 +1,5 @@
-import torch
+from comfy_api.torch_helpers import set_torch_compile_wrapper
+
 
 class TorchCompileModel:
     @classmethod
@@ -14,7 +15,7 @@ class TorchCompileModel:
 
     def patch(self, model, backend):
         m = model.clone()
-        m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend))
+        set_torch_compile_wrapper(model=m, backend=backend)
         return (m, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_wan.py

@@ -268,8 +268,9 @@ class WanVaceToVideo:
             trim_latent = reference_image.shape[2]
 
         mask = mask.unsqueeze(0)
-        positive = node_helpers.conditioning_set_values(positive, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
-        negative = node_helpers.conditioning_set_values(negative, {"vace_frames": control_video_latent, "vace_mask": mask, "vace_strength": strength})
+
+        positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
+        negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
 
         latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device())
         out_latent = {}
@@ -344,6 +345,44 @@ class WanCameraImageToVideo:
         out_latent["samples"] = latent
         return (positive, negative, out_latent)
 
+class WanPhantomSubjectToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                             "length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"images": ("IMAGE", ),
+                }}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative_text", "negative_img_text", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/video_models"
+
+    def encode(self, positive, negative, vae, width, height, length, batch_size, images):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        cond2 = negative
+        if images is not None:
+            images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            latent_images = []
+            for i in images:
+                latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])]
+            concat_latent_image = torch.cat(latent_images, dim=2)
+
+            positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image})
+            cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image})
+            negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))})
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return (positive, cond2, negative, out_latent)
+
 NODE_CLASS_MAPPINGS = {
     "WanImageToVideo": WanImageToVideo,
     "WanFunControlToVideo": WanFunControlToVideo,
@@ -352,4 +391,5 @@ NODE_CLASS_MAPPINGS = {
     "WanVaceToVideo": WanVaceToVideo,
     "TrimVideoLatent": TrimVideoLatent,
     "WanCameraImageToVideo": WanCameraImageToVideo,
+    "WanPhantomSubjectToVideo": WanPhantomSubjectToVideo,
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.34"
+__version__ = "0.3.40"

execution.py

@@ -909,7 +909,6 @@ class PromptQueue:
         self.currently_running = {}
         self.history = {}
         self.flags = {}
-        server.prompt_queue = self
 
     def put(self, item):
         with self.mutex:
@@ -954,6 +953,7 @@ class PromptQueue:
             self.history[prompt[1]].update(history_result)
             self.server.queue_updated()
 
+    # Note: slow
     def get_current_queue(self):
         with self.mutex:
             out = []
@@ -961,6 +961,13 @@ class PromptQueue:
                 out += [x]
             return (out, copy.deepcopy(self.queue))
 
+    # read-safe as long as queue items are immutable
+    def get_current_queue_volatile(self):
+        with self.mutex:
+            running = [x for x in self.currently_running.values()]
+            queued = copy.copy(self.queue)
+            return (running, queued)
+
     def get_tasks_remaining(self):
         with self.mutex:
             return len(self.queue) + len(self.currently_running)

main.py

@@ -260,7 +260,6 @@ def start_comfyui(asyncio_loop=None):
         asyncio_loop = asyncio.new_event_loop()
         asyncio.set_event_loop(asyncio_loop)
     prompt_server = server.PromptServer(asyncio_loop)
-    q = execution.PromptQueue(prompt_server)
 
     hook_breaker_ac10a0.save_functions()
     nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes, init_api_nodes=not args.disable_api_nodes)
@@ -271,7 +270,7 @@ def start_comfyui(asyncio_loop=None):
     prompt_server.add_routes()
     hijack_progress(prompt_server)
 
-    threading.Thread(target=prompt_worker, daemon=True, args=(q, prompt_server,)).start()
+    threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start()
 
     if args.quick_test_for_ci:
         exit(0)

node_helpers.py

@@ -5,12 +5,18 @@ from comfy.cli_args import args
 
 from PIL import ImageFile, UnidentifiedImageError
 
-def conditioning_set_values(conditioning, values={}):
+def conditioning_set_values(conditioning, values={}, append=False):
     c = []
     for t in conditioning:
         n = [t[0], t[1].copy()]
         for k in values:
-            n[1][k] = values[k]
+            val = values[k]
+            if append:
+                old_val = n[1].get(k, None)
+                if old_val is not None:
+                    val = old_val + val
+
+            n[1][k] = val
         c.append(n)
 
     return c

nodes.py

@@ -1103,16 +1103,7 @@ class unCLIPConditioning:
         if strength == 0:
             return (conditioning, )
 
-        c = []
-        for t in conditioning:
-            o = t[1].copy()
-            x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}
-            if "unclip_conditioning" in o:
-                o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x]
-            else:
-                o["unclip_conditioning"] = [x]
-            n = [t[0], o]
-            c.append(n)
+        c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True)
         return (c, )
 
 class GLIGENLoader:
@@ -2070,11 +2061,13 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImagePadForOutpaint": "Pad Image for Outpainting",
     "ImageBatch": "Batch Images",
     "ImageCrop": "Image Crop",
+    "ImageStitch": "Image Stitch",
     "ImageBlend": "Image Blend",
     "ImageBlur": "Image Blur",
     "ImageQuantize": "Image Quantize",
     "ImageSharpen": "Image Sharpen",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
+    "GetImageSize": "Get Image Size",
     # _for_testing
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
@@ -2290,6 +2283,10 @@ def init_builtin_api_nodes():
         "nodes_pixverse.py",
         "nodes_stability.py",
         "nodes_pika.py",
+        "nodes_runway.py",
+        "nodes_tripo.py",
+        "nodes_rodin.py",
+        "nodes_gemini.py",
     ]
 
     if not load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"):

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.34"
+version = "0.3.40"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,6 @@
-comfyui-frontend-package==1.19.9
-comfyui-workflow-templates==0.1.14
+comfyui-frontend-package==1.21.7
+comfyui-workflow-templates==0.1.25
+comfyui-embedded-docs==0.2.0
 torch
 torchsde
 torchvision

server.py

@@ -29,6 +29,7 @@ import comfy.model_management
 import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager
+
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
 from app.custom_node_manager import CustomNodeManager
@@ -159,7 +160,7 @@ class PromptServer():
         self.custom_node_manager = CustomNodeManager()
         self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
-        self.prompt_queue = None
+        self.prompt_queue = execution.PromptQueue(self)
         self.loop = loop
         self.messages = asyncio.Queue()
         self.client_session:Optional[aiohttp.ClientSession] = None
@@ -226,7 +227,7 @@ class PromptServer():
             return response
 
         @routes.get("/embeddings")
-        def get_embeddings(self):
+        def get_embeddings(request):
             embeddings = folder_paths.get_filename_list("embeddings")
             return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
 
@@ -282,7 +283,6 @@ class PromptServer():
                     a.update(f.read())
                     b.update(image.file.read())
                     image.file.seek(0)
-                    f.close()
                 return a.hexdigest() == b.hexdigest()
             return False
 
@@ -390,7 +390,7 @@ class PromptServer():
         async def view_image(request):
             if "filename" in request.rel_url.query:
                 filename = request.rel_url.query["filename"]
-                filename,output_dir = folder_paths.annotated_filepath(filename)
+                filename, output_dir = folder_paths.annotated_filepath(filename)
 
                 if not filename:
                     return web.Response(status=400)
@@ -476,9 +476,8 @@ class PromptServer():
                         # Get content type from mimetype, defaulting to 'application/octet-stream'
                         content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream'
 
-                        # For security, force certain extensions to download instead of display
-                        file_extension = os.path.splitext(filename)[1].lower()
-                        if file_extension in {'.html', '.htm', '.js', '.css'}:
+                        # For security, force certain mimetypes to download instead of display
+                        if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}:
                             content_type = 'application/octet-stream'  # Forces download
 
                         return web.FileResponse(
@@ -621,7 +620,7 @@ class PromptServer():
         @routes.get("/queue")
         async def get_queue(request):
             queue_info = {}
-            current_queue = self.prompt_queue.get_current_queue()
+            current_queue = self.prompt_queue.get_current_queue_volatile()
             queue_info['queue_running'] = current_queue[0]
             queue_info['queue_pending'] = current_queue[1]
             return web.json_response(queue_info)
@@ -746,6 +745,13 @@ class PromptServer():
                 web.static('/templates', workflow_templates_path)
             ])
 
+        # Serve embedded documentation from the package
+        embedded_docs_path = FrontendManager.embedded_docs_path()
+        if embedded_docs_path:
+            self.app.add_routes([
+                web.static('/docs', embedded_docs_path)
+            ])
+
         self.app.add_routes([
             web.static('/', self.web_root),
         ])
@@ -782,7 +788,7 @@ class PromptServer():
             if hasattr(Image, 'Resampling'):
                 resampling = Image.Resampling.BILINEAR
             else:
-                resampling = Image.ANTIALIAS
+                resampling = Image.Resampling.LANCZOS
 
             image = ImageOps.contain(image, (max_size, max_size), resampling)
         type_num = 1

tests-unit/comfy_extras_test/image_stitch_test.py

@@ -0,0 +1,243 @@
+import torch
+from unittest.mock import patch, MagicMock
+
+# Mock nodes module to prevent CUDA initialization during import
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+
+# Mock server module for PromptServer
+mock_server = MagicMock()
+
+with patch.dict('sys.modules', {'nodes': mock_nodes, 'server': mock_server}):
+    from comfy_extras.nodes_images import ImageStitch
+
+
+class TestImageStitch:
+
+    def create_test_image(self, batch_size=1, height=64, width=64, channels=3):
+        """Helper to create test images with specific dimensions"""
+        return torch.rand(batch_size, height, width, channels)
+
+    def test_no_image2_passthrough(self):
+        """Test that when image2 is None, image1 is returned unchanged"""
+        node = ImageStitch()
+        image1 = self.create_test_image()
+
+        result = node.stitch(image1, "right", True, 0, "white", image2=None)
+
+        assert len(result) == 1
+        assert torch.equal(result[0], image1)
+
+    def test_basic_horizontal_stitch_right(self):
+        """Test basic horizontal stitching to the right"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=24)
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 32, 56, 3)  # 32 + 24 width
+
+    def test_basic_horizontal_stitch_left(self):
+        """Test basic horizontal stitching to the left"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=24)
+
+        result = node.stitch(image1, "left", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 32, 56, 3)  # 24 + 32 width
+
+    def test_basic_vertical_stitch_down(self):
+        """Test basic vertical stitching downward"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=24, width=32)
+
+        result = node.stitch(image1, "down", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 56, 32, 3)  # 32 + 24 height
+
+    def test_basic_vertical_stitch_up(self):
+        """Test basic vertical stitching upward"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=24, width=32)
+
+        result = node.stitch(image1, "up", False, 0, "white", image2)
+
+        assert result[0].shape == (1, 56, 32, 3)  # 24 + 32 height
+
+    def test_size_matching_horizontal(self):
+        """Test size matching for horizontal concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=64, width=64)
+        image2 = self.create_test_image(height=32, width=32)  # Different aspect ratio
+
+        result = node.stitch(image1, "right", True, 0, "white", image2)
+
+        # image2 should be resized to match image1's height (64) with preserved aspect ratio
+        expected_width = 64 + 64  # original + resized (32*64/32 = 64)
+        assert result[0].shape == (1, 64, expected_width, 3)
+
+    def test_size_matching_vertical(self):
+        """Test size matching for vertical concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=64, width=64)
+        image2 = self.create_test_image(height=32, width=32)
+
+        result = node.stitch(image1, "down", True, 0, "white", image2)
+
+        # image2 should be resized to match image1's width (64) with preserved aspect ratio
+        expected_height = 64 + 64  # original + resized (32*64/32 = 64)
+        assert result[0].shape == (1, expected_height, 64, 3)
+
+    def test_padding_for_mismatched_heights_horizontal(self):
+        """Test padding when heights don't match in horizontal concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=64, width=32)
+        image2 = self.create_test_image(height=48, width=24)  # Shorter height
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Both images should be padded to height 64
+        assert result[0].shape == (1, 64, 56, 3)  # 32 + 24 width, max(64,48) height
+
+    def test_padding_for_mismatched_widths_vertical(self):
+        """Test padding when widths don't match in vertical concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=64)
+        image2 = self.create_test_image(height=24, width=48)  # Narrower width
+
+        result = node.stitch(image1, "down", False, 0, "white", image2)
+
+        # Both images should be padded to width 64
+        assert result[0].shape == (1, 56, 64, 3)  # 32 + 24 height, max(64,48) width
+
+    def test_spacing_horizontal(self):
+        """Test spacing addition in horizontal concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=24)
+        spacing_width = 16
+
+        result = node.stitch(image1, "right", False, spacing_width, "white", image2)
+
+        # Expected width: 32 + 16 (spacing) + 24 = 72
+        assert result[0].shape == (1, 32, 72, 3)
+
+    def test_spacing_vertical(self):
+        """Test spacing addition in vertical concatenation"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=24, width=32)
+        spacing_width = 16
+
+        result = node.stitch(image1, "down", False, spacing_width, "white", image2)
+
+        # Expected height: 32 + 16 (spacing) + 24 = 72
+        assert result[0].shape == (1, 72, 32, 3)
+
+    def test_spacing_color_values(self):
+        """Test that spacing colors are applied correctly"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        # Test white spacing
+        result_white = node.stitch(image1, "right", False, 16, "white", image2)
+        # Check that spacing region contains white values (close to 1.0)
+        spacing_region = result_white[0][:, :, 32:48, :]  # Middle 16 pixels
+        assert torch.all(spacing_region >= 0.9)  # Should be close to white
+
+        # Test black spacing
+        result_black = node.stitch(image1, "right", False, 16, "black", image2)
+        spacing_region = result_black[0][:, :, 32:48, :]
+        assert torch.all(spacing_region <= 0.1)  # Should be close to black
+
+    def test_odd_spacing_width_made_even(self):
+        """Test that odd spacing widths are made even"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        # Use odd spacing width
+        result = node.stitch(image1, "right", False, 15, "white", image2)
+
+        # Should be made even (16), so total width = 32 + 16 + 32 = 80
+        assert result[0].shape == (1, 32, 80, 3)
+
+    def test_batch_size_matching(self):
+        """Test that different batch sizes are handled correctly"""
+        node = ImageStitch()
+        image1 = self.create_test_image(batch_size=2, height=32, width=32)
+        image2 = self.create_test_image(batch_size=1, height=32, width=32)
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Should match larger batch size
+        assert result[0].shape == (2, 32, 64, 3)
+
+    def test_channel_matching_rgb_to_rgba(self):
+        """Test that channel differences are handled (RGB + alpha)"""
+        node = ImageStitch()
+        image1 = self.create_test_image(channels=3)  # RGB
+        image2 = self.create_test_image(channels=4)  # RGBA
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Should have 4 channels (RGBA)
+        assert result[0].shape[-1] == 4
+
+    def test_channel_matching_rgba_to_rgb(self):
+        """Test that channel differences are handled (RGBA + RGB)"""
+        node = ImageStitch()
+        image1 = self.create_test_image(channels=4)  # RGBA
+        image2 = self.create_test_image(channels=3)  # RGB
+
+        result = node.stitch(image1, "right", False, 0, "white", image2)
+
+        # Should have 4 channels (RGBA)
+        assert result[0].shape[-1] == 4
+
+    def test_all_color_options(self):
+        """Test all available color options"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        colors = ["white", "black", "red", "green", "blue"]
+
+        for color in colors:
+            result = node.stitch(image1, "right", False, 16, color, image2)
+            assert result[0].shape == (1, 32, 80, 3)  # Basic shape check
+
+    def test_all_directions(self):
+        """Test all direction options"""
+        node = ImageStitch()
+        image1 = self.create_test_image(height=32, width=32)
+        image2 = self.create_test_image(height=32, width=32)
+
+        directions = ["right", "left", "up", "down"]
+
+        for direction in directions:
+            result = node.stitch(image1, direction, False, 0, "white", image2)
+            assert result[0].shape == (1, 32, 64, 3) if direction in ["right", "left"] else (1, 64, 32, 3)
+
+    def test_batch_size_channel_spacing_integration(self):
+        """Test integration of batch matching, channel matching, size matching, and spacings"""
+        node = ImageStitch()
+        image1 = self.create_test_image(batch_size=2, height=64, width=48, channels=3)
+        image2 = self.create_test_image(batch_size=1, height=32, width=32, channels=4)
+
+        result = node.stitch(image1, "right", True, 8, "red", image2)
+
+        # Should handle: batch matching, size matching, channel matching, spacing
+        assert result[0].shape[0] == 2  # Batch size matched
+        assert result[0].shape[-1] == 4  # Channels matched to max
+        assert result[0].shape[1] == 64  # Height from image1 (size matching)
+        # Width should be: 48 + 8 (spacing) + resized_image2_width
+        expected_image2_width = int(64 * (32/32))  # Resized to height 64
+        expected_total_width = 48 + 8 + expected_image2_width
+        assert result[0].shape[2] == expected_total_width
+