generate

Dockerfile

@@ -35,7 +35,7 @@ COPY --chown=user . $HOME/app
 
 # Download and uzip truepic-size from Google Drive
 RUN pip install gdown
-RUN gdown --id 1MdXMu8xSuG8WClY3JSgtL3Vy1IWTmWZO
+RUN gdown 1MdXMu8xSuG8WClY3JSgtL3Vy1IWTmWZO
 RUN tar -xf truepic-sign-v0.1.0-ubuntu-latest.tar.gz
 RUN chmod +x truepic-sign
 

app.py

@@ -2,25 +2,36 @@ import gradio as gr
 import torch
 import requests
 from torchvision import transforms
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, EulerDiscreteScheduler
+
 
 model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
 response = requests.get("https://git.io/JJkYN")
 labels = response.text.split("\n")
 
 
-def predict(inp):
-    inp = transforms.ToTensor()(inp).unsqueeze(0)
-    with torch.no_grad():
-        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-        confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-    return confidences
+def generate(inp):
+    torch.cuda.empty_cache()
+    print(f"Is CUDA available: {torch.cuda.is_available()}")
+
+    pipeline = StableDiffusionPipeline.from_pretrained(str(model), torch_dtype=torch.float16)
+    
+    #pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+    #another comment
+    pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
+    
+    pipeline = pipeline.to("cuda")
+    image = pipeline(inp, height=512, width=512).images[0]
+
+    return image
 
 
 def run():
     demo = gr.Interface(
-        fn=predict,
-        inputs=gr.inputs.Image(type="pil"),
-        outputs=gr.outputs.Label(num_top_classes=3),
+        fn=generate,
+        inputs=gr.inputs.Text(label="Prompt"),
+        outputs=gr.outputs.Image(type="pil"),
+        
     )
 
     demo.launch(server_name="0.0.0.0", server_port=7860)