Update app.py

app.py

@@ -11,17 +11,42 @@ openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16)
 pipe = StableDiffusionControlNetPipeline.from_pretrained("helkoo/jelaba_2HR", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16)
 
- #optimizations
+#optimizations
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_xformers_memory_efficient_attention()
-pipe.enable_model_cpu_offload()
-
+#pipe.enable_xformers_memory_efficient_attention()
+#pipe.enable_model_cpu_offload()
+pipe = pipe.to("cuda")
 
 def generate(image,prompt):
     image = openpose(image)
-    print(image)
+    #image = image
+    image = pipe(prompt, image, num_inference_steps=20).images[0]
+    return image
+
+gr.Interface(fn=generate, inputs=["image","text"], outputs="image").launch(share=True, debug=True)
+
+import numpy as np
+import requests
+def generate2(prompt,taille):
+    if taille == "S":
+      image = Image.open(requests.get('https://mode-et-caftan.com/757-large_default/jellaba-salsa-marocaine-femme.jpg', stream=True).raw)
+
+    if taille == "XL":
+      image = Image.open(requests.get('https://i.pinimg.com/236x/03/f1/36/03f136b83bb37c9f17c3764f1b36f9fa--big-is-beautiful-curvy-fashion.jpg', stream=True).raw)
+    
+    if taille == "L":
+      image = Image.open(requests.get('https://mode-et-caftan.com/757-large_default/jellaba-salsa-marocaine-femme.jpg', stream=True).raw)
+
+    # convert image to numpy array
+    image = np.array(image)
+    image = openpose(image)
+    #image = image
     image = pipe(prompt, image, num_inference_steps=20).images[0]
     return image
 
-gr.Interface(fn=generate, inputs=["image","text"], outputs="image").launch(share=True)
+gr.Interface(fn=generate2, inputs=["text",
+                                   gr.Dropdown(
+                                  ["S", "L", "XL"], label="taille", info="choisie la taille"
+        ),
+        ], outputs="image").launch(share=True, debug=True)