Changes

app.py

@@ -15,11 +15,11 @@ from huggingface_hub import login
 login(token='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')
 
 
-# pipe = StableDiffusionPipeline.from_pretrained("Divyanshu04/Finetuned-model", safety_checker=None, torch_dtype=torch.float16).to("cpu")
+pipe = StableDiffusionPipeline.from_pretrained("Divyanshu04/Finetuned-model", safety_checker=None, torch_dtype=torch.float16).to("cpu")
 
-# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 # pipe.enable_xformers_memory_efficient_attention()  #if gpu is available
-# g_cuda = None
+g_cuda = None
          
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[0]  # YOLOv5 root directory
@@ -29,16 +29,6 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))
 
 app = Flask(__name__)
 
-import requests
-
-API_URL = "https://api-inference.huggingface.co/models/Divyanshu04/Finetuned-model"
-headers = {"Authorization": "Bearer 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"}
-
-def query(payload):
-	response = requests.post(API_URL, headers=headers, json=payload)
-	return response.content
-
-  
 
 # @app.route("/", methods=["POST"])
 def generate():
@@ -52,37 +42,30 @@ def generate():
 
     if Submit:
 
-        image_bytes = query({"inputs": prompt,})
-
-        img_io = io.BytesIO(image_bytes)
-
-        image = Image.open(img_io)
-        img_io.seek(0)
-        byteImg = img_io.read()
-
-        # guidance_scale = 7.5
-        # num_inference_steps = 24
-        # height = 512
-        # width = 512
-
-        # g_cuda = torch.Generator(device='cpu')
-        # seed = 52362
-        # g_cuda.manual_seed(seed)
-
-
-        # with autocast("cpu"), torch.inference_mode():
-        #     images = pipe(
-        #         prompt,
-        #         height=height,
-        #         width=width,
-        #         negative_prompt=negative_prompt,
-        #         num_images_per_prompt=num_samples,
-        #         num_inference_steps=num_inference_steps,
-        #         guidance_scale=guidance_scale,
-        #         generator=g_cuda
-        #     ).images
+  
+        guidance_scale = 7.5
+        num_inference_steps = 24
+        height = 512
+        width = 512
+
+        g_cuda = torch.Generator(device='cpu')
+        seed = 52362
+        g_cuda.manual_seed(seed)
+
+
+        with autocast("cpu"), torch.inference_mode():
+            images = pipe(
+                prompt,
+                height=height,
+                width=width,
+                negative_prompt=negative_prompt,
+                num_images_per_prompt=num_samples,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                generator=g_cuda
+            ).images
         
-        st.image(byteImg)
+        st.image(images)
     
     else:
         st.write('<Enter parameters to generate image>')