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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,
StabilityAspectRatio,
Stability_SD3_5_Model,
Stability_SD3_5_GenerationMode,
get_stability_style_presets,
)
from comfy_api_nodes.apis.client import (
ApiEndpoint,
HttpMethod,
SynchronousOperation,
PollingOperation,
EmptyRequest,
)
from comfy_api_nodes.apinode_utils import (
bytesio_to_image_tensor,
tensor_to_bytesio,
validate_string,
)
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:
"""
Generates images synchronously based on prompt and resolution.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/Stability AI"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" +
"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. " +
"To control the weight of a given word use the format `(word:weight)`," +
"where `word` is the word you'd like to control the weight of and `weight`" +
"is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" +
"would convey a sky that was blue and green, but more green than blue."
},
),
"aspect_ratio": ([x.value for x in StabilityAspectRatio],
{
"default": StabilityAspectRatio.ratio_1_1,
"tooltip": "Aspect ratio of generated 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": {
"image": (IO.IMAGE,),
"negative_prompt": (
IO.STRING,
{
"default": "",
"forceInput": True,
"tooltip": "A blurb of text describing what you do not wish to see in the output image. This is an advanced feature."
},
),
"image_denoise": (
IO.FLOAT,
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int,
negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None,
**kwargs):
validate_string(prompt, strip_whitespace=False)
# prepare image binary if image present
image_binary = None
if image is not None:
image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
else:
image_denoise = None
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/generate/ultra",
method=HttpMethod.POST,
request_model=StabilityStableUltraRequest,
response_model=StabilityStableUltraResponse,
),
request=StabilityStableUltraRequest(
prompt=prompt,
negative_prompt=negative_prompt,
aspect_ratio=aspect_ratio,
seed=seed,
strength=image_denoise,
style_preset=style_preset,
),
files=files,
content_type="multipart/form-data",
auth_kwargs=kwargs,
)
response_api = operation.execute()
if response_api.finish_reason != "SUCCESS":
raise Exception(f"Stable Image Ultra 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 StabilityStableImageSD_3_5Node:
"""
Generates images synchronously based on prompt and resolution.
"""
RETURN_TYPES = (IO.IMAGE,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/image/Stability AI"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"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."
},
),
"model": ([x.value for x in Stability_SD3_5_Model],),
"aspect_ratio": ([x.value for x in StabilityAspectRatio],
{
"default": StabilityAspectRatio.ratio_1_1,
"tooltip": "Aspect ratio of generated image.",
},
),
"style_preset": (get_stability_style_presets(),
{
"tooltip": "Optional desired style of generated image.",
},
),
"cfg_scale": (
IO.FLOAT,
{
"default": 4.0,
"min": 1.0,
"max": 10.0,
"step": 0.1,
"tooltip": "How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)",
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 4294967294,
"control_after_generate": True,
"tooltip": "The random seed used for creating the noise.",
},
),
},
"optional": {
"image": (IO.IMAGE,),
"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."
},
),
"image_denoise": (
IO.FLOAT,
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
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,
**kwargs):
validate_string(prompt, strip_whitespace=False)
# prepare image binary if image present
image_binary = None
mode = Stability_SD3_5_GenerationMode.text_to_image
if image is not None:
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
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/generate/sd3",
method=HttpMethod.POST,
request_model=StabilityStable3_5Request,
response_model=StabilityStableUltraResponse,
),
request=StabilityStable3_5Request(
prompt=prompt,
negative_prompt=negative_prompt,
aspect_ratio=aspect_ratio,
seed=seed,
strength=image_denoise,
style_preset=style_preset,
cfg_scale=cfg_scale,
model=model,
mode=mode,
),
files=files,
content_type="multipart/form-data",
auth_kwargs=kwargs,
)
response_api = operation.execute()
if response_api.finish_reason != "SUCCESS":
raise Exception(f"Stable Diffusion 3.5 Image 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 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 AI"
@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",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None,
**kwargs):
validate_string(prompt, strip_whitespace=False)
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_kwargs=kwargs,
)
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 AI"
@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",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None,
**kwargs):
validate_string(prompt, strip_whitespace=False)
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_kwargs=kwargs,
)
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_kwargs=kwargs,
)
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 AI"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": (IO.IMAGE,),
},
"optional": {
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
"comfy_api_key": "API_KEY_COMFY_ORG",
},
}
def api_call(self, image: torch.Tensor,
**kwargs):
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_kwargs=kwargs,
)
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,
"StabilityUpscaleConservativeNode": StabilityUpscaleConservativeNode,
"StabilityUpscaleCreativeNode": StabilityUpscaleCreativeNode,
"StabilityUpscaleFastNode": StabilityUpscaleFastNode,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"StabilityStableImageUltraNode": "Stability AI Stable Image Ultra",
"StabilityStableImageSD_3_5Node": "Stability AI Stable Diffusion 3.5 Image",
"StabilityUpscaleConservativeNode": "Stability AI Upscale Conservative",
"StabilityUpscaleCreativeNode": "Stability AI Upscale Creative",
"StabilityUpscaleFastNode": "Stability AI Upscale Fast",
}
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