Update app.py

app.py

@@ -1,146 +1,155 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler
+import spaces
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16
 
-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)
+repo = "stabilityai/stable-diffusion-3-medium-diffusers"
+pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+MAX_IMAGE_SIZE = 1344
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+def infer(prompts, negative_prompts, seeds, randomize_seeds, widths, heights, guidance_scales, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+    images = []
+    for i, prompt in enumerate(prompts):
+        if randomize_seeds[i]:
+            seeds[i] = random.randint(0, MAX_SEED)
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
-    
-    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] 
-    
-    return image
+        generator = torch.Generator().manual_seed(seeds[i])
+
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompts[i],
+            guidance_scale=guidance_scales[i],
+            num_inference_steps=num_inference_steps[i],
+            width=widths[i],
+            height=heights[i],
+            generator=generator
+        ).images[0]
+
+        images.append(image)
+
+    return images, seeds
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    ["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", "A blurry astronaut", 0, True, 512, 512, 7.5, 28],
+    ["An astronaut riding a green horse", "Astronaut on a regular horse", 0, True, 512, 512, 7.5, 28],
+    ["A delicious ceviche cheesecake slice", "A cheesecake that looks boring", 0, True, 512, 512, 7.5, 28],
 ]
 
 css="""
 #col-container {
     margin: 0 auto;
-    max-width: 520px;
+    max-width: 580px;
 }
 """
 
-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}.
+        # Demo [Automated Stable Diffusion 3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
         """)
-        
-        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)
 
-        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(
+        with gr.Row():
+            prompt_group = gr.Group(elem_id="prompt_group")
+            with prompt_group:
+                prompt_input = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                negative_prompt_input = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    placeholder="Enter a negative prompt",
+                )
+                seed_input = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed_input = gr.Checkbox(label="Randomize seed", value=True)
+                width_input = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
-                    step=32,
+                    step=64,
                     value=512,
                 )
-                
-                height = gr.Slider(
+                height_input = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
-                    step=32,
+                    step=64,
                     value=512,
                 )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
+                guidance_scale_input = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,
+                    value=7.5,
                 )
-                
-                num_inference_steps = gr.Slider(
+                num_inference_steps_input = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=50,
                     step=1,
-                    value=2,
+                    value=28,
                 )
-        
+            run_button = gr.Button("Run", scale=0)
+
+        result = gr.Gallery(label="Results", show_label=False, columns=4, rows=1)
+        add_button = gr.Button("Add Prompt")
+
+        with gr.Accordion("Advanced Settings", open=False):
+            pass
+
         gr.Examples(
             examples = examples,
-            inputs = [prompt]
+            inputs = [
+                prompt_input,
+                negative_prompt_input,
+                seed_input,
+                randomize_seed_input,
+                width_input,
+                height_input,
+                guidance_scale_input,
+                num_inference_steps_input
+            ]
         )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+    def add_prompt():
+        prompt_group.duplicate()
+    
+    def clear_prompts():
+        prompt_group.clear()
 
-demo.queue().launch()
+    add_button.click(add_prompt)
+    gr.on(
+        triggers=[run_button.click, prompt_input.submit, negative_prompt_input.submit],
+        fn=infer,
+        inputs=[
+            prompt_input,
+            negative_prompt_input,
+            seed_input,
+            randomize_seed_input,
+            width_input,
+            height_input,
+            guidance_scale_input,
+            num_inference_steps_input
+        ],
+        outputs=[result, seed_input],
+        api_name="infer"
+    )
+    demo.launch()