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Dan Bochman
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@@ -0,0 +1,3 @@
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from .fashn_viton import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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+177
@@ -0,0 +1,177 @@
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import base64
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import os
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import time
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from io import BytesIO
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import numpy as np
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import requests
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import torch
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from comfy.utils import ProgressBar
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from PIL import Image
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class FASHN:
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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_image": ("IMAGE",),
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"garment_image": ("IMAGE",),
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"category": (["tops", "bottoms", "one-pieces"],),
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},
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"optional": {
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"flat_lay": ("BOOLEAN", {"default": False}),
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"nsfw_filter": ("BOOLEAN", {"default": True}),
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"cover_feet": ("BOOLEAN", {"default": False}),
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"adjust_hands": ("BOOLEAN", {"default": False}),
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"restore_background": ("BOOLEAN", {"default": False}),
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"restore_clothes": ("BOOLEAN", {"default": False}),
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"guidance_scale": ("FLOAT", {"default": 2.5, "min": 1.5, "max": 5.0, "step": 0.1}),
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"timesteps": ("INT", {"default": 50, "min": 20, "max": 50, "step": 1}),
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"seed": ("INT", {"default": 42}),
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"num_samples": ("INT", {"default": 1, "min": 1, "max": 4, "step": 1}),
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"fashn_api_key": ("STRING", {"multiline": False}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "fashn_tryon"
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CATEGORY = "FASHN AI"
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@staticmethod
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def encode_img_to_base64(img):
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buffered = BytesIO()
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img.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return f"data:image/jpeg;base64,{img_str}"
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@staticmethod
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def maybe_resize_image(img: Image.Image, target_width: int = 384, target_height: int = 576):
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if img.width > target_width or img.height > target_height:
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img.thumbnail((target_width, target_height), resample=Image.Resampling.LANCZOS)
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return img
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@staticmethod
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def loadimage_to_pil(img_tensor_bhwc: torch.Tensor):
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img_np = img_tensor_bhwc.squeeze(0).numpy()
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return Image.fromarray((img_np * 255).astype(np.uint8))
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@staticmethod
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def pil_load_image_from_http(session, url: str) -> Image.Image:
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response = session.get(url, stream=True)
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response.raise_for_status()
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# Check if the content type is an image
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content_type = response.headers.get("Content-Type", "")
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if not content_type.startswith("image/"):
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raise ValueError(f"The URL's Content-Type is not an image. Content-Type: {content_type}")
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# Use BytesIO to create an in-memory binary stream
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img_bytes = BytesIO(response.content)
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with Image.open(img_bytes) as img:
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img.load() # Load the image here to ensure it can be opened
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return img.copy() # Return a copy to ensure the file pointer doesn't affect the image
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@staticmethod
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def pil_to_torch_hwc(img: Image.Image):
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img = np.array(img)
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img = torch.from_numpy(img).to(dtype=torch.float32) / 255.0
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return img
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def fashn_tryon(
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self,
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model_image,
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garment_image,
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category,
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flat_lay,
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nsfw_filter,
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cover_feet,
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adjust_hands,
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restore_background,
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restore_clothes,
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guidance_scale,
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timesteps,
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seed,
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num_samples,
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fashn_api_key=None,
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):
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# Environment variables
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ENDPOINT_URL = os.getenv("FASHN_ENDPOINT_URL", "https://api.fashn.ai/v1/")
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API_KEY = fashn_api_key or os.getenv("FASHN_API_KEY")
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if not API_KEY:
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raise ValueError("FASHN_API_KEY must be set in environment variables or provided as fashn_api_key.")
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# Progress bar
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pbar = ProgressBar(total=7 + num_samples)
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# Preprocess images
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model_image, garment_image = map(self.loadimage_to_pil, [model_image, garment_image])
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model_image, garment_image = map(self.maybe_resize_image, [model_image, garment_image])
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model_image, garment_image = map(self.encode_img_to_base64, [model_image, garment_image])
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pbar.update(1)
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# Prepare API request
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {API_KEY}",
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}
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inputs = {
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"model_image": model_image,
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"garment_image": garment_image,
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"category": category,
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"flat_lay": flat_lay,
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"nsfw_filter": nsfw_filter,
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"cover_feet": cover_feet,
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"adjust_hands": adjust_hands,
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"restore_background": restore_background,
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"restore_clothes": restore_clothes,
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"guidance_scale": guidance_scale,
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"timesteps": timesteps,
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"seed": seed,
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"num_samples": num_samples,
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}
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# Make API request
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session = requests.Session()
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response = session.post(f"{ENDPOINT_URL}/run", headers=headers, json=inputs, timeout=10)
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response.raise_for_status()
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pred_id = response.json().get("id")
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pbar.update(1)
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# Poll the status of the prediction
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start_time = time.time()
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while True:
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if time.time() - start_time > 180: # 3 minutes timeout
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raise Exception("Maximum polling time exceeded.")
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status_response = session.get(f"{ENDPOINT_URL}/status/{pred_id}", headers=headers, timeout=10)
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status_response.raise_for_status()
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status_data = status_response.json()
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if status_data["status"] == "completed":
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break
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elif status_data["status"] not in ["starting", "in_queue", "processing"]:
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raise Exception(f"Prediction failed: {status_data.get('error')}")
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pbar.update(1)
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time.sleep(3)
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# Get the result images
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pbar.update(1)
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urls = status_data["output"]
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result_imgs = []
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for output_url in urls:
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pil_img = self.pil_load_image_from_http(session, output_url)
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result_imgs.append(self.pil_to_torch_hwc(pil_img))
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session.close()
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result_tensor = torch.stack(result_imgs, dim=0)
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return (result_tensor,)
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NODE_CLASS_MAPPINGS = {"FASHN": FASHN}
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NODE_DISPLAY_NAME_MAPPINGS = {"FASHN": "FASHN Virtual Try-On"}
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