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https://git.datalinker.icu/comfyanonymous/ComfyUI
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Added initial BFL Flux 1.1 [pro] Ultra node (#11)
This commit is contained in:
Jedrzej Kosinski
Robin Huang
parent
1d24f0a68f
commit
c6d9c77e86
@ -14,6 +14,9 @@ from comfy_api_nodes.apis import (
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MinimaxTaskResultResponse,
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IdeogramGenerateRequest,
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IdeogramGenerateResponse,
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BFLFluxProGenerateRequest,
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BFLFluxProGenerateResponse,
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BFLStatus,
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ImageRequest,
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Model
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)
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@ -29,12 +32,13 @@ import logging
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import json
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import av
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import os
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import time
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import folder_paths
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def downscale_input(image):
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def downscale_input(image, total_pixels=1536*1024):
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samples = image.movedim(-1,1)
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#downscaling input images to roughly the same size as the outputs
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total = int(1536 * 1024)
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total = int(total_pixels)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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if scale_by >= 1:
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return image
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@ -83,6 +87,25 @@ def validate_and_cast_response(response):
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return torch.stack(image_tensors, dim=0)
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def validate_aspect_ratio(aspect_ratio: str, minimum_ratio: float, maximum_ratio: float, minimum_ratio_str: str, maximum_ratio_str: str):
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# get ratio values
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numbers = aspect_ratio.split(':')
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if len(numbers) != 2:
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raise Exception(f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}.")
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try:
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numerator = int(numbers[0])
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denominator = int(numbers[1])
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except ValueError:
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raise Exception(f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}.")
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calculated_ratio = numerator/denominator
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# if not close to minimum and maximum, check bounds
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if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose(calculated_ratio, maximum_ratio):
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if calculated_ratio < minimum_ratio:
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raise Exception(f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio}).")
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elif calculated_ratio > maximum_ratio:
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raise Exception(f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio}).")
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return aspect_ratio
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class OpenAIDalle2(ComfyNodeABC):
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"""
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Generates images synchronously via OpenAI's DALL·E 2 endpoint.
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@ -595,6 +618,156 @@ class IdeogramTextToImage(ComfyNodeABC):
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#def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
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# return ""
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class FluxProUltraImageNode(ComfyNodeABC):
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"""
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Generates images synchronously based on prompt and resolution.
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"""
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MINIMUM_RATIO = 1/4
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MAXIMUM_RATIO = 4/1
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MINIMUM_RATIO_STR = "1:4"
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MAXIMUM_RATIO_STR = "4:1"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"prompt": (IO.STRING, {
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"multiline": True,
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"default": "",
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"tooltip": "Prompt for the image generation",
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}),
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"prompt_upsampling": (IO.BOOLEAN, {
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"default": False,
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"tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result)."
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}),
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"seed": (IO.INT, {
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"control_after_generate": True,
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"tooltip": "The random seed used for creating the noise.",
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}),
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"aspect_ratio": (IO.STRING, {
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"default": "16:9",
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"tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
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}),
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"raw": (IO.BOOLEAN, {
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"default": False,
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"tooltip": "When True, generate less processed, more natural-looking images."
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}),
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},
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"optional": {
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"image_prompt": (IO.IMAGE, ),
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"image_prompt_strength": (IO.FLOAT, {
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"default": 0.1,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Blend between the prompt and the image prompt.",
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}),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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}
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}
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@classmethod
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def VALIDATE_INPUTS(cls, aspect_ratio: str):
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try:
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validate_aspect_ratio(aspect_ratio, minimum_ratio=cls.MINIMUM_RATIO, maximum_ratio=cls.MAXIMUM_RATIO,
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minimum_ratio_str=cls.MINIMUM_RATIO_STR, maximum_ratio_str=cls.MAXIMUM_RATIO_STR)
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except Exception as e:
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return str(e)
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return True
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RETURN_TYPES = (IO.IMAGE,)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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FUNCTION = "api_call"
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API_NODE = True
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CATEGORY = "api node"
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def api_call(self, prompt: str, aspect_ratio: str, prompt_upsampling=False, raw=False, seed=0, image_prompt=None, image_prompt_strength=0.1, auth_token=None, **kwargs):
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/bfl/flux-pro-1.1-ultra/generate",
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method=HttpMethod.POST,
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request_model=BFLFluxProGenerateRequest,
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response_model=BFLFluxProGenerateResponse
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),
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request=BFLFluxProGenerateRequest(
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prompt=prompt,
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prompt_upsampling=prompt_upsampling,
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seed=seed,
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aspect_ratio=validate_aspect_ratio(aspect_ratio, minimum_ratio=self.MINIMUM_RATIO, maximum_ratio=self.MAXIMUM_RATIO,
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minimum_ratio_str=self.MINIMUM_RATIO_STR, maximum_ratio_str=self.MAXIMUM_RATIO_STR),
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raw=raw,
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image_prompt=image_prompt if image_prompt is None else self._convert_image_to_base64(image_prompt),
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image_prompt_strength=None if image_prompt is None else round(image_prompt_strength, 2),
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),
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auth_token=auth_token
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)
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output_image = self._handle_bfl_synchronous_operation(operation)
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return (output_image,)
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def _handle_bfl_synchronous_operation(self, operation: SynchronousOperation, timeout_bfl_calls=360):
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response_api: BFLFluxProGenerateResponse = operation.execute()
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return self._poll_until_generated(response_api.polling_url, timeout=timeout_bfl_calls)
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def _poll_until_generated(self, polling_url: str, timeout=360):
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# used bfl-comfy-nodes to verify code implementation:
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# https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
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start_time = time.time()
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retries_404 = 0
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max_retries_404 = 5
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retry_404_seconds = 2
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retry_202_seconds = 2
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retry_pending_seconds = 1
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request = requests.Request(method=HttpMethod.GET, url=polling_url)
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# NOTE: should True loop be replaced with checking if workflow has been interrupted?
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while True:
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response = requests.Session().send(request.prepare())
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if response.status_code == 200:
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result = response.json()
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if result["status"] == BFLStatus.ready:
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img_url = result["result"]["sample"]
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img_response = requests.get(img_url)
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return self._process_bfl_image_response(img_response)
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elif result["status"] in [BFLStatus.request_moderated, BFLStatus.content_moderated]:
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status = result["status"]
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raise Exception(f"BFL API did not return an image due to: {status}.")
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elif result["status"] == BFLStatus.error:
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raise Exception(f"BFL API encountered an error: {result}.")
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elif result["status"] == BFLStatus.pending:
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time.sleep(retry_pending_seconds)
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continue
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elif response.status_code == 404:
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if retries_404 < max_retries_404:
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retries_404 += 1
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time.sleep(retry_404_seconds)
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continue
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raise Exception(f"BFL API could not find task after {max_retries_404} tries.")
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elif response.status_code == 202:
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time.sleep(retry_202_seconds)
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elif time.time() - start_time > timeout:
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raise Exception(f"BFL API experienced a timeout; could not return request under {timeout} seconds.")
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else:
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raise Exception(f"BFL API encountered an error: {response.json()}")
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def _process_bfl_image_response(self, response: requests.Response):
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image = Image.open(io.BytesIO(response.content)).convert("RGBA")
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image_array = np.array(image).astype(np.float32) / 255.0
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return torch.from_numpy(image_array).unsqueeze(0)
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def _convert_image_to_base64(self, image: torch.Tensor):
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scaled_image = downscale_input(image, total_pixels=2048*2048)
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# remove batch dimension if present
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if len(scaled_image.shape) > 3:
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scaled_image = scaled_image[0]
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image_np = (scaled_image.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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img_byte_arr = io.BytesIO()
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img.save(img_byte_arr, format='PNG')
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return base64.b64encode(img_byte_arr.getvalue()).decode()
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class MinimaxTextToVideoNode:
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"""
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@ -772,6 +945,7 @@ NODE_CLASS_MAPPINGS = {
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"OpenAIDalle3": OpenAIDalle3,
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"OpenAIGPTImage1": OpenAIGPTImage1,
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"IdeogramTextToImage": IdeogramTextToImage,
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"FluxProUltraImageNode": FluxProUltraImageNode,
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"MinimaxTextToVideoNode": MinimaxTextToVideoNode,
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}
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@ -781,5 +955,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"OpenAIDalle3": "OpenAI DALL·E 3",
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"OpenAIGPTImage1": "OpenAI GPT Image 1",
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"IdeogramTextToImage": "Ideogram Text to Image",
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"FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
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"MinimaxTextToVideoNode": "Minimax Text to Video",
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}
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