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[Partner Nodes] feat(Bytedance): add new Seedream node with Fast Mode widget (#15750)

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Signed-off-by: Alexander Piskun <bigcat88@icloud.com>
(cherry picked from commit 18c9aa4873)
This commit is contained in:
Alexander Piskun
2026-08-20 20:07:44 +04:00
committed by Purz
parent 8268a79890
commit db387a6f4e
2 changed files with 274 additions and 114 deletions
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3
comfy_api_nodes/apis/bytedance.py
View File

@@ -18,7 +18,8 @@ class Seedream4Options(BaseModel):
class Seedream5OptimizePromptOptions(BaseModel):
thinking: Literal["auto", "enabled", "disabled"] = Field(...)
thinking: Literal["auto", "enabled", "disabled"] | None = Field(None)
mode: Literal["standard", "fast"] | None = Field(None)
class Seedream4TaskCreationRequest(BaseModel):

385
comfy_api_nodes/nodes_bytedance.py
View File

@@ -753,6 +753,8 @@ def _seedream_model_inputs(
max_width: int = 6240,
max_height: int = 4992,
supports_batch: bool = True,
supports_fast: bool = False,
include_common: bool = False,
):
inputs = [
IO.Combo.Input(
@@ -813,16 +815,282 @@ def _seedream_model_inputs(
advanced=True,
)
)
if supports_fast:
inputs.append(
IO.Combo.Input(
"prompt_optimization",
options=["standard", "fast"],
default="standard",
tooltip="Prompt-optimization mode when reference images are provided: "
"'standard' gives higher quality, 'fast' shorter generation time.",
advanced=True,
)
)
if include_common:
inputs.extend(
[
IO.Int.Input(
"seed",
default=42,
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,
),
IO.Boolean.Input(
"thinking",
default=True,
tooltip=(
"Enable the model's prompt-optimization reasoning ('thinking') for better adherence. "
"Can substantially increase generation time — notably on Seedream 5.0 Pro. "
"Can only be disabled for text-to-image (not when reference images are provided)."
),
advanced=True,
),
]
)
return inputs
class ByteDanceSeedreamNodeV2(IO.ComfyNode):
class ByteDanceSeedreamNodeV3(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceSeedreamNodeV3",
display_name="ByteDance Seedream 4.5 & 5.0",
category="partner/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 pro",
_seedream_model_inputs(
max_ref_images=10,
presets=RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
max_width=3136,
max_height=2496,
supports_batch=False,
supports_fast=True,
include_common=True,
),
),
IO.DynamicCombo.Option(
"seedream 5.0 lite",
_seedream_model_inputs(
max_ref_images=14,
presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
include_common=True,
),
),
IO.DynamicCombo.Option(
"seedream-4-5-251128",
_seedream_model_inputs(
max_ref_images=10,
presets=RECOMMENDED_PRESETS_SEEDREAM_4_5,
include_common=True,
),
),
IO.DynamicCombo.Option(
"seedream-4-0-250828",
_seedream_model_inputs(
max_ref_images=10,
presets=RECOMMENDED_PRESETS_SEEDREAM_4_0,
include_common=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", "model.size_preset", "model.width", "model.height"],
input_groups=["model.images"],
),
expr="""
(
$model := $string(widgets.model);
$sp := $string($lookup(widgets, "model.size_preset"));
$w := $lookup(widgets, "model.width");
$h := $lookup(widgets, "model.height");
$px := ($type($w) = "number" and $type($h) = "number") ? $w * $h : 0;
$refs := $lookup(inputGroups, "model.images");
$extra := ($type($refs) = "number" and $refs > 1) ? ($refs - 1) * 0.003 : 0;
$isPro := $contains($model, "5.0 pro");
$isCustom := $contains($sp, "custom");
$sizeKnown := $isCustom ? $px > 0 : ($contains($sp, "1k") or $contains($sp, "2k"));
$proPrice := $isCustom
? ($px < 2610000 ? 0.045 : 0.09)
: ($contains($sp, "1k") ? 0.045 : 0.09);
($isPro and ($sizeKnown = false))
? {
"type": "range_usd",
"min_usd": 0.045 + $extra,
"max_usd": 0.09 + $extra,
"format": { "suffix": "/Image", "approximate": true }
}
: {
"type": "usd",
"usd": $isPro ? $proPrice + $extra
: $contains($model, "5.0 lite") ? 0.035
: $contains($model, "4-5") ? 0.04
: 0.03,
"format": { "suffix": $isPro ? "/Image" : " x images/Run", "approximate": true }
}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
seed: int = 0,
watermark: bool = False,
thinking: bool = True,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
model_id = SEEDREAM_MODELS[model["model"]]
presets = SEEDREAM_PRESETS[model_id]
is_pro = "seedream-5-0-pro" in 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)
prompt_optimization = model.get("prompt_optimization", "standard")
seed = model.get("seed", seed)
watermark = model.get("watermark", watermark)
thinking = model.get("thinking", thinking)
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 is_pro:
if out_num_pixels < 921_600:
raise ValueError(
f"Minimum image resolution for the selected model is 0.92MP, but {mp_provided:.2f}MP provided."
)
if out_num_pixels > 4_194_304:
raise ValueError(
f"Maximum image resolution for the selected model is 4.19MP, but {mp_provided:.2f}MP provided."
)
else:
if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3_686_400:
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 < 921_600:
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."
)
if not thinking and n_input_images > 0:
raise ValueError(
"'thinking' can only be disabled for text-to-image; enable it when using reference images."
)
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",
)
optimize_prompt_options = None
if n_input_images == 0:
optimize_prompt_options = Seedream5OptimizePromptOptions(thinking="enabled" if thinking else "disabled")
elif prompt_optimization == "fast":
optimize_prompt_options = Seedream5OptimizePromptOptions(mode="fast")
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=None if is_pro else sequential_image_generation,
sequential_image_generation_options=None if is_pro else Seedream4Options(max_images=max_images),
watermark=watermark,
optimize_prompt_options=optimize_prompt_options,
),
)
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 ByteDanceSeedreamNodeV2(ByteDanceSeedreamNodeV3):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="ByteDanceSeedreamNodeV2",
display_name="ByteDance Seedream 4.5 & 5.0",
display_name="ByteDance Seedream 4.5 & 5.0 (Legacy)",
category="partner/image/ByteDance",
description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
inputs=[
@@ -896,6 +1164,7 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
is_deprecated=True,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(
widgets=["model", "model.size_preset", "model.width", "model.height"]
@@ -924,117 +1193,6 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
seed: int = 0,
watermark: bool = False,
thinking: bool = True,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
model_id = SEEDREAM_MODELS[model["model"]]
presets = SEEDREAM_PRESETS[model_id]
is_pro = "seedream-5-0-pro" in 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 is_pro:
if out_num_pixels < 921_600:
raise ValueError(
f"Minimum image resolution for the selected model is 0.92MP, but {mp_provided:.2f}MP provided."
)
if out_num_pixels > 4_194_304:
raise ValueError(
f"Maximum image resolution for the selected model is 4.19MP, but {mp_provided:.2f}MP provided."
)
else:
if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3_686_400:
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 < 921_600:
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."
)
if not thinking and n_input_images > 0:
raise ValueError(
"'thinking' can only be disabled for text-to-image; enable it when using reference images."
)
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",
)
optimize_prompt_options = None
if n_input_images == 0:
optimize_prompt_options = Seedream5OptimizePromptOptions(thinking="enabled" if thinking else "disabled")
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=None if is_pro else sequential_image_generation,
sequential_image_generation_options=None if is_pro else Seedream4Options(max_images=max_images),
watermark=watermark,
optimize_prompt_options=optimize_prompt_options,
),
)
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 ByteDanceSeedreamLayerSeparationNode(IO.ComfyNode):
@classmethod
@@ -3512,6 +3670,7 @@ class ByteDanceExtension(ComfyExtension):
ByteDanceImageNode,
ByteDanceSeedreamNode,
ByteDanceSeedreamNodeV2,
ByteDanceSeedreamNodeV3,
ByteDanceSeedreamLayerSeparationNode,
ByteDanceTextToVideoNode,
ByteDanceImageToVideoNode,