252 lines
8.0 KiB
Python
252 lines
8.0 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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from io import BytesIO
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import torch
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class DrawThingsTxt2Img:
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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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"model": ("STRING", {"default": "flux_1_dev_q8p.ckpt"}),
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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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"sampler": (
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[
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"UniPC",
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"DPM++ 2M Karras",
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"Euler Ancestral",
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"DPM++ SDE Karras",
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"PLMS",
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"DDIM",
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"LCM",
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"Euler A Substep",
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"DPM++ SDE Substep",
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"TCD",
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"DPM++ 2M Trailing",
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"Euler A Trailing",
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"DPM++ SDE Trailing",
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"DDIM Trailing",
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"DPM++ 2M AYS",
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"Euler A AYS",
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"DPM++ SDE AYS",
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],
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{"default": "Euler A Trailing"},
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),
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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(
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self, model, prompt, seed, width, height, guidance_scale, sampler, steps
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):
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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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"model": model,
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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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"sampler": sampler,
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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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def image_to_base64(image_tensor):
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# Convert the image tensor to a NumPy array and scale it to the range 0-255
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i = 255.0 * image_tensor.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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# Save the image to a BytesIO object (in memory) rather than to a file
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buffered = BytesIO()
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img.save(buffered, format="PNG")
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# Encode the image as base64
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encoded_string = base64.b64encode(buffered.getvalue()).decode("utf-8")
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return encoded_string
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def resize_for_inpainting(pixels, mask=None):
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x = (pixels.shape[1] // 64) * 64
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y = (pixels.shape[2] // 64) * 64
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# mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
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orig_pixels = pixels
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pixels = orig_pixels.clone()
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 64) // 2
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y_offset = (pixels.shape[2] % 64) // 2
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pixels = pixels[:, x_offset : x + x_offset, y_offset : y + y_offset, :]
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# pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset]
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# mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
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# m = (1.0 - mask.round()).squeeze(1)
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# for i in range(3):
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# pixels[:,:,:,i] -= 0.5
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# pixels[:,:,:,i] *= m
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# pixels[:,:,:,i] += 0.5
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return pixels
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def get_image_size(pixels):
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"""
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Get image size from a size image, i.e. assumed input size is [H, W, C]
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"""
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x = (pixels.shape[0] // 64) * 64
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y = (pixels.shape[1] // 64) * 64
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return x, y
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class DrawThingsImg2Img:
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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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"images": ("IMAGE", {"tooltip": "input image"}),
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"model": ("STRING", {"default": "flux_1_dev_q8p.ckpt"}),
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"prompt": ("STRING", {"default": ""}),
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"seed": ("INT", {"default": 42}),
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"guidance_scale": (
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"FLOAT",
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{"default": 3.5, "min": 0, "max": 25, "step": 0.1},
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),
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"sampler": (
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[
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"UniPC",
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"DPM++ 2M Karras",
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"Euler Ancestral",
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"DPM++ SDE Karras",
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"PLMS",
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"DDIM",
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"LCM",
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"Euler A Substep",
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"DPM++ SDE Substep",
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"TCD",
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"DPM++ 2M Trailing",
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"Euler A Trailing",
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"DPM++ SDE Trailing",
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"DDIM Trailing",
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"DPM++ 2M AYS",
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"Euler A AYS",
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"DPM++ SDE AYS",
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],
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{"default": "Euler A Trailing"},
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),
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"steps": ("INT", {"default": 20, "min": 1, "max": 150, "step": 1}),
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"denoise": (
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"FLOAT",
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{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01},
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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 = ("generated_image",)
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FUNCTION = "generate_image"
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def generate_image(
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self, images, model, prompt, seed, guidance_scale, sampler, steps, denoise
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):
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# Call the Draw Things API
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api_url = "http://127.0.0.1:7860/sdapi/v1/img2img"
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encoded_images = []
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images_resized = resize_for_inpainting(images)
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for image_tensor in images_resized:
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encoded_images.append(image_to_base64(image_tensor))
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height, width = get_image_size(images_resized[0])
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payload = {
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"model": model,
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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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"sampler": sampler,
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"steps": steps,
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"init_images": encoded_images,
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"strength": denoise,
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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 = {
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"DrawThingsTxt2Img": DrawThingsTxt2Img,
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"DrawThingsImg2Img": DrawThingsImg2Img,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DrawThingsTxt2Img": "Draw Things Txt2Img",
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"DrawThingsImg2Img": "Draw Things Img2Img",
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}
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