74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
#!/usr/bin/env python3
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"""
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Wrapper nodes for calling Draw Things from ComfyUI
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"""
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import base64
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import numpy as np
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import requests
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from PIL import Image
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import io
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import torch
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class DrawThingsWrapper:
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def __init__(self):
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pass
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CATEGORY = "DrawThingsWrapper"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"default": ""}),
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"seed": ("INT", {"default": 42}),
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"width": ("INT", {"default": 512}),
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"height": ("INT", {"default": 512}),
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"guidance_scale": ("FLOAT", {"default": 3.5}),
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"steps": ("INT", {"default": 20}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("generated_image",)
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FUNCTION = "generate_image"
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def generate_image(self, prompt, seed, width, height, guidance_scale, steps):
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# Call the Draw Things API
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api_url = "http://127.0.0.1:7860/sdapi/v1/txt2img"
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payload = {
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"prompt": prompt,
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"seed": seed,
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"width": width,
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"height": height,
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"guidance_scale": guidance_scale,
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"steps": steps,
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}
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response = requests.post(api_url, json=payload)
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# Raise an error if the request failed
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response.raise_for_status()
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# Parse the JSON response
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data = response.json()
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# Process the images (assuming they are base64 encoded or raw binary data)
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images = []
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for img_data in data["images"]:
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image_bytes = base64.b64decode(img_data)
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# Convert the image data to a Pillow Image object
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image = Image.open(io.BytesIO(image_bytes))
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image_np = np.array(image)
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# Convert to float32 tensor and normalize
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tensor_image = torch.from_numpy(image_np.astype(np.float32) / 255.0)
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images.append(tensor_image)
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return (torch.stack(images),)
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NODE_CLASS_MAPPINGS = {"DrawThingsWrapper": DrawThingsWrapper}
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NODE_DISPLAY_NAME_MAPPINGS = {"DrawThingsWrapper": "Draw Things Wrapper"}
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