@@ -43,3 +43,4 @@ from ml_model.services.whisper import Whisper
 from ml_model.services.qwen_235B import Qwen_235B
 from ml_model.services.minimaxvideo import Minimaxvideo
 from ml_model.services.kling import Kling
+from ml_model.services.fluxkrea import Fluxkrea
@@ -0,0 +1,89 @@
+import base64
+import time
+from datetime import timedelta
+from decimal import Decimal
+from io import BytesIO
+
+import filetype
+import requests
+from django.core.files import File
+
+from messages.models import Message
+from ml_model.models import (
+    NeuronModel,
+)
+from ml_model.services.base import SimpleService
+from ml_model.tasks import replicate_run
+
+
+class Fluxkrea(SimpleService):
+    """
+    Flux Service
+    contains abstract method make, which makes a generation
+    """
+
+    TOKENS_COST = {
+        'flux-krea-dev': {
+            'input_imgs': Decimal('7.5'),
+        },
+    }
+
+    def calculate_price(self, input_message: Message, version: str) -> Decimal:
+        price_map = self.TOKENS_COST[version]
+        price = price_map['input_imgs']
+        if image_count := input_message.info.get('num_outputs'):
+            price = price * image_count
+        return price.quantize(Decimal('0.1'), rounding='ROUND_UP')
+
+    _CALLBACK_BASE = 'black-forest-labs/'
+
+    @property
+    def neuron_model(self):
+        return NeuronModel.objects.get(title='Flux')
+
+    def save_results(
+        self,
+        prompt: str,
+        images: list,
+        time: timedelta,
+        save: bool = True,
+    ) -> list[Message]:
+        messages: list[Message] = []
+        for image in images:
+            messages.append(
+                Message(
+                    content_object=self.store,
+                    elapsed_time=time,
+                    content=prompt,
+                    file=File(BytesIO(requests.get(image).content), '.png'),
+                )
+            )
+        if save:
+            return Message.objects.bulk_create(messages)
+        return messages
+
+    def make(self, input_message: Message, save: bool = True) -> list[Message]:
+        start_time = time.time()
+        version = input_message.info.get('version')
+        callback_data = dict(
+            {
+                'prompt': self.translate_prompt(input_message.content),
+                **input_message.info,
+            }
+        )
+        if input_message.file:
+            kind = filetype.guess(input_message.file.read(20))
+            mime = kind.mime if kind else 'application/octet-stream'
+            input_message.file.seek(0)
+            image = f'data:{mime};base64,{base64.b64encode(input_message.file.read()).decode("utf-8")}'
+            input_message.file.close()
+            callback_data.update({'image': image})
+        runner = replicate_run(
+            f'{self._CALLBACK_BASE}{callback_data.get("version", "flux-krea-dev")}',
+            callback_data,
+        )
+        images = runner if isinstance(runner, list) else [runner]
+        process_time = timedelta(seconds=(time.time() - start_time))
+        self.handle_invoice(input_message.content_object.model, input_message=input_message, version=version)
+        msgs = self.save_results(input_message.content, images, process_time, save)
+        return msgs