dev

modules/api/api.py

@@ -490,11 +490,16 @@ class Api:
         return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
     def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
+        from modules.env_to_yaml import get_env_var
+        from modules.s3 import s3_client
+        from modules.download_from_s3 import get_photo_base64
+
         task_id = img2imgreq.force_task_id or create_task_id("img2img")
 
-        init_images = img2imgreq.init_images
-        if init_images is None:
+
+        if img2imgreq.init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
+        init_images = [get_photo_base64(s3_url=img2imgreq.init_images)]
 
         mask = img2imgreq.mask
         if mask:
@@ -562,8 +567,6 @@ class Api:
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
-        from modules.s3 import s3_client
-        from modules.env_to_yaml import get_env_var
         import uuid
 
         b64_string = b64images[0].decode("utf-8")

modules/api/models.py

@@ -117,7 +117,7 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     StableDiffusionProcessingImg2Img,
     [
         {"key": "sampler_index", "type": str, "default": "Euler"},
-        {"key": "init_images", "type": list, "default": None},
+        {"key": "init_images", "type": str, "default": None},
         {"key": "denoising_strength", "type": float, "default": 0.75},
         {"key": "mask", "type": str, "default": None},
         {"key": "include_init_images", "type": bool, "default": False, "exclude" : True},

modules/download_from_s3.py

@@ -0,0 +1,22 @@
+import base64
+from modules.s3 import s3_client
+from urllib.parse import urlparse
+
+
+def get_photo_base64(s3_url) -> str:
+    try:
+        s3_url = s3_url
+        parsed = urlparse(s3_url)
+        path_parts = parsed.path.lstrip('/').split('/', 1)
+
+        if len(path_parts) != 2:
+            raise ValueError("Не удалось извлечь bucket и key из URL.")
+
+        bucket_name, s3_key = path_parts
+
+        response = s3_client.get_object(Bucket=bucket_name, Key=s3_key)
+        image_data = response['Body'].read()
+        return base64.b64encode(image_data).decode('utf-8')
+    except Exception as e:
+        print(f"Ошибка при получении файла: {e}")
+        return ""

modules/env_to_yaml.py

@@ -5,4 +5,4 @@ load_dotenv()
 
 def get_env_var(key: str) -> str:
     value = os.getenv(key)
-    return value
+    return value

modules/s3.py

@@ -1,3 +1,4 @@
+
 import boto3
 from botocore.client import Config
 
@@ -11,5 +12,3 @@ s3_client = boto3.client(
     region_name='ru-1',
     config=Config(s3={'addressing_style': 'path'})
 )
-
-

requirements.txt

@@ -13,18 +13,19 @@ gradio==3.41.2
 inflection
 jsonmerge
 kornia
+urllib
 lark
 numpy
 omegaconf
 open-clip-torch
-
+dotenv
 piexif
 protobuf==3.20.0
 psutil
 pytorch_lightning
 requests
 resize-right
-
+botocore
 safetensors
 scikit-image>=0.19
 tomesd