git commit -am 'Update space' && git push

app.py

@@ -1,146 +1,53 @@
+import matplotlib.pyplot as plt
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+from diffusers import StableDiffusionPipeline
+import matplotlib.pyplot as plt
+import torch
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+model_id1 = "dreamlike-art/dreamlike-diffusion-1.0"
+model_id2 = "stabilityai/stable-diffusion-xl-base-1.0"
+model_id3 = "stabilityai/stable-diffusion-2"
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16, use_safetensors=True)
+pipe = pipe.to("cuda")
+
+def generate_image_interface(prompt, num_inference_steps, height, width):
+    params = {
+        'prompt': prompt,
+        'num_inference_steps': num_inference_steps,
+        'num_images_per_prompt': 2,
+        'height': height,
+        'width': width
+    }
     
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+    img = pipe(**params).images  # Ensure the `pipe` call correctly matches the expected API
     
-    return image
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
+    num_images = len(img)
+    if num_images > 1:
+        fig, ax = plt.subplots(nrows=1, ncols=num_images, figsize=(15, 5))
+        for i in range(num_images):
+            ax[i].imshow(img[i])
+            ax[i].axis('off')
+    else:
+        fig = plt.figure()
+        plt.imshow(img[0])
+        plt.axis('off')
         
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+    plt.tight_layout()
+    plt.show()
+    return fig
+
+# Define the Gradio interface
+inputs = [
+    gr.Textbox(label="Enter your prompt"),
+    gr.Slider(minimum=1, maximum=100, value=50, label="Number of Inference Steps"),
+    gr.Slider(minimum=512, maximum=1024, value=768, label="Height"),
+    gr.Slider(minimum=512, maximum=1024, value=768, label="Width")
+]
+outputs = gr.Plot()
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+demo = gr.Interface(fn=generate_image_interface, inputs=inputs, outputs=outputs)
+demo.launch(share=True)
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
 
-demo.queue().launch()