82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
import replicate
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import os
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import requests
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import torch
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import numpy as np
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from PIL import Image
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import io
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class FluxKontextReplicate:
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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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"image": ("IMAGE",),
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"prompt": ("STRING", {"multiline": True, "default": "Make this a 90s cartoon"}),
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"api_key": ("STRING", {"default": ""}),
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"model": (["flux-kontext-dev", "flux-kontext-max", "flux-kontext-pro"], {"default": "flux-kontext-dev"}),
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"aspect_ratio": (["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3", "5:4", "4:5", "21:9", "9:21", "2:1", "1:2", "match_input_image"], {"default": "match_input_image"}),
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"output_format": (["jpg", "png"], {"default": "jpg"}),
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"safety_tolerance": ("INT", {"default": 2, "min": 0, "max": 6, "step": 1}),
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"seed": ("INT", {"default": 69, "min": 1, "max": 2147483646, "step": 1}),
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"prompt_upsampling": ("BOOLEAN", {"default": False})
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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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FUNCTION = "generate_image"
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CATEGORY = "image/edit"
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def generate_image(self, image, prompt, api_key, model, aspect_ratio, output_format, safety_tolerance, seed, prompt_upsampling):
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try:
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os.environ["REPLICATE_API_TOKEN"] = api_key
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# Convert tensor to PIL and save to buffer
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tensor = image.squeeze(0) if len(image.shape) == 4 else image
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if tensor.max() <= 1.0:
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tensor = (tensor * 255).clamp(0, 255).byte()
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pil_image = Image.fromarray(tensor.cpu().numpy(), 'RGB')
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img_buffer = io.BytesIO()
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pil_image.save(img_buffer, format='PNG')
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img_buffer.seek(0)
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# Build input dict with all parameters for both models
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replicate_input = {
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"prompt": prompt,
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"input_image": img_buffer,
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"aspect_ratio": aspect_ratio,
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"output_format": output_format,
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"safety_tolerance": safety_tolerance,
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"seed": seed,
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"prompt_upsampling": prompt_upsampling
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}
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# Run Replicate model with selected model
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output = replicate.run(
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f"black-forest-labs/{model}",
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input=replicate_input
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)
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# Get URL from output
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output_url = output if isinstance(output, str) else (output[0] if isinstance(output, list) and output else str(output))
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# Download and convert back to tensor
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response = requests.get(output_url, timeout=30)
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response.raise_for_status()
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downloaded_image = Image.open(io.BytesIO(response.content))
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if downloaded_image.mode != 'RGB':
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downloaded_image = downloaded_image.convert('RGB')
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np_image = np.array(downloaded_image).astype(np.float32) / 255.0
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output_tensor = torch.from_numpy(np_image).unsqueeze(0)
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return (output_tensor,)
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except Exception as e:
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raise RuntimeError(f"Flux Kontext generation failed: {str(e)}") from e
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NODE_CLASS_MAPPINGS = {"FluxKontextReplicate": FluxKontextReplicate}
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NODE_DISPLAY_NAME_MAPPINGS = {"FluxKontextReplicate": "Flux Kontext (Replicate)"} |