razv23andClaude
728d83611f
Moved all files from comfyui-razv-wavespeed-custom/ subfolder directly to razv-wavespeed/ root directory to fix ComfyUI custom node loading. ComfyUI expects node files to be in the main directory, not in a subfolder. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
63 lines
3.5 KiB
Python
63 lines
3.5 KiB
Python
from typing import Optional, Union, List, Dict, Any
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from pydantic import BaseModel, Field
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from ..utils import BaseRequest, normalization_loras
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class LoraWeightItem(BaseModel):
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path: str = Field(..., description="Path to the LoRA model")
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scale: float = Field(..., description="Scale of the LoRA model (range: 0.0 ~ 4.0)", ge=0.0, le=4.0)
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# link api json(api_doc/data/flux-dev-fill.json)
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class FluxDevFill(BaseRequest):
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"""
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FLUX.1 [dev] Fill is a high-performance endpoint for the FLUX.1 [dev] model that enables rapid transformation of existing images, delivering high-quality style transfers and image modifications with the core FLUX capabilities.
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"""
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image: str = Field(..., description="The URL of the image to generate an image from.")
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mask_image: Optional[str] = Field(default=None, description="The URL of the mask image to generate an image from.")
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prompt: Optional[str] = Field(default=None, description="The prompt to generate an image from.")
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num_inference_steps: Optional[int] = Field(default=28, description="The number of inference steps to perform.")
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seed: Optional[int] = Field(default=-1, description=" The same seed and the same prompt given to the same version of the model will output the same image every time. ")
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guidance_scale: Optional[float] = Field(default=30, 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(default=1, description="The number of images to generate.")
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loras: Optional[List[LoraWeightItem]] = Field(None, description="List of LoRAs to apply (max 3)", max_items=3)
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enable_safety_checker: Optional[bool] = Field(default=True, description="If set to true, the safety checker will be enabled.")
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width: Optional[int] = Field(default=864, description="The width of the generated image.", ge=512, le=1536)
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height: Optional[int] = Field(default=1536, description="The height of the generated image.", ge=512, le=1536)
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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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"image": self.image,
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"mask_image": self.mask_image,
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"prompt": self.prompt,
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"size": f"{self.width}*{self.height}",
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"num_inference_steps": self.num_inference_steps,
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"seed": self.seed,
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"guidance_scale": self.guidance_scale,
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"num_images": self.num_images,
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"loras": [lora.model_dump() for lora in self.loras] if self.loras else [],
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"enable_safety_checker": self.enable_safety_checker,
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}
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if "loras" in payload and hasattr(self, "loras") and self.loras:
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LORA_SCALE_MAX_FROM_JSON = 4.0
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LORA_SCALE_DEFAULT_FROM_JSON = 1.0
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loras_for_normalization = [lora.model_dump() for lora in self.loras]
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payload["loras"] = normalization_loras(loras_for_normalization, LORA_SCALE_MAX_FROM_JSON, LORA_SCALE_DEFAULT_FROM_JSON)
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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/wavespeed-ai/flux-fill-dev"
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def field_required(self):
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return ['image']
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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 ['image', 'mask_image', 'prompt', 'size', 'num_inference_steps', 'seed', 'guidance_scale', 'num_images', 'loras', 'enable_safety_checker']
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