Merge branch 'Comfy-Org:master' into master

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azazeal04 2026-05-11 13:16:32 +02:00 committed by GitHub
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29 changed files with 22074 additions and 541 deletions

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@ -4234,7 +4234,7 @@
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78
160
],
"flags": {},
"order": 3,
"order": 7,
"mode": 0,
"inputs": [
{
"localized_name": "images",
"name": "images",
"type": "IMAGE",
"link": 250
"localized_name": "input",
"name": "input",
"type": "IMAGE,MASK",
"link": 281
},
{
"localized_name": "audio",
"name": "audio",
"shape": 7,
"type": "AUDIO",
"link": 251
},
{
"localized_name": "fps",
"name": "fps",
"type": "FLOAT",
"localized_name": "resize_type",
"name": "resize_type",
"type": "COMFY_DYNAMICCOMBO_V3",
"widget": {
"name": "fps"
"name": "resize_type"
},
"link": 252
"link": null
},
{
"localized_name": "width",
"name": "resize_type.width",
"type": "INT",
"widget": {
"name": "resize_type.width"
},
"link": 279
},
{
"localized_name": "height",
"name": "resize_type.height",
"type": "INT",
"widget": {
"name": "resize_type.height"
},
"link": 280
},
{
"localized_name": "crop",
"name": "resize_type.crop",
"type": "COMBO",
"widget": {
"name": "resize_type.crop"
},
"link": null
},
{
"localized_name": "scale_method",
"name": "scale_method",
"type": "COMBO",
"widget": {
"name": "scale_method"
},
"link": null
}
],
"outputs": [
{
"localized_name": "VIDEO",
"name": "VIDEO",
"type": "VIDEO",
"localized_name": "resized",
"name": "resized",
"type": "*",
"links": [
255
282
]
}
],
"properties": {
"cnr_id": "comfy-core",
"ver": "0.13.0",
"Node name for S&R": "CreateVideo"
"Node name for S&R": "ResizeImageMaskNode"
},
"widgets_values": [
30
"scale dimensions",
512,
512,
"center",
"area"
]
}
],
@ -500,14 +760,6 @@
"target_slot": 1,
"type": "IMAGE"
},
{
"id": 250,
"origin_id": 79,
"origin_slot": 0,
"target_id": 80,
"target_slot": 0,
"type": "IMAGE"
},
{
"id": 251,
"origin_id": 77,
@ -579,6 +831,62 @@
"target_id": 79,
"target_slot": 5,
"type": "COMBO"
},
{
"id": 266,
"origin_id": 79,
"origin_slot": 0,
"target_id": 90,
"target_slot": 0,
"type": "IMAGE"
},
{
"id": 274,
"origin_id": 90,
"origin_slot": 0,
"target_id": 95,
"target_slot": 0,
"type": "INT"
},
{
"id": 276,
"origin_id": 90,
"origin_slot": 1,
"target_id": 96,
"target_slot": 0,
"type": "INT"
},
{
"id": 279,
"origin_id": 95,
"origin_slot": 1,
"target_id": 97,
"target_slot": 2,
"type": "INT"
},
{
"id": 280,
"origin_id": 96,
"origin_slot": 1,
"target_id": 97,
"target_slot": 3,
"type": "INT"
},
{
"id": 281,
"origin_id": 79,
"origin_slot": 0,
"target_id": 97,
"target_slot": 0,
"type": "IMAGE"
},
{
"id": 282,
"origin_id": 97,
"origin_slot": 0,
"target_id": 80,
"target_slot": 0,
"type": "IMAGE"
}
],
"extra": {
@ -588,5 +896,6 @@
"description": "Stitches multiple video clips into a single sequential video file."
}
]
}
},
"extra": {}
}

View File

@ -1207,7 +1207,7 @@ def model_trange(*args, **kwargs):
pbar.i1_time = time.time()
pbar.set_postfix_str(" Model Initialization complete! ")
elif pbar._i == 2:
#bring forward the effective start time based the the diff between first and second iteration
#bring forward the effective start time based the diff between first and second iteration
#to attempt to remove load overhead from the final step rate estimate.
pbar.start_t = pbar.i1_time - (time.time() - pbar.i1_time)
pbar.set_postfix_str("")

View File

@ -23,7 +23,7 @@ class BriaEditImageRequest(BaseModel):
None,
description="Mask image (black and white). Black areas will be preserved, white areas will be edited. "
"If omitted, the edit applies to the entire image. "
"The input image and the the input mask must be of the same size.",
"The input image and the input mask must be of the same size.",
)
negative_prompt: str | None = Field(None)
guidance_scale: float = Field(...)

View File

@ -198,6 +198,62 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [
("Custom", None, None),
]
_PRESETS_SEEDREAM_1K = [
("(1K) 1024x1024 (1:1)", 1024, 1024),
("(1K) 864x1152 (3:4)", 864, 1152),
("(1K) 1152x864 (4:3)", 1152, 864),
("(1K) 1312x736 (16:9)", 1312, 736),
("(1K) 736x1312 (9:16)", 736, 1312),
("(1K) 832x1248 (2:3)", 832, 1248),
("(1K) 1248x832 (3:2)", 1248, 832),
("(1K) 1568x672 (21:9)", 1568, 672),
]
_PRESETS_SEEDREAM_2K = [
("(2K) 2048x2048 (1:1)", 2048, 2048),
("(2K) 1728x2304 (3:4)", 1728, 2304),
("(2K) 2304x1728 (4:3)", 2304, 1728),
("(2K) 2848x1600 (16:9)", 2848, 1600),
("(2K) 1600x2848 (9:16)", 1600, 2848),
("(2K) 1664x2496 (2:3)", 1664, 2496),
("(2K) 2496x1664 (3:2)", 2496, 1664),
("(2K) 3136x1344 (21:9)", 3136, 1344),
]
_PRESETS_SEEDREAM_3K = [
("(3K) 3072x3072 (1:1)", 3072, 3072),
("(3K) 2592x3456 (3:4)", 2592, 3456),
("(3K) 3456x2592 (4:3)", 3456, 2592),
("(3K) 4096x2304 (16:9)", 4096, 2304),
("(3K) 2304x4096 (9:16)", 2304, 4096),
("(3K) 2496x3744 (2:3)", 2496, 3744),
("(3K) 3744x2496 (3:2)", 3744, 2496),
("(3K) 4704x2016 (21:9)", 4704, 2016),
]
_PRESETS_SEEDREAM_4K = [
("(4K) 4096x4096 (1:1)", 4096, 4096),
("(4K) 3520x4704 (3:4)", 3520, 4704),
("(4K) 4704x3520 (4:3)", 4704, 3520),
("(4K) 5504x3040 (16:9)", 5504, 3040),
("(4K) 3040x5504 (9:16)", 3040, 5504),
("(4K) 3328x4992 (2:3)", 3328, 4992),
("(4K) 4992x3328 (3:2)", 4992, 3328),
("(4K) 6240x2656 (21:9)", 6240, 2656),
]
_CUSTOM_PRESET = [("Custom", None, None)]
RECOMMENDED_PRESETS_SEEDREAM_5_LITE = (
_PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_3K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
)
RECOMMENDED_PRESETS_SEEDREAM_4_5 = (
_PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
)
RECOMMENDED_PRESETS_SEEDREAM_4_0 = (
_PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
)
# Seedance 2.0 reference video pixel count limits per model and output resolution.
SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = {
"dreamina-seedance-2-0-260128": {

View File

@ -596,6 +596,7 @@ class Flux2ProImageNode(IO.ComfyNode):
depends_on=IO.PriceBadgeDepends(widgets=["width", "height"], inputs=["images"]),
expr=cls.PRICE_BADGE_EXPR,
),
is_deprecated=True,
)
@classmethod
@ -674,6 +675,175 @@ class Flux2MaxImageNode(Flux2ProImageNode):
"""
_FLUX2_MODEL_ENDPOINTS = {
"Flux.2 [pro]": "/proxy/bfl/flux-2-pro/generate",
"Flux.2 [max]": "/proxy/bfl/flux-2-max/generate",
}
def _flux2_model_inputs():
return [
IO.Int.Input(
"width",
default=1024,
min=256,
max=2048,
step=32,
),
IO.Int.Input(
"height",
default=768,
min=256,
max=2048,
step=32,
),
IO.Autogrow.Input(
"images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("image"),
names=[f"image_{i}" for i in range(1, 9)],
min=0,
),
tooltip="Optional reference image(s) for image-to-image generation. Up to 8 images.",
),
]
class Flux2ImageNode(IO.ComfyNode):
@classmethod
def define_schema(cls) -> IO.Schema:
return IO.Schema(
node_id="Flux2ImageNode",
display_name="Flux.2 Image",
category="api node/image/BFL",
description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt for the image generation or edit",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option("Flux.2 [pro]", _flux2_model_inputs()),
IO.DynamicCombo.Option("Flux.2 [max]", _flux2_model_inputs()),
],
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=0xFFFFFFFFFFFFFFFF,
control_after_generate=True,
tooltip="The random seed used for creating the noise.",
),
],
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,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(
widgets=["model", "model.width", "model.height"],
input_groups=["model.images"],
),
expr="""
(
$isMax := widgets.model = "flux.2 [max]";
$MP := 1024 * 1024;
$w := $lookup(widgets, "model.width");
$h := $lookup(widgets, "model.height");
$outMP := $max([1, $floor((($w * $h) + $MP - 1) / $MP)]);
$outputCost := $isMax
? (0.07 + 0.03 * ($outMP - 1))
: (0.03 + 0.015 * ($outMP - 1));
$refMin := $isMax ? 0.03 : 0.015;
$refMax := $isMax ? 0.24 : 0.12;
$hasRefs := $lookup(inputGroups, "model.images") > 0;
$hasRefs
? {
"type": "range_usd",
"min_usd": $outputCost + $refMin,
"max_usd": $outputCost + $refMax,
"format": { "approximate": true }
}
: {"type": "usd", "usd": $outputCost}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
seed: int,
) -> IO.NodeOutput:
model_choice = model["model"]
endpoint = _FLUX2_MODEL_ENDPOINTS[model_choice]
width = model["width"]
height = model["height"]
images_dict = model.get("images") or {}
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
n_images = sum(get_number_of_images(t) for t in image_tensors)
if n_images > 8:
raise ValueError("The current maximum number of supported images is 8.")
flat_tensors: list[torch.Tensor] = []
for tensor in image_tensors:
if len(tensor.shape) == 4:
flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
else:
flat_tensors.append(tensor)
reference_images: dict[str, str] = {}
for idx, tensor in enumerate(flat_tensors):
key_name = f"input_image_{idx + 1}" if idx else "input_image"
reference_images[key_name] = tensor_to_base64_string(tensor, total_pixels=2048 * 2048)
initial_response = await sync_op(
cls,
ApiEndpoint(path=endpoint, method="POST"),
response_model=BFLFluxProGenerateResponse,
data=Flux2ProGenerateRequest(
prompt=prompt,
width=width,
height=height,
seed=seed,
**reference_images,
),
)
def price_extractor(_r: BaseModel) -> float | None:
return None if initial_response.cost is None else initial_response.cost / 100
response = await poll_op(
cls,
ApiEndpoint(initial_response.polling_url),
response_model=BFLFluxStatusResponse,
status_extractor=lambda r: r.status,
progress_extractor=lambda r: r.progress,
price_extractor=price_extractor,
completed_statuses=[BFLStatus.ready],
failed_statuses=[
BFLStatus.request_moderated,
BFLStatus.content_moderated,
BFLStatus.error,
BFLStatus.task_not_found,
],
queued_statuses=[],
)
return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
class BFLExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@ -685,6 +855,7 @@ class BFLExtension(ComfyExtension):
FluxProFillNode,
Flux2ProImageNode,
Flux2MaxImageNode,
Flux2ImageNode,
]

View File

@ -10,6 +10,9 @@ from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.bytedance import (
RECOMMENDED_PRESETS,
RECOMMENDED_PRESETS_SEEDREAM_4,
RECOMMENDED_PRESETS_SEEDREAM_4_0,
RECOMMENDED_PRESETS_SEEDREAM_4_5,
RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
SEEDANCE2_PRICE_PER_1K_TOKENS,
SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
VIDEO_TASKS_EXECUTION_TIME,
@ -68,6 +71,12 @@ SEEDREAM_MODELS = {
"seedream-4-0-250828": "seedream-4-0-250828",
}
SEEDREAM_PRESETS = {
"seedream-5-0-260128": RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
"seedream-4-5-251128": RECOMMENDED_PRESETS_SEEDREAM_4_5,
"seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0,
}
# Long-running tasks endpoints(e.g., video)
BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks"
BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id}
@ -562,6 +571,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
)
""",
),
is_deprecated=True,
)
@classmethod
@ -651,6 +661,226 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
def _seedream_model_inputs(*, max_ref_images: int, presets: list):
return [
IO.Combo.Input(
"size_preset",
options=[label for label, _, _ in presets],
tooltip="Pick a recommended size. Select Custom to use the width and height below.",
),
IO.Int.Input(
"width",
default=2048,
min=1024,
max=6240,
step=2,
tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
),
IO.Int.Input(
"height",
default=2048,
min=1024,
max=4992,
step=2,
tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
),
IO.Int.Input(
"max_images",
default=1,
min=1,
max=max_ref_images,
step=1,
display_mode=IO.NumberDisplay.number,
tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
"With >1, the model generates between 1 and max_images related images "
"(e.g., story scenes, character variations). "
"Total images (input + generated) cannot exceed 15.",
),
IO.Autogrow.Input(
"images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("image"),
names=[f"image_{i}" for i in range(1, max_ref_images + 1)],
min=0,
),
tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. "
f"Up to {max_ref_images} images.",
),
IO.Boolean.Input(
"fail_on_partial",
default=False,
tooltip="If enabled, abort execution if any requested images are missing or return an error.",
advanced=True,
),
]
class ByteDanceSeedreamNodeV2(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceSeedreamNodeV2",
display_name="ByteDance Seedream 4.5 & 5.0",
category="api node/image/ByteDance",
description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Text prompt for creating or editing an image.",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"seedream 5.0 lite",
_seedream_model_inputs(max_ref_images=14, presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE),
),
IO.DynamicCombo.Option(
"seedream-4-5-251128",
_seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_5),
),
IO.DynamicCombo.Option(
"seedream-4-0-250828",
_seedream_model_inputs(max_ref_images=10, presets=RECOMMENDED_PRESETS_SEEDREAM_4_0),
),
],
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation.",
),
IO.Boolean.Input(
"watermark",
default=False,
tooltip='Whether to add an "AI generated" watermark to the image.',
advanced=True,
),
],
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,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model"]),
expr="""
(
$price := $contains(widgets.model, "5.0 lite") ? 0.035 :
$contains(widgets.model, "4-5") ? 0.04 : 0.03;
{
"type":"usd",
"usd": $price,
"format": { "suffix":" x images/Run", "approximate": true }
}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
seed: int = 0,
watermark: bool = False,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
model_id = SEEDREAM_MODELS[model["model"]]
presets = SEEDREAM_PRESETS[model_id]
size_preset = model.get("size_preset", presets[0][0])
width = model.get("width", 2048)
height = model.get("height", 2048)
max_images = model.get("max_images", 1)
sequential_image_generation = "disabled" if max_images == 1 else "auto"
images_dict = model.get("images") or {}
fail_on_partial = model.get("fail_on_partial", False)
w = h = None
for label, tw, th in presets:
if label == size_preset:
w, h = tw, th
break
if w is None or h is None:
w, h = width, height
out_num_pixels = w * h
mp_provided = out_num_pixels / 1_000_000.0
if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3686400:
raise ValueError(
f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
)
if "seedream-4-0" in model_id and out_num_pixels < 921600:
raise ValueError(
f"Minimum image resolution that the selected model can generate is 0.92MP, "
f"but {mp_provided:.2f}MP provided."
)
if out_num_pixels > 16_777_216:
raise ValueError(
f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
)
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
n_input_images = sum(get_number_of_images(t) for t in image_tensors)
max_num_of_images = 14 if model_id == "seedream-5-0-260128" else 10
if n_input_images > max_num_of_images:
raise ValueError(
f"Maximum of {max_num_of_images} reference images are supported, but {n_input_images} received."
)
if sequential_image_generation == "auto" and n_input_images + max_images > 15:
raise ValueError(
"The maximum number of generated images plus the number of reference images cannot exceed 15."
)
reference_images_urls: list[str] = []
if image_tensors:
for tensor in image_tensors:
validate_image_aspect_ratio(tensor, (1, 3), (3, 1))
reference_images_urls = await upload_images_to_comfyapi(
cls,
image_tensors,
max_images=n_input_images,
mime_type="image/png",
wait_label="Uploading reference images",
)
response = await sync_op(
cls,
ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
response_model=ImageTaskCreationResponse,
data=Seedream4TaskCreationRequest(
model=model_id,
prompt=prompt,
image=reference_images_urls,
size=f"{w}x{h}",
seed=seed,
sequential_image_generation=sequential_image_generation,
sequential_image_generation_options=Seedream4Options(max_images=max_images),
watermark=watermark,
),
)
if len(response.data) == 1:
return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d]
if fail_on_partial and len(urls) < len(response.data):
raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.")
return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
class ByteDanceTextToVideoNode(IO.ComfyNode):
@classmethod
@ -2105,6 +2335,7 @@ class ByteDanceExtension(ComfyExtension):
return [
ByteDanceImageNode,
ByteDanceSeedreamNode,
ByteDanceSeedreamNodeV2,
ByteDanceTextToVideoNode,
ByteDanceImageToVideoNode,
ByteDanceFirstLastFrameNode,

View File

@ -162,6 +162,61 @@ class GrokImageNode(IO.ComfyNode):
)
_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS = [
"auto",
"1:1",
"2:3",
"3:2",
"3:4",
"4:3",
"9:16",
"16:9",
"9:19.5",
"19.5:9",
"9:20",
"20:9",
"1:2",
"2:1",
]
def _grok_image_edit_model_inputs(*, max_ref_images: int, with_aspect_ratio: bool):
inputs = [
IO.Autogrow.Input(
"images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("image"),
names=[f"image_{i}" for i in range(1, max_ref_images + 1)],
min=1,
),
tooltip=(
"Reference image to edit."
if max_ref_images == 1
else f"Reference image(s) to edit. Up to {max_ref_images} images."
),
),
IO.Combo.Input("resolution", options=["1K", "2K"]),
IO.Int.Input(
"number_of_images",
default=1,
min=1,
max=10,
step=1,
tooltip="Number of edited images to generate",
display_mode=IO.NumberDisplay.number,
),
]
if with_aspect_ratio:
inputs.append(
IO.Combo.Input(
"aspect_ratio",
options=_GROK_IMAGE_EDIT_ASPECT_RATIO_OPTIONS,
tooltip="Only allowed when multiple images are connected.",
)
)
return inputs
class GrokImageEditNode(IO.ComfyNode):
@classmethod
@ -256,6 +311,7 @@ class GrokImageEditNode(IO.ComfyNode):
)
""",
),
is_deprecated=True,
)
@classmethod
@ -303,6 +359,143 @@ class GrokImageEditNode(IO.ComfyNode):
)
class GrokImageEditNodeV2(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="GrokImageEditNodeV2",
display_name="Grok Image Edit",
category="api node/image/Grok",
description="Modify an existing image based on a text prompt",
inputs=[
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="The text prompt used to generate the image",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"grok-imagine-image-quality",
_grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True),
),
IO.DynamicCombo.Option(
"grok-imagine-image-pro",
_grok_image_edit_model_inputs(max_ref_images=1, with_aspect_ratio=False),
),
IO.DynamicCombo.Option(
"grok-imagine-image",
_grok_image_edit_model_inputs(max_ref_images=3, with_aspect_ratio=True),
),
],
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to determine if node should re-run; "
"actual results are nondeterministic regardless of seed.",
),
],
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,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(
widgets=["model", "model.resolution", "model.number_of_images"],
),
expr="""
(
$isQualityModel := widgets.model = "grok-imagine-image-quality";
$isPro := $contains(widgets.model, "pro");
$res := $lookup(widgets, "model.resolution");
$n := $lookup(widgets, "model.number_of_images");
$rate := $isQualityModel
? ($res = "1k" ? 0.05 : 0.07)
: ($isPro ? 0.07 : 0.02);
$base := $isQualityModel ? 0.01 : 0.002;
$output := $rate * $n;
$isPro
? {"type":"usd","usd": $base + $output}
: {"type":"range_usd","min_usd": $base + $output, "max_usd": 3 * $base + $output}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
seed: int,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
model_id = model["model"]
resolution = model["resolution"]
number_of_images = model["number_of_images"]
images_dict = model.get("images") or {}
aspect_ratio = model.get("aspect_ratio", "auto")
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
n_images = sum(get_number_of_images(t) for t in image_tensors)
if n_images < 1:
raise ValueError("At least one image is required for editing.")
if model_id == "grok-imagine-image-pro" and n_images > 1:
raise ValueError("The pro model supports only 1 input image.")
if model_id != "grok-imagine-image-pro" and n_images > 3:
raise ValueError("A maximum of 3 input images is supported.")
if aspect_ratio != "auto" and n_images == 1:
raise ValueError(
"Custom aspect ratio is only allowed when multiple images are connected to the image input."
)
flat_tensors: list[torch.Tensor] = []
for tensor in image_tensors:
if len(tensor.shape) == 4:
flat_tensors.extend(tensor[i] for i in range(tensor.shape[0]))
else:
flat_tensors.append(tensor)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/xai/v1/images/edits", method="POST"),
data=ImageEditRequest(
model=model_id,
images=[
InputUrlObject(url=f"data:image/png;base64,{tensor_to_base64_string(i)}") for i in flat_tensors
],
prompt=prompt,
resolution=resolution.lower(),
n=number_of_images,
seed=seed,
aspect_ratio=None if aspect_ratio == "auto" else aspect_ratio,
),
response_model=ImageGenerationResponse,
price_extractor=_extract_grok_price,
)
if len(response.data) == 1:
return IO.NodeOutput(await download_url_to_image_tensor(response.data[0].url))
return IO.NodeOutput(
torch.cat(
[await download_url_to_image_tensor(i) for i in [str(d.url) for d in response.data if d.url]],
)
)
class GrokVideoNode(IO.ComfyNode):
@classmethod
@ -737,6 +930,7 @@ class GrokExtension(ComfyExtension):
return [
GrokImageNode,
GrokImageEditNode,
GrokImageEditNodeV2,
GrokVideoNode,
GrokVideoReferenceNode,
GrokVideoEditNode,

View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.20.1"
__version__ = "0.21.0"

View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.20.1"
version = "0.21.0"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.10"