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Add 8 nodes - 4 BFL, 4 Stability (#117)

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Jedrzej Kosinski 2025-05-03 18:23:27 -05:00 committed by GitHub
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5 changed files with 399 additions and 37 deletions
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18
comfy_api_nodes/apinode_utils.py
View File

@ -524,3 +524,21 @@ def upload_images_to_comfyapi(
if idx_image >= batch_length:
break
return download_urls
def resize_mask_to_image(mask: torch.Tensor, image: torch.Tensor,
upscale_method="nearest-exact", crop="disabled",
allow_gradient=True, add_channel_dim=False):
"""
Resize mask to be the same dimensions as an image, while maintaining proper format for API calls.
"""
_, H, W, _ = image.shape
mask = mask.unsqueeze(-1)
mask = mask.movedim(-1,1)
mask = common_upscale(mask, width=W, height=H, upscale_method=upscale_method, crop=crop)
mask = mask.movedim(1,-1)
if not add_channel_dim:
mask = mask.squeeze(-1)
if not allow_gradient:
mask = (mask > 0.5).float()
return mask

37
comfy_api_nodes/apis/stability_api.py
View File

@ -53,8 +53,8 @@ class StabilityStylePreset(str, Enum):
class Stability_SD3_5_Model(str, Enum):
sd3_5_large = "sd3.5-large"
sd3_5_large_turbo = "sd3.5-large-turbo"
#sd3_5_medium = "sd3.5-medium"
# sd3_5_large_turbo = "sd3.5-large-turbo"
sd3_5_medium = "sd3.5-medium"
class Stability_SD3_5_GenerationMode(str, Enum):
@ -76,6 +76,25 @@ class StabilityStable3_5Request(BaseModel):
strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None)
class StabilityUpscaleConservativeRequest(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
seed: Optional[int] = Field(None)
output_format: Optional[str] = Field(StabilityFormat.png.value)
image: Optional[str] = Field(None)
creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None)
class StabilityUpscaleCreativeRequest(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
seed: Optional[int] = Field(None)
output_format: Optional[str] = Field(StabilityFormat.png.value)
image: Optional[str] = Field(None)
creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None)
style_preset: Optional[str] = Field(None)
class StabilityStableUltraRequest(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
@ -92,3 +111,17 @@ class StabilityStableUltraResponse(BaseModel):
finish_reason: Optional[str] = Field(None)
seed: Optional[int] = Field(None)
class StabilityResultsGetResponse(BaseModel):
image: Optional[str] = Field(None)
finish_reason: Optional[str] = Field(None)
seed: Optional[int] = Field(None)
id: Optional[str] = Field(None)
name: Optional[str] = Field(None)
errors: Optional[list[str]] = Field(None)
status: Optional[str] = Field(None)
result: Optional[str] = Field(None)
class StabilityAsyncResponse(BaseModel):
id: Optional[str] = Field(None)

52
comfy_api_nodes/nodes_bfl.py
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@ -20,6 +20,7 @@ from comfy_api_nodes.apinode_utils import (
downscale_image_tensor,
validate_aspect_ratio,
process_image_response,
resize_mask_to_image,
)
import numpy as np
@ -589,9 +590,11 @@ class FluxProFillNode(ComfyNodeABC):
auth_token=None,
**kwargs,
):
# prepare mask
mask = resize_mask_to_image(mask, image)
mask = convert_image_to_base64(convert_mask_to_image(mask))
# make sure image will have alpha channel removed
image = convert_image_to_base64(image[:,:,:,:3])
mask = convert_image_to_base64(convert_mask_to_image(mask))
operation = SynchronousOperation(
endpoint=ApiEndpoint(
@ -641,20 +644,22 @@ class FluxProCannyNode(ComfyNodeABC):
},
),
"canny_low_threshold": (
IO.INT,
IO.FLOAT,
{
"default": 0,
"min": 0,
"max": 500,
"default": 0.1,
"min": 0.01,
"max": 0.99,
"step": 0.01,
"tooltip": "Low threshold for Canny edge detection; ignored if skip_processing is True"
},
),
"canny_high_threshold": (
IO.INT,
IO.FLOAT,
{
"default": 0,
"min": 0,
"max": 500,
"default": 0.4,
"min": 0.01,
"max": 0.99,
"step": 0.01,
"tooltip": "High threshold for Canny edge detection; ignored if skip_processing is True"
},
),
@ -712,8 +717,8 @@ class FluxProCannyNode(ComfyNodeABC):
control_image: torch.Tensor,
prompt: str,
prompt_upsampling: bool,
canny_low_threshold: int,
canny_high_threshold: int,
canny_low_threshold: float,
canny_high_threshold: float,
skip_preprocessing: bool,
steps: int,
guidance: float,
@ -724,6 +729,13 @@ class FluxProCannyNode(ComfyNodeABC):
control_image = convert_image_to_base64(control_image[:,:,:,:3])
preprocessed_image = None
# scale canny threshold between 0-500, to match BFL's API
def scale_value(value: float, min_val=0, max_val=500):
return min_val + value * (max_val - min_val)
canny_low_threshold = int(round(scale_value(canny_low_threshold)))
canny_high_threshold = int(round(scale_value(canny_high_threshold)))
if skip_preprocessing:
preprocessed_image = control_image
control_image = None
@ -849,7 +861,7 @@ class FluxProDepthNode(ComfyNodeABC):
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/bfl/flux-pro-1.0-canny/generate",
path="/proxy/bfl/flux-pro-1.0-depth/generate",
method=HttpMethod.POST,
request_model=BFLFluxDepthImageRequest,
response_model=BFLFluxProGenerateResponse,
@ -874,18 +886,18 @@ class FluxProDepthNode(ComfyNodeABC):
NODE_CLASS_MAPPINGS = {
"FluxProUltraImageNode": FluxProUltraImageNode,
# "FluxProImageNode": FluxProImageNode,
# "FluxProExpandNode": FluxProExpandNode,
# "FluxProFillNode": FluxProFillNode,
# "FluxProCannyNode": FluxProCannyNode,
# "FluxProDepthNode": FluxProDepthNode,
"FluxProExpandNode": FluxProExpandNode,
"FluxProFillNode": FluxProFillNode,
"FluxProCannyNode": FluxProCannyNode,
"FluxProDepthNode": FluxProDepthNode,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
# "FluxProImageNode": "Flux 1.1 [pro] Image",
# "FluxProExpandNode": "Flux.1 Expand Image",
# "FluxProFillNode": "Flux.1 Fill Image",
# "FluxProCannyNode": "Flux.1 Canny Control Image",
# "FluxProDepthNode": "Flux.1 Depth Control Image",
"FluxProExpandNode": "Flux.1 Expand Image",
"FluxProFillNode": "Flux.1 Fill Image",
"FluxProCannyNode": "Flux.1 Canny Control Image",
"FluxProDepthNode": "Flux.1 Depth Control Image",
}

10
comfy_api_nodes/nodes_recraft.py
View File

@ -1,6 +1,6 @@
from __future__ import annotations
from inspect import cleandoc
from comfy.utils import ProgressBar, common_upscale
from comfy.utils import ProgressBar
from comfy.comfy_types.node_typing import IO
from comfy_api_nodes.apis.recraft_api import (
RecraftImageGenerationRequest,
@ -25,6 +25,7 @@ from comfy_api_nodes.apinode_utils import (
bytesio_to_image_tensor,
download_url_to_bytesio,
tensor_to_bytesio,
resize_mask_to_image,
)
import folder_paths
import json
@ -654,12 +655,7 @@ class RecraftImageInpaintingNode:
)
# prepare mask tensor
_, H, W, _ = image.shape
mask = mask.unsqueeze(-1)
mask = mask.movedim(-1,1)
mask = common_upscale(mask, width=W, height=H, upscale_method="nearest-exact", crop="disabled")
mask = mask.movedim(1,-1)
mask = (mask > 0.5).float()
mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True)
images = []
total = image.shape[0]

319
comfy_api_nodes/nodes_stability.py
View File

@ -1,6 +1,10 @@
from inspect import cleandoc
from comfy.comfy_types.node_typing import IO
from comfy_api_nodes.apis.stability_api import (
StabilityUpscaleConservativeRequest,
StabilityUpscaleCreativeRequest,
StabilityAsyncResponse,
StabilityResultsGetResponse,
StabilityStable3_5Request,
StabilityStableUltraRequest,
StabilityStableUltraResponse,
@ -13,6 +17,8 @@ from comfy_api_nodes.apis.client import (
ApiEndpoint,
HttpMethod,
SynchronousOperation,
PollingOperation,
EmptyRequest,
)
from comfy_api_nodes.apinode_utils import (
bytesio_to_image_tensor,
@ -22,8 +28,22 @@ from comfy_api_nodes.apinode_utils import (
import torch
import base64
from io import BytesIO
from enum import Enum
class StabilityPollStatus(str, Enum):
finished = "finished"
in_progress = "in_progress"
failed = "failed"
def get_async_dummy_status(x: StabilityResultsGetResponse):
if x.name is not None or x.errors is not None:
return StabilityPollStatus.failed
elif x.finish_reason is not None:
return StabilityPollStatus.finished
return StabilityPollStatus.in_progress
class StabilityStableImageUltraNode:
"""
@ -108,7 +128,7 @@ class StabilityStableImageUltraNode:
# prepare image binary if image present
image_binary = None
if image is not None:
image_binary = tensor_to_bytesio(image, 1504 * 1504).read()
image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
else:
image_denoise = None
@ -174,6 +194,7 @@ class StabilityStableImageSD_3_5Node:
"tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results."
},
),
"model": ([x.value for x in Stability_SD3_5_Model],),
"aspect_ratio": ([x.value for x in StabilityAspectRatio],
{
"default": StabilityAspectRatio.ratio_1_1,
@ -232,16 +253,16 @@ class StabilityStableImageSD_3_5Node:
},
}
def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float,
def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float,
negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None,
auth_token=None):
model = Stability_SD3_5_Model.sd3_5_large.value
# prepare image binary if image present
image_binary = None
mode = Stability_SD3_5_GenerationMode.text_to_image.value
mode = Stability_SD3_5_GenerationMode.text_to_image
if image is not None:
image_binary = tensor_to_bytesio(image, 1504 * 1504).read()
mode = Stability_SD3_5_GenerationMode.image_to_image.value
image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
mode = Stability_SD3_5_GenerationMode.image_to_image
aspect_ratio = None
else:
image_denoise = None
@ -287,15 +308,297 @@ class StabilityStableImageSD_3_5Node:
return (returned_image,)
class StabilityUpscaleConservativeNode:
"""
Upscale image with minimal alterations to 4K resolution.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/stability"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (IO.IMAGE,),
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results."
},
),
"creativity": (
IO.FLOAT,
{
"default": 0.35,
"min": 0.2,
"max": 0.5,
"step": 0.01,
"tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.",
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 4294967294,
"control_after_generate": True,
"tooltip": "The random seed used for creating the noise.",
},
),
},
"optional": {
"negative_prompt": (
IO.STRING,
{
"default": "",
"forceInput": True,
"tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature."
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
},
}
def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None,
auth_token=None):
image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
if not negative_prompt:
negative_prompt = None
files = {
"image": image_binary
}
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/stability/v2beta/stable-image/upscale/conservative",
method=HttpMethod.POST,
request_model=StabilityUpscaleConservativeRequest,
response_model=StabilityStableUltraResponse,
),
request=StabilityUpscaleConservativeRequest(
prompt=prompt,
negative_prompt=negative_prompt,
creativity=round(creativity,2),
seed=seed,
),
files=files,
content_type="multipart/form-data",
auth_token=auth_token,
)
response_api = operation.execute()
if response_api.finish_reason != "SUCCESS":
raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.")
image_data = base64.b64decode(response_api.image)
returned_image = bytesio_to_image_tensor(BytesIO(image_data))
return (returned_image,)
class StabilityUpscaleCreativeNode:
"""
Upscale image with minimal alterations to 4K resolution.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/stability"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (IO.IMAGE,),
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results."
},
),
"creativity": (
IO.FLOAT,
{
"default": 0.3,
"min": 0.1,
"max": 0.5,
"step": 0.01,
"tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.",
},
),
"style_preset": (get_stability_style_presets(),
{
"tooltip": "Optional desired style of generated image.",
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 4294967294,
"control_after_generate": True,
"tooltip": "The random seed used for creating the noise.",
},
),
},
"optional": {
"negative_prompt": (
IO.STRING,
{
"default": "",
"forceInput": True,
"tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature."
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
},
}
def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None,
auth_token=None):
image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
if not negative_prompt:
negative_prompt = None
if style_preset == "None":
style_preset = None
files = {
"image": image_binary
}
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/stability/v2beta/stable-image/upscale/creative",
method=HttpMethod.POST,
request_model=StabilityUpscaleCreativeRequest,
response_model=StabilityAsyncResponse,
),
request=StabilityUpscaleCreativeRequest(
prompt=prompt,
negative_prompt=negative_prompt,
creativity=round(creativity,2),
style_preset=style_preset,
seed=seed,
),
files=files,
content_type="multipart/form-data",
auth_token=auth_token,
)
response_api = operation.execute()
operation = PollingOperation(
poll_endpoint=ApiEndpoint(
path=f"/proxy/stability/v2beta/results/{response_api.id}",
method=HttpMethod.GET,
request_model=EmptyRequest,
response_model=StabilityResultsGetResponse,
),
poll_interval=3,
completed_statuses=[StabilityPollStatus.finished],
failed_statuses=[StabilityPollStatus.failed],
status_extractor=lambda x: get_async_dummy_status(x),
auth_token=auth_token,
)
response_poll: StabilityResultsGetResponse = operation.execute()
if response_poll.finish_reason != "SUCCESS":
raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.")
image_data = base64.b64decode(response_poll.result)
returned_image = bytesio_to_image_tensor(BytesIO(image_data))
return (returned_image,)
class StabilityUpscaleFastNode:
"""
Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/stability"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (IO.IMAGE,),
},
"optional": {
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
},
}
def api_call(self, image: torch.Tensor,
auth_token=None):
image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read()
files = {
"image": image_binary
}
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/stability/v2beta/stable-image/upscale/fast",
method=HttpMethod.POST,
request_model=EmptyRequest,
response_model=StabilityStableUltraResponse,
),
request=EmptyRequest(),
files=files,
content_type="multipart/form-data",
auth_token=auth_token,
)
response_api = operation.execute()
if response_api.finish_reason != "SUCCESS":
raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.")
image_data = base64.b64decode(response_api.image)
returned_image = bytesio_to_image_tensor(BytesIO(image_data))
return (returned_image,)
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"StabilityStableImageUltraNode": StabilityStableImageUltraNode,
# "StabilityStableImageSD_3_5Node": StabilityStableImageSD_3_5Node,
"StabilityStableImageSD_3_5Node": StabilityStableImageSD_3_5Node,
"StabilityUpscaleConservativeNode": StabilityUpscaleConservativeNode,
"StabilityUpscaleCreativeNode": StabilityUpscaleCreativeNode,
"StabilityUpscaleFastNode": StabilityUpscaleFastNode,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"StabilityStableImageUltraNode": "Stability Stable Image Ultra",
# "StabilityStableImageSD_3_5Node": "Stability Stable Diffusion 3.5 Image",
"StabilityStableImageSD_3_5Node": "Stability Stable Diffusion 3.5 Image",
"StabilityUpscaleConservativeNode": "Stability Upscale Conservative",
"StabilityUpscaleCreativeNode": "Stability Upscale Creative",
"StabilityUpscaleFastNode": "Stability Upscale Fast",
}