jasonandjason.mei
681f6ebc5f
* add gitignore * add video api * fix: export to video * fix * fix * fix * fix * fix * add i2v * fix * fix * add openai * fix: add base url * change default models * fix: add base url * add v1 * fix models * fix models * fix models * fix models * fix models * fix bug * fix * try to fix * use IO.STRING test * image fix * add chat content * add files * fix optional * fix file read * fix file read * change name * add get models * add unused apikey * change model sort * fix IMAGE_LIST * fix: change to image * add model cache * remove json_object * change max_tokens to 65536 * change max_tokens to 6000 --------- Co-authored-by: jason.mei <jason.mei@ucloud.cn>
82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
from typing import Optional
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from pydantic import Field
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from ..utils import BaseRequest, image_to_base64
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from torch import Tensor
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class FluxDev(BaseRequest):
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"""
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Flux-dev text to image model, 12 billion parameter rectified flow transformer
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"""
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API_PATH = "/v1/images/generations"
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guidance_scale: Optional[float] = Field(
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default=2.5, description="The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt when looking for a related image to show you.")
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num_images: Optional[int] = Field(
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default=1, description="The number of images to generate.")
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num_inference_steps: Optional[int] = Field(
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default=28, description="The number of inference steps to perform.")
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prompt: str = Field(...,
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description="The prompt to generate an image from.")
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image: Optional[Tensor] = Field(
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default=None, description="The image for reference.")
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seed: Optional[int] = Field(
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default=-1, description="\n The same seed and the same prompt given to the same version of the model\n will output the same image every time.\n ")
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width: Optional[int] = Field(
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default=1024, description="The width of the generated image.", ge=256, le=1536)
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height: Optional[int] = Field(
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default=1024, description="The height of the generated image.", ge=256, le=1536)
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strength: Optional[float] = Field(
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default=0.8, description="Strength indicates extent to transform the reference image", ge=0, le=1)
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def __init__(
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self,
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prompt: str,
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image: Optional[Tensor] = None,
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guidance_scale: Optional[float] = 2.5,
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num_images: Optional[int] = 1,
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num_inference_steps: Optional[int] = 28,
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seed: Optional[int] = -1,
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width: Optional[int] = 1024,
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height: Optional[int] = 1024,
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strength: Optional[float] = 0.8,
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**kwargs):
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super().__init__(**kwargs)
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self.prompt = prompt
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self.guidance_scale = guidance_scale
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self.num_images = num_images
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self.num_inference_steps = num_inference_steps
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self.seed = seed
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self.width = width
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self.height = height
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self.strength = strength
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self.image = image_to_base64(
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image) if isinstance(image, Tensor) else None
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def build_payload(self) -> dict:
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"""Builds the request payload dictionary."""
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payload = {
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"model": "black-forest-labs/flux.1-dev",
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"prompt": self.prompt,
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"image": self.image,
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"guidance_scale": self.guidance_scale,
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"n": self.num_images,
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"steps": self.num_inference_steps,
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"seed": self.seed,
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"size": f"{self.width}x{self.height}",
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"strength": self.strength,
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"response_format": "url"
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}
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return self._remove_empty_fields(payload)
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# def get_api_path(self):
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# """Gets the API path. Corresponds to api_path in the JSON."""
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# return "/api/v3/images/generations"
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def field_required(self):
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return ["prompt"]
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def field_order(self):
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"""Corresponds to x-order-properties in the JSON request_schema."""
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return ["prompt", "image", "strength", "size", "num_inference_steps", "seed", "guidance_scale", "n", "response_format"]
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