method name

api/core/controllers/text2image.py

@@ -1,4 +1,4 @@
-from diffusers import DiffusionPipeline as Pipe
+from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, DiffusionPipeline as Pipe
 import torch
 
 class Generador:
@@ -48,4 +48,17 @@ class Generador:
             _response = bytes(str(e), 'utf-8')
         finally:
             return _response
-    
+class Difusor:
+    def using_runway_sd_15(prompt:str)->bytes:
+        try:
+            controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd")
+            _generador = StableDiffusionControlNetPipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16)
+            _generador.to("cuda")
+            _imagen = _generador(prompt).images[0]
+            _response = bytes(Generador.img_to_bytes(image=_imagen))
+        except Exception as e:
+            print(e)
+            _response = bytes(str(e), 'utf-8')
+        finally:
+            return _response

api/main.py

@@ -54,7 +54,7 @@ def get_text2speach(data:dict) -> dict:
 
 
 @api.post("/imagen_a_imagen/", status_code=201, responses = {201: {"content": {"image/png": {}}}} ,response_class=__main.api_response)
-def get_text2img(data:dict, file: bytes = __main.fileupload()):
+def get_img2img(data:dict, file: bytes = __main.fileupload()):
     __response=dict({"request_data":data})
     try:
         if data and 'texto' in data and 'modelo' in data and file is not None: