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36
.github/workflows/release-stable-all.yml
vendored
36
.github/workflows/release-stable-all.yml
vendored
@@ -20,29 +20,12 @@ jobs:
|
||||
git_tag: ${{ inputs.git_tag }}
|
||||
cache_tag: "cu130"
|
||||
python_minor: "13"
|
||||
python_patch: "11"
|
||||
python_patch: "12"
|
||||
rel_name: "nvidia"
|
||||
rel_extra_name: ""
|
||||
test_release: true
|
||||
secrets: inherit
|
||||
|
||||
release_nvidia_cu128:
|
||||
permissions:
|
||||
contents: "write"
|
||||
packages: "write"
|
||||
pull-requests: "read"
|
||||
name: "Release NVIDIA cu128"
|
||||
uses: ./.github/workflows/stable-release.yml
|
||||
with:
|
||||
git_tag: ${{ inputs.git_tag }}
|
||||
cache_tag: "cu128"
|
||||
python_minor: "12"
|
||||
python_patch: "10"
|
||||
rel_name: "nvidia"
|
||||
rel_extra_name: "_cu128"
|
||||
test_release: true
|
||||
secrets: inherit
|
||||
|
||||
release_nvidia_cu126:
|
||||
permissions:
|
||||
contents: "write"
|
||||
@@ -76,3 +59,20 @@ jobs:
|
||||
rel_extra_name: ""
|
||||
test_release: false
|
||||
secrets: inherit
|
||||
|
||||
release_xpu:
|
||||
permissions:
|
||||
contents: "write"
|
||||
packages: "write"
|
||||
pull-requests: "read"
|
||||
name: "Release Intel XPU"
|
||||
uses: ./.github/workflows/stable-release.yml
|
||||
with:
|
||||
git_tag: ${{ inputs.git_tag }}
|
||||
cache_tag: "xpu"
|
||||
python_minor: "13"
|
||||
python_patch: "12"
|
||||
rel_name: "intel"
|
||||
rel_extra_name: ""
|
||||
test_release: true
|
||||
secrets: inherit
|
||||
|
||||
@@ -61,6 +61,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
|
||||
|
||||
## Features
|
||||
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
|
||||
- NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI.
|
||||
- Image Models
|
||||
- SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
|
||||
- [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
|
||||
@@ -136,7 +137,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
|
||||
- Builds a new release using the latest stable core version
|
||||
|
||||
3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
|
||||
- Weekly frontend updates are merged into the core repository
|
||||
- Every 2+ weeks frontend updates are merged into the core repository
|
||||
- Features are frozen for the upcoming core release
|
||||
- Development continues for the next release cycle
|
||||
|
||||
@@ -275,7 +276,7 @@ Nvidia users should install stable pytorch using this command:
|
||||
|
||||
This is the command to install pytorch nightly instead which might have performance improvements.
|
||||
|
||||
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
|
||||
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132```
|
||||
|
||||
#### Troubleshooting
|
||||
|
||||
|
||||
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@@ -1 +1,322 @@
|
||||
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|
||||
{
|
||||
"revision": 0,
|
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"last_node_id": 29,
|
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"last_link_id": 0,
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"pos": [
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1970,
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|
||||
],
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"size": [
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180,
|
||||
86
|
||||
],
|
||||
"flags": {},
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"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"label": "image",
|
||||
"localized_name": "images.image0",
|
||||
"name": "images.image0",
|
||||
"type": "IMAGE",
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"label": "R",
|
||||
"localized_name": "IMAGE0",
|
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"name": "IMAGE0",
|
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"type": "IMAGE",
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"links": []
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{
|
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"label": "G",
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"name": "IMAGE1",
|
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"type": "IMAGE",
|
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"links": []
|
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},
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|
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"label": "B",
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|
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"name": "IMAGE2",
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"type": "IMAGE",
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|
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},
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{
|
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"label": "A",
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"localized_name": "IMAGE3",
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"name": "IMAGE3",
|
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"type": "IMAGE",
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1820,
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|
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|
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|
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"ver": "0.10.0",
|
||||
"Node name for S&R": "ImageUpscaleWithModel"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 11,
|
||||
"type": "CreateVideo",
|
||||
"pos": [
|
||||
1110,
|
||||
550
|
||||
],
|
||||
"size": [
|
||||
320,
|
||||
78
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 13
|
||||
},
|
||||
{
|
||||
"localized_name": "audio",
|
||||
"name": "audio",
|
||||
"shape": 7,
|
||||
"type": "AUDIO",
|
||||
"link": 16
|
||||
},
|
||||
{
|
||||
"localized_name": "fps",
|
||||
"name": "fps",
|
||||
"type": "FLOAT",
|
||||
"widget": {
|
||||
"name": "fps"
|
||||
},
|
||||
"link": 12
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "VIDEO",
|
||||
"name": "VIDEO",
|
||||
"type": "VIDEO",
|
||||
"links": [
|
||||
15
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.10.0",
|
||||
"Node name for S&R": "CreateVideo"
|
||||
},
|
||||
"widgets_values": [
|
||||
30
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "GetVideoComponents",
|
||||
"pos": [
|
||||
1110,
|
||||
330
|
||||
],
|
||||
"size": [
|
||||
320,
|
||||
70
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "video",
|
||||
"name": "video",
|
||||
"type": "VIDEO",
|
||||
"link": 10
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "images",
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
14
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "audio",
|
||||
"name": "audio",
|
||||
"type": "AUDIO",
|
||||
"links": [
|
||||
16
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "fps",
|
||||
"name": "fps",
|
||||
"type": "FLOAT",
|
||||
"links": [
|
||||
12
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.10.0",
|
||||
"Node name for S&R": "GetVideoComponents"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "UpscaleModelLoader",
|
||||
"pos": [
|
||||
750,
|
||||
450
|
||||
],
|
||||
"size": [
|
||||
280,
|
||||
60
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "model_name",
|
||||
"name": "model_name",
|
||||
"type": "COMBO",
|
||||
"widget": {
|
||||
"name": "model_name"
|
||||
},
|
||||
"link": 19
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "UPSCALE_MODEL",
|
||||
"name": "UPSCALE_MODEL",
|
||||
"type": "UPSCALE_MODEL",
|
||||
"links": [
|
||||
1
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.10.0",
|
||||
"Node name for S&R": "UpscaleModelLoader",
|
||||
"models": [
|
||||
{
|
||||
"name": "RealESRGAN_x4plus.safetensors",
|
||||
"url": "https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors",
|
||||
"directory": "upscale_models"
|
||||
}
|
||||
]
|
||||
},
|
||||
"widgets_values": [
|
||||
"RealESRGAN_x4plus.safetensors"
|
||||
]
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"links": [
|
||||
{
|
||||
"id": 1,
|
||||
"origin_id": 1,
|
||||
"origin_slot": 0,
|
||||
"target_id": 2,
|
||||
"target_slot": 0,
|
||||
"type": "UPSCALE_MODEL"
|
||||
},
|
||||
{
|
||||
"id": 14,
|
||||
"origin_id": 10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 2,
|
||||
"target_slot": 1,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"origin_id": 2,
|
||||
"origin_slot": 0,
|
||||
"target_id": 11,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 16,
|
||||
"origin_id": 10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 11,
|
||||
"target_slot": 1,
|
||||
"type": "AUDIO"
|
||||
},
|
||||
{
|
||||
"id": 12,
|
||||
"origin_id": 10,
|
||||
"origin_slot": 2,
|
||||
"target_id": 11,
|
||||
"target_slot": 2,
|
||||
"type": "FLOAT"
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 10,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 15,
|
||||
"origin_id": 11,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 0,
|
||||
"type": "VIDEO"
|
||||
},
|
||||
{
|
||||
"id": 19,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 1,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
}
|
||||
],
|
||||
"extra": {
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"category": "Video generation and editing/Enhance video"
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
|
||||
@@ -611,6 +611,7 @@ class AceStepDiTModel(nn.Module):
|
||||
intermediate_size,
|
||||
patch_size,
|
||||
audio_acoustic_hidden_dim,
|
||||
condition_dim=None,
|
||||
layer_types=None,
|
||||
sliding_window=128,
|
||||
rms_norm_eps=1e-6,
|
||||
@@ -640,7 +641,7 @@ class AceStepDiTModel(nn.Module):
|
||||
|
||||
self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
|
||||
self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations)
|
||||
self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
|
||||
self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device)
|
||||
|
||||
if layer_types is None:
|
||||
layer_types = ["full_attention"] * num_layers
|
||||
@@ -1035,6 +1036,9 @@ class AceStepConditionGenerationModel(nn.Module):
|
||||
fsq_dim=2048,
|
||||
fsq_levels=[8, 8, 8, 5, 5, 5],
|
||||
fsq_input_num_quantizers=1,
|
||||
encoder_hidden_size=2048,
|
||||
encoder_intermediate_size=6144,
|
||||
encoder_num_heads=16,
|
||||
audio_model=None,
|
||||
dtype=None,
|
||||
device=None,
|
||||
@@ -1054,24 +1058,24 @@ class AceStepConditionGenerationModel(nn.Module):
|
||||
|
||||
self.decoder = AceStepDiTModel(
|
||||
in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size,
|
||||
layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.encoder = AceStepConditionEncoder(
|
||||
text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps,
|
||||
text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.tokenizer = AceStepAudioTokenizer(
|
||||
audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
|
||||
audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.detokenizer = AudioTokenDetokenizer(
|
||||
hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device))
|
||||
|
||||
def prepare_condition(
|
||||
self,
|
||||
|
||||
@@ -155,6 +155,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
def __init__(self, embed_dim: int, **kwargs):
|
||||
self.max_batch_size = kwargs.pop("max_batch_size", None)
|
||||
ddconfig = kwargs.pop("ddconfig")
|
||||
decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig)
|
||||
super().__init__(
|
||||
encoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
|
||||
@@ -162,7 +163,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
},
|
||||
decoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
|
||||
"params": ddconfig,
|
||||
"params": decoder_ddconfig,
|
||||
},
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -3,12 +3,9 @@ from ..diffusionmodules.openaimodel import Timestep
|
||||
import torch
|
||||
|
||||
class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
|
||||
def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
|
||||
def __init__(self, *args, timestep_dim=256, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
if clip_stats_path is None:
|
||||
clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
|
||||
else:
|
||||
clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
|
||||
clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
|
||||
self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
|
||||
self.register_buffer("data_std", clip_std[None, :], persistent=False)
|
||||
self.time_embed = Timestep(timestep_dim)
|
||||
|
||||
@@ -696,6 +696,15 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys:
|
||||
dit_config = {}
|
||||
dit_config["audio_model"] = "ace1.5"
|
||||
head_dim = 128
|
||||
dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
|
||||
|
||||
dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim
|
||||
dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0]
|
||||
dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.')
|
||||
return dit_config
|
||||
|
||||
if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4
|
||||
|
||||
13
comfy/sd.py
13
comfy/sd.py
@@ -556,12 +556,19 @@ class VAE:
|
||||
old_memory_used_decode = self.memory_used_decode
|
||||
self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0
|
||||
|
||||
decoder_ch = sd['decoder.conv_in.weight'].shape[0] // ddconfig['ch_mult'][-1]
|
||||
if decoder_ch != ddconfig['ch']:
|
||||
decoder_ddconfig = ddconfig.copy()
|
||||
decoder_ddconfig['ch'] = decoder_ch
|
||||
else:
|
||||
decoder_ddconfig = None
|
||||
|
||||
if 'post_quant_conv.weight' in sd:
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {}))
|
||||
else:
|
||||
self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
|
||||
encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': decoder_ddconfig if decoder_ddconfig is not None else ddconfig})
|
||||
elif "decoder.layers.1.layers.0.beta" in sd:
|
||||
config = {}
|
||||
param_key = None
|
||||
@@ -1745,6 +1752,8 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
|
||||
temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
|
||||
if len(temp_sd) > 0:
|
||||
sd = temp_sd
|
||||
if custom_operations is None:
|
||||
sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
|
||||
|
||||
parameters = comfy.utils.calculate_parameters(sd)
|
||||
weight_dtype = comfy.utils.weight_dtype(sd)
|
||||
|
||||
@@ -9,6 +9,7 @@ from comfy_api.latest._input import (
|
||||
CurveInput,
|
||||
MonotoneCubicCurve,
|
||||
LinearCurve,
|
||||
RangeInput,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
@@ -21,4 +22,5 @@ __all__ = [
|
||||
"CurveInput",
|
||||
"MonotoneCubicCurve",
|
||||
"LinearCurve",
|
||||
"RangeInput",
|
||||
]
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
|
||||
from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve
|
||||
from .range_types import RangeInput
|
||||
from .video_types import VideoInput
|
||||
|
||||
__all__ = [
|
||||
@@ -12,4 +13,5 @@ __all__ = [
|
||||
"CurveInput",
|
||||
"MonotoneCubicCurve",
|
||||
"LinearCurve",
|
||||
"RangeInput",
|
||||
]
|
||||
|
||||
70
comfy_api/latest/_input/range_types.py
Normal file
70
comfy_api/latest/_input/range_types.py
Normal file
@@ -0,0 +1,70 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RangeInput:
|
||||
"""Represents a levels/range adjustment: input range [min, max] with
|
||||
optional midpoint (gamma control).
|
||||
|
||||
Generates a 1D LUT identical to GIMP's levels mapping:
|
||||
1. Normalize input to [0, 1] using [min, max]
|
||||
2. Apply gamma correction: pow(value, 1/gamma)
|
||||
3. Clamp to [0, 1]
|
||||
|
||||
The midpoint field is a position in [0, 1] representing where the
|
||||
midtone falls within [min, max]. It maps to gamma via:
|
||||
gamma = -log2(midpoint)
|
||||
So midpoint=0.5 → gamma=1.0 (linear).
|
||||
"""
|
||||
|
||||
def __init__(self, min_val: float, max_val: float, midpoint: float | None = None):
|
||||
self.min_val = min_val
|
||||
self.max_val = max_val
|
||||
self.midpoint = midpoint
|
||||
|
||||
@staticmethod
|
||||
def from_raw(data) -> RangeInput:
|
||||
if isinstance(data, RangeInput):
|
||||
return data
|
||||
if isinstance(data, dict):
|
||||
return RangeInput(
|
||||
min_val=float(data.get("min", 0.0)),
|
||||
max_val=float(data.get("max", 1.0)),
|
||||
midpoint=float(data["midpoint"]) if data.get("midpoint") is not None else None,
|
||||
)
|
||||
raise TypeError(f"Cannot convert {type(data)} to RangeInput")
|
||||
|
||||
def to_lut(self, size: int = 256) -> np.ndarray:
|
||||
"""Generate a float64 lookup table mapping [0, 1] input through this
|
||||
levels adjustment.
|
||||
|
||||
The LUT maps normalized input values (0..1) to output values (0..1),
|
||||
matching the GIMP levels formula.
|
||||
"""
|
||||
xs = np.linspace(0.0, 1.0, size, dtype=np.float64)
|
||||
|
||||
in_range = self.max_val - self.min_val
|
||||
if abs(in_range) < 1e-10:
|
||||
return np.where(xs >= self.min_val, 1.0, 0.0).astype(np.float64)
|
||||
|
||||
# Normalize: map [min, max] → [0, 1]
|
||||
result = (xs - self.min_val) / in_range
|
||||
result = np.clip(result, 0.0, 1.0)
|
||||
|
||||
# Gamma correction from midpoint
|
||||
if self.midpoint is not None and self.midpoint > 0 and self.midpoint != 0.5:
|
||||
gamma = max(-math.log2(self.midpoint), 0.001)
|
||||
inv_gamma = 1.0 / gamma
|
||||
mask = result > 0
|
||||
result[mask] = np.power(result[mask], inv_gamma)
|
||||
|
||||
return result
|
||||
|
||||
def __repr__(self) -> str:
|
||||
mid = f", midpoint={self.midpoint}" if self.midpoint is not None else ""
|
||||
return f"RangeInput(min={self.min_val}, max={self.max_val}{mid})"
|
||||
@@ -1266,6 +1266,43 @@ class Histogram(ComfyTypeIO):
|
||||
Type = list[int]
|
||||
|
||||
|
||||
@comfytype(io_type="RANGE")
|
||||
class Range(ComfyTypeIO):
|
||||
from comfy_api.input import RangeInput
|
||||
if TYPE_CHECKING:
|
||||
Type = RangeInput
|
||||
|
||||
class Input(WidgetInput):
|
||||
def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
|
||||
socketless: bool=True, default: dict=None,
|
||||
display: str=None,
|
||||
gradient_stops: list=None,
|
||||
show_midpoint: bool=None,
|
||||
midpoint_scale: str=None,
|
||||
value_min: float=None,
|
||||
value_max: float=None,
|
||||
advanced: bool=None):
|
||||
super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
|
||||
if default is None:
|
||||
self.default = {"min": 0.0, "max": 1.0}
|
||||
self.display = display
|
||||
self.gradient_stops = gradient_stops
|
||||
self.show_midpoint = show_midpoint
|
||||
self.midpoint_scale = midpoint_scale
|
||||
self.value_min = value_min
|
||||
self.value_max = value_max
|
||||
|
||||
def as_dict(self):
|
||||
return super().as_dict() | prune_dict({
|
||||
"display": self.display,
|
||||
"gradient_stops": self.gradient_stops,
|
||||
"show_midpoint": self.show_midpoint,
|
||||
"midpoint_scale": self.midpoint_scale,
|
||||
"value_min": self.value_min,
|
||||
"value_max": self.value_max,
|
||||
})
|
||||
|
||||
|
||||
DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
|
||||
def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
|
||||
DYNAMIC_INPUT_LOOKUP[io_type] = func
|
||||
@@ -2276,5 +2313,6 @@ __all__ = [
|
||||
"BoundingBox",
|
||||
"Curve",
|
||||
"Histogram",
|
||||
"Range",
|
||||
"NodeReplace",
|
||||
]
|
||||
|
||||
226
comfy_api_nodes/apis/wan.py
Normal file
226
comfy_api_nodes/apis/wan.py
Normal file
@@ -0,0 +1,226 @@
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class Text2ImageInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
negative_prompt: str | None = Field(None)
|
||||
|
||||
|
||||
class Image2ImageInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
negative_prompt: str | None = Field(None)
|
||||
images: list[str] = Field(..., min_length=1, max_length=2)
|
||||
|
||||
|
||||
class Text2VideoInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
negative_prompt: str | None = Field(None)
|
||||
audio_url: str | None = Field(None)
|
||||
|
||||
|
||||
class Image2VideoInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
negative_prompt: str | None = Field(None)
|
||||
img_url: str = Field(...)
|
||||
audio_url: str | None = Field(None)
|
||||
|
||||
|
||||
class Reference2VideoInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
negative_prompt: str | None = Field(None)
|
||||
reference_video_urls: list[str] = Field(...)
|
||||
|
||||
|
||||
class Txt2ImageParametersField(BaseModel):
|
||||
size: str = Field(...)
|
||||
n: int = Field(1, description="Number of images to generate.") # we support only value=1
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
prompt_extend: bool = Field(True)
|
||||
watermark: bool = Field(False)
|
||||
|
||||
|
||||
class Image2ImageParametersField(BaseModel):
|
||||
size: str | None = Field(None)
|
||||
n: int = Field(1, description="Number of images to generate.") # we support only value=1
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
watermark: bool = Field(False)
|
||||
|
||||
|
||||
class Text2VideoParametersField(BaseModel):
|
||||
size: str = Field(...)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
duration: int = Field(5, ge=5, le=15)
|
||||
prompt_extend: bool = Field(True)
|
||||
watermark: bool = Field(False)
|
||||
audio: bool = Field(False, description="Whether to generate audio automatically.")
|
||||
shot_type: str = Field("single")
|
||||
|
||||
|
||||
class Image2VideoParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
duration: int = Field(5, ge=5, le=15)
|
||||
prompt_extend: bool = Field(True)
|
||||
watermark: bool = Field(False)
|
||||
audio: bool = Field(False, description="Whether to generate audio automatically.")
|
||||
shot_type: str = Field("single")
|
||||
|
||||
|
||||
class Reference2VideoParametersField(BaseModel):
|
||||
size: str = Field(...)
|
||||
duration: int = Field(5, ge=5, le=15)
|
||||
shot_type: str = Field("single")
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
watermark: bool = Field(False)
|
||||
|
||||
|
||||
class Text2ImageTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Text2ImageInputField = Field(...)
|
||||
parameters: Txt2ImageParametersField = Field(...)
|
||||
|
||||
|
||||
class Image2ImageTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Image2ImageInputField = Field(...)
|
||||
parameters: Image2ImageParametersField = Field(...)
|
||||
|
||||
|
||||
class Text2VideoTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Text2VideoInputField = Field(...)
|
||||
parameters: Text2VideoParametersField = Field(...)
|
||||
|
||||
|
||||
class Image2VideoTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Image2VideoInputField = Field(...)
|
||||
parameters: Image2VideoParametersField = Field(...)
|
||||
|
||||
|
||||
class Reference2VideoTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Reference2VideoInputField = Field(...)
|
||||
parameters: Reference2VideoParametersField = Field(...)
|
||||
|
||||
|
||||
class Wan27MediaItem(BaseModel):
|
||||
type: str = Field(...)
|
||||
url: str = Field(...)
|
||||
|
||||
|
||||
class Wan27ReferenceVideoInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
negative_prompt: str | None = Field(None)
|
||||
media: list[Wan27MediaItem] = Field(...)
|
||||
|
||||
|
||||
class Wan27ReferenceVideoParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
ratio: str | None = Field(None)
|
||||
duration: int = Field(5, ge=2, le=10)
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
|
||||
class Wan27ReferenceVideoTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Wan27ReferenceVideoInputField = Field(...)
|
||||
parameters: Wan27ReferenceVideoParametersField = Field(...)
|
||||
|
||||
|
||||
class Wan27ImageToVideoInputField(BaseModel):
|
||||
prompt: str | None = Field(None)
|
||||
negative_prompt: str | None = Field(None)
|
||||
media: list[Wan27MediaItem] = Field(...)
|
||||
|
||||
|
||||
class Wan27ImageToVideoParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
duration: int = Field(5, ge=2, le=15)
|
||||
prompt_extend: bool = Field(True)
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
|
||||
class Wan27ImageToVideoTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Wan27ImageToVideoInputField = Field(...)
|
||||
parameters: Wan27ImageToVideoParametersField = Field(...)
|
||||
|
||||
|
||||
class Wan27VideoEditInputField(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
media: list[Wan27MediaItem] = Field(...)
|
||||
|
||||
|
||||
class Wan27VideoEditParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
ratio: str | None = Field(None)
|
||||
duration: int = Field(0)
|
||||
audio_setting: str = Field("auto")
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
|
||||
class Wan27VideoEditTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Wan27VideoEditInputField = Field(...)
|
||||
parameters: Wan27VideoEditParametersField = Field(...)
|
||||
|
||||
|
||||
class Wan27Text2VideoParametersField(BaseModel):
|
||||
resolution: str = Field(...)
|
||||
ratio: str | None = Field(None)
|
||||
duration: int = Field(5, ge=2, le=15)
|
||||
prompt_extend: bool = Field(True)
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
|
||||
class Wan27Text2VideoTaskCreationRequest(BaseModel):
|
||||
model: str = Field(...)
|
||||
input: Text2VideoInputField = Field(...)
|
||||
parameters: Wan27Text2VideoParametersField = Field(...)
|
||||
|
||||
|
||||
class TaskCreationOutputField(BaseModel):
|
||||
task_id: str = Field(...)
|
||||
task_status: str = Field(...)
|
||||
|
||||
|
||||
class TaskCreationResponse(BaseModel):
|
||||
output: TaskCreationOutputField | None = Field(None)
|
||||
request_id: str = Field(...)
|
||||
code: str | None = Field(None, description="Error code for the failed request.")
|
||||
message: str | None = Field(None, description="Details about the failed request.")
|
||||
|
||||
|
||||
class TaskResult(BaseModel):
|
||||
url: str | None = Field(None)
|
||||
code: str | None = Field(None)
|
||||
message: str | None = Field(None)
|
||||
|
||||
|
||||
class ImageTaskStatusOutputField(TaskCreationOutputField):
|
||||
task_id: str = Field(...)
|
||||
task_status: str = Field(...)
|
||||
results: list[TaskResult] | None = Field(None)
|
||||
|
||||
|
||||
class VideoTaskStatusOutputField(TaskCreationOutputField):
|
||||
task_id: str = Field(...)
|
||||
task_status: str = Field(...)
|
||||
video_url: str | None = Field(None)
|
||||
code: str | None = Field(None)
|
||||
message: str | None = Field(None)
|
||||
|
||||
|
||||
class ImageTaskStatusResponse(BaseModel):
|
||||
output: ImageTaskStatusOutputField | None = Field(None)
|
||||
request_id: str = Field(...)
|
||||
|
||||
|
||||
class VideoTaskStatusResponse(BaseModel):
|
||||
output: VideoTaskStatusOutputField | None = Field(None)
|
||||
request_id: str = Field(...)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -80,7 +80,7 @@ class EmptyAceStepLatentAudio(io.ComfyNode):
|
||||
@classmethod
|
||||
def execute(cls, seconds, batch_size) -> io.NodeOutput:
|
||||
length = int(seconds * 44100 / 512 / 8)
|
||||
latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device())
|
||||
latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
|
||||
return io.NodeOutput({"samples": latent, "type": "audio"})
|
||||
|
||||
|
||||
@@ -103,7 +103,7 @@ class EmptyAceStep15LatentAudio(io.ComfyNode):
|
||||
@classmethod
|
||||
def execute(cls, seconds, batch_size) -> io.NodeOutput:
|
||||
length = round((seconds * 48000 / 1920))
|
||||
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
|
||||
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
|
||||
return io.NodeOutput({"samples": latent, "type": "audio"})
|
||||
|
||||
class ReferenceAudio(io.ComfyNode):
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
from comfy_api.input import CurveInput
|
||||
from typing_extensions import override
|
||||
@@ -32,10 +34,58 @@ class CurveEditor(io.ComfyNode):
|
||||
return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result)
|
||||
|
||||
|
||||
class ImageHistogram(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ImageHistogram",
|
||||
display_name="Image Histogram",
|
||||
category="utils",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
],
|
||||
outputs=[
|
||||
io.Histogram.Output("rgb"),
|
||||
io.Histogram.Output("luminance"),
|
||||
io.Histogram.Output("red"),
|
||||
io.Histogram.Output("green"),
|
||||
io.Histogram.Output("blue"),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image) -> io.NodeOutput:
|
||||
img = image[0].cpu().numpy()
|
||||
img_uint8 = np.clip(img * 255, 0, 255).astype(np.uint8)
|
||||
|
||||
def bincount(data):
|
||||
return np.bincount(data.ravel(), minlength=256)[:256]
|
||||
|
||||
hist_r = bincount(img_uint8[:, :, 0])
|
||||
hist_g = bincount(img_uint8[:, :, 1])
|
||||
hist_b = bincount(img_uint8[:, :, 2])
|
||||
|
||||
# Average of R, G, B histograms (same as Photoshop's RGB composite)
|
||||
rgb = ((hist_r + hist_g + hist_b) // 3).tolist()
|
||||
|
||||
# ITU-R BT.709-6, Item 3.2 (p.6) — Derivation of luminance signal
|
||||
# https://www.itu.int/rec/R-REC-BT.709-6-201506-I/en
|
||||
lum = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2]
|
||||
luminance = bincount(np.clip(lum * 255, 0, 255).astype(np.uint8)).tolist()
|
||||
|
||||
return io.NodeOutput(
|
||||
rgb,
|
||||
luminance,
|
||||
hist_r.tolist(),
|
||||
hist_g.tolist(),
|
||||
hist_b.tolist(),
|
||||
)
|
||||
|
||||
|
||||
class CurveExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self):
|
||||
return [CurveEditor]
|
||||
return [CurveEditor, ImageHistogram]
|
||||
|
||||
|
||||
async def comfy_entrypoint():
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
comfyui-frontend-package==1.42.8
|
||||
comfyui-workflow-templates==0.9.39
|
||||
comfyui-workflow-templates==0.9.44
|
||||
comfyui-embedded-docs==0.4.3
|
||||
torch
|
||||
torchsde
|
||||
|
||||
@@ -146,6 +146,10 @@ def is_loopback(host):
|
||||
def create_origin_only_middleware():
|
||||
@web.middleware
|
||||
async def origin_only_middleware(request: web.Request, handler):
|
||||
if 'Sec-Fetch-Site' in request.headers:
|
||||
sec_fetch_site = request.headers['Sec-Fetch-Site']
|
||||
if sec_fetch_site == 'cross-site':
|
||||
return web.Response(status=403)
|
||||
#this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason.
|
||||
#in that case the Host and Origin hostnames won't match
|
||||
#I know the proper fix would be to add a cookie but this should take care of the problem in the meantime
|
||||
|
||||
Reference in New Issue
Block a user