Merge branch 'master' of github.com:comfyanonymous/ComfyUI into feat/linux-amd-setup

This commit is contained in:
AKharytonchyk 2025-07-16 11:30:13 +02:00
commit 2530ec5ad1
10 changed files with 81 additions and 35 deletions

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@ -0,0 +1,40 @@
name: Check for Windows Line Endings
on:
pull_request:
branches: ['*'] # Trigger on all pull requests to any branch
jobs:
check-line-endings:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0 # Fetch all history to compare changes
- name: Check for Windows line endings (CRLF)
run: |
# Get the list of changed files in the PR
CHANGED_FILES=$(git diff --name-only origin/${{ github.base_ref }}..HEAD)
# Flag to track if CRLF is found
CRLF_FOUND=false
# Loop through each changed file
for FILE in $CHANGED_FILES; do
# Check if the file exists and is a text file
if [ -f "$FILE" ] && file "$FILE" | grep -q "text"; then
# Check for CRLF line endings
if grep -UP '\r$' "$FILE"; then
echo "Error: Windows line endings (CRLF) detected in $FILE"
CRLF_FOUND=true
fi
fi
done
# Exit with error if CRLF was found
if [ "$CRLF_FOUND" = true ]; then
exit 1
fi

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@ -144,6 +144,7 @@ class PerformanceFeature(enum.Enum):
parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")
parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")

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@ -1214,7 +1214,7 @@ class Omnigen2(supported_models_base.BASE):
def clip_target(self, state_dict={}):
pref = self.text_encoder_key_prefix[0]
hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref))
return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.LuminaTokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]

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@ -31,6 +31,7 @@ from einops import rearrange
from comfy.cli_args import args
MMAP_TORCH_FILES = args.mmap_torch_files
DISABLE_MMAP = args.disable_mmap
ALWAYS_SAFE_LOAD = False
if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated
@ -58,7 +59,10 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
sd = {}
for k in f.keys():
sd[k] = f.get_tensor(k)
tensor = f.get_tensor(k)
if DISABLE_MMAP: # TODO: Not sure if this is the best way to bypass the mmap issues
tensor = tensor.to(device=device, copy=True)
sd[k] = tensor
if return_metadata:
metadata = f.metadata()
except Exception as e:

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@ -406,7 +406,7 @@ class GeminiInputFiles(ComfyNodeABC):
def create_file_part(self, file_path: str) -> GeminiPart:
mime_type = (
GeminiMimeType.pdf
GeminiMimeType.application_pdf
if file_path.endswith(".pdf")
else GeminiMimeType.text_plain
)

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@ -247,7 +247,7 @@ class MaskComposite:
visible_width, visible_height = (right - left, bottom - top,)
source_portion = source[:, :visible_height, :visible_width]
destination_portion = destination[:, top:bottom, left:right]
destination_portion = output[:, top:bottom, left:right]
if operation == "multiply":
output[:, top:bottom, left:right] = destination_portion * source_portion

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@ -1,24 +1,24 @@
from nodes import MAX_RESOLUTION
class CLIPTextEncodePixArtAlpha:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
# "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma."
def encode(self, clip, width, height, text):
tokens = clip.tokenize(text)
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),)
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha,
}
from nodes import MAX_RESOLUTION
class CLIPTextEncodePixArtAlpha:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
# "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma."
def encode(self, clip, width, height, text):
tokens = clip.tokenize(text)
return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),)
NODE_CLASS_MAPPINGS = {
"CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha,
}

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@ -1,5 +1,5 @@
comfyui-frontend-package==1.23.4
comfyui-workflow-templates==0.1.35
comfyui-workflow-templates==0.1.36
comfyui-embedded-docs==0.2.4
torch
torchsde

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@ -10,11 +10,11 @@ import urllib.parse
server_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())
def queue_prompt(prompt):
p = {"prompt": prompt, "client_id": client_id}
def queue_prompt(prompt, prompt_id):
p = {"prompt": prompt, "client_id": client_id, "prompt_id": prompt_id}
data = json.dumps(p).encode('utf-8')
req = urllib.request.Request("http://{}/prompt".format(server_address), data=data)
return json.loads(urllib.request.urlopen(req).read())
req = urllib.request.Request("http://{}/prompt".format(server_address), data=data)
urllib.request.urlopen(req).read()
def get_image(filename, subfolder, folder_type):
data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
@ -27,7 +27,8 @@ def get_history(prompt_id):
return json.loads(response.read())
def get_images(ws, prompt):
prompt_id = queue_prompt(prompt)['prompt_id']
prompt_id = str(uuid.uuid4())
queue_prompt(prompt, prompt_id)
output_images = {}
while True:
out = ws.recv()

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@ -678,7 +678,7 @@ class PromptServer():
if "prompt" in json_data:
prompt = json_data["prompt"]
prompt_id = str(uuid.uuid4())
prompt_id = str(json_data.get("prompt_id", uuid.uuid4()))
valid = await execution.validate_prompt(prompt_id, prompt)
extra_data = {}
if "extra_data" in json_data: