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dev/Combo-
| Author | SHA1 | Date | |
|---|---|---|---|
|
d25d14dfb6 |
36
.github/workflows/release-stable-all.yml
vendored
36
.github/workflows/release-stable-all.yml
vendored
@@ -20,12 +20,29 @@ jobs:
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git_tag: ${{ inputs.git_tag }}
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cache_tag: "cu130"
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python_minor: "13"
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python_patch: "12"
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python_patch: "11"
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rel_name: "nvidia"
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rel_extra_name: ""
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test_release: true
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secrets: inherit
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release_nvidia_cu128:
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permissions:
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contents: "write"
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packages: "write"
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pull-requests: "read"
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name: "Release NVIDIA cu128"
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uses: ./.github/workflows/stable-release.yml
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with:
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git_tag: ${{ inputs.git_tag }}
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cache_tag: "cu128"
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python_minor: "12"
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python_patch: "10"
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rel_name: "nvidia"
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rel_extra_name: "_cu128"
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test_release: true
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secrets: inherit
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release_nvidia_cu126:
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permissions:
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contents: "write"
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@@ -59,20 +76,3 @@ jobs:
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rel_extra_name: ""
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test_release: false
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secrets: inherit
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release_xpu:
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permissions:
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contents: "write"
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packages: "write"
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pull-requests: "read"
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name: "Release Intel XPU"
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uses: ./.github/workflows/stable-release.yml
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with:
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git_tag: ${{ inputs.git_tag }}
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cache_tag: "xpu"
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python_minor: "13"
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python_patch: "12"
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rel_name: "intel"
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rel_extra_name: ""
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test_release: true
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secrets: inherit
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@@ -61,7 +61,6 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo
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## Features
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- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
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- 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.
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- Image Models
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- SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
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- [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
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@@ -137,7 +136,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
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- Builds a new release using the latest stable core version
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3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
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- Every 2+ weeks frontend updates are merged into the core repository
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- Weekly frontend updates are merged into the core repository
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- Features are frozen for the upcoming core release
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- Development continues for the next release cycle
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@@ -276,7 +275,7 @@ Nvidia users should install stable pytorch using this command:
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This is the command to install pytorch nightly instead which might have performance improvements.
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```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132```
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```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
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#### Troubleshooting
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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File diff suppressed because one or more lines are too long
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File diff suppressed because one or more lines are too long
@@ -1,322 +1 @@
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"type": "IMAGE",
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||||
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|
||||
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|
||||
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|
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|
||||
}
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|
||||
"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": {}
|
||||
}
|
||||
{"revision": 0, "last_node_id": 13, "last_link_id": 0, "nodes": [{"id": 13, "type": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "pos": [1120, 330], "size": [240, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [{"localized_name": "video", "name": "video", "type": "VIDEO", "link": null}, {"name": "model_name", "type": "COMBO", "widget": {"name": "model_name"}, "link": null}], "outputs": [{"localized_name": "VIDEO", "name": "VIDEO", "type": "VIDEO", "links": []}], "title": "Video Upscale(GAN x4)", "properties": {"proxyWidgets": [["-1", "model_name"]], "cnr_id": "comfy-core", "ver": "0.14.1"}, "widgets_values": ["RealESRGAN_x4plus.safetensors"]}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "cf95b747-3e17-46cb-8097-cac60ff9b2e1", "version": 1, "state": {"lastGroupId": 0, "lastNodeId": 13, "lastLinkId": 19, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Video Upscale(GAN x4)", "inputNode": {"id": -10, "bounding": [550, 460, 120, 80]}, "outputNode": {"id": -20, "bounding": [1490, 460, 120, 60]}, "inputs": [{"id": "666d633e-93e7-42dc-8d11-2b7b99b0f2a6", "name": "video", "type": "VIDEO", "linkIds": [10], "localized_name": "video", "pos": [650, 480]}, {"id": "2e23a087-caa8-4d65-99e6-662761aa905a", "name": "model_name", "type": "COMBO", "linkIds": [19], "pos": [650, 500]}], "outputs": [{"id": "0c1768ea-3ec2-412f-9af6-8e0fa36dae70", "name": "VIDEO", "type": "VIDEO", "linkIds": [15], "localized_name": "VIDEO", "pos": [1510, 480]}], "widgets": [], "nodes": [{"id": 2, "type": "ImageUpscaleWithModel", "pos": [1110, 450], "size": [320, 46], "flags": {}, "order": 1, "mode": 0, "inputs": [{"localized_name": "upscale_model", "name": "upscale_model", "type": "UPSCALE_MODEL", "link": 1}, {"localized_name": "image", "name": "image", "type": "IMAGE", "link": 14}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [13]}], "properties": {"cnr_id": "comfy-core", "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": {}}
|
||||
|
||||
@@ -49,7 +49,7 @@ parser.add_argument("--temp-directory", type=str, default=None, help="Set the Co
|
||||
parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
|
||||
parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
|
||||
parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
|
||||
parser.add_argument("--cuda-device", type=str, default=None, metavar="DEVICE_ID", help="Set the ids of cuda devices this instance will use. All other devices will not be visible.")
|
||||
parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.")
|
||||
parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
|
||||
cm_group = parser.add_mutually_exclusive_group()
|
||||
cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
|
||||
|
||||
@@ -15,14 +15,13 @@
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
import torch
|
||||
from enum import Enum
|
||||
import math
|
||||
import os
|
||||
import logging
|
||||
import copy
|
||||
import comfy.utils
|
||||
import comfy.model_management
|
||||
import comfy.model_detection
|
||||
@@ -39,7 +38,7 @@ import comfy.ldm.hydit.controlnet
|
||||
import comfy.ldm.flux.controlnet
|
||||
import comfy.ldm.qwen_image.controlnet
|
||||
import comfy.cldm.dit_embedder
|
||||
from typing import TYPE_CHECKING, Union
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.hooks import HookGroup
|
||||
|
||||
@@ -65,18 +64,6 @@ class StrengthType(Enum):
|
||||
CONSTANT = 1
|
||||
LINEAR_UP = 2
|
||||
|
||||
class ControlIsolation:
|
||||
'''Temporarily set a ControlBase object's previous_controlnet to None to prevent cascading calls.'''
|
||||
def __init__(self, control: ControlBase):
|
||||
self.control = control
|
||||
self.orig_previous_controlnet = control.previous_controlnet
|
||||
|
||||
def __enter__(self):
|
||||
self.control.previous_controlnet = None
|
||||
|
||||
def __exit__(self, *args):
|
||||
self.control.previous_controlnet = self.orig_previous_controlnet
|
||||
|
||||
class ControlBase:
|
||||
def __init__(self):
|
||||
self.cond_hint_original = None
|
||||
@@ -90,7 +77,7 @@ class ControlBase:
|
||||
self.compression_ratio = 8
|
||||
self.upscale_algorithm = 'nearest-exact'
|
||||
self.extra_args = {}
|
||||
self.previous_controlnet: Union[ControlBase, None] = None
|
||||
self.previous_controlnet = None
|
||||
self.extra_conds = []
|
||||
self.strength_type = StrengthType.CONSTANT
|
||||
self.concat_mask = False
|
||||
@@ -98,7 +85,6 @@ class ControlBase:
|
||||
self.extra_concat = None
|
||||
self.extra_hooks: HookGroup = None
|
||||
self.preprocess_image = lambda a: a
|
||||
self.multigpu_clones: dict[torch.device, ControlBase] = {}
|
||||
|
||||
def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
|
||||
self.cond_hint_original = cond_hint
|
||||
@@ -125,38 +111,17 @@ class ControlBase:
|
||||
def cleanup(self):
|
||||
if self.previous_controlnet is not None:
|
||||
self.previous_controlnet.cleanup()
|
||||
for device_cnet in self.multigpu_clones.values():
|
||||
with ControlIsolation(device_cnet):
|
||||
device_cnet.cleanup()
|
||||
|
||||
self.cond_hint = None
|
||||
self.extra_concat = None
|
||||
self.timestep_range = None
|
||||
|
||||
def get_models(self):
|
||||
out = []
|
||||
for device_cnet in self.multigpu_clones.values():
|
||||
out += device_cnet.get_models_only_self()
|
||||
if self.previous_controlnet is not None:
|
||||
out += self.previous_controlnet.get_models()
|
||||
return out
|
||||
|
||||
def get_models_only_self(self):
|
||||
'Calls get_models, but temporarily sets previous_controlnet to None.'
|
||||
with ControlIsolation(self):
|
||||
return self.get_models()
|
||||
|
||||
def get_instance_for_device(self, device):
|
||||
'Returns instance of this Control object intended for selected device.'
|
||||
return self.multigpu_clones.get(device, self)
|
||||
|
||||
def deepclone_multigpu(self, load_device, autoregister=False):
|
||||
'''
|
||||
Create deep clone of Control object where model(s) is set to other devices.
|
||||
|
||||
When autoregister is set to True, the deep clone is also added to multigpu_clones dict.
|
||||
'''
|
||||
raise NotImplementedError("Classes inheriting from ControlBase should define their own deepclone_multigpu funtion.")
|
||||
|
||||
def get_extra_hooks(self):
|
||||
out = []
|
||||
if self.extra_hooks is not None:
|
||||
@@ -165,7 +130,7 @@ class ControlBase:
|
||||
out += self.previous_controlnet.get_extra_hooks()
|
||||
return out
|
||||
|
||||
def copy_to(self, c: ControlBase):
|
||||
def copy_to(self, c):
|
||||
c.cond_hint_original = self.cond_hint_original
|
||||
c.strength = self.strength
|
||||
c.timestep_percent_range = self.timestep_percent_range
|
||||
@@ -319,14 +284,6 @@ class ControlNet(ControlBase):
|
||||
self.copy_to(c)
|
||||
return c
|
||||
|
||||
def deepclone_multigpu(self, load_device, autoregister=False):
|
||||
c = self.copy()
|
||||
c.control_model = copy.deepcopy(c.control_model)
|
||||
c.control_model_wrapped = comfy.model_patcher.ModelPatcher(c.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
|
||||
if autoregister:
|
||||
self.multigpu_clones[load_device] = c
|
||||
return c
|
||||
|
||||
def get_models(self):
|
||||
out = super().get_models()
|
||||
out.append(self.control_model_wrapped)
|
||||
@@ -949,14 +906,6 @@ class T2IAdapter(ControlBase):
|
||||
self.copy_to(c)
|
||||
return c
|
||||
|
||||
def deepclone_multigpu(self, load_device, autoregister=False):
|
||||
c = self.copy()
|
||||
c.t2i_model = copy.deepcopy(c.t2i_model)
|
||||
c.device = load_device
|
||||
if autoregister:
|
||||
self.multigpu_clones[load_device] = c
|
||||
return c
|
||||
|
||||
def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
|
||||
compression_ratio = 8
|
||||
upscale_algorithm = 'nearest-exact'
|
||||
|
||||
@@ -611,7 +611,6 @@ 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,
|
||||
@@ -641,7 +640,7 @@ 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(condition_dim, hidden_size, dtype=dtype, device=device)
|
||||
self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device)
|
||||
|
||||
if layer_types is None:
|
||||
layer_types = ["full_attention"] * num_layers
|
||||
@@ -1036,9 +1035,6 @@ 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,
|
||||
@@ -1058,24 +1054,24 @@ 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, condition_dim=encoder_hidden_size,
|
||||
intermediate_size, patch_size, audio_acoustic_hidden_dim,
|
||||
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, encoder_hidden_size, num_lyric_layers, num_timbre_layers,
|
||||
encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps,
|
||||
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,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.tokenizer = AceStepAudioTokenizer(
|
||||
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,
|
||||
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,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
|
||||
self.detokenizer = AudioTokenDetokenizer(
|
||||
encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim,
|
||||
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, encoder_hidden_size, dtype=dtype, device=device))
|
||||
self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device))
|
||||
|
||||
def prepare_condition(
|
||||
self,
|
||||
|
||||
@@ -155,7 +155,6 @@ 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",
|
||||
@@ -163,7 +162,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
|
||||
},
|
||||
decoder_config={
|
||||
"target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
|
||||
"params": decoder_ddconfig,
|
||||
"params": ddconfig,
|
||||
},
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -3,9 +3,12 @@ from ..diffusionmodules.openaimodel import Timestep
|
||||
import torch
|
||||
|
||||
class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
|
||||
def __init__(self, *args, timestep_dim=256, **kwargs):
|
||||
def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
|
||||
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")
|
||||
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)
|
||||
|
||||
@@ -696,15 +696,6 @@ 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
|
||||
|
||||
@@ -15,7 +15,6 @@
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import psutil
|
||||
import logging
|
||||
@@ -33,11 +32,6 @@ import comfy.memory_management
|
||||
import comfy.utils
|
||||
import comfy.quant_ops
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
|
||||
|
||||
class VRAMState(Enum):
|
||||
DISABLED = 0 #No vram present: no need to move models to vram
|
||||
NO_VRAM = 1 #Very low vram: enable all the options to save vram
|
||||
@@ -212,25 +206,6 @@ def get_torch_device():
|
||||
else:
|
||||
return torch.device(torch.cuda.current_device())
|
||||
|
||||
def get_all_torch_devices(exclude_current=False):
|
||||
global cpu_state
|
||||
devices = []
|
||||
if cpu_state == CPUState.GPU:
|
||||
if is_nvidia():
|
||||
for i in range(torch.cuda.device_count()):
|
||||
devices.append(torch.device(i))
|
||||
elif is_intel_xpu():
|
||||
for i in range(torch.xpu.device_count()):
|
||||
devices.append(torch.device(i))
|
||||
elif is_ascend_npu():
|
||||
for i in range(torch.npu.device_count()):
|
||||
devices.append(torch.device(i))
|
||||
else:
|
||||
devices.append(get_torch_device())
|
||||
if exclude_current:
|
||||
devices.remove(get_torch_device())
|
||||
return devices
|
||||
|
||||
def get_total_memory(dev=None, torch_total_too=False):
|
||||
global directml_enabled
|
||||
if dev is None:
|
||||
@@ -519,13 +494,9 @@ try:
|
||||
logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
|
||||
except:
|
||||
logging.warning("Could not pick default device.")
|
||||
try:
|
||||
for device in get_all_torch_devices(exclude_current=True):
|
||||
logging.info("Device: {}".format(get_torch_device_name(device)))
|
||||
except:
|
||||
pass
|
||||
|
||||
current_loaded_models: list[LoadedModel] = []
|
||||
|
||||
current_loaded_models = []
|
||||
|
||||
def module_size(module):
|
||||
module_mem = 0
|
||||
@@ -558,7 +529,7 @@ def module_mmap_residency(module, free=False):
|
||||
return mmap_touched_mem, module_mem
|
||||
|
||||
class LoadedModel:
|
||||
def __init__(self, model: ModelPatcher):
|
||||
def __init__(self, model):
|
||||
self._set_model(model)
|
||||
self.device = model.load_device
|
||||
self.real_model = None
|
||||
@@ -566,7 +537,7 @@ class LoadedModel:
|
||||
self.model_finalizer = None
|
||||
self._patcher_finalizer = None
|
||||
|
||||
def _set_model(self, model: ModelPatcher):
|
||||
def _set_model(self, model):
|
||||
self._model = weakref.ref(model)
|
||||
if model.parent is not None:
|
||||
self._parent_model = weakref.ref(model.parent)
|
||||
@@ -577,7 +548,6 @@ class LoadedModel:
|
||||
model = self._parent_model()
|
||||
if model is not None:
|
||||
self._set_model(model)
|
||||
self.device = model.load_device
|
||||
|
||||
@property
|
||||
def model(self):
|
||||
@@ -1809,34 +1779,7 @@ def soft_empty_cache(force=False):
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
def unload_all_models():
|
||||
for device in get_all_torch_devices():
|
||||
free_memory(1e30, device)
|
||||
|
||||
def unload_model_and_clones(model: ModelPatcher, unload_additional_models=True, all_devices=False):
|
||||
'Unload only model and its clones - primarily for multigpu cloning purposes.'
|
||||
initial_keep_loaded: list[LoadedModel] = current_loaded_models.copy()
|
||||
additional_models = []
|
||||
if unload_additional_models:
|
||||
additional_models = model.get_nested_additional_models()
|
||||
keep_loaded = []
|
||||
for loaded_model in initial_keep_loaded:
|
||||
if loaded_model.model is not None:
|
||||
if model.clone_base_uuid == loaded_model.model.clone_base_uuid:
|
||||
continue
|
||||
# check additional models if they are a match
|
||||
skip = False
|
||||
for add_model in additional_models:
|
||||
if add_model.clone_base_uuid == loaded_model.model.clone_base_uuid:
|
||||
skip = True
|
||||
break
|
||||
if skip:
|
||||
continue
|
||||
keep_loaded.append(loaded_model)
|
||||
if not all_devices:
|
||||
free_memory(1e30, get_torch_device(), keep_loaded)
|
||||
else:
|
||||
for device in get_all_torch_devices():
|
||||
free_memory(1e30, device, keep_loaded)
|
||||
free_memory(1e30, get_torch_device())
|
||||
|
||||
def debug_memory_summary():
|
||||
if is_amd() or is_nvidia():
|
||||
|
||||
@@ -23,7 +23,6 @@ import inspect
|
||||
import logging
|
||||
import math
|
||||
import uuid
|
||||
import copy
|
||||
from typing import Callable, Optional
|
||||
|
||||
import torch
|
||||
@@ -76,15 +75,12 @@ def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_
|
||||
def create_model_options_clone(orig_model_options: dict):
|
||||
return comfy.patcher_extension.copy_nested_dicts(orig_model_options)
|
||||
|
||||
def create_hook_patches_clone(orig_hook_patches, copy_tuples=False):
|
||||
def create_hook_patches_clone(orig_hook_patches):
|
||||
new_hook_patches = {}
|
||||
for hook_ref in orig_hook_patches:
|
||||
new_hook_patches[hook_ref] = {}
|
||||
for k in orig_hook_patches[hook_ref]:
|
||||
new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:]
|
||||
if copy_tuples:
|
||||
for i in range(len(new_hook_patches[hook_ref][k])):
|
||||
new_hook_patches[hook_ref][k][i] = tuple(new_hook_patches[hook_ref][k][i])
|
||||
return new_hook_patches
|
||||
|
||||
def wipe_lowvram_weight(m):
|
||||
@@ -276,10 +272,7 @@ class ModelPatcher:
|
||||
self.is_clip = False
|
||||
self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed
|
||||
|
||||
self.cached_patcher_init: tuple[Callable, tuple] | tuple[Callable, tuple, int] | None = None
|
||||
self.is_multigpu_base_clone = False
|
||||
self.clone_base_uuid = uuid.uuid4()
|
||||
|
||||
self.cached_patcher_init: tuple[Callable, tuple] | None = None
|
||||
if not hasattr(self.model, 'model_loaded_weight_memory'):
|
||||
self.model.model_loaded_weight_memory = 0
|
||||
|
||||
@@ -333,8 +326,6 @@ class ModelPatcher:
|
||||
if self.cached_patcher_init is None:
|
||||
raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.")
|
||||
temp_model_patcher = self.cached_patcher_init[0](*self.cached_patcher_init[1], disable_dynamic=True)
|
||||
if len(self.cached_patcher_init) > 2:
|
||||
temp_model_patcher = temp_model_patcher[self.cached_patcher_init[2]]
|
||||
model_override = temp_model_patcher.get_clone_model_override()
|
||||
if model_override is None:
|
||||
model_override = self.get_clone_model_override()
|
||||
@@ -393,106 +384,19 @@ class ModelPatcher:
|
||||
n.hook_mode = self.hook_mode
|
||||
|
||||
n.cached_patcher_init = self.cached_patcher_init
|
||||
n.is_multigpu_base_clone = self.is_multigpu_base_clone
|
||||
n.clone_base_uuid = self.clone_base_uuid
|
||||
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):
|
||||
callback(self, n)
|
||||
return n
|
||||
|
||||
def deepclone_multigpu(self, new_load_device=None, models_cache: dict[uuid.UUID,ModelPatcher]=None):
|
||||
logging.info(f"Creating deepclone of {self.model.__class__.__name__} for {new_load_device if new_load_device else self.load_device}.")
|
||||
comfy.model_management.unload_model_and_clones(self)
|
||||
n = self.clone()
|
||||
# set load device, if present
|
||||
if new_load_device is not None:
|
||||
n.load_device = new_load_device
|
||||
if self.cached_patcher_init is not None:
|
||||
temp_model_patcher: ModelPatcher | list[ModelPatcher] = self.cached_patcher_init[0](*self.cached_patcher_init[1])
|
||||
if len(self.cached_patcher_init) > 2:
|
||||
temp_model_patcher = temp_model_patcher[self.cached_patcher_init[2]]
|
||||
n.model = temp_model_patcher.model
|
||||
else:
|
||||
n.model = copy.deepcopy(n.model)
|
||||
# Clear VBAR state so the clone gets fresh, device-specific VBARs during load().
|
||||
# deep-copied ModelVBAR objects share raw C pointers with the original, which causes
|
||||
# double-free and thread-safety issues (concurrent vbar_fault on shared global state).
|
||||
if hasattr(n.model, "dynamic_vbars"):
|
||||
n.model.dynamic_vbars = {}
|
||||
for m in n.model.modules():
|
||||
if hasattr(m, "_v"):
|
||||
delattr(m, "_v")
|
||||
# unlike for normal clone, backup dicts that shared same ref should not;
|
||||
# otherwise, patchers that have deep copies of base models will erroneously influence each other.
|
||||
n.backup = copy.deepcopy(n.backup)
|
||||
n.object_patches_backup = copy.deepcopy(n.object_patches_backup)
|
||||
n.hook_backup = copy.deepcopy(n.hook_backup)
|
||||
# multigpu clone should not have multigpu additional_models entry
|
||||
n.remove_additional_models("multigpu")
|
||||
# multigpu_clone all stored additional_models; make sure circular references are properly handled
|
||||
if models_cache is None:
|
||||
models_cache = {}
|
||||
for key, model_list in n.additional_models.items():
|
||||
for i in range(len(model_list)):
|
||||
add_model = n.additional_models[key][i]
|
||||
if add_model.clone_base_uuid not in models_cache:
|
||||
models_cache[add_model.clone_base_uuid] = add_model.deepclone_multigpu(new_load_device=new_load_device, models_cache=models_cache)
|
||||
n.additional_models[key][i] = models_cache[add_model.clone_base_uuid]
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_DEEPCLONE_MULTIGPU):
|
||||
callback(self, n)
|
||||
return n
|
||||
|
||||
def match_multigpu_clones(self):
|
||||
multigpu_models = self.get_additional_models_with_key("multigpu")
|
||||
if len(multigpu_models) > 0:
|
||||
new_multigpu_models = []
|
||||
for mm in multigpu_models:
|
||||
# clone main model, but bring over relevant props from existing multigpu clone
|
||||
n = self.clone()
|
||||
n.load_device = mm.load_device
|
||||
n.backup = mm.backup
|
||||
n.object_patches_backup = mm.object_patches_backup
|
||||
n.hook_backup = mm.hook_backup
|
||||
n.model = mm.model
|
||||
n.is_multigpu_base_clone = mm.is_multigpu_base_clone
|
||||
n.remove_additional_models("multigpu")
|
||||
orig_additional_models: dict[str, list[ModelPatcher]] = comfy.patcher_extension.copy_nested_dicts(n.additional_models)
|
||||
n.additional_models = comfy.patcher_extension.copy_nested_dicts(mm.additional_models)
|
||||
# figure out which additional models are not present in multigpu clone
|
||||
models_cache = {}
|
||||
for mm_add_model in mm.get_additional_models():
|
||||
models_cache[mm_add_model.clone_base_uuid] = mm_add_model
|
||||
remove_models_uuids = set(list(models_cache.keys()))
|
||||
for key, model_list in orig_additional_models.items():
|
||||
for orig_add_model in model_list:
|
||||
if orig_add_model.clone_base_uuid not in models_cache:
|
||||
models_cache[orig_add_model.clone_base_uuid] = orig_add_model.deepclone_multigpu(new_load_device=n.load_device, models_cache=models_cache)
|
||||
existing_list = n.get_additional_models_with_key(key)
|
||||
existing_list.append(models_cache[orig_add_model.clone_base_uuid])
|
||||
n.set_additional_models(key, existing_list)
|
||||
if orig_add_model.clone_base_uuid in remove_models_uuids:
|
||||
remove_models_uuids.remove(orig_add_model.clone_base_uuid)
|
||||
# remove duplicate additional models
|
||||
for key, model_list in n.additional_models.items():
|
||||
new_model_list = [x for x in model_list if x.clone_base_uuid not in remove_models_uuids]
|
||||
n.set_additional_models(key, new_model_list)
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_MATCH_MULTIGPU_CLONES):
|
||||
callback(self, n)
|
||||
new_multigpu_models.append(n)
|
||||
self.set_additional_models("multigpu", new_multigpu_models)
|
||||
|
||||
def is_clone(self, other):
|
||||
if hasattr(other, 'model') and self.model is other.model:
|
||||
return True
|
||||
return False
|
||||
|
||||
def clone_has_same_weights(self, clone: ModelPatcher, allow_multigpu=False):
|
||||
if allow_multigpu:
|
||||
if self.clone_base_uuid != clone.clone_base_uuid:
|
||||
return False
|
||||
else:
|
||||
if not self.is_clone(clone):
|
||||
return False
|
||||
def clone_has_same_weights(self, clone: 'ModelPatcher'):
|
||||
if not self.is_clone(clone):
|
||||
return False
|
||||
|
||||
if self.current_hooks != clone.current_hooks:
|
||||
return False
|
||||
@@ -1263,7 +1167,7 @@ class ModelPatcher:
|
||||
return self.additional_models.get(key, [])
|
||||
|
||||
def get_additional_models(self):
|
||||
all_models: list[ModelPatcher] = []
|
||||
all_models = []
|
||||
for models in self.additional_models.values():
|
||||
all_models.extend(models)
|
||||
return all_models
|
||||
@@ -1317,13 +1221,9 @@ class ModelPatcher:
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN):
|
||||
callback(self)
|
||||
|
||||
def prepare_state(self, timestep, model_options, ignore_multigpu=False):
|
||||
def prepare_state(self, timestep):
|
||||
for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE):
|
||||
callback(self, timestep, model_options, ignore_multigpu)
|
||||
if not ignore_multigpu and "multigpu_clones" in model_options:
|
||||
for p in model_options["multigpu_clones"].values():
|
||||
p: ModelPatcher
|
||||
p.prepare_state(timestep, model_options, ignore_multigpu=True)
|
||||
callback(self, timestep)
|
||||
|
||||
def restore_hook_patches(self):
|
||||
if self.hook_patches_backup is not None:
|
||||
@@ -1336,18 +1236,12 @@ class ModelPatcher:
|
||||
def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]):
|
||||
curr_t = t[0]
|
||||
reset_current_hooks = False
|
||||
multigpu_kf_changed_cache = None
|
||||
transformer_options = model_options.get("transformer_options", {})
|
||||
for hook in hook_group.hooks:
|
||||
changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options)
|
||||
# if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref;
|
||||
# this will cause the weights to be recalculated when sampling
|
||||
if changed:
|
||||
# cache changed for multigpu usage
|
||||
if "multigpu_clones" in model_options:
|
||||
if multigpu_kf_changed_cache is None:
|
||||
multigpu_kf_changed_cache = []
|
||||
multigpu_kf_changed_cache.append(hook)
|
||||
# reset current_hooks if contains hook that changed
|
||||
if self.current_hooks is not None:
|
||||
for current_hook in self.current_hooks.hooks:
|
||||
@@ -1359,28 +1253,6 @@ class ModelPatcher:
|
||||
self.cached_hook_patches.pop(cached_group)
|
||||
if reset_current_hooks:
|
||||
self.patch_hooks(None)
|
||||
if "multigpu_clones" in model_options:
|
||||
for p in model_options["multigpu_clones"].values():
|
||||
p: ModelPatcher
|
||||
p._handle_changed_hook_keyframes(multigpu_kf_changed_cache)
|
||||
|
||||
def _handle_changed_hook_keyframes(self, kf_changed_cache: list[comfy.hooks.Hook]):
|
||||
'Used to handle multigpu behavior inside prepare_hook_patches_current_keyframe.'
|
||||
if kf_changed_cache is None:
|
||||
return
|
||||
reset_current_hooks = False
|
||||
# reset current_hooks if contains hook that changed
|
||||
for hook in kf_changed_cache:
|
||||
if self.current_hooks is not None:
|
||||
for current_hook in self.current_hooks.hooks:
|
||||
if current_hook == hook:
|
||||
reset_current_hooks = True
|
||||
break
|
||||
for cached_group in list(self.cached_hook_patches.keys()):
|
||||
if cached_group.contains(hook):
|
||||
self.cached_hook_patches.pop(cached_group)
|
||||
if reset_current_hooks:
|
||||
self.patch_hooks(None)
|
||||
|
||||
def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None,
|
||||
registered: comfy.hooks.HookGroup = None):
|
||||
|
||||
@@ -1,230 +0,0 @@
|
||||
from __future__ import annotations
|
||||
import queue
|
||||
import threading
|
||||
import torch
|
||||
import logging
|
||||
|
||||
from collections import namedtuple
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
import comfy.utils
|
||||
import comfy.patcher_extension
|
||||
import comfy.model_management
|
||||
|
||||
|
||||
class MultiGPUThreadPool:
|
||||
"""Persistent thread pool for multi-GPU work distribution.
|
||||
|
||||
Maintains one worker thread per extra GPU device. Each thread calls
|
||||
torch.cuda.set_device() once at startup so that compiled kernel caches
|
||||
(inductor/triton) stay warm across diffusion steps.
|
||||
"""
|
||||
|
||||
def __init__(self, devices: list[torch.device]):
|
||||
self._workers: list[threading.Thread] = []
|
||||
self._work_queues: dict[torch.device, queue.Queue] = {}
|
||||
self._result_queues: dict[torch.device, queue.Queue] = {}
|
||||
|
||||
for device in devices:
|
||||
wq = queue.Queue()
|
||||
rq = queue.Queue()
|
||||
self._work_queues[device] = wq
|
||||
self._result_queues[device] = rq
|
||||
t = threading.Thread(target=self._worker_loop, args=(device, wq, rq), daemon=True)
|
||||
t.start()
|
||||
self._workers.append(t)
|
||||
|
||||
def _worker_loop(self, device: torch.device, work_q: queue.Queue, result_q: queue.Queue):
|
||||
try:
|
||||
torch.cuda.set_device(device)
|
||||
except Exception as e:
|
||||
logging.error(f"MultiGPUThreadPool: failed to set device {device}: {e}")
|
||||
while True:
|
||||
item = work_q.get()
|
||||
if item is None:
|
||||
return
|
||||
result_q.put((None, e))
|
||||
return
|
||||
while True:
|
||||
item = work_q.get()
|
||||
if item is None:
|
||||
break
|
||||
fn, args, kwargs = item
|
||||
try:
|
||||
result = fn(*args, **kwargs)
|
||||
result_q.put((result, None))
|
||||
except Exception as e:
|
||||
result_q.put((None, e))
|
||||
|
||||
def submit(self, device: torch.device, fn, *args, **kwargs):
|
||||
self._work_queues[device].put((fn, args, kwargs))
|
||||
|
||||
def get_result(self, device: torch.device):
|
||||
return self._result_queues[device].get()
|
||||
|
||||
@property
|
||||
def devices(self) -> list[torch.device]:
|
||||
return list(self._work_queues.keys())
|
||||
|
||||
def shutdown(self):
|
||||
for wq in self._work_queues.values():
|
||||
wq.put(None) # sentinel
|
||||
for t in self._workers:
|
||||
t.join(timeout=5.0)
|
||||
|
||||
|
||||
class GPUOptions:
|
||||
def __init__(self, device_index: int, relative_speed: float):
|
||||
self.device_index = device_index
|
||||
self.relative_speed = relative_speed
|
||||
|
||||
def clone(self):
|
||||
return GPUOptions(self.device_index, self.relative_speed)
|
||||
|
||||
def create_dict(self):
|
||||
return {
|
||||
"relative_speed": self.relative_speed
|
||||
}
|
||||
|
||||
class GPUOptionsGroup:
|
||||
def __init__(self):
|
||||
self.options: dict[int, GPUOptions] = {}
|
||||
|
||||
def add(self, info: GPUOptions):
|
||||
self.options[info.device_index] = info
|
||||
|
||||
def clone(self):
|
||||
c = GPUOptionsGroup()
|
||||
for opt in self.options.values():
|
||||
c.add(opt)
|
||||
return c
|
||||
|
||||
def register(self, model: ModelPatcher):
|
||||
opts_dict = {}
|
||||
# get devices that are valid for this model
|
||||
devices: list[torch.device] = [model.load_device]
|
||||
for extra_model in model.get_additional_models_with_key("multigpu"):
|
||||
extra_model: ModelPatcher
|
||||
devices.append(extra_model.load_device)
|
||||
# create dictionary with actual device mapped to its GPUOptions
|
||||
device_opts_list: list[GPUOptions] = []
|
||||
for device in devices:
|
||||
device_opts = self.options.get(device.index, GPUOptions(device_index=device.index, relative_speed=1.0))
|
||||
opts_dict[device] = device_opts.create_dict()
|
||||
device_opts_list.append(device_opts)
|
||||
# make relative_speed relative to 1.0
|
||||
min_speed = min([x.relative_speed for x in device_opts_list])
|
||||
for value in opts_dict.values():
|
||||
value['relative_speed'] /= min_speed
|
||||
model.model_options['multigpu_options'] = opts_dict
|
||||
|
||||
|
||||
def create_multigpu_deepclones(model: ModelPatcher, max_gpus: int, gpu_options: GPUOptionsGroup=None, reuse_loaded=False):
|
||||
'Prepare ModelPatcher to contain deepclones of its BaseModel and related properties.'
|
||||
model = model.clone()
|
||||
# check if multigpu is already prepared - get the load devices from them if possible to exclude
|
||||
skip_devices = set()
|
||||
multigpu_models = model.get_additional_models_with_key("multigpu")
|
||||
if len(multigpu_models) > 0:
|
||||
for mm in multigpu_models:
|
||||
skip_devices.add(mm.load_device)
|
||||
skip_devices = list(skip_devices)
|
||||
|
||||
full_extra_devices = comfy.model_management.get_all_torch_devices(exclude_current=True)
|
||||
limit_extra_devices = full_extra_devices[:max_gpus-1]
|
||||
extra_devices = limit_extra_devices.copy()
|
||||
# exclude skipped devices
|
||||
for skip in skip_devices:
|
||||
if skip in extra_devices:
|
||||
extra_devices.remove(skip)
|
||||
# create new deepclones
|
||||
if len(extra_devices) > 0:
|
||||
for device in extra_devices:
|
||||
device_patcher = None
|
||||
if reuse_loaded:
|
||||
# check if there are any ModelPatchers currently loaded that could be referenced here after a clone
|
||||
loaded_models: list[ModelPatcher] = comfy.model_management.loaded_models()
|
||||
for lm in loaded_models:
|
||||
if lm.model is not None and lm.clone_base_uuid == model.clone_base_uuid and lm.load_device == device:
|
||||
device_patcher = lm.clone()
|
||||
logging.info(f"Reusing loaded deepclone of {device_patcher.model.__class__.__name__} for {device}")
|
||||
break
|
||||
if device_patcher is None:
|
||||
device_patcher = model.deepclone_multigpu(new_load_device=device)
|
||||
device_patcher.is_multigpu_base_clone = True
|
||||
multigpu_models = model.get_additional_models_with_key("multigpu")
|
||||
multigpu_models.append(device_patcher)
|
||||
model.set_additional_models("multigpu", multigpu_models)
|
||||
model.match_multigpu_clones()
|
||||
if gpu_options is None:
|
||||
gpu_options = GPUOptionsGroup()
|
||||
gpu_options.register(model)
|
||||
else:
|
||||
logging.info("No extra torch devices need initialization, skipping initializing MultiGPU Work Units.")
|
||||
# TODO: only keep model clones that don't go 'past' the intended max_gpu count
|
||||
# multigpu_models = model.get_additional_models_with_key("multigpu")
|
||||
# new_multigpu_models = []
|
||||
# for m in multigpu_models:
|
||||
# if m.load_device in limit_extra_devices:
|
||||
# new_multigpu_models.append(m)
|
||||
# model.set_additional_models("multigpu", new_multigpu_models)
|
||||
# persist skip_devices for use in sampling code
|
||||
# if len(skip_devices) > 0 or "multigpu_skip_devices" in model.model_options:
|
||||
# model.model_options["multigpu_skip_devices"] = skip_devices
|
||||
return model
|
||||
|
||||
|
||||
LoadBalance = namedtuple('LoadBalance', ['work_per_device', 'idle_time'])
|
||||
def load_balance_devices(model_options: dict[str], total_work: int, return_idle_time=False, work_normalized: int=None):
|
||||
'Optimize work assigned to different devices, accounting for their relative speeds and splittable work.'
|
||||
opts_dict = model_options['multigpu_options']
|
||||
devices = list(model_options['multigpu_clones'].keys())
|
||||
speed_per_device = []
|
||||
work_per_device = []
|
||||
# get sum of each device's relative_speed
|
||||
total_speed = 0.0
|
||||
for opts in opts_dict.values():
|
||||
total_speed += opts['relative_speed']
|
||||
# get relative work for each device;
|
||||
# obtained by w = (W*r)/R
|
||||
for device in devices:
|
||||
relative_speed = opts_dict[device]['relative_speed']
|
||||
relative_work = (total_work*relative_speed) / total_speed
|
||||
speed_per_device.append(relative_speed)
|
||||
work_per_device.append(relative_work)
|
||||
# relative work must be expressed in whole numbers, but likely is a decimal;
|
||||
# perform rounding while maintaining total sum equal to total work (sum of relative works)
|
||||
work_per_device = round_preserved(work_per_device)
|
||||
dict_work_per_device = {}
|
||||
for device, relative_work in zip(devices, work_per_device):
|
||||
dict_work_per_device[device] = relative_work
|
||||
if not return_idle_time:
|
||||
return LoadBalance(dict_work_per_device, None)
|
||||
# divide relative work by relative speed to get estimated completion time of said work by each device;
|
||||
# time here is relative and does not correspond to real-world units
|
||||
completion_time = [w/r for w,r in zip(work_per_device, speed_per_device)]
|
||||
# calculate relative time spent by the devices waiting on each other after their work is completed
|
||||
idle_time = abs(min(completion_time) - max(completion_time))
|
||||
# if need to compare work idle time, need to normalize to a common total work
|
||||
if work_normalized:
|
||||
idle_time *= (work_normalized/total_work)
|
||||
|
||||
return LoadBalance(dict_work_per_device, idle_time)
|
||||
|
||||
def round_preserved(values: list[float]):
|
||||
'Round all values in a list, preserving the combined sum of values.'
|
||||
# get floor of values; casting to int does it too
|
||||
floored = [int(x) for x in values]
|
||||
total_floored = sum(floored)
|
||||
# get remainder to distribute
|
||||
remainder = round(sum(values)) - total_floored
|
||||
# pair values with fractional portions
|
||||
fractional = [(i, x-floored[i]) for i, x in enumerate(values)]
|
||||
# sort by fractional part in descending order
|
||||
fractional.sort(key=lambda x: x[1], reverse=True)
|
||||
# distribute the remainder
|
||||
for i in range(remainder):
|
||||
index = fractional[i][0]
|
||||
floored[index] += 1
|
||||
return floored
|
||||
@@ -3,8 +3,6 @@ from typing import Callable
|
||||
|
||||
class CallbacksMP:
|
||||
ON_CLONE = "on_clone"
|
||||
ON_DEEPCLONE_MULTIGPU = "on_deepclone_multigpu"
|
||||
ON_MATCH_MULTIGPU_CLONES = "on_match_multigpu_clones"
|
||||
ON_LOAD = "on_load_after"
|
||||
ON_DETACH = "on_detach_after"
|
||||
ON_CLEANUP = "on_cleanup"
|
||||
|
||||
@@ -20,6 +20,7 @@ try:
|
||||
if cuda_version < (13,):
|
||||
ck.registry.disable("cuda")
|
||||
logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
|
||||
|
||||
ck.registry.disable("triton")
|
||||
for k, v in ck.list_backends().items():
|
||||
logging.info(f"Found comfy_kitchen backend {k}: {v}")
|
||||
|
||||
@@ -1,18 +1,16 @@
|
||||
from __future__ import annotations
|
||||
import torch
|
||||
import uuid
|
||||
import math
|
||||
import collections
|
||||
import comfy.model_management
|
||||
import comfy.conds
|
||||
import comfy.model_patcher
|
||||
import comfy.utils
|
||||
import comfy.hooks
|
||||
import comfy.patcher_extension
|
||||
from typing import TYPE_CHECKING
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_base import BaseModel
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from comfy.model_base import BaseModel
|
||||
from comfy.controlnet import ControlBase
|
||||
|
||||
def prepare_mask(noise_mask, shape, device):
|
||||
@@ -120,47 +118,6 @@ def cleanup_additional_models(models):
|
||||
if hasattr(m, 'cleanup'):
|
||||
m.cleanup()
|
||||
|
||||
def preprocess_multigpu_conds(conds: dict[str, list[dict[str]]], model: ModelPatcher, model_options: dict[str]):
|
||||
'''If multigpu acceleration required, creates deepclones of ControlNets and GLIGEN per device.'''
|
||||
multigpu_models: list[ModelPatcher] = model.get_additional_models_with_key("multigpu")
|
||||
if len(multigpu_models) == 0:
|
||||
return
|
||||
extra_devices = [x.load_device for x in multigpu_models]
|
||||
# handle controlnets
|
||||
controlnets: set[ControlBase] = set()
|
||||
for k in conds:
|
||||
for kk in conds[k]:
|
||||
if 'control' in kk:
|
||||
controlnets.add(kk['control'])
|
||||
if len(controlnets) > 0:
|
||||
# first, unload all controlnet clones
|
||||
for cnet in list(controlnets):
|
||||
cnet_models = cnet.get_models()
|
||||
for cm in cnet_models:
|
||||
comfy.model_management.unload_model_and_clones(cm, unload_additional_models=True)
|
||||
|
||||
# next, make sure each controlnet has a deepclone for all relevant devices
|
||||
for cnet in controlnets:
|
||||
curr_cnet = cnet
|
||||
while curr_cnet is not None:
|
||||
for device in extra_devices:
|
||||
if device not in curr_cnet.multigpu_clones:
|
||||
curr_cnet.deepclone_multigpu(device, autoregister=True)
|
||||
curr_cnet = curr_cnet.previous_controlnet
|
||||
# since all device clones are now present, recreate the linked list for cloned cnets per device
|
||||
for cnet in controlnets:
|
||||
curr_cnet = cnet
|
||||
while curr_cnet is not None:
|
||||
prev_cnet = curr_cnet.previous_controlnet
|
||||
for device in extra_devices:
|
||||
device_cnet = curr_cnet.get_instance_for_device(device)
|
||||
prev_device_cnet = None
|
||||
if prev_cnet is not None:
|
||||
prev_device_cnet = prev_cnet.get_instance_for_device(device)
|
||||
device_cnet.set_previous_controlnet(prev_device_cnet)
|
||||
curr_cnet = prev_cnet
|
||||
# potentially handle gligen - since not widely used, ignored for now
|
||||
|
||||
def estimate_memory(model, noise_shape, conds):
|
||||
cond_shapes = collections.defaultdict(list)
|
||||
cond_shapes_min = {}
|
||||
@@ -185,8 +142,7 @@ def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None
|
||||
return executor.execute(model, noise_shape, conds, model_options=model_options, force_full_load=force_full_load, force_offload=force_offload)
|
||||
|
||||
def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None, force_full_load=False, force_offload=False):
|
||||
model.match_multigpu_clones()
|
||||
preprocess_multigpu_conds(conds, model, model_options)
|
||||
real_model: BaseModel = None
|
||||
models, inference_memory = get_additional_models(conds, model.model_dtype())
|
||||
models += get_additional_models_from_model_options(model_options)
|
||||
models += model.get_nested_additional_models() # TODO: does this require inference_memory update?
|
||||
@@ -198,7 +154,7 @@ def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=Non
|
||||
memory_required += inference_memory
|
||||
minimum_memory_required += inference_memory
|
||||
comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required, force_full_load=force_full_load)
|
||||
real_model: BaseModel = model.model
|
||||
real_model = model.model
|
||||
|
||||
return real_model, conds, models
|
||||
|
||||
@@ -244,18 +200,3 @@ def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict):
|
||||
comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name],
|
||||
copy_dict1=False)
|
||||
return to_load_options
|
||||
|
||||
def prepare_model_patcher_multigpu_clones(model_patcher: ModelPatcher, loaded_models: list[ModelPatcher], model_options: dict):
|
||||
'''
|
||||
In case multigpu acceleration is enabled, prep ModelPatchers for each device.
|
||||
'''
|
||||
multigpu_patchers: list[ModelPatcher] = [x for x in loaded_models if x.is_multigpu_base_clone]
|
||||
if len(multigpu_patchers) > 0:
|
||||
multigpu_dict: dict[torch.device, ModelPatcher] = {}
|
||||
multigpu_dict[model_patcher.load_device] = model_patcher
|
||||
for x in multigpu_patchers:
|
||||
x.hook_patches = comfy.model_patcher.create_hook_patches_clone(model_patcher.hook_patches, copy_tuples=True)
|
||||
x.hook_mode = model_patcher.hook_mode # match main model's hook_mode
|
||||
multigpu_dict[x.load_device] = x
|
||||
model_options["multigpu_clones"] = multigpu_dict
|
||||
return multigpu_patchers
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import comfy.model_management
|
||||
from .k_diffusion import sampling as k_diffusion_sampling
|
||||
from .extra_samplers import uni_pc
|
||||
from typing import TYPE_CHECKING, Callable, NamedTuple, Any
|
||||
from typing import TYPE_CHECKING, Callable, NamedTuple
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
from comfy.model_base import BaseModel
|
||||
@@ -18,7 +16,6 @@ import comfy.model_patcher
|
||||
import comfy.patcher_extension
|
||||
import comfy.hooks
|
||||
import comfy.context_windows
|
||||
import comfy.multigpu
|
||||
import comfy.utils
|
||||
import scipy.stats
|
||||
import numpy
|
||||
@@ -144,7 +141,7 @@ def can_concat_cond(c1, c2):
|
||||
|
||||
return cond_equal_size(c1.conditioning, c2.conditioning)
|
||||
|
||||
def cond_cat(c_list, device=None):
|
||||
def cond_cat(c_list):
|
||||
temp = {}
|
||||
for x in c_list:
|
||||
for k in x:
|
||||
@@ -156,8 +153,6 @@ def cond_cat(c_list, device=None):
|
||||
for k in temp:
|
||||
conds = temp[k]
|
||||
out[k] = conds[0].concat(conds[1:])
|
||||
if device is not None and hasattr(out[k], 'to'):
|
||||
out[k] = out[k].to(device)
|
||||
|
||||
return out
|
||||
|
||||
@@ -217,9 +212,7 @@ def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torc
|
||||
)
|
||||
return executor.execute(model, conds, x_in, timestep, model_options)
|
||||
|
||||
def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
|
||||
if 'multigpu_clones' in model_options:
|
||||
return _calc_cond_batch_multigpu(model, conds, x_in, timestep, model_options)
|
||||
def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
|
||||
out_conds = []
|
||||
out_counts = []
|
||||
# separate conds by matching hooks
|
||||
@@ -251,7 +244,7 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
|
||||
if has_default_conds:
|
||||
finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
|
||||
|
||||
model.current_patcher.prepare_state(timestep, model_options)
|
||||
model.current_patcher.prepare_state(timestep)
|
||||
|
||||
# run every hooked_to_run separately
|
||||
for hooks, to_run in hooked_to_run.items():
|
||||
@@ -352,212 +345,6 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
|
||||
|
||||
return out_conds
|
||||
|
||||
def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
|
||||
out_conds = []
|
||||
out_counts = []
|
||||
# separate conds by matching hooks
|
||||
hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {}
|
||||
default_conds = []
|
||||
has_default_conds = False
|
||||
|
||||
output_device = x_in.device
|
||||
|
||||
for i in range(len(conds)):
|
||||
out_conds.append(torch.zeros_like(x_in))
|
||||
out_counts.append(torch.ones_like(x_in) * 1e-37)
|
||||
|
||||
cond = conds[i]
|
||||
default_c = []
|
||||
if cond is not None:
|
||||
for x in cond:
|
||||
if 'default' in x:
|
||||
default_c.append(x)
|
||||
has_default_conds = True
|
||||
continue
|
||||
p = get_area_and_mult(x, x_in, timestep)
|
||||
if p is None:
|
||||
continue
|
||||
if p.hooks is not None:
|
||||
model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
|
||||
hooked_to_run.setdefault(p.hooks, list())
|
||||
hooked_to_run[p.hooks] += [(p, i)]
|
||||
default_conds.append(default_c)
|
||||
|
||||
if has_default_conds:
|
||||
finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
|
||||
|
||||
model.current_patcher.prepare_state(timestep, model_options)
|
||||
|
||||
devices = [dev_m for dev_m in model_options['multigpu_clones'].keys()]
|
||||
device_batched_hooked_to_run: dict[torch.device, list[tuple[comfy.hooks.HookGroup, tuple]]] = {}
|
||||
|
||||
total_conds = 0
|
||||
for to_run in hooked_to_run.values():
|
||||
total_conds += len(to_run)
|
||||
conds_per_device = max(1, math.ceil(total_conds//len(devices)))
|
||||
index_device = 0
|
||||
current_device = devices[index_device]
|
||||
# run every hooked_to_run separately
|
||||
for hooks, to_run in hooked_to_run.items():
|
||||
while len(to_run) > 0:
|
||||
current_device = devices[index_device % len(devices)]
|
||||
batched_to_run = device_batched_hooked_to_run.setdefault(current_device, [])
|
||||
# keep track of conds currently scheduled onto this device
|
||||
batched_to_run_length = 0
|
||||
for btr in batched_to_run:
|
||||
batched_to_run_length += len(btr[1])
|
||||
|
||||
first = to_run[0]
|
||||
first_shape = first[0][0].shape
|
||||
to_batch_temp = []
|
||||
# make sure not over conds_per_device limit when creating temp batch
|
||||
for x in range(len(to_run)):
|
||||
if can_concat_cond(to_run[x][0], first[0]) and len(to_batch_temp) < (conds_per_device - batched_to_run_length):
|
||||
to_batch_temp += [x]
|
||||
|
||||
to_batch_temp.reverse()
|
||||
to_batch = to_batch_temp[:1]
|
||||
|
||||
free_memory = comfy.model_management.get_free_memory(current_device)
|
||||
for i in range(1, len(to_batch_temp) + 1):
|
||||
batch_amount = to_batch_temp[:len(to_batch_temp)//i]
|
||||
input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
|
||||
if model.memory_required(input_shape) * 1.5 < free_memory:
|
||||
to_batch = batch_amount
|
||||
break
|
||||
conds_to_batch = []
|
||||
for x in to_batch:
|
||||
conds_to_batch.append(to_run.pop(x))
|
||||
batched_to_run_length += len(conds_to_batch)
|
||||
|
||||
batched_to_run.append((hooks, conds_to_batch))
|
||||
if batched_to_run_length >= conds_per_device:
|
||||
index_device += 1
|
||||
|
||||
class thread_result(NamedTuple):
|
||||
output: Any
|
||||
mult: Any
|
||||
area: Any
|
||||
batch_chunks: int
|
||||
cond_or_uncond: Any
|
||||
error: Exception = None
|
||||
|
||||
def _handle_batch(device: torch.device, batch_tuple: tuple[comfy.hooks.HookGroup, tuple], results: list[thread_result]):
|
||||
try:
|
||||
torch.cuda.set_device(device)
|
||||
model_current: BaseModel = model_options["multigpu_clones"][device].model
|
||||
# run every hooked_to_run separately
|
||||
with torch.no_grad():
|
||||
for hooks, to_batch in batch_tuple:
|
||||
input_x = []
|
||||
mult = []
|
||||
c = []
|
||||
cond_or_uncond = []
|
||||
uuids = []
|
||||
area = []
|
||||
control: ControlBase = None
|
||||
patches = None
|
||||
for x in to_batch:
|
||||
o = x
|
||||
p = o[0]
|
||||
input_x.append(p.input_x)
|
||||
mult.append(p.mult)
|
||||
c.append(p.conditioning)
|
||||
area.append(p.area)
|
||||
cond_or_uncond.append(o[1])
|
||||
uuids.append(p.uuid)
|
||||
control = p.control
|
||||
patches = p.patches
|
||||
|
||||
batch_chunks = len(cond_or_uncond)
|
||||
input_x = torch.cat(input_x).to(device)
|
||||
c = cond_cat(c, device=device)
|
||||
timestep_ = torch.cat([timestep.to(device)] * batch_chunks)
|
||||
|
||||
transformer_options = model_current.current_patcher.apply_hooks(hooks=hooks)
|
||||
if 'transformer_options' in model_options:
|
||||
transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options,
|
||||
model_options['transformer_options'],
|
||||
copy_dict1=False)
|
||||
|
||||
if patches is not None:
|
||||
transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts(
|
||||
transformer_options.get("patches", {}),
|
||||
patches
|
||||
)
|
||||
|
||||
transformer_options["cond_or_uncond"] = cond_or_uncond[:]
|
||||
transformer_options["uuids"] = uuids[:]
|
||||
transformer_options["sigmas"] = timestep.to(device)
|
||||
transformer_options["sample_sigmas"] = transformer_options["sample_sigmas"].to(device)
|
||||
transformer_options["multigpu_thread_device"] = device
|
||||
|
||||
cast_transformer_options(transformer_options, device=device)
|
||||
c['transformer_options'] = transformer_options
|
||||
|
||||
if control is not None:
|
||||
device_control = control.get_instance_for_device(device)
|
||||
c['control'] = device_control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options)
|
||||
|
||||
if 'model_function_wrapper' in model_options:
|
||||
output = model_options['model_function_wrapper'](model_current.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).to(output_device).chunk(batch_chunks)
|
||||
else:
|
||||
output = model_current.apply_model(input_x, timestep_, **c).to(output_device).chunk(batch_chunks)
|
||||
results.append(thread_result(output, mult, area, batch_chunks, cond_or_uncond))
|
||||
except Exception as e:
|
||||
results.append(thread_result(None, None, None, None, None, error=e))
|
||||
raise
|
||||
|
||||
|
||||
def _handle_batch_pooled(device, batch_tuple):
|
||||
worker_results = []
|
||||
_handle_batch(device, batch_tuple, worker_results)
|
||||
return worker_results
|
||||
|
||||
results: list[thread_result] = []
|
||||
thread_pool: comfy.multigpu.MultiGPUThreadPool = model_options.get("multigpu_thread_pool")
|
||||
|
||||
# Submit all GPU work to pool threads
|
||||
pool_devices = []
|
||||
for device, batch_tuple in device_batched_hooked_to_run.items():
|
||||
if thread_pool is not None:
|
||||
thread_pool.submit(device, _handle_batch_pooled, device, batch_tuple)
|
||||
pool_devices.append(device)
|
||||
else:
|
||||
# Fallback: no pool, run everything on main thread
|
||||
_handle_batch(device, batch_tuple, results)
|
||||
|
||||
# Collect results from pool workers
|
||||
for device in pool_devices:
|
||||
worker_results, error = thread_pool.get_result(device)
|
||||
if error is not None:
|
||||
raise error
|
||||
results.extend(worker_results)
|
||||
|
||||
for output, mult, area, batch_chunks, cond_or_uncond, error in results:
|
||||
if error is not None:
|
||||
raise error
|
||||
for o in range(batch_chunks):
|
||||
cond_index = cond_or_uncond[o]
|
||||
a = area[o]
|
||||
if a is None:
|
||||
out_conds[cond_index] += output[o] * mult[o]
|
||||
out_counts[cond_index] += mult[o]
|
||||
else:
|
||||
out_c = out_conds[cond_index]
|
||||
out_cts = out_counts[cond_index]
|
||||
dims = len(a) // 2
|
||||
for i in range(dims):
|
||||
out_c = out_c.narrow(i + 2, a[i + dims], a[i])
|
||||
out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
|
||||
out_c += output[o] * mult[o]
|
||||
out_cts += mult[o]
|
||||
|
||||
for i in range(len(out_conds)):
|
||||
out_conds[i] /= out_counts[i]
|
||||
|
||||
return out_conds
|
||||
|
||||
def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
|
||||
logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
|
||||
return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
|
||||
@@ -862,8 +649,6 @@ def pre_run_control(model, conds):
|
||||
percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
|
||||
if 'control' in x:
|
||||
x['control'].pre_run(model, percent_to_timestep_function)
|
||||
for device_cnet in x['control'].multigpu_clones.values():
|
||||
device_cnet.pre_run(model, percent_to_timestep_function)
|
||||
|
||||
def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
|
||||
cond_cnets = []
|
||||
@@ -1106,9 +891,7 @@ def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
|
||||
to_load_options = model_options.get("to_load_options", None)
|
||||
if to_load_options is None:
|
||||
return
|
||||
cast_transformer_options(to_load_options, device, dtype)
|
||||
|
||||
def cast_transformer_options(transformer_options: dict[str], device=None, dtype=None):
|
||||
casts = []
|
||||
if device is not None:
|
||||
casts.append(device)
|
||||
@@ -1117,17 +900,18 @@ def cast_transformer_options(transformer_options: dict[str], device=None, dtype=
|
||||
# if nothing to apply, do nothing
|
||||
if len(casts) == 0:
|
||||
return
|
||||
|
||||
# try to call .to on patches
|
||||
if "patches" in transformer_options:
|
||||
patches = transformer_options["patches"]
|
||||
if "patches" in to_load_options:
|
||||
patches = to_load_options["patches"]
|
||||
for name in patches:
|
||||
patch_list = patches[name]
|
||||
for i in range(len(patch_list)):
|
||||
if hasattr(patch_list[i], "to"):
|
||||
for cast in casts:
|
||||
patch_list[i] = patch_list[i].to(cast)
|
||||
if "patches_replace" in transformer_options:
|
||||
patches = transformer_options["patches_replace"]
|
||||
if "patches_replace" in to_load_options:
|
||||
patches = to_load_options["patches_replace"]
|
||||
for name in patches:
|
||||
patch_list = patches[name]
|
||||
for k in patch_list:
|
||||
@@ -1137,8 +921,8 @@ def cast_transformer_options(transformer_options: dict[str], device=None, dtype=
|
||||
# try to call .to on any wrappers/callbacks
|
||||
wrappers_and_callbacks = ["wrappers", "callbacks"]
|
||||
for wc_name in wrappers_and_callbacks:
|
||||
if wc_name in transformer_options:
|
||||
wc: dict[str, list] = transformer_options[wc_name]
|
||||
if wc_name in to_load_options:
|
||||
wc: dict[str, list] = to_load_options[wc_name]
|
||||
for wc_dict in wc.values():
|
||||
for wc_list in wc_dict.values():
|
||||
for i in range(len(wc_list)):
|
||||
@@ -1146,6 +930,7 @@ def cast_transformer_options(transformer_options: dict[str], device=None, dtype=
|
||||
for cast in casts:
|
||||
wc_list[i] = wc_list[i].to(cast)
|
||||
|
||||
|
||||
class CFGGuider:
|
||||
def __init__(self, model_patcher: ModelPatcher):
|
||||
self.model_patcher = model_patcher
|
||||
@@ -1200,31 +985,16 @@ class CFGGuider:
|
||||
self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
|
||||
device = self.model_patcher.load_device
|
||||
|
||||
multigpu_patchers = comfy.sampler_helpers.prepare_model_patcher_multigpu_clones(self.model_patcher, self.loaded_models, self.model_options)
|
||||
|
||||
# Create persistent thread pool for all GPU devices (main + extras)
|
||||
if multigpu_patchers:
|
||||
extra_devices = [p.load_device for p in multigpu_patchers]
|
||||
all_devices = [device] + extra_devices
|
||||
self.model_options["multigpu_thread_pool"] = comfy.multigpu.MultiGPUThreadPool(all_devices)
|
||||
noise = noise.to(device=device, dtype=torch.float32)
|
||||
latent_image = latent_image.to(device=device, dtype=torch.float32)
|
||||
sigmas = sigmas.to(device)
|
||||
cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
|
||||
|
||||
try:
|
||||
noise = noise.to(device=device, dtype=torch.float32)
|
||||
latent_image = latent_image.to(device=device, dtype=torch.float32)
|
||||
sigmas = sigmas.to(device)
|
||||
cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
|
||||
|
||||
self.model_patcher.pre_run()
|
||||
for multigpu_patcher in multigpu_patchers:
|
||||
multigpu_patcher.pre_run()
|
||||
output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
|
||||
finally:
|
||||
thread_pool = self.model_options.pop("multigpu_thread_pool", None)
|
||||
if thread_pool is not None:
|
||||
thread_pool.shutdown()
|
||||
self.model_patcher.cleanup()
|
||||
for multigpu_patcher in multigpu_patchers:
|
||||
multigpu_patcher.cleanup()
|
||||
|
||||
comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
|
||||
del self.inner_model
|
||||
|
||||
18
comfy/sd.py
18
comfy/sd.py
@@ -556,19 +556,12 @@ 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], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {}))
|
||||
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
|
||||
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': decoder_ddconfig if decoder_ddconfig is not None else ddconfig})
|
||||
decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
|
||||
elif "decoder.layers.1.layers.0.beta" in sd:
|
||||
config = {}
|
||||
param_key = None
|
||||
@@ -1596,7 +1589,10 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
|
||||
out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)
|
||||
if out is None:
|
||||
raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd)))
|
||||
out[0].cached_patcher_init = (load_checkpoint_guess_config, (ckpt_path, False, False, False, embedding_directory, output_model, model_options, te_model_options), 0)
|
||||
if output_model and out[0] is not None:
|
||||
out[0].cached_patcher_init = (load_checkpoint_guess_config_model_only, (ckpt_path, embedding_directory, model_options, te_model_options))
|
||||
if output_clip and out[1] is not None:
|
||||
out[1].patcher.cached_patcher_init = (load_checkpoint_guess_config_clip_only, (ckpt_path, embedding_directory, model_options, te_model_options))
|
||||
return out
|
||||
|
||||
def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directory=None, model_options={}, te_model_options={}, disable_dynamic=False):
|
||||
@@ -1749,8 +1745,6 @@ 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)
|
||||
|
||||
@@ -43,9 +43,55 @@ class UploadType(str, Enum):
|
||||
model = "file_upload"
|
||||
|
||||
|
||||
class RemoteItemSchema:
|
||||
"""Describes how to map API response objects to rich dropdown items.
|
||||
|
||||
All *_field parameters use dot-path notation (e.g. ``"labels.gender"``).
|
||||
``label_field`` additionally supports template strings with ``{field}``
|
||||
placeholders (e.g. ``"{name} ({labels.accent})"``).
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
value_field: str,
|
||||
label_field: str,
|
||||
preview_url_field: str | None = None,
|
||||
preview_type: Literal["image", "video", "audio"] = "image",
|
||||
description_field: str | None = None,
|
||||
search_fields: list[str] | None = None,
|
||||
filter_field: str | None = None,
|
||||
):
|
||||
self.value_field = value_field
|
||||
"""Dot-path to the unique identifier within each item. This value is stored in the widget and passed to execute()."""
|
||||
self.label_field = label_field
|
||||
"""Dot-path to the display name, or a template string with {field} placeholders."""
|
||||
self.preview_url_field = preview_url_field
|
||||
"""Dot-path to a preview media URL. If None, no preview is shown."""
|
||||
self.preview_type = preview_type
|
||||
"""How to render the preview: "image", "video", or "audio"."""
|
||||
self.description_field = description_field
|
||||
"""Optional dot-path or template for a subtitle line shown below the label."""
|
||||
self.search_fields = search_fields
|
||||
"""Dot-paths to fields included in the search index. Defaults to [label_field]."""
|
||||
self.filter_field = filter_field
|
||||
"""Optional dot-path to a categorical field for filter tabs."""
|
||||
|
||||
def as_dict(self):
|
||||
return prune_dict({
|
||||
"value_field": self.value_field,
|
||||
"label_field": self.label_field,
|
||||
"preview_url_field": self.preview_url_field,
|
||||
"preview_type": self.preview_type,
|
||||
"description_field": self.description_field,
|
||||
"search_fields": self.search_fields,
|
||||
"filter_field": self.filter_field,
|
||||
})
|
||||
|
||||
|
||||
class RemoteOptions:
|
||||
def __init__(self, route: str, refresh_button: bool, control_after_refresh: Literal["first", "last"]="first",
|
||||
timeout: int=None, max_retries: int=None, refresh: int=None):
|
||||
timeout: int=None, max_retries: int=None, refresh: int=None,
|
||||
response_key: str=None, query_params: dict[str, str]=None,
|
||||
item_schema: RemoteItemSchema=None):
|
||||
self.route = route
|
||||
"""The route to the remote source."""
|
||||
self.refresh_button = refresh_button
|
||||
@@ -58,6 +104,12 @@ class RemoteOptions:
|
||||
"""The maximum number of retries before aborting the request."""
|
||||
self.refresh = refresh
|
||||
"""The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
|
||||
self.response_key = response_key
|
||||
"""Dot-path to the items array in the response. If None, the entire response is used."""
|
||||
self.query_params = query_params
|
||||
"""Static query parameters appended to the request URL."""
|
||||
self.item_schema = item_schema
|
||||
"""When present, the frontend renders a rich dropdown with previews instead of a plain combo widget."""
|
||||
|
||||
def as_dict(self):
|
||||
return prune_dict({
|
||||
@@ -67,6 +119,9 @@ class RemoteOptions:
|
||||
"timeout": self.timeout,
|
||||
"max_retries": self.max_retries,
|
||||
"refresh": self.refresh,
|
||||
"response_key": self.response_key,
|
||||
"query_params": self.query_params,
|
||||
"item_schema": self.item_schema.as_dict() if self.item_schema else None,
|
||||
})
|
||||
|
||||
|
||||
@@ -2184,6 +2239,7 @@ class NodeReplace:
|
||||
__all__ = [
|
||||
"FolderType",
|
||||
"UploadType",
|
||||
"RemoteItemSchema",
|
||||
"RemoteOptions",
|
||||
"NumberDisplay",
|
||||
"ControlAfterGenerate",
|
||||
|
||||
49
comfy_api_nodes/apis/phota_labs.py
Normal file
49
comfy_api_nodes/apis/phota_labs.py
Normal file
@@ -0,0 +1,49 @@
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class PhotaGenerateRequest(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
num_output_images: int = Field(1)
|
||||
aspect_ratio: str = Field(...)
|
||||
resolution: str = Field(...)
|
||||
profile_ids: list[str] | None = Field(None)
|
||||
|
||||
|
||||
class PhotaEditRequest(BaseModel):
|
||||
prompt: str = Field(...)
|
||||
images: list[str] = Field(...)
|
||||
num_output_images: int = Field(1)
|
||||
aspect_ratio: str = Field(...)
|
||||
resolution: str = Field(...)
|
||||
profile_ids: list[str] | None = Field(None)
|
||||
|
||||
|
||||
class PhotaEnhanceRequest(BaseModel):
|
||||
image: str = Field(...)
|
||||
num_output_images: int = Field(1)
|
||||
|
||||
|
||||
class PhotaKnownGeneratedSubjectCounts(BaseModel):
|
||||
counts: dict[str, int] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class PhotoStudioResponse(BaseModel):
|
||||
images: list[str] = Field(..., description="Base64-encoded PNG output images.")
|
||||
known_subjects: PhotaKnownGeneratedSubjectCounts = Field(default_factory=PhotaKnownGeneratedSubjectCounts)
|
||||
|
||||
|
||||
class PhotaAddProfileRequest(BaseModel):
|
||||
image_urls: list[str] = Field(...)
|
||||
|
||||
|
||||
class PhotaAddProfileResponse(BaseModel):
|
||||
profile_id: str = Field(...)
|
||||
|
||||
|
||||
class PhotaProfileStatusResponse(BaseModel):
|
||||
profile_id: str = Field(...)
|
||||
status: str = Field(
|
||||
...,
|
||||
description="Current profile status: VALIDATING, QUEUING, IN_PROGRESS, READY, ERROR, or INACTIVE.",
|
||||
)
|
||||
message: str | None = Field(default=None, description="Optional error or status message.")
|
||||
@@ -1,226 +0,0 @@
|
||||
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(...)
|
||||
@@ -233,6 +233,45 @@ class ElevenLabsVoiceSelector(IO.ComfyNode):
|
||||
return IO.NodeOutput(voice_id)
|
||||
|
||||
|
||||
class ElevenLabsRichVoiceSelector(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
return IO.Schema(
|
||||
node_id="ElevenLabsRichVoiceSelector",
|
||||
display_name="ElevenLabs Voice Selector (Rich)",
|
||||
category="api node/audio/ElevenLabs",
|
||||
description="Select an ElevenLabs voice with audio preview and rich metadata.",
|
||||
inputs=[
|
||||
IO.Combo.Input(
|
||||
"voice",
|
||||
options=ELEVENLABS_VOICE_OPTIONS,
|
||||
remote=IO.RemoteOptions(
|
||||
route="http://localhost:9000/elevenlabs/voices",
|
||||
refresh_button=True,
|
||||
item_schema=IO.RemoteItemSchema(
|
||||
value_field="voice_id",
|
||||
label_field="name",
|
||||
preview_url_field="preview_url",
|
||||
preview_type="audio",
|
||||
search_fields=["name", "labels.gender", "labels.accent"],
|
||||
filter_field="labels.use_case",
|
||||
),
|
||||
),
|
||||
tooltip="Choose a voice with audio preview.",
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Custom(ELEVENLABS_VOICE).Output(display_name="voice"),
|
||||
],
|
||||
is_api_node=False,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, voice: str) -> IO.NodeOutput:
|
||||
# voice is already the voice_id from item_schema.value_field
|
||||
return IO.NodeOutput(voice)
|
||||
|
||||
|
||||
class ElevenLabsTextToSpeech(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
@@ -911,6 +950,7 @@ class ElevenLabsExtension(ComfyExtension):
|
||||
return [
|
||||
ElevenLabsSpeechToText,
|
||||
ElevenLabsVoiceSelector,
|
||||
ElevenLabsRichVoiceSelector,
|
||||
ElevenLabsTextToSpeech,
|
||||
ElevenLabsAudioIsolation,
|
||||
ElevenLabsTextToSoundEffects,
|
||||
|
||||
350
comfy_api_nodes/nodes_phota_labs.py
Normal file
350
comfy_api_nodes/nodes_phota_labs.py
Normal file
@@ -0,0 +1,350 @@
|
||||
import base64
|
||||
from io import BytesIO
|
||||
|
||||
from typing_extensions import override
|
||||
|
||||
from comfy_api.latest import IO, ComfyExtension, Input
|
||||
from comfy_api_nodes.apis.phota_labs import (
|
||||
PhotaAddProfileRequest,
|
||||
PhotaAddProfileResponse,
|
||||
PhotaEditRequest,
|
||||
PhotaEnhanceRequest,
|
||||
PhotaGenerateRequest,
|
||||
PhotaProfileStatusResponse,
|
||||
PhotoStudioResponse,
|
||||
)
|
||||
from comfy_api_nodes.util import (
|
||||
ApiEndpoint,
|
||||
bytesio_to_image_tensor,
|
||||
poll_op,
|
||||
sync_op,
|
||||
upload_images_to_comfyapi,
|
||||
upload_image_to_comfyapi,
|
||||
validate_string,
|
||||
)
|
||||
|
||||
# Direct API endpoint (comment out this class to use proxy)
|
||||
class ApiEndpoint(ApiEndpoint):
|
||||
"""Temporary override to use direct API instead of proxy."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
path: str,
|
||||
method: str = "GET",
|
||||
*,
|
||||
query_params: dict | None = None,
|
||||
headers: dict | None = None,
|
||||
):
|
||||
self.path = path.replace("/proxy/phota/", "https://api.photalabs.com/")
|
||||
self.method = method
|
||||
self.query_params = query_params or {}
|
||||
self.headers = headers or {}
|
||||
if "api.photalabs.com" in self.path:
|
||||
self.headers["X-API-Key"] = "YOUR_PHOTA_API_KEY"
|
||||
|
||||
|
||||
PHOTA_LABS_PROFILE_ID = "PHOTA_LABS_PROFILE_ID"
|
||||
|
||||
|
||||
class PhotaLabsGenerate(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PhotaLabsGenerate",
|
||||
display_name="Phota Labs Generate",
|
||||
category="api node/image/Phota Labs",
|
||||
description="Generate images from a text prompt using Phota Labs.",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Text prompt describing the desired image.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"aspect_ratio",
|
||||
options=["auto", "1:1", "3:4", "4:3", "9:16", "16:9"],
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=["1K", "4K"],
|
||||
),
|
||||
],
|
||||
outputs=[IO.Image.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
aspect_ratio: str,
|
||||
resolution: str,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=False, min_length=1)
|
||||
pid_list = None # list(profile_ids.values()) if profile_ids else None
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/phota/v1/phota/generate", method="POST"),
|
||||
response_model=PhotoStudioResponse,
|
||||
data=PhotaGenerateRequest(
|
||||
prompt=prompt,
|
||||
aspect_ratio=aspect_ratio,
|
||||
resolution=resolution,
|
||||
profile_ids=pid_list or None,
|
||||
),
|
||||
)
|
||||
return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(base64.b64decode(response.images[0]))))
|
||||
|
||||
|
||||
class PhotaLabsEdit(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PhotaLabsEdit",
|
||||
display_name="Phota Labs Edit",
|
||||
category="api node/image/Phota Labs",
|
||||
description="Edit images based on a text prompt using Phota Labs. "
|
||||
"Provide input images and a prompt describing the desired edit.",
|
||||
inputs=[
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplatePrefix(
|
||||
IO.Image.Input("image"),
|
||||
prefix="image",
|
||||
min=1,
|
||||
max=10,
|
||||
),
|
||||
),
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"aspect_ratio",
|
||||
options=["auto", "1:1", "3:4", "4:3", "9:16", "16:9"],
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=["1K", "4K"],
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"profile_ids",
|
||||
template=IO.Autogrow.TemplatePrefix(
|
||||
IO.Custom(PHOTA_LABS_PROFILE_ID).Input("profile_id"),
|
||||
prefix="profile_id",
|
||||
min=0,
|
||||
max=5,
|
||||
),
|
||||
),
|
||||
],
|
||||
outputs=[IO.Image.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
images: IO.Autogrow.Type,
|
||||
prompt: str,
|
||||
aspect_ratio: str,
|
||||
resolution: str,
|
||||
profile_ids: IO.Autogrow.Type = None,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, strip_whitespace=False, min_length=1)
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/phota/v1/phota/edit", method="POST"),
|
||||
response_model=PhotoStudioResponse,
|
||||
data=PhotaEditRequest(
|
||||
prompt=prompt,
|
||||
images=await upload_images_to_comfyapi(cls, list(images.values()), max_images=10),
|
||||
aspect_ratio=aspect_ratio,
|
||||
resolution=resolution,
|
||||
profile_ids=list(profile_ids.values()) if profile_ids else None,
|
||||
),
|
||||
)
|
||||
return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(base64.b64decode(response.images[0]))))
|
||||
|
||||
|
||||
class PhotaLabsEnhance(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PhotaLabsEnhance",
|
||||
display_name="Phota Labs Enhance",
|
||||
category="api node/image/Phota Labs",
|
||||
description="Automatically enhance a photo using Phota Labs. "
|
||||
"No text prompt is required — enhancement parameters are inferred automatically.",
|
||||
inputs=[
|
||||
IO.Image.Input(
|
||||
"image",
|
||||
tooltip="Input image to enhance.",
|
||||
),
|
||||
IO.Autogrow.Input(
|
||||
"profile_ids",
|
||||
template=IO.Autogrow.TemplatePrefix(
|
||||
IO.Custom(PHOTA_LABS_PROFILE_ID).Input("profile_id"),
|
||||
prefix="profile_id",
|
||||
min=0,
|
||||
max=5,
|
||||
),
|
||||
),
|
||||
],
|
||||
outputs=[IO.Image.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
image: Input.Image,
|
||||
profile_ids: IO.Autogrow.Type = None,
|
||||
) -> IO.NodeOutput:
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/phota/v1/phota/enhance", method="POST"),
|
||||
response_model=PhotoStudioResponse,
|
||||
data=PhotaEnhanceRequest(
|
||||
image=await upload_image_to_comfyapi(cls, image),
|
||||
),
|
||||
)
|
||||
return IO.NodeOutput(bytesio_to_image_tensor(BytesIO(base64.b64decode(response.images[0]))))
|
||||
|
||||
|
||||
class PhotaLabsSelectProfile(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PhotaLabsSelectProfile",
|
||||
display_name="Phota Labs Select Profile",
|
||||
category="api node/image/Phota Labs",
|
||||
description="Select a trained Phota Labs profile for use in generation.",
|
||||
inputs=[
|
||||
IO.Combo.Input(
|
||||
"profile_id",
|
||||
options=[],
|
||||
remote=IO.RemoteOptions(
|
||||
route="http://localhost:9000/phota/profiles",
|
||||
refresh_button=True,
|
||||
item_schema=IO.RemoteItemSchema(
|
||||
value_field="profile_id",
|
||||
label_field="profile_id",
|
||||
preview_url_field="preview_url",
|
||||
preview_type="image",
|
||||
),
|
||||
),
|
||||
),
|
||||
],
|
||||
outputs=[IO.Custom(PHOTA_LABS_PROFILE_ID).Output(display_name="profile_id")],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(cls, profile_id: str) -> IO.NodeOutput:
|
||||
return IO.NodeOutput(profile_id)
|
||||
|
||||
|
||||
class PhotaLabsAddProfile(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="PhotaLabsAddProfile",
|
||||
display_name="Phota Labs Add Profile",
|
||||
category="api node/image/Phota Labs",
|
||||
description="Create a training profile from 30-50 reference images using Phota Labs. "
|
||||
"Uploads images and starts asynchronous training, returning the profile ID once training is queued.",
|
||||
inputs=[
|
||||
IO.Autogrow.Input(
|
||||
"images",
|
||||
template=IO.Autogrow.TemplatePrefix(
|
||||
IO.Image.Input("image"),
|
||||
prefix="image",
|
||||
min=30,
|
||||
max=50,
|
||||
),
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Custom(PHOTA_LABS_PROFILE_ID).Output(display_name="profile_id"),
|
||||
IO.String.Output(display_name="status"),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
images: IO.Autogrow.Type,
|
||||
) -> IO.NodeOutput:
|
||||
image_urls = await upload_images_to_comfyapi(
|
||||
cls,
|
||||
list(images.values()),
|
||||
max_images=50,
|
||||
wait_label="Uploading training images",
|
||||
)
|
||||
response = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/phota/v1/phota/profiles/add", method="POST"),
|
||||
response_model=PhotaAddProfileResponse,
|
||||
data=PhotaAddProfileRequest(image_urls=image_urls),
|
||||
)
|
||||
# Poll until validation passes and training is queued/in-progress/ready
|
||||
status_response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path=f"/proxy/phota/v1/phota/profiles/{response.profile_id}/status"
|
||||
),
|
||||
response_model=PhotaProfileStatusResponse,
|
||||
status_extractor=lambda r: r.status,
|
||||
completed_statuses=["QUEUING", "IN_PROGRESS", "READY"],
|
||||
failed_statuses=["ERROR", "INACTIVE"],
|
||||
)
|
||||
return IO.NodeOutput(response.profile_id, status_response.status)
|
||||
|
||||
|
||||
class PhotaLabsExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
return [
|
||||
PhotaLabsGenerate,
|
||||
PhotaLabsEdit,
|
||||
PhotaLabsEnhance,
|
||||
PhotaLabsSelectProfile,
|
||||
PhotaLabsAddProfile,
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> PhotaLabsExtension:
|
||||
return PhotaLabsExtension()
|
||||
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(), dtype=comfy.model_management.intermediate_dtype())
|
||||
latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device())
|
||||
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(), dtype=comfy.model_management.intermediate_dtype())
|
||||
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
|
||||
return io.NodeOutput({"samples": latent, "type": "audio"})
|
||||
|
||||
class ReferenceAudio(io.ComfyNode):
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
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
|
||||
@@ -34,58 +32,10 @@ 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, ImageHistogram]
|
||||
return [CurveEditor]
|
||||
|
||||
|
||||
async def comfy_entrypoint():
|
||||
|
||||
@@ -1,89 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from inspect import cleandoc
|
||||
from typing import TYPE_CHECKING
|
||||
from typing_extensions import override
|
||||
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from comfy.model_patcher import ModelPatcher
|
||||
import comfy.multigpu
|
||||
|
||||
|
||||
class MultiGPUCFGSplitNode(io.ComfyNode):
|
||||
"""
|
||||
Prepares model to have sampling accelerated via splitting work units.
|
||||
|
||||
Should be placed after nodes that modify the model object itself, such as compile or attention-switch nodes.
|
||||
|
||||
Other than those exceptions, this node can be placed in any order.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="MultiGPU_WorkUnits",
|
||||
display_name="MultiGPU CFG Split",
|
||||
category="advanced/multigpu",
|
||||
description=cleandoc(cls.__doc__),
|
||||
inputs=[
|
||||
io.Model.Input("model"),
|
||||
io.Int.Input("max_gpus", default=2, min=1, step=1),
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model: ModelPatcher, max_gpus: int) -> io.NodeOutput:
|
||||
model = comfy.multigpu.create_multigpu_deepclones(model, max_gpus, reuse_loaded=True)
|
||||
return io.NodeOutput(model)
|
||||
|
||||
|
||||
class MultiGPUOptionsNode(io.ComfyNode):
|
||||
"""
|
||||
Select the relative speed of GPUs in the special case they have significantly different performance from one another.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="MultiGPU_Options",
|
||||
display_name="MultiGPU Options",
|
||||
category="advanced/multigpu",
|
||||
description=cleandoc(cls.__doc__),
|
||||
inputs=[
|
||||
io.Int.Input("device_index", default=0, min=0, max=64),
|
||||
io.Float.Input("relative_speed", default=1.0, min=0.0, step=0.01),
|
||||
io.Custom("GPU_OPTIONS").Input("gpu_options", optional=True),
|
||||
],
|
||||
outputs=[
|
||||
io.Custom("GPU_OPTIONS").Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, device_index: int, relative_speed: float, gpu_options: comfy.multigpu.GPUOptionsGroup = None) -> io.NodeOutput:
|
||||
if not gpu_options:
|
||||
gpu_options = comfy.multigpu.GPUOptionsGroup()
|
||||
gpu_options.clone()
|
||||
|
||||
opt = comfy.multigpu.GPUOptions(device_index=device_index, relative_speed=relative_speed)
|
||||
gpu_options.add(opt)
|
||||
|
||||
return io.NodeOutput(gpu_options)
|
||||
|
||||
|
||||
class MultiGPUExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
MultiGPUCFGSplitNode,
|
||||
# MultiGPUOptionsNode,
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> MultiGPUExtension:
|
||||
return MultiGPUExtension()
|
||||
@@ -991,6 +991,10 @@ async def validate_inputs(prompt_id, prompt, item, validated):
|
||||
|
||||
if isinstance(input_type, list) or input_type == io.Combo.io_type:
|
||||
if input_type == io.Combo.io_type:
|
||||
# Skip validation for combos with remote options — options
|
||||
# are fetched client-side and not available on the server.
|
||||
if extra_info.get("remote"):
|
||||
continue
|
||||
combo_options = extra_info.get("options", [])
|
||||
else:
|
||||
combo_options = input_type
|
||||
|
||||
1
nodes.py
1
nodes.py
@@ -2412,7 +2412,6 @@ async def init_builtin_extra_nodes():
|
||||
"nodes_lt_audio.py",
|
||||
"nodes_lt.py",
|
||||
"nodes_hooks.py",
|
||||
"nodes_multigpu.py",
|
||||
"nodes_load_3d.py",
|
||||
"nodes_cosmos.py",
|
||||
"nodes_video.py",
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
comfyui-frontend-package==1.42.8
|
||||
comfyui-workflow-templates==0.9.44
|
||||
comfyui-workflow-templates==0.9.39
|
||||
comfyui-embedded-docs==0.4.3
|
||||
torch
|
||||
torchsde
|
||||
|
||||
@@ -146,10 +146,6 @@ 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