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Content-Type: {content_type}") + + # Use BytesIO to create an in-memory binary stream + img_bytes = BytesIO(response.content) + with Image.open(img_bytes) as img: + img.load() # Load the image here to ensure it can be opened + return img.copy() # Return a copy to ensure the file pointer doesn't affect the image + + @staticmethod + def pil_to_torch_hwc(img: Image.Image): + img = np.array(img) + img = torch.from_numpy(img).to(dtype=torch.float32) / 255.0 + return img + + def fashn_tryon( + self, + model_image, + garment_image, + category, + flat_lay, + nsfw_filter, + cover_feet, + adjust_hands, + restore_background, + restore_clothes, + guidance_scale, + timesteps, + seed, + num_samples, + fashn_api_key=None, + ): + # Environment variables + ENDPOINT_URL = os.getenv("FASHN_ENDPOINT_URL", "https://api.fashn.ai/v1/") + API_KEY = fashn_api_key or os.getenv("FASHN_API_KEY") + + if not API_KEY: + raise ValueError("FASHN_API_KEY must be set in environment variables or provided as fashn_api_key.") + + # Progress bar + pbar = ProgressBar(total=7 + num_samples) + + # Preprocess images + model_image, garment_image = map(self.loadimage_to_pil, [model_image, garment_image]) + model_image, garment_image = map(self.maybe_resize_image, [model_image, garment_image]) + model_image, garment_image = map(self.encode_img_to_base64, [model_image, garment_image]) + pbar.update(1) + + # Prepare API request + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {API_KEY}", + } + inputs = { + "model_image": model_image, + "garment_image": garment_image, + "category": category, + "flat_lay": flat_lay, + "nsfw_filter": nsfw_filter, + "cover_feet": cover_feet, + "adjust_hands": adjust_hands, + "restore_background": restore_background, + "restore_clothes": restore_clothes, + "guidance_scale": guidance_scale, + "timesteps": timesteps, + "seed": seed, + "num_samples": num_samples, + } + + # Make API request + session = requests.Session() + response = session.post(f"{ENDPOINT_URL}/run", headers=headers, json=inputs, timeout=10) + response.raise_for_status() + pred_id = response.json().get("id") + pbar.update(1) + + # Poll the status of the prediction + start_time = time.time() + while True: + if time.time() - start_time > 180: # 3 minutes timeout + raise Exception("Maximum polling time exceeded.") + + status_response = session.get(f"{ENDPOINT_URL}/status/{pred_id}", headers=headers, timeout=10) + status_response.raise_for_status() + status_data = status_response.json() + + if status_data["status"] == "completed": + break + elif status_data["status"] not in ["starting", "in_queue", "processing"]: + raise Exception(f"Prediction failed: {status_data.get('error')}") + + pbar.update(1) + time.sleep(3) + + # Get the result images + pbar.update(1) + urls = status_data["output"] + result_imgs = [] + for output_url in urls: + pil_img = self.pil_load_image_from_http(session, output_url) + result_imgs.append(self.pil_to_torch_hwc(pil_img)) + + session.close() + + result_tensor = torch.stack(result_imgs, dim=0) + return (result_tensor,) + + +NODE_CLASS_MAPPINGS = {"FASHN": FASHN} + +NODE_DISPLAY_NAME_MAPPINGS = {"FASHN": "FASHN Virtual Try-On"}