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feat(api-nodes): add dedicated OpenAI GPT-Image-2 node #13506

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289 changes: 278 additions & 11 deletions comfy_api_nodes/nodes_openai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -357,13 +357,18 @@ def calculate_tokens_price_image_1_5(response: OpenAIImageGenerationResponse) ->
return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 32.0)) / 1_000_000.0


def calculate_tokens_price_image_2(response: OpenAIImageGenerationResponse) -> float | None:
# https://platform.openai.com/docs/pricing - gpt-image-2: input $8/1M, output $30/1M
return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 30.0)) / 1_000_000.0


class OpenAIGPTImage1(IO.ComfyNode):

@classmethod
def define_schema(cls):
return IO.Schema(
node_id="OpenAIGPTImage1",
display_name="OpenAI GPT Image 2",
display_name="OpenAI GPT Image 1 & 1.5",
category="api node/image/OpenAI",
description="Generates images synchronously via OpenAI's GPT Image endpoint.",
inputs=[
Expand Down Expand Up @@ -427,8 +432,8 @@ def define_schema(cls):
),
IO.Combo.Input(
"model",
options=["gpt-image-1", "gpt-image-1.5", 'gpt-image-2'],
default="gpt-image-2",
options=["gpt-image-1", "gpt-image-1.5"],
default="gpt-image-1.5",
optional=True,
),
],
Expand All @@ -442,14 +447,22 @@ def define_schema(cls):
],
is_api_node=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["quality", "n"]),
depends_on=IO.PriceBadgeDepends(widgets=["quality", "n", "model"]),
expr="""
(
$ranges := {
"low": [0.011, 0.02],
"medium": [0.046, 0.07],
"high": [0.167, 0.3]
};
$m := widgets.model;
$ranges :=
$contains($m, "gpt-image-1.5")
? {
"low": [0.009, 0.016],
"medium": [0.037, 0.056],
"high": [0.134, 0.240]
}
: {
"low": [0.011, 0.020],
"medium": [0.046, 0.070],
"high": [0.167, 0.300]
};
$range := $lookup($ranges, widgets.quality);
$n := widgets.n;
($n = 1)
Expand Down Expand Up @@ -487,8 +500,6 @@ async def execute(
price_extractor = calculate_tokens_price_image_1
elif model == "gpt-image-1.5":
price_extractor = calculate_tokens_price_image_1_5
elif model == "gpt-image-2":
price_extractor = calculate_tokens_price_image_1_5
else:
raise ValueError(f"Unknown model: {model}")

Expand Down Expand Up @@ -566,6 +577,261 @@ async def execute(
return IO.NodeOutput(await validate_and_cast_response(response))


_GPT_IMAGE_2_SIZES = [
"auto",
"1024x1024",
"1536x1024",
"1024x1536",
"2048x2048",
"2048x1152",
"3840x2160",
"2160x3840",
]


def _resolve_gpt_image_2_size(size: str, custom_width: int, custom_height: int) -> str:
if custom_width <= 0 or custom_height <= 0:
return size
w, h = custom_width, custom_height
if max(w, h) > 3840:
raise ValueError(f"Maximum edge length must be ≤ 3840px, got {max(w, h)}")
if w % 16 != 0 or h % 16 != 0:
raise ValueError(f"Both edges must be multiples of 16px, got {w}x{h}")
if max(w, h) / min(w, h) > 3:
raise ValueError(f"Long-to-short edge ratio must not exceed 3:1, got {max(w, h) / min(w, h):.2f}:1")
total = w * h
if total < 655_360 or total > 8_294_400:
raise ValueError(f"Total pixels must be between 655,360 and 8,294,400, got {total:,}")
return f"{w}x{h}"


class OpenAIGPTImage2(IO.ComfyNode):

@classmethod
def define_schema(cls):
return IO.Schema(
node_id="OpenAIGPTImage2",
display_name="OpenAI GPT Image 2",
category="api node/image/OpenAI",
description="Generates images synchronously via OpenAI's GPT-Image-2 endpoint.",
inputs=[
IO.String.Input(
"prompt",
default="",
multiline=True,
tooltip="Text prompt for GPT Image 2",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2**31 - 1,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="not implemented yet in backend",
optional=True,
),
IO.Combo.Input(
"quality",
default="auto",
options=["auto", "low", "medium", "high"],
tooltip="Image quality. 'auto' lets the model decide based on the prompt. Square images are typically fastest.",
optional=True,
),
IO.Combo.Input(
"background",
default="auto",
options=["auto", "opaque"],
tooltip="Background style. GPT-Image-2 does not support transparent backgrounds.",
optional=True,
),
IO.Combo.Input(
"size",
default="auto",
options=_GPT_IMAGE_2_SIZES,
tooltip="Output image dimensions. Ignored when custom_width and custom_height are both non-zero.",
optional=True,
),
IO.Int.Input(
"custom_width",
default=0,
min=0,
max=3840,
step=16,
display_mode=IO.NumberDisplay.number,
tooltip="Custom output width in pixels. Set to 0 (default) to use the size preset. When both width and height are non-zero, they override the size preset. Slider enforces multiples of 16 and max edge 3840px. Additional constraints checked at generation: ratio ≤ 3:1, total pixels 655,360–8,294,400.",
optional=True,
),
IO.Int.Input(
"custom_height",
default=0,
min=0,
max=3840,
step=16,
display_mode=IO.NumberDisplay.number,
tooltip="Custom output height in pixels. Set to 0 (default) to use the size preset. When both width and height are non-zero, they override the size preset. Slider enforces multiples of 16 and max edge 3840px. Additional constraints checked at generation: ratio ≤ 3:1, total pixels 655,360–8,294,400.",
optional=True,
),
IO.Int.Input(
"num_images",
default=1,
min=1,
max=8,
step=1,
tooltip="Number of images to generate per run.",
display_mode=IO.NumberDisplay.number,
optional=True,
),
IO.Image.Input(
"image",
tooltip="Optional reference image for image editing.",
optional=True,
),
IO.Mask.Input(
"mask",
tooltip="Optional mask for inpainting (white areas will be replaced).",
optional=True,
),
IO.Combo.Input(
"model",
options=["gpt-image-2"],
default="gpt-image-2",
tooltip="Model used for image generation.",
optional=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=["quality", "num_images"]),
expr="""
(
$ranges := {
"low": [0.005, 0.010],
"medium": [0.041, 0.060],
"high": [0.165, 0.250]
};
$q := widgets.quality;
$n := widgets.num_images;
$n := ($n != null and $n != 0) ? $n : 1;
$range := $lookup($ranges, $q);
$lo := $range ? $range[0] : 0.005;
$hi := $range ? $range[1] : 0.250;
($n = 1)
? {"type":"range_usd","min_usd": $lo, "max_usd": $hi, "format": {"approximate": ($range ? false : true)}}
: {
"type":"range_usd",
"min_usd": $lo,
"max_usd": $hi,
"format": {"approximate": ($range ? false : true), "suffix": " x " & $string($n) & "/Run"}
}
)
""",
),
Comment on lines +713 to +738
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@coderabbitai coderabbitai Bot Apr 22, 2026

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⚠️ Potential issue | 🟡 Minor

Price badge fallback range is suspiciously wide — worth a quick sanity check.

When quality == "auto", $lookup($ranges, $q) is falsy, so the badge falls back to [0.005, 0.250] with approximate: true (Lines 726–727). That spans a 50× ratio, which is technically honest ("we don't know until the model decides") but potentially alarming in the UI. If product is OK with that, great — this is just a heads-up that it'll render as "~$0.005–$0.250" by default on a fresh node drop, since "auto" is the default quality (Line 637).

🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@comfy_api_nodes/nodes_openai.py` around lines 713 - 738, The price-badge JQ
expression in IO.PriceBadge uses a very wide fallback range [0.005, 0.250] when
$lookup($ranges, widgets.quality) is falsy (e.g., quality == "auto"); tighten
this by either mapping "auto" explicitly to a specific bucket (e.g., "medium")
or replacing the fallback bounds with a narrower, product-approved default (for
example set $lo/$hi to something closer to the existing medium/high ranges) and
keep approximate: true; update the expr in the IO.PriceBadge (the
$ranges/$lookup/$range/$lo/$hi logic) so new default behavior applies when
widgets.quality is not found.

)

@classmethod
async def execute(
cls,
prompt: str,
seed: int = 0,
quality: str = "auto",
background: str = "auto",
image: Input.Image | None = None,
mask: Input.Image | None = None,
num_images: int = 1,
size: str = "auto",
custom_width: int = 0,
custom_height: int = 0,
model: str = "gpt-image-2",
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False)

if mask is not None and image is None:
raise ValueError("Cannot use a mask without an input image")

resolved_size = _resolve_gpt_image_2_size(size, custom_width, custom_height)

if image is not None:
files = []
batch_size = image.shape[0]
for i in range(batch_size):
single_image = image[i : i + 1]
scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()

image_np = (scaled_image.numpy() * 255).astype(np.uint8)
img = Image.fromarray(image_np)
img_byte_arr = BytesIO()
img.save(img_byte_arr, format="PNG")
img_byte_arr.seek(0)

if batch_size == 1:
files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png")))
else:
files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png")))

if mask is not None:
if image.shape[0] != 1:
raise Exception("Cannot use a mask with multiple image")
if mask.shape[1:] != image.shape[1:-1]:
raise Exception("Mask and Image must be the same size")
_, height, width = mask.shape
rgba_mask = torch.zeros(height, width, 4, device="cpu")
rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()

scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048 * 2048).squeeze()

mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
mask_img = Image.fromarray(mask_np)
mask_img_byte_arr = BytesIO()
mask_img.save(mask_img_byte_arr, format="PNG")
mask_img_byte_arr.seek(0)
files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png")))

response = await sync_op(
cls,
ApiEndpoint(path="/proxy/openai/images/edits", method="POST"),
response_model=OpenAIImageGenerationResponse,
data=OpenAIImageEditRequest(
model=model,
prompt=prompt,
quality=quality,
background=background,
n=num_images,
size=resolved_size,
moderation="low",
),
content_type="multipart/form-data",
files=files,
price_extractor=calculate_tokens_price_image_2,
)
else:
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
response_model=OpenAIImageGenerationResponse,
data=OpenAIImageGenerationRequest(
model=model,
prompt=prompt,
quality=quality,
background=background,
n=num_images,
size=resolved_size,
moderation="low",
),
price_extractor=calculate_tokens_price_image_2,
)
return IO.NodeOutput(await validate_and_cast_response(response))
Comment on lines +741 to +832
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@coderabbitai coderabbitai Bot Apr 22, 2026

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⚠️ Potential issue | 🟠 Major

seed is accepted by the node but silently dropped on the wire.

OpenAIGPTImage2.execute declares seed: int = 0 and the schema exposes a seed input with control_after_generate=True, but neither OpenAIImageEditRequest(...) at Lines 803–811 nor OpenAIImageGenerationRequest(...) at Lines 821–829 forwards seed=seed. Compare with OpenAIGPTImage1.execute, which does pass seed=seed in both branches (Lines 552 and 571). Users wiring a deterministic seed control into this node will quietly get non-reproducible results without any error.

If the backend genuinely doesn't accept seed for gpt-image-2 yet, please drop the input from the schema rather than leaving a ghost widget. Otherwise, plumb it through like the Image 1 node does:

🔧 Proposed forwarding 
                 data=OpenAIImageEditRequest(
                     model=model,
                     prompt=prompt,
                     quality=quality,
                     background=background,
                     n=num_images,
+                    seed=seed,
                     size=resolved_size,
                     moderation="low",
                 ),
                 data=OpenAIImageGenerationRequest(
                     model=model,
                     prompt=prompt,
                     quality=quality,
                     background=background,
                     n=num_images,
+                    seed=seed,
                     size=resolved_size,
                     moderation="low",
                 ),
🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@comfy_api_nodes/nodes_openai.py` around lines 741 - 832,
OpenAIGPTImage2.execute currently accepts seed but never forwards it in the
request payload; update the calls that construct OpenAIImageEditRequest and
OpenAIImageGenerationRequest inside OpenAIGPTImage2.execute to include seed=seed
(matching how OpenAIGPTImage1.execute forwards seed), or if the backend truly
doesn't support seeding for gpt-image-2, remove the seed parameter from the node
schema and function signature instead; locate the two request constructions
named OpenAIImageEditRequest(...) and OpenAIImageGenerationRequest(...) in
OpenAIGPTImage2.execute and either add seed=seed to both or delete/disable the
seed input upstream.



class OpenAIChatNode(IO.ComfyNode):
"""
Node to generate text responses from an OpenAI model.
Expand Down Expand Up @@ -915,6 +1181,7 @@ async def get_node_list(self) -> list[type[IO.ComfyNode]]:
OpenAIDalle2,
OpenAIDalle3,
OpenAIGPTImage1,
OpenAIGPTImage2,
OpenAIChatNode,
OpenAIInputFiles,
OpenAIChatConfig,
Expand Down
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