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Merge branch 'master' into v3-match-type

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Jedrzej Kosinski 2025-11-12 23:25:58 -08:00
commit ef4179e894
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8
.github/ISSUE_TEMPLATE/bug-report.yml vendored
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@ -8,13 +8,15 @@ body:
Before submitting a **Bug Report**, please ensure the following:
- **1:** You are running the latest version of ComfyUI.
- **2:** You have looked at the existing bug reports and made sure this isn't already reported.
- **2:** You have your ComfyUI logs and relevant workflow on hand and will post them in this bug report.
- **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing
`--disable-all-custom-nodes` command line argument.
`--disable-all-custom-nodes` command line argument. If you have custom node try updating them to the latest version.
- **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact
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.
## Very Important
Please make sure that you post ALL your ComfyUI logs in the bug report. A bug report without logs will likely be ignored.
- type: checkboxes
id: custom-nodes-test
attributes:

20
.github/workflows/test-ci.yml vendored
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@ -21,14 +21,15 @@ jobs:
fail-fast: false
matrix:
# os: [macos, linux, windows]
os: [macos, linux]
python_version: ["3.9", "3.10", "3.11", "3.12"]
# os: [macos, linux]
os: [linux]
python_version: ["3.10", "3.11", "3.12"]
cuda_version: ["12.1"]
torch_version: ["stable"]
include:
- os: macos
runner_label: [self-hosted, macOS]
flags: "--use-pytorch-cross-attention"
# - os: macos
# runner_label: [self-hosted, macOS]
# flags: "--use-pytorch-cross-attention"
- os: linux
runner_label: [self-hosted, Linux]
flags: ""
@ -73,14 +74,15 @@ jobs:
strategy:
fail-fast: false
matrix:
os: [macos, linux]
# os: [macos, linux]
os: [linux]
python_version: ["3.11"]
cuda_version: ["12.1"]
torch_version: ["nightly"]
include:
- os: macos
runner_label: [self-hosted, macOS]
flags: "--use-pytorch-cross-attention"
# - os: macos
# runner_label: [self-hosted, macOS]
# flags: "--use-pytorch-cross-attention"
- os: linux
runner_label: [self-hosted, Linux]
flags: ""

13
README.md
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@ -112,10 +112,11 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
## Release Process
ComfyUI follows a weekly release cycle targeting Friday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
ComfyUI follows a weekly release cycle targeting Monday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
- Releases a new stable version (e.g., v0.7.0)
- Releases a new stable version (e.g., v0.7.0) roughly every week.
- Commits outside of the stable release tags may be very unstable and break many custom nodes.
- Serves as the foundation for the desktop release
2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
@ -199,7 +200,7 @@ comfy install
## Manual Install (Windows, Linux)
Python 3.14 will work if you comment out the `kornia` dependency in the requirements.txt file (breaks the canny node) but it is not recommended.
Python 3.14 works but you may encounter issues with the torch compile node. The free threaded variant is still missing some dependencies.
Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
@ -241,7 +242,7 @@ RDNA 4 (RX 9000 series):
### Intel GPUs (Windows and Linux)
(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
1. To install PyTorch xpu, use the following command:
@ -251,10 +252,6 @@ This is the command to install the Pytorch xpu nightly which might have some per
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
### NVIDIA
Nvidia users should install stable pytorch using this command:

3
comfy/cli_args.py
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@ -145,10 +145,11 @@ class PerformanceFeature(enum.Enum):
Fp8MatrixMultiplication = "fp8_matrix_mult"
CublasOps = "cublas_ops"
AutoTune = "autotune"
PinnedMem = "pinned_memory"
parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.")
parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")

4
comfy/ldm/flux/layers.py
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@ -195,8 +195,8 @@ class DoubleStreamBlock(nn.Module):
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
# calculate the img bloks
img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
img += apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img)
img += apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img)
# calculate the txt bloks
txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt)

10
comfy/ldm/flux/math.py
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@ -7,15 +7,7 @@ import comfy.model_management
def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
q_shape = q.shape
k_shape = k.shape
if pe is not None:
q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2)
k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2)
q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
q, k = apply_rope(q, k, pe)
heads = q.shape[1]
x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
return x

22
comfy/ldm/flux/model.py
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@ -210,7 +210,7 @@ class Flux(nn.Module):
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img
def process_img(self, x, index=0, h_offset=0, w_offset=0):
def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
bs, c, h, w = x.shape
patch_size = self.patch_size
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
@ -222,10 +222,22 @@ class Flux(nn.Module):
h_offset = ((h_offset + (patch_size // 2)) // patch_size)
w_offset = ((w_offset + (patch_size // 2)) // patch_size)
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
steps_h = h_len
steps_w = w_len
rope_options = transformer_options.get("rope_options", None)
if rope_options is not None:
h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
index += rope_options.get("shift_t", 0.0)
h_offset += rope_options.get("shift_y", 0.0)
w_offset += rope_options.get("shift_x", 0.0)
img_ids = torch.zeros((steps_h, steps_w, 3), device=x.device, dtype=x.dtype)
img_ids[:, :, 0] = img_ids[:, :, 1] + index
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=steps_h, device=x.device, dtype=x.dtype).unsqueeze(1)
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=steps_w, device=x.device, dtype=x.dtype).unsqueeze(0)
return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
@ -241,7 +253,7 @@ class Flux(nn.Module):
h_len = ((h_orig + (patch_size // 2)) // patch_size)
w_len = ((w_orig + (patch_size // 2)) // patch_size)
img, img_ids = self.process_img(x)
img, img_ids = self.process_img(x, transformer_options=transformer_options)
img_tokens = img.shape[1]
if ref_latents is not None:
h = 0

88
comfy/ldm/lightricks/model.py
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@ -3,12 +3,11 @@ from torch import nn
import comfy.patcher_extension
import comfy.ldm.modules.attention
import comfy.ldm.common_dit
from einops import rearrange
import math
from typing import Dict, Optional, Tuple
from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
from comfy.ldm.flux.math import apply_rope1
def get_timestep_embedding(
timesteps: torch.Tensor,
@ -238,20 +237,6 @@ class FeedForward(nn.Module):
return self.net(x)
def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
cos_freqs = freqs_cis[0]
sin_freqs = freqs_cis[1]
t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
t1, t2 = t_dup.unbind(dim=-1)
t_dup = torch.stack((-t2, t1), dim=-1)
input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
return out
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
super().__init__()
@ -281,8 +266,8 @@ class CrossAttention(nn.Module):
k = self.k_norm(k)
if pe is not None:
q = apply_rotary_emb(q, pe)
k = apply_rotary_emb(k, pe)
q = apply_rope1(q.unsqueeze(1), pe).squeeze(1)
k = apply_rope1(k.unsqueeze(1), pe).squeeze(1)
if mask is None:
out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
@ -306,12 +291,17 @@ class BasicTransformerBlock(nn.Module):
def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa
attn1_input = comfy.ldm.common_dit.rms_norm(x)
attn1_input = torch.addcmul(attn1_input, attn1_input, scale_msa).add_(shift_msa)
attn1_input = self.attn1(attn1_input, pe=pe, transformer_options=transformer_options)
x.addcmul_(attn1_input, gate_msa)
del attn1_input
x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
x += self.ff(y) * gate_mlp
y = comfy.ldm.common_dit.rms_norm(x)
y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)
x.addcmul_(self.ff(y), gate_mlp)
return x
@ -327,41 +317,35 @@ def get_fractional_positions(indices_grid, max_pos):
def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
dtype = torch.float32 #self.dtype
dtype = torch.float32
device = indices_grid.device
# Get fractional positions and compute frequency indices
fractional_positions = get_fractional_positions(indices_grid, max_pos)
indices = theta ** torch.linspace(0, 1, dim // 6, device=device, dtype=dtype) * math.pi / 2
start = 1
end = theta
device = fractional_positions.device
# Compute frequencies and apply cos/sin
freqs = (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)).transpose(-1, -2).flatten(2)
cos_vals = freqs.cos().repeat_interleave(2, dim=-1)
sin_vals = freqs.sin().repeat_interleave(2, dim=-1)
indices = theta ** (
torch.linspace(
math.log(start, theta),
math.log(end, theta),
dim // 6,
device=device,
dtype=dtype,
)
)
indices = indices.to(dtype=dtype)
indices = indices * math.pi / 2
freqs = (
(indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
.transpose(-1, -2)
.flatten(2)
)
cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
# Pad if dim is not divisible by 6
if dim % 6 != 0:
cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
padding_size = dim % 6
cos_vals = torch.cat([torch.ones_like(cos_vals[:, :, :padding_size]), cos_vals], dim=-1)
sin_vals = torch.cat([torch.zeros_like(sin_vals[:, :, :padding_size]), sin_vals], dim=-1)
# Reshape and extract one value per pair (since repeat_interleave duplicates each value)
cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0].to(out_dtype) # [B, N, dim//2]
sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0].to(out_dtype) # [B, N, dim//2]
# Build rotation matrix [[cos, -sin], [sin, cos]] and add heads dimension
freqs_cis = torch.stack([
torch.stack([cos_vals, -sin_vals], dim=-1),
torch.stack([sin_vals, cos_vals], dim=-1)
], dim=-2).unsqueeze(1) # [B, 1, N, dim//2, 2, 2]
return freqs_cis
class LTXVModel(torch.nn.Module):
@ -501,7 +485,7 @@ class LTXVModel(torch.nn.Module):
shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
x = self.norm_out(x)
# Modulation
x = x * (1 + scale) + shift
x = torch.addcmul(x, x, scale).add_(shift)
x = self.proj_out(x)
x = self.patchifier.unpatchify(

2
comfy/ldm/qwen_image/controlnet.py
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@ -44,7 +44,7 @@ class QwenImageControlNetModel(QwenImageTransformer2DModel):
txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
ids = torch.cat((txt_ids, img_ids), dim=1)
image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
del ids, txt_ids, img_ids
hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint)

56
comfy/ldm/qwen_image/model.py
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@ -10,6 +10,7 @@ from comfy.ldm.modules.attention import optimized_attention_masked
from comfy.ldm.flux.layers import EmbedND
import comfy.ldm.common_dit
import comfy.patcher_extension
from comfy.ldm.flux.math import apply_rope1
class GELU(nn.Module):
def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None):
@ -134,33 +135,34 @@ class Attention(nn.Module):
image_rotary_emb: Optional[torch.Tensor] = None,
transformer_options={},
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size = hidden_states.shape[0]
seq_img = hidden_states.shape[1]
seq_txt = encoder_hidden_states.shape[1]
img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1))
img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1))
img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1))
# Project and reshape to BHND format (batch, heads, seq, dim)
img_query = self.to_q(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
img_key = self.to_k(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2).contiguous()
img_value = self.to_v(hidden_states).view(batch_size, seq_img, self.heads, -1).transpose(1, 2)
txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
txt_query = self.add_q_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()
txt_key = self.add_k_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2).contiguous()
txt_value = self.add_v_proj(encoder_hidden_states).view(batch_size, seq_txt, self.heads, -1).transpose(1, 2)
img_query = self.norm_q(img_query)
img_key = self.norm_k(img_key)
txt_query = self.norm_added_q(txt_query)
txt_key = self.norm_added_k(txt_key)
joint_query = torch.cat([txt_query, img_query], dim=1)
joint_key = torch.cat([txt_key, img_key], dim=1)
joint_value = torch.cat([txt_value, img_value], dim=1)
joint_query = torch.cat([txt_query, img_query], dim=2)
joint_key = torch.cat([txt_key, img_key], dim=2)
joint_value = torch.cat([txt_value, img_value], dim=2)
joint_query = apply_rotary_emb(joint_query, image_rotary_emb)
joint_key = apply_rotary_emb(joint_key, image_rotary_emb)
joint_query = apply_rope1(joint_query, image_rotary_emb)
joint_key = apply_rope1(joint_key, image_rotary_emb)
joint_query = joint_query.flatten(start_dim=2)
joint_key = joint_key.flatten(start_dim=2)
joint_value = joint_value.flatten(start_dim=2)
joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options)
joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
attention_mask, transformer_options=transformer_options,
skip_reshape=True)
txt_attn_output = joint_hidden_states[:, :seq_txt, :]
img_attn_output = joint_hidden_states[:, seq_txt:, :]
@ -234,10 +236,10 @@ class QwenImageTransformerBlock(nn.Module):
img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)
txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)
img_normed = self.img_norm1(hidden_states)
img_modulated, img_gate1 = self._modulate(img_normed, img_mod1)
txt_normed = self.txt_norm1(encoder_hidden_states)
txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1)
img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1)
del img_mod1
txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1)
del txt_mod1
img_attn_output, txt_attn_output = self.attn(
hidden_states=img_modulated,
@ -246,16 +248,20 @@ class QwenImageTransformerBlock(nn.Module):
image_rotary_emb=image_rotary_emb,
transformer_options=transformer_options,
)
del img_modulated
del txt_modulated
hidden_states = hidden_states + img_gate1 * img_attn_output
encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
del img_attn_output
del txt_attn_output
del img_gate1
del txt_gate1
img_normed2 = self.img_norm2(hidden_states)
img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2)
img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2)
hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2))
txt_normed2 = self.txt_norm2(encoder_hidden_states)
txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2)
txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2)
encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2))
return encoder_hidden_states, hidden_states
@ -413,7 +419,7 @@ class QwenImageTransformer2DModel(nn.Module):
txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
ids = torch.cat((txt_ids, img_ids), dim=1)
image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
image_rotary_emb = self.pe_embedder(ids).to(x.dtype).contiguous()
del ids, txt_ids, img_ids
hidden_states = self.img_in(hidden_states)

1
comfy/ldm/wan/model.py
View File

@ -232,6 +232,7 @@ class WanAttentionBlock(nn.Module):
# assert e[0].dtype == torch.float32
# self-attention
x = x.contiguous() # otherwise implicit in LayerNorm
y = self.self_attn(
torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
freqs, transformer_options=transformer_options)

65
comfy/model_management.py
View File

@ -504,6 +504,7 @@ class LoadedModel:
if use_more_vram == 0:
use_more_vram = 1e32
self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights)
real_model = self.model.model
if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None:
@ -689,7 +690,10 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
current_free_mem = get_free_memory(torch_dev) + loaded_memory
lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory)
lowvram_model_memory = lowvram_model_memory - loaded_memory
if lowvram_model_memory == 0:
lowvram_model_memory = 0.1
if vram_set_state == VRAMState.NO_VRAM:
lowvram_model_memory = 0.1
@ -1082,32 +1086,75 @@ def cast_to_device(tensor, device, dtype, copy=False):
non_blocking = device_supports_non_blocking(device)
return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
PINNED_MEMORY = {}
TOTAL_PINNED_MEMORY = 0
MAX_PINNED_MEMORY = -1
if not args.disable_pinned_memory:
if is_nvidia() or is_amd():
if WINDOWS:
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45 # Windows limit is apparently 50%
else:
MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))
def pin_memory(tensor):
if PerformanceFeature.PinnedMem not in args.fast:
global TOTAL_PINNED_MEMORY
if MAX_PINNED_MEMORY <= 0:
return False
if not is_nvidia():
if type(tensor) is not torch.nn.parameter.Parameter:
return False
if not is_device_cpu(tensor.device):
return False
if torch.cuda.cudart().cudaHostRegister(tensor.data_ptr(), tensor.numel() * tensor.element_size(), 1) == 0:
if tensor.is_pinned():
#NOTE: Cuda does detect when a tensor is already pinned and would
#error below, but there are proven cases where this also queues an error
#on the GPU async. So dont trust the CUDA API and guard here
return False
if not tensor.is_contiguous():
return False
size = tensor.numel() * tensor.element_size()
if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:
return False
ptr = tensor.data_ptr()
if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0:
PINNED_MEMORY[ptr] = size
TOTAL_PINNED_MEMORY += size
return True
return False
def unpin_memory(tensor):
if PerformanceFeature.PinnedMem not in args.fast:
return False
if not is_nvidia():
global TOTAL_PINNED_MEMORY
if MAX_PINNED_MEMORY <= 0:
return False
if not is_device_cpu(tensor.device):
return False
if torch.cuda.cudart().cudaHostUnregister(tensor.data_ptr()) == 0:
ptr = tensor.data_ptr()
size = tensor.numel() * tensor.element_size()
size_stored = PINNED_MEMORY.get(ptr, None)
if size_stored is None:
logging.warning("Tried to unpin tensor not pinned by ComfyUI")
return False
if size != size_stored:
logging.warning("Size of pinned tensor changed")
return False
if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
if len(PINNED_MEMORY) == 0:
TOTAL_PINNED_MEMORY = 0
return True
return False

24
comfy/model_patcher.py
View File

@ -843,7 +843,7 @@ class ModelPatcher:
self.object_patches_backup.clear()
def partially_unload(self, device_to, memory_to_free=0):
def partially_unload(self, device_to, memory_to_free=0, force_patch_weights=False):
with self.use_ejected():
hooks_unpatched = False
memory_freed = 0
@ -887,13 +887,19 @@ class ModelPatcher:
module_mem += move_weight_functions(m, device_to)
if lowvram_possible:
if weight_key in self.patches:
_, set_func, convert_func = get_key_weight(self.model, weight_key)
m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func))
patch_counter += 1
if force_patch_weights:
self.patch_weight_to_device(weight_key)
else:
_, set_func, convert_func = get_key_weight(self.model, weight_key)
m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func))
patch_counter += 1
if bias_key in self.patches:
_, set_func, convert_func = get_key_weight(self.model, bias_key)
m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func))
patch_counter += 1
if force_patch_weights:
self.patch_weight_to_device(bias_key)
else:
_, set_func, convert_func = get_key_weight(self.model, bias_key)
m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func))
patch_counter += 1
cast_weight = True
if cast_weight:
@ -909,6 +915,7 @@ class ModelPatcher:
self.model.model_lowvram = True
self.model.lowvram_patch_counter += patch_counter
self.model.model_loaded_weight_memory -= memory_freed
logging.info("loaded partially: {:.2f} MB loaded, lowvram patches: {}".format(self.model.model_loaded_weight_memory / (1024 * 1024), self.model.lowvram_patch_counter))
return memory_freed
def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
@ -921,6 +928,9 @@ class ModelPatcher:
extra_memory += (used - self.model.model_loaded_weight_memory)
self.patch_model(load_weights=False)
if extra_memory < 0 and not unpatch_weights:
self.partially_unload(self.offload_device, -extra_memory, force_patch_weights=force_patch_weights)
return 0
full_load = False
if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0:
self.apply_hooks(self.forced_hooks, force_apply=True)

4
comfy/ops.py
View File

@ -110,9 +110,9 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
for f in s.bias_function:
bias = f(bias)
weight = weight.to(dtype=dtype)
if weight_has_function:
if weight_has_function or weight.dtype != dtype:
with wf_context:
weight = weight.to(dtype=dtype)
for f in s.weight_function:
weight = f(weight)

17
comfy_api_nodes/apis/PixverseController.py
View File

@ -1,17 +0,0 @@
# generated by datamodel-codegen:
# filename: filtered-openapi.yaml
# timestamp: 2025-04-29T23:44:54+00:00
from __future__ import annotations
from typing import Optional
from pydantic import BaseModel
from . import PixverseDto
class ResponseData(BaseModel):
ErrCode: Optional[int] = None
ErrMsg: Optional[str] = None
Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None

57
comfy_api_nodes/apis/PixverseDto.py
View File

@ -1,57 +0,0 @@
# generated by datamodel-codegen:
# filename: filtered-openapi.yaml
# timestamp: 2025-04-29T23:44:54+00:00
from __future__ import annotations
from typing import Optional
from pydantic import BaseModel, Field
class V2OpenAPII2VResp(BaseModel):
video_id: Optional[int] = Field(None, description='Video_id')
class V2OpenAPIT2VReq(BaseModel):
aspect_ratio: str = Field(
..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9']
)
duration: int = Field(
...,
description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)',
examples=[5],
)
model: str = Field(
..., description='Model version (only supports v3.5)', examples=['v3.5']
)
motion_mode: Optional[str] = Field(
'normal',
description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)',
examples=['normal'],
)
negative_prompt: Optional[str] = Field(
None, description='Negative prompt\n', max_length=2048
)
prompt: str = Field(..., description='Prompt', max_length=2048)
quality: str = Field(
...,
description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")',
examples=['540p'],
)
seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647')
style: Optional[str] = Field(
None,
description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed',
examples=['anime'],
)
template_id: Optional[int] = Field(
None,
description='Template ID (template_id must be activated before use)',
examples=[302325299692608],
)
water_mark: Optional[bool] = Field(
False,
description='Watermark (true: add watermark, false: no watermark)',
examples=[False],
)

981
comfy_api_nodes/apis/client.py
View File

@ -1,981 +0,0 @@
"""
API Client Framework for api.comfy.org.
This module provides a flexible framework for making API requests from ComfyUI nodes.
It supports both synchronous and asynchronous API operations with proper type validation.
Key Components:
--------------
1. ApiClient - Handles HTTP requests with authentication and error handling
2. ApiEndpoint - Defines a single HTTP endpoint with its request/response models
3. ApiOperation - Executes a single synchronous API operation
Usage Examples:
--------------
# Example 1: Synchronous API Operation
# ------------------------------------
# For a simple API call that returns the result immediately:
# 1. Create the API client
api_client = ApiClient(
base_url="https://api.example.com",
auth_token="your_auth_token_here",
comfy_api_key="your_comfy_api_key_here",
timeout=30.0,
verify_ssl=True
)
# 2. Define the endpoint
user_info_endpoint = ApiEndpoint(
path="/v1/users/me",
method=HttpMethod.GET,
request_model=EmptyRequest, # No request body needed
response_model=UserProfile, # Pydantic model for the response
query_params=None
)
# 3. Create the request object
request = EmptyRequest()
# 4. Create and execute the operation
operation = ApiOperation(
endpoint=user_info_endpoint,
request=request
)
user_profile = await operation.execute(client=api_client) # Returns immediately with the result
# Example 2: Asynchronous API Operation with Polling
# -------------------------------------------------
# For an API that starts a task and requires polling for completion:
# 1. Define the endpoints (initial request and polling)
generate_image_endpoint = ApiEndpoint(
path="/v1/images/generate",
method=HttpMethod.POST,
request_model=ImageGenerationRequest,
response_model=TaskCreatedResponse,
query_params=None
)
check_task_endpoint = ApiEndpoint(
path="/v1/tasks/{task_id}",
method=HttpMethod.GET,
request_model=EmptyRequest,
response_model=ImageGenerationResult,
query_params=None
)
# 2. Create the request object
request = ImageGenerationRequest(
prompt="a beautiful sunset over mountains",
width=1024,
height=1024,
num_images=1
)
# 3. Create and execute the polling operation
operation = PollingOperation(
initial_endpoint=generate_image_endpoint,
initial_request=request,
poll_endpoint=check_task_endpoint,
task_id_field="task_id",
status_field="status",
completed_statuses=["completed"],
failed_statuses=["failed", "error"]
)
# This will make the initial request and then poll until completion
result = await operation.execute(client=api_client) # Returns the final ImageGenerationResult when done
"""
from __future__ import annotations
import aiohttp
import asyncio
import logging
import io
import os
import socket
from aiohttp.client_exceptions import ClientError, ClientResponseError
from typing import Type, Optional, Any, TypeVar, Generic, Callable
from enum import Enum
import json
from urllib.parse import urljoin, urlparse
from pydantic import BaseModel, Field
import uuid # For generating unique operation IDs
from server import PromptServer
from comfy.cli_args import args
from comfy import utils
from . import request_logger
T = TypeVar("T", bound=BaseModel)
R = TypeVar("R", bound=BaseModel)
P = TypeVar("P", bound=BaseModel) # For poll response
PROGRESS_BAR_MAX = 100
class NetworkError(Exception):
"""Base exception for network-related errors with diagnostic information."""
pass
class LocalNetworkError(NetworkError):
"""Exception raised when local network connectivity issues are detected."""
pass
class ApiServerError(NetworkError):
"""Exception raised when the API server is unreachable but internet is working."""
pass
class EmptyRequest(BaseModel):
"""Base class for empty request bodies.
For GET requests, fields will be sent as query parameters."""
pass
class UploadRequest(BaseModel):
file_name: str = Field(..., description="Filename to upload")
content_type: Optional[str] = Field(
None,
description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
)
class UploadResponse(BaseModel):
download_url: str = Field(..., description="URL to GET uploaded file")
upload_url: str = Field(..., description="URL to PUT file to upload")
class HttpMethod(str, Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
PATCH = "PATCH"
class ApiClient:
"""
Client for making HTTP requests to an API with authentication, error handling, and retry logic.
"""
def __init__(
self,
base_url: str,
auth_token: Optional[str] = None,
comfy_api_key: Optional[str] = None,
timeout: float = 3600.0,
verify_ssl: bool = True,
max_retries: int = 3,
retry_delay: float = 1.0,
retry_backoff_factor: float = 2.0,
retry_status_codes: Optional[tuple[int, ...]] = None,
session: Optional[aiohttp.ClientSession] = None,
):
self.base_url = base_url
self.auth_token = auth_token
self.comfy_api_key = comfy_api_key
self.timeout = timeout
self.verify_ssl = verify_ssl
self.max_retries = max_retries
self.retry_delay = retry_delay
self.retry_backoff_factor = retry_backoff_factor
# Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests),
# 500, 502, 503, 504 (Server Errors)
self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504)
self._session: Optional[aiohttp.ClientSession] = session
self._owns_session = session is None # Track if we have to close it
@staticmethod
def _generate_operation_id(path: str) -> str:
"""Generates a unique operation ID for logging."""
return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}"
@staticmethod
def _create_json_payload_args(
data: Optional[dict[str, Any]] = None,
headers: Optional[dict[str, str]] = None,
) -> dict[str, Any]:
return {
"json": data,
"headers": headers,
}
def _create_form_data_args(
self,
data: dict[str, Any] | None,
files: dict[str, Any] | None,
headers: Optional[dict[str, str]] = None,
multipart_parser: Callable | None = None,
) -> dict[str, Any]:
if headers and "Content-Type" in headers:
del headers["Content-Type"]
if multipart_parser and data:
data = multipart_parser(data)
if isinstance(data, aiohttp.FormData):
form = data # If the parser already returned a FormData, pass it through
else:
form = aiohttp.FormData(default_to_multipart=True)
if data: # regular text fields
for k, v in data.items():
if v is None:
continue # aiohttp fails to serialize "None" values
# aiohttp expects strings or bytes; convert enums etc.
form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v)
if files:
file_iter = files if isinstance(files, list) else files.items()
for field_name, file_obj in file_iter:
if file_obj is None:
continue # aiohttp fails to serialize "None" values
# file_obj can be (filename, bytes/io.BytesIO, content_type) tuple
if isinstance(file_obj, tuple):
filename, file_value, content_type = self._unpack_tuple(file_obj)
else:
file_value = file_obj
filename = getattr(file_obj, "name", field_name)
content_type = "application/octet-stream"
form.add_field(
name=field_name,
value=file_value,
filename=filename,
content_type=content_type,
)
return {"data": form, "headers": headers or {}}
@staticmethod
def _create_urlencoded_form_data_args(
data: dict[str, Any],
headers: Optional[dict[str, str]] = None,
) -> dict[str, Any]:
headers = headers or {}
headers["Content-Type"] = "application/x-www-form-urlencoded"
return {
"data": data,
"headers": headers,
}
def get_headers(self) -> dict[str, str]:
"""Get headers for API requests, including authentication if available"""
headers = {"Content-Type": "application/json", "Accept": "application/json"}
if self.auth_token:
headers["Authorization"] = f"Bearer {self.auth_token}"
elif self.comfy_api_key:
headers["X-API-KEY"] = self.comfy_api_key
return headers
async def _check_connectivity(self, target_url: str) -> dict[str, bool]:
"""
Check connectivity to determine if network issues are local or server-related.
Args:
target_url: URL to check connectivity to
Returns:
Dictionary with connectivity status details
"""
results = {
"internet_accessible": False,
"api_accessible": False,
"is_local_issue": False,
"is_api_issue": False,
}
timeout = aiohttp.ClientTimeout(total=5.0)
async with aiohttp.ClientSession(timeout=timeout) as session:
try:
async with session.get("https://www.google.com", ssl=self.verify_ssl) as resp:
results["internet_accessible"] = resp.status < 500
except (ClientError, asyncio.TimeoutError, socket.gaierror):
results["is_local_issue"] = True
return results # cannot reach the internet – early exit
# Now check API health endpoint
parsed = urlparse(target_url)
health_url = f"{parsed.scheme}://{parsed.netloc}/health"
try:
async with session.get(health_url, ssl=self.verify_ssl) as resp:
results["api_accessible"] = resp.status < 500
except ClientError:
pass # leave as False
results["is_api_issue"] = results["internet_accessible"] and not results["api_accessible"]
return results
async def request(
self,
method: str,
path: str,
params: Optional[dict[str, Any]] = None,
data: Optional[dict[str, Any]] = None,
files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None,
headers: Optional[dict[str, str]] = None,
content_type: str = "application/json",
multipart_parser: Callable | None = None,
retry_count: int = 0, # Used internally for tracking retries
) -> dict[str, Any]:
"""
Make an HTTP request to the API with automatic retries for transient errors.
Args:
method: HTTP method (GET, POST, etc.)
path: API endpoint path (will be joined with base_url)
params: Query parameters
data: body data
files: Files to upload
headers: Additional headers
content_type: Content type of the request. Defaults to application/json.
retry_count: Internal parameter for tracking retries, do not set manually
Returns:
Parsed JSON response
Raises:
LocalNetworkError: If local network connectivity issues are detected
ApiServerError: If the API server is unreachable but internet is working
Exception: For other request failures
"""
# Build full URL and merge headers
relative_path = path.lstrip("/")
url = urljoin(self.base_url, relative_path)
self._check_auth(self.auth_token, self.comfy_api_key)
request_headers = self.get_headers()
if headers:
request_headers.update(headers)
if files:
request_headers.pop("Content-Type", None)
if params:
params = {k: v for k, v in params.items() if v is not None} # aiohttp fails to serialize None values
logging.debug("[DEBUG] Request Headers: %s", request_headers)
logging.debug("[DEBUG] Files: %s", files)
logging.debug("[DEBUG] Params: %s", params)
logging.debug("[DEBUG] Data: %s", data)
if content_type == "application/x-www-form-urlencoded":
payload_args = self._create_urlencoded_form_data_args(data or {}, request_headers)
elif content_type == "multipart/form-data":
payload_args = self._create_form_data_args(data, files, request_headers, multipart_parser)
else:
payload_args = self._create_json_payload_args(data, request_headers)
operation_id = self._generate_operation_id(path)
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
request_headers=request_headers,
request_params=params,
request_data=data if content_type == "application/json" else "[form-data or other]",
)
session = await self._get_session()
try:
async with session.request(
method,
url,
params=params,
ssl=self.verify_ssl,
**payload_args,
) as resp:
if resp.status >= 400:
try:
error_data = await resp.json()
except (aiohttp.ContentTypeError, json.JSONDecodeError):
error_data = await resp.text()
return await self._handle_http_error(
ClientResponseError(resp.request_info, resp.history, status=resp.status, message=error_data),
operation_id,
method,
url,
params,
data,
files,
headers,
content_type,
multipart_parser,
retry_count=retry_count,
response_content=error_data,
)
# Success – parse JSON (safely) and log
try:
payload = await resp.json()
response_content_to_log = payload
except (aiohttp.ContentTypeError, json.JSONDecodeError):
payload = {}
response_content_to_log = await resp.text()
request_logger.log_request_response(
operation_id=operation_id,
request_method=method,
request_url=url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content=response_content_to_log,
)
return payload
except (ClientError, asyncio.TimeoutError, socket.gaierror) as e:
# Treat as *connection* problem – optionally retry, else escalate
if retry_count < self.max_retries:
delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
logging.warning("Connection error. Retrying in %.2fs (%s/%s): %s", delay, retry_count + 1,
self.max_retries, str(e))
await asyncio.sleep(delay)
return await self.request(
method,
path,
params=params,
data=data,
files=files,
headers=headers,
content_type=content_type,
multipart_parser=multipart_parser,
retry_count=retry_count + 1,
)
# One final connectivity check for diagnostics
connectivity = await self._check_connectivity(self.base_url)
if connectivity["is_local_issue"]:
raise LocalNetworkError(
"Unable to connect to the API server due to local network issues. "
"Please check your internet connection and try again."
) from e
raise ApiServerError(
f"The API server at {self.base_url} is currently unreachable. "
f"The service may be experiencing issues. Please try again later."
) from e
@staticmethod
def _check_auth(auth_token, comfy_api_key):
"""Verify that an auth token is present or comfy_api_key is present"""
if auth_token is None and comfy_api_key is None:
raise Exception("Unauthorized: Please login first to use this node.")
return auth_token or comfy_api_key
@staticmethod
async def upload_file(
upload_url: str,
file: io.BytesIO | str,
content_type: str | None = None,
max_retries: int = 3,
retry_delay: float = 1.0,
retry_backoff_factor: float = 2.0,
) -> aiohttp.ClientResponse:
"""Upload a file to the API with retry logic.
Args:
upload_url: The URL to upload to
file: Either a file path string, BytesIO object, or tuple of (file_path, filename)
content_type: Optional mime type to set for the upload
max_retries: Maximum number of retry attempts
retry_delay: Initial delay between retries in seconds
retry_backoff_factor: Multiplier for the delay after each retry
"""
headers: dict[str, str] = {}
skip_auto_headers: set[str] = set()
if content_type:
headers["Content-Type"] = content_type
else:
# tell aiohttp not to add Content-Type that will break the request signature and result in a 403 status.
skip_auto_headers.add("Content-Type")
# Extract file bytes
if isinstance(file, io.BytesIO):
file.seek(0)
data = file.read()
elif isinstance(file, str):
with open(file, "rb") as f:
data = f.read()
else:
raise ValueError("File must be BytesIO or str path")
parsed = urlparse(upload_url)
basename = os.path.basename(parsed.path) or parsed.netloc or "upload"
operation_id = f"upload_{basename}_{uuid.uuid4().hex[:8]}"
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
request_headers=headers,
request_data=f"[File data {len(data)} bytes]",
)
delay = retry_delay
for attempt in range(max_retries + 1):
try:
timeout = aiohttp.ClientTimeout(total=None) # honour server side timeouts
async with aiohttp.ClientSession(timeout=timeout) as session:
async with session.put(
upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers,
) as resp:
resp.raise_for_status()
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
response_status_code=resp.status,
response_headers=dict(resp.headers),
response_content="File uploaded successfully.",
)
return resp
except (ClientError, asyncio.TimeoutError) as e:
request_logger.log_request_response(
operation_id=operation_id,
request_method="PUT",
request_url=upload_url,
response_status_code=e.status if hasattr(e, "status") else None,
response_headers=dict(e.headers) if hasattr(e, "headers") else None,
response_content=None,
error_message=f"{type(e).__name__}: {str(e)}",
)
if attempt < max_retries:
logging.warning(
"Upload failed (%s/%s). Retrying in %.2fs. %s", attempt + 1, max_retries, delay, str(e)
)
await asyncio.sleep(delay)
delay *= retry_backoff_factor
else:
raise NetworkError(f"Failed to upload file after {max_retries + 1} attempts: {e}") from e
async def _handle_http_error(
self,
exc: ClientResponseError,
operation_id: str,
*req_meta,
retry_count: int,
response_content: dict | str = "",
) -> dict[str, Any]:
status_code = exc.status
if status_code == 401:
user_friendly = "Unauthorized: Please login first to use this node."
elif status_code == 402:
user_friendly = "Payment Required: Please add credits to your account to use this node."
elif status_code == 409:
user_friendly = "There is a problem with your account. Please contact support@comfy.org."
elif status_code == 429:
user_friendly = "Rate Limit Exceeded: Please try again later."
else:
if isinstance(response_content, dict):
if "error" in response_content and "message" in response_content["error"]:
user_friendly = f"API Error: {response_content['error']['message']}"
if "type" in response_content["error"]:
user_friendly += f" (Type: {response_content['error']['type']})"
else: # Handle cases where error is just a JSON dict with unknown format
user_friendly = f"API Error: {json.dumps(response_content)}"
else:
if len(response_content) < 200: # Arbitrary limit for display
user_friendly = f"API Error (raw): {response_content}"
else:
user_friendly = f"API Error (raw, status {response_content})"
request_logger.log_request_response(
operation_id=operation_id,
request_method=req_meta[0],
request_url=req_meta[1],
response_status_code=exc.status,
response_headers=dict(req_meta[5]) if req_meta[5] else None,
response_content=response_content,
error_message=f"HTTP Error {exc.status}",
)
logging.debug("[DEBUG] API Error: %s (Status: %s)", user_friendly, status_code)
if response_content:
logging.debug("[DEBUG] Response content: %s", response_content)
# Retry if eligible
if status_code in self.retry_status_codes and retry_count < self.max_retries:
delay = self.retry_delay * (self.retry_backoff_factor ** retry_count)
logging.warning(
"HTTP error %s. Retrying in %.2fs (%s/%s)",
status_code,
delay,
retry_count + 1,
self.max_retries,
)
await asyncio.sleep(delay)
return await self.request(
req_meta[0], # method
req_meta[1].replace(self.base_url, ""), # path
params=req_meta[2],
data=req_meta[3],
files=req_meta[4],
headers=req_meta[5],
content_type=req_meta[6],
multipart_parser=req_meta[7],
retry_count=retry_count + 1,
)
raise Exception(user_friendly) from exc
@staticmethod
def _unpack_tuple(t):
"""Helper to normalise (filename, file, content_type) tuples."""
if len(t) == 3:
return t
elif len(t) == 2:
return t[0], t[1], "application/octet-stream"
else:
raise ValueError("files tuple must be (filename, file[, content_type])")
async def _get_session(self) -> aiohttp.ClientSession:
if self._session is None or self._session.closed:
timeout = aiohttp.ClientTimeout(total=self.timeout)
self._session = aiohttp.ClientSession(timeout=timeout)
self._owns_session = True
return self._session
async def close(self) -> None:
if self._owns_session and self._session and not self._session.closed:
await self._session.close()
async def __aenter__(self) -> "ApiClient":
"""Allow usage as async‑context‑manager – ensures clean teardown"""
return self
async def __aexit__(self, exc_type, exc, tb):
await self.close()
class ApiEndpoint(Generic[T, R]):
"""Defines an API endpoint with its request and response types"""
def __init__(
self,
path: str,
method: HttpMethod,
request_model: Type[T],
response_model: Type[R],
query_params: Optional[dict[str, Any]] = None,
):
"""Initialize an API endpoint definition.
Args:
path: The URL path for this endpoint, can include placeholders like {id}
method: The HTTP method to use (GET, POST, etc.)
request_model: Pydantic model class that defines the structure and validation rules for API requests to this endpoint
response_model: Pydantic model class that defines the structure and validation rules for API responses from this endpoint
query_params: Optional dictionary of query parameters to include in the request
"""
self.path = path
self.method = method
self.request_model = request_model
self.response_model = response_model
self.query_params = query_params or {}
class SynchronousOperation(Generic[T, R]):
"""Represents a single synchronous API operation."""
def __init__(
self,
endpoint: ApiEndpoint[T, R],
request: T,
files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None,
api_base: str | None = None,
auth_token: Optional[str] = None,
comfy_api_key: Optional[str] = None,
auth_kwargs: Optional[dict[str, str]] = None,
timeout: float = 7200.0,
verify_ssl: bool = True,
content_type: str = "application/json",
multipart_parser: Callable | None = None,
max_retries: int = 3,
retry_delay: float = 1.0,
retry_backoff_factor: float = 2.0,
) -> None:
self.endpoint = endpoint
self.request = request
self.files = files
self.api_base: str = api_base or args.comfy_api_base
self.auth_token = auth_token
self.comfy_api_key = comfy_api_key
if auth_kwargs is not None:
self.auth_token = auth_kwargs.get("auth_token", self.auth_token)
self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key)
self.timeout = timeout
self.verify_ssl = verify_ssl
self.content_type = content_type
self.multipart_parser = multipart_parser
self.max_retries = max_retries
self.retry_delay = retry_delay
self.retry_backoff_factor = retry_backoff_factor
async def execute(self, client: Optional[ApiClient] = None) -> R:
owns_client = client is None
if owns_client:
client = ApiClient(
base_url=self.api_base,
auth_token=self.auth_token,
comfy_api_key=self.comfy_api_key,
timeout=self.timeout,
verify_ssl=self.verify_ssl,
max_retries=self.max_retries,
retry_delay=self.retry_delay,
retry_backoff_factor=self.retry_backoff_factor,
)
try:
request_dict: Optional[dict[str, Any]]
if isinstance(self.request, EmptyRequest):
request_dict = None
else:
request_dict = self.request.model_dump(exclude_none=True)
for k, v in list(request_dict.items()):
if isinstance(v, Enum):
request_dict[k] = v.value
logging.debug("[DEBUG] API Request: %s %s", self.endpoint.method.value, self.endpoint.path)
logging.debug("[DEBUG] Request Data: %s", json.dumps(request_dict, indent=2))
logging.debug("[DEBUG] Query Params: %s", self.endpoint.query_params)
response_json = await client.request(
self.endpoint.method.value,
self.endpoint.path,
params=self.endpoint.query_params,
data=request_dict,
files=self.files,
content_type=self.content_type,
multipart_parser=self.multipart_parser,
)
logging.debug("=" * 50)
logging.debug("[DEBUG] RESPONSE DETAILS:")
logging.debug("[DEBUG] Status Code: 200 (Success)")
logging.debug("[DEBUG] Response Body: %s", json.dumps(response_json, indent=2))
logging.debug("=" * 50)
parsed_response = self.endpoint.response_model.model_validate(response_json)
logging.debug("[DEBUG] Parsed Response: %s", parsed_response)
return parsed_response
finally:
if owns_client:
await client.close()
class TaskStatus(str, Enum):
"""Enum for task status values"""
COMPLETED = "completed"
FAILED = "failed"
PENDING = "pending"
class PollingOperation(Generic[T, R]):
"""Represents an asynchronous API operation that requires polling for completion."""
def __init__(
self,
poll_endpoint: ApiEndpoint[EmptyRequest, R],
completed_statuses: list[str],
failed_statuses: list[str],
*,
status_extractor: Callable[[R], Optional[str]],
progress_extractor: Callable[[R], Optional[float]] | None = None,
result_url_extractor: Callable[[R], Optional[str]] | None = None,
price_extractor: Callable[[R], Optional[float]] | None = None,
request: Optional[T] = None,
api_base: str | None = None,
auth_token: Optional[str] = None,
comfy_api_key: Optional[str] = None,
auth_kwargs: Optional[dict[str, str]] = None,
poll_interval: float = 5.0,
max_poll_attempts: int = 120, # Default max polling attempts (10 minutes with 5s interval)
max_retries: int = 3, # Max retries per individual API call
retry_delay: float = 1.0,
retry_backoff_factor: float = 2.0,
estimated_duration: Optional[float] = None,
node_id: Optional[str] = None,
) -> None:
self.poll_endpoint = poll_endpoint
self.request = request
self.api_base: str = api_base or args.comfy_api_base
self.auth_token = auth_token
self.comfy_api_key = comfy_api_key
if auth_kwargs is not None:
self.auth_token = auth_kwargs.get("auth_token", self.auth_token)
self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key)
self.poll_interval = poll_interval
self.max_poll_attempts = max_poll_attempts
self.max_retries = max_retries
self.retry_delay = retry_delay
self.retry_backoff_factor = retry_backoff_factor
self.estimated_duration = estimated_duration
self.status_extractor = status_extractor or (lambda x: getattr(x, "status", None))
self.progress_extractor = progress_extractor
self.result_url_extractor = result_url_extractor
self.price_extractor = price_extractor
self.node_id = node_id
self.completed_statuses = completed_statuses
self.failed_statuses = failed_statuses
self.final_response: Optional[R] = None
self.extracted_price: Optional[float] = None
async def execute(self, client: Optional[ApiClient] = None) -> R:
owns_client = client is None
if owns_client:
client = ApiClient(
base_url=self.api_base,
auth_token=self.auth_token,
comfy_api_key=self.comfy_api_key,
max_retries=self.max_retries,
retry_delay=self.retry_delay,
retry_backoff_factor=self.retry_backoff_factor,
)
try:
return await self._poll_until_complete(client)
finally:
if owns_client:
await client.close()
def _display_text_on_node(self, text: str):
if not self.node_id:
return
if self.extracted_price is not None:
text = f"Price: ${self.extracted_price}\n{text}"
PromptServer.instance.send_progress_text(text, self.node_id)
def _display_time_progress_on_node(self, time_completed: int | float):
if not self.node_id:
return
if self.estimated_duration is not None:
remaining = max(0, int(self.estimated_duration) - time_completed)
message = f"Task in progress: {time_completed}s (~{remaining}s remaining)"
else:
message = f"Task in progress: {time_completed}s"
self._display_text_on_node(message)
def _check_task_status(self, response: R) -> TaskStatus:
try:
status = self.status_extractor(response)
if status in self.completed_statuses:
return TaskStatus.COMPLETED
if status in self.failed_statuses:
return TaskStatus.FAILED
return TaskStatus.PENDING
except Exception as e:
logging.error("Error extracting status: %s", e)
return TaskStatus.PENDING
async def _poll_until_complete(self, client: ApiClient) -> R:
"""Poll until the task is complete"""
consecutive_errors = 0
max_consecutive_errors = min(5, self.max_retries * 2) # Limit consecutive errors
if self.progress_extractor:
progress = utils.ProgressBar(PROGRESS_BAR_MAX)
status = TaskStatus.PENDING
for poll_count in range(1, self.max_poll_attempts + 1):
try:
logging.debug("[DEBUG] Polling attempt #%s", poll_count)
request_dict = None if self.request is None else self.request.model_dump(exclude_none=True)
if poll_count == 1:
logging.debug(
"[DEBUG] Poll Request: %s %s",
self.poll_endpoint.method.value,
self.poll_endpoint.path,
)
logging.debug(
"[DEBUG] Poll Request Data: %s",
json.dumps(request_dict, indent=2) if request_dict else "None",
)
# Query task status
resp = await client.request(
self.poll_endpoint.method.value,
self.poll_endpoint.path,
params=self.poll_endpoint.query_params,
data=request_dict,
)
consecutive_errors = 0 # reset on success
response_obj: R = self.poll_endpoint.response_model.model_validate(resp)
# Check if task is complete
status = self._check_task_status(response_obj)
logging.debug("[DEBUG] Task Status: %s", status)
# If progress extractor is provided, extract progress
if self.progress_extractor:
new_progress = self.progress_extractor(response_obj)
if new_progress is not None:
progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX)
if self.price_extractor:
price = self.price_extractor(response_obj)
if price is not None:
self.extracted_price = price
if status == TaskStatus.COMPLETED:
message = "Task completed successfully"
if self.result_url_extractor:
result_url = self.result_url_extractor(response_obj)
if result_url:
message = f"Result URL: {result_url}"
logging.debug("[DEBUG] %s", message)
self._display_text_on_node(message)
self.final_response = response_obj
if self.progress_extractor:
progress.update(100)
return self.final_response
if status == TaskStatus.FAILED:
message = f"Task failed: {json.dumps(resp)}"
logging.error("[DEBUG] %s", message)
raise Exception(message)
logging.debug("[DEBUG] Task still pending, continuing to poll...")
# Task pending – wait
for i in range(int(self.poll_interval)):
self._display_time_progress_on_node((poll_count - 1) * self.poll_interval + i)
await asyncio.sleep(1)
except (LocalNetworkError, ApiServerError, NetworkError) as e:
consecutive_errors += 1
if consecutive_errors >= max_consecutive_errors:
raise Exception(
f"Polling aborted after {consecutive_errors} network errors: {str(e)}"
) from e
logging.warning(
"Network error (%s/%s): %s",
consecutive_errors,
max_consecutive_errors,
str(e),
)
await asyncio.sleep(self.poll_interval)
except Exception as e:
# For other errors, increment count and potentially abort
consecutive_errors += 1
if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED:
raise Exception(
f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
) from e
logging.error("[DEBUG] Polling error: %s", str(e))
logging.warning(
"Error during polling (attempt %s/%s): %s. Will retry in %s seconds.",
poll_count,
self.max_poll_attempts,
str(e),
self.poll_interval,
)
await asyncio.sleep(self.poll_interval)
# If we've exhausted all polling attempts
raise Exception(
f"Polling timed out after {self.max_poll_attempts} attempts (" f"{self.max_poll_attempts * self.poll_interval} seconds). "
"The operation may still be running on the server but is taking longer than expected."
)

196
comfy_api_nodes/nodes_rodin.py
View File

@ -5,12 +5,9 @@ Rodin API docs: https://developer.hyper3d.ai/
"""
from __future__ import annotations
from inspect import cleandoc
import folder_paths as comfy_paths
import aiohttp
import os
import asyncio
import logging
import math
from typing import Optional
@ -26,11 +23,11 @@ from comfy_api_nodes.apis.rodin_api import (
Rodin3DDownloadResponse,
JobStatus,
)
from comfy_api_nodes.apis.client import (
from comfy_api_nodes.util import (
sync_op,
poll_op,
ApiEndpoint,
HttpMethod,
SynchronousOperation,
PollingOperation,
download_url_to_bytesio,
)
from comfy_api.latest import ComfyExtension, IO
@ -121,35 +118,31 @@ def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
async def create_generate_task(
cls: type[IO.ComfyNode],
images=None,
seed=1,
material="PBR",
quality_override=18000,
tier="Regular",
mesh_mode="Quad",
TAPose = False,
auth_kwargs: Optional[dict[str, str]] = None,
ta_pose: bool = False,
):
if images is 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(
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/rodin/api/v2/rodin", method="POST"),
response_model=Rodin3DGenerateResponse,
data=Rodin3DGenerateRequest(
seed=seed,
tier=tier,
material=material,
quality_override=quality_override,
mesh_mode=mesh_mode,
TAPose=TAPose,
TAPose=ta_pose,
),
files=[
(
@ -159,11 +152,8 @@ async def create_generate_task(
for image in images if image is not None
],
content_type="multipart/form-data",
auth_kwargs=auth_kwargs,
)
response = await operation.execute()
if hasattr(response, "error"):
error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
logging.error(error_message)
@ -187,74 +177,46 @@ def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
return "DONE"
return "Generating"
def extract_progress(response: Rodin3DCheckStatusResponse) -> Optional[int]:
if not response.jobs:
return None
completed_count = sum(1 for job in response.jobs if job.status == JobStatus.Done)
return int((completed_count / len(response.jobs)) * 100)
async def poll_for_task_status(
subscription_key, auth_kwargs: Optional[dict[str, str]] = None,
) -> Rodin3DCheckStatusResponse:
poll_operation = PollingOperation(
poll_endpoint=ApiEndpoint(
path="/proxy/rodin/api/v2/status",
method=HttpMethod.POST,
request_model=Rodin3DCheckStatusRequest,
response_model=Rodin3DCheckStatusResponse,
),
request=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
completed_statuses=["DONE"],
failed_statuses=["FAILED"],
status_extractor=check_rodin_status,
poll_interval=3.0,
auth_kwargs=auth_kwargs,
)
async def poll_for_task_status(subscription_key: str, cls: type[IO.ComfyNode]) -> Rodin3DCheckStatusResponse:
logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
return await poll_operation.execute()
async def get_rodin_download_list(uuid, auth_kwargs: Optional[dict[str, str]] = None) -> Rodin3DDownloadResponse:
logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/rodin/api/v2/download",
method=HttpMethod.POST,
request_model=Rodin3DDownloadRequest,
response_model=Rodin3DDownloadResponse,
),
request=Rodin3DDownloadRequest(task_uuid=uuid),
auth_kwargs=auth_kwargs,
return await poll_op(
cls,
ApiEndpoint(path="/proxy/rodin/api/v2/status", method="POST"),
response_model=Rodin3DCheckStatusResponse,
data=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
status_extractor=check_rodin_status,
progress_extractor=extract_progress,
)
return await operation.execute()
async def download_files(url_list, task_uuid):
async def get_rodin_download_list(uuid: str, cls: type[IO.ComfyNode]) -> Rodin3DDownloadResponse:
logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
return await sync_op(
cls,
ApiEndpoint(path="/proxy/rodin/api/v2/download", method="POST"),
response_model=Rodin3DDownloadResponse,
data=Rodin3DDownloadRequest(task_uuid=uuid),
monitor_progress=False,
)
async def download_files(url_list, task_uuid: str):
result_folder_name = f"Rodin3D_{task_uuid}"
save_path = os.path.join(comfy_paths.get_output_directory(), result_folder_name)
os.makedirs(save_path, exist_ok=True)
model_file_path = None
async with aiohttp.ClientSession() as session:
for i in url_list.list:
file_path = os.path.join(save_path, i.name)
if file_path.endswith(".glb"):
model_file_path = os.path.join(result_folder_name, i.name)
logging.info("[ Rodin3D API - download_files ] Downloading file: %s", file_path)
max_retries = 5
for attempt in range(max_retries):
try:
async with session.get(i.url) as resp:
resp.raise_for_status()
with open(file_path, "wb") as f:
async for chunk in resp.content.iter_chunked(32 * 1024):
f.write(chunk)
break
except Exception as e:
logging.info("[ Rodin3D API - download_files ] Error downloading %s:%s", file_path, str(e))
if attempt < max_retries - 1:
logging.info("Retrying...")
await asyncio.sleep(2)
else:
logging.info(
"[ Rodin3D API - download_files ] Failed to download %s after %s attempts.",
file_path,
max_retries,
)
for i in url_list.list:
file_path = os.path.join(save_path, i.name)
if file_path.endswith(".glb"):
model_file_path = os.path.join(result_folder_name, i.name)
await download_url_to_bytesio(i.url, file_path)
return model_file_path
@ -276,6 +238,7 @@ class Rodin3D_Regular(IO.ComfyNode):
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@ -294,21 +257,17 @@ class Rodin3D_Regular(IO.ComfyNode):
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
cls,
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
await poll_for_task_status(subscription_key, cls)
download_list = await get_rodin_download_list(task_uuid, cls)
model = await download_files(download_list, task_uuid)
return IO.NodeOutput(model)
@ -332,6 +291,7 @@ class Rodin3D_Detail(IO.ComfyNode):
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@ -350,21 +310,17 @@ class Rodin3D_Detail(IO.ComfyNode):
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
cls,
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
await poll_for_task_status(subscription_key, cls)
download_list = await get_rodin_download_list(task_uuid, cls)
model = await download_files(download_list, task_uuid)
return IO.NodeOutput(model)
@ -388,6 +344,7 @@ class Rodin3D_Smooth(IO.ComfyNode):
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@ -400,27 +357,22 @@ class Rodin3D_Smooth(IO.ComfyNode):
Material_Type,
Polygon_count,
) -> IO.NodeOutput:
tier = "Smooth"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
cls,
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
tier="Smooth",
mesh_mode=mesh_mode,
auth_kwargs=auth,
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
await poll_for_task_status(subscription_key, cls)
download_list = await get_rodin_download_list(task_uuid, cls)
model = await download_files(download_list, task_uuid)
return IO.NodeOutput(model)
@ -451,6 +403,7 @@ class Rodin3D_Sketch(IO.ComfyNode):
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@ -461,29 +414,21 @@ class Rodin3D_Sketch(IO.ComfyNode):
Images,
Seed,
) -> IO.NodeOutput:
tier = "Sketch"
num_images = Images.shape[0]
m_images = []
for i in range(num_images):
m_images.append(Images[i])
material_type = "PBR"
quality_override = 18000
mesh_mode = "Quad"
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
cls,
images=m_images,
seed=Seed,
material=material_type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
auth_kwargs=auth,
material="PBR",
quality_override=18000,
tier="Sketch",
mesh_mode="Quad",
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
await poll_for_task_status(subscription_key, cls)
download_list = await get_rodin_download_list(task_uuid, cls)
model = await download_files(download_list, task_uuid)
return IO.NodeOutput(model)
@ -522,6 +467,7 @@ class Rodin3D_Gen2(IO.ComfyNode):
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@ -541,22 +487,18 @@ class Rodin3D_Gen2(IO.ComfyNode):
for i in range(num_images):
m_images.append(Images[i])
mesh_mode, quality_override = get_quality_mode(Polygon_count)
auth = {
"auth_token": cls.hidden.auth_token_comfy_org,
"comfy_api_key": cls.hidden.api_key_comfy_org,
}
task_uuid, subscription_key = await create_generate_task(
cls,
images=m_images,
seed=Seed,
material=Material_Type,
quality_override=quality_override,
tier=tier,
mesh_mode=mesh_mode,
TAPose=TAPose,
auth_kwargs=auth,
ta_pose=TAPose,
)
await poll_for_task_status(subscription_key, auth_kwargs=auth)
download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
await poll_for_task_status(subscription_key, cls)
download_list = await get_rodin_download_list(task_uuid, cls)
model = await download_files(download_list, task_uuid)
return IO.NodeOutput(model)

4
comfy_api_nodes/util/client.py
View File

@ -16,9 +16,9 @@ from pydantic import BaseModel
from comfy import utils
from comfy_api.latest import IO
from comfy_api_nodes.apis import request_logger
from server import PromptServer
from . import request_logger
from ._helpers import (
default_base_url,
get_auth_header,
@ -77,7 +77,7 @@ class _PollUIState:
_RETRY_STATUS = {408, 429, 500, 502, 503, 504}
COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished"]
COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done"]
FAILED_STATUSES = ["cancelled", "canceled", "fail", "failed", "error"]
QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"]

2
comfy_api_nodes/util/download_helpers.py
View File

@ -12,8 +12,8 @@ from aiohttp.client_exceptions import ClientError, ContentTypeError
from comfy_api.input_impl import VideoFromFile
from comfy_api.latest import IO as COMFY_IO
from comfy_api_nodes.apis import request_logger
from . import request_logger
from ._helpers import (
default_base_url,
get_auth_header,

4
comfy_api_nodes/apis/request_logger.py → comfy_api_nodes/util/request_logger.py
View File

@ -1,11 +1,11 @@
from __future__ import annotations
import os
import datetime
import hashlib
import json
import logging
import os
import re
import hashlib
from typing import Any
import folder_paths

2
comfy_api_nodes/util/upload_helpers.py
View File

@ -13,8 +13,8 @@ from pydantic import BaseModel, Field
from comfy_api.latest import IO, Input
from comfy_api.util import VideoCodec, VideoContainer
from comfy_api_nodes.apis import request_logger
from . import request_logger
from ._helpers import is_processing_interrupted, sleep_with_interrupt
from .client import (
ApiEndpoint,

2
comfy_execution/caching.py
View File

@ -399,6 +399,8 @@ class RAMPressureCache(LRUCache):
ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
def scan_list_for_ram_usage(outputs):
nonlocal ram_usage
if outputs is None:
return
for output in outputs:
if isinstance(output, list):
scan_list_for_ram_usage(output)

2
comfyui_version.py
View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.3.67"
__version__ = "0.3.68"

2
pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.3.67"
version = "0.3.68"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"