113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
from .modelverse_api.utils import imageurl2tensor
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from .modelverse_api.client import ModelverseClient
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from .modelverse_api.requests.step1x_edit import Step1xEdit
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import torch
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import asyncio
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class Step1xEditNode:
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"""
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Step1X Edit Node
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This node uses Modelverse's Step1X Edit API to transform photos with simple instructions.
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"""
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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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"client": ("MODELVERSE_API_CLIENT",),
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"prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "The prompt to guide the image edit."}),
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"negative_prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "The negative prompt to use."}),
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"image": ("IMAGE", {"default": None, "tooltip": "The image to be edited."}),
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"num_requests": ("INT", {
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"default": 1,
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"min": 1,
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"max": 10,
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"step": 1,
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"display": "number",
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"tooltip": "Number of request to make (1 to 10)"
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}),
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"seed": ("INT", {
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"default": -1,
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"min": -1,
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"max": 0xffffffffffffffff,
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"control_after_generate": True,
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"tooltip": "Random seed for reproducible results. -1 for random seed"
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}),
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"num_inference_steps": ("INT", {
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"default": 30,
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"min": 1,
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"max": 50,
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"step": 1,
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"display": "number",
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"tooltip": "Number of inference steps (1 to 50)"
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}),
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"guidance_scale": ("FLOAT", {
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"default": 4.0,
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"min": 0.0,
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"max": 20.0,
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"step": 0.1,
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"display": "number",
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"tooltip": "The CFG scale (0.0 to 20.0)"
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}),
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},
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}
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image", )
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CATEGORY = "UCLOUD_MODELVERSE"
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FUNCTION = "execute"
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async def execute(self,
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client,
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prompt,
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image,
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negative_prompt,
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num_requests=1,
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seed=-1,
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num_inference_steps=30,
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guidance_scale=4.0):
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if prompt is None or prompt == "":
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raise ValueError("Prompt is required")
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if image is None:
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raise ValueError("Input image is required")
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print("INFO:", "Running Step1X-Edit.")
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client = ModelverseClient(client["api_key"])
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tasks = [client.async_send_request(Step1xEdit(
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prompt=prompt,
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image=image,
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negative_prompt=negative_prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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seed=seed+i,
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))
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for i in range(num_requests)]
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image_urls = await client.run_tasks(tasks)
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output_images_list = []
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for image_url in image_urls:
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if not image_url:
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print(
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"WARN:", "No image URLs in the generated result in current request. Skipping...")
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output_images = imageurl2tensor(image_url)
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output_images_list.append(output_images)
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print(
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"INFO:", f"{len(output_images_list)}/{num_requests} request made successfully.")
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return (torch.cat(output_images_list, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"Modelverse Step1xEditNode": Step1xEditNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Modelverse Step1xEditNode": "Modelverse Step1X Edit"
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}
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