Update app.py

app.py

@@ -1,41 +1,126 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
+from diffusers import AutoencoderKL, DiffusionPipeline
 import torch
+from __future__ import annotations
+import os
+import PIL.Image 
+import spaces
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+MARKDOWN = """
+The demo is based on <a href="https://huggingface.co/dataautogpt3/OpenDalleV1.1">OpenDalle V1.1</a> by @dataautogpt3
+The demo is based on the fusion of different models to provide better performance, comparatively. 
 
-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)
+You can try out the prompts and check for yourself. 
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+**Parts of codes are adopted from [@hysts's SD-XL demo](https://huggingface.co/spaces/hysts/SD-XL) running on A10G GPU **
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+You can check out more of my spaces. Demo by [Sunder Ali Khowaja](https://sander-ali.github.io) - [Github](https://github.com/sander-ali)
+"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<h1>Running on CPU 🥶 This demo does not work on CPU. </h1>"
 
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
+ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0") == "1"
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+    return seed
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+    pipe = DiffusionPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe.enable_xformers_memory_efficient_attention()
+    if ENABLE_REFINER:
+        refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    if ENABLE_CPU_OFFLOAD:
+        pipe.enable_model_cpu_offload()
+        if ENABLE_REFINER:
+            refiner.enable_model_cpu_offload()
+    else:
+        pipe.to(device)
+        if ENABLE_REFINER:
+            refiner.to(device)
+    if USE_TORCH_COMPILE:
+        pipe.unet = torch.compile(pipe.unet, mode='reduce-overhead', fullgraph=True)
+        if ENABLE_REFINER:
+            refiner.unet = torch.compile(refiner.unet, mode="reduce_overhead", fullgraph=True)
+
+@spaces.GPU
+@spaces.GPU
+def infer(
+    prompt: str,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale_base: float = 5.0,
+    guidance_scale_refiner: float = 5.0,
+    num_inference_steps_base: int = 25,
+    num_inference_steps_refiner: int = 25,
+    apply_refiner: bool = False,
+    negative_prompt: str = "",
+    prompt_2: str = "",
+    negative_prompt_2: str = "",
+    use_negative_prompt: bool = False,
+    use_prompt_2: bool = False,
+    use_negative_prompt_2: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+) -> PIL.Image.Image:
+    print(f"** Generating image for: \"{prompt}\" **")
     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
+
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    if not use_prompt_2:
+        prompt_2 = None  # type: ignore
+    if not use_negative_prompt_2:
+        negative_prompt_2 = None  # type: ignore
+
+    if not apply_refiner:
+        return pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_2=prompt_2,
+            negative_prompt_2=negative_prompt_2,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale_base,
+            num_inference_steps=num_inference_steps_base,
+            generator=generator,
+            output_type="pil",
+        ).images[0]
+    else:
+        latents = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_2=prompt_2,
+            negative_prompt_2=negative_prompt_2,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale_base,
+            num_inference_steps=num_inference_steps_base,
+            generator=generator,
+            output_type="latent",
+        ).images
+        image = refiner(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            prompt_2=prompt_2,
+            negative_prompt_2=negative_prompt_2,
+            guidance_scale=guidance_scale_refiner,
+            num_inference_steps=num_inference_steps_refiner,
+            image=latents,
+            generator=generator,
+        ).images[0]
+        return image
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -50,97 +135,233 @@ css="""
 }
 """
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
+# if torch.cuda.is_available():
+#     power_device = "GPU"
+# else:
+#     power_device = "CPU"
+theme = gr.themes.Glass(
+    primary_hue="blue",
+    secondary_hue="blue",
+    neutral_hue="gray",
+    text_size="md",
+    spacing_size="md",
+    radius_size="md",
+    font=[gr.themes.GoogleFont('Source Sans Pro'), 'ui-sans-serif', 'system-ui', 'sans-serif'],
+).set(
+    body_background_fill_dark='*background_fill_primary',
+    background_fill_primary_dark='*neutral_950',
+    background_fill_secondary='*neutral_50',
+    background_fill_secondary_dark='*neutral_900',
+    border_color_primary_dark='*neutral_700',
+    block_background_fill='*background_fill_primary',
+    block_background_fill_dark='*neutral_800',
+    block_border_width='1px',
+    block_label_background_fill='*background_fill_primary',
+    block_label_background_fill_dark='*background_fill_secondary',
+    block_label_text_color='*neutral_500',
+    block_label_text_size='*text_sm',
+    block_label_text_weight='400',
+    block_shadow='none',
+    block_shadow_dark='none',
+    block_title_text_color='*neutral_500',
+    block_title_text_weight='400',
+    panel_border_width='0',
+    panel_border_width_dark='0',
+    checkbox_background_color_dark='*neutral_800',
+    checkbox_border_width='*input_border_width',
+    checkbox_label_border_width='*input_border_width',
+    input_background_fill='*neutral_100',
+    input_background_fill_dark='*neutral_700',
+    input_border_color_focus_dark='*neutral_700',
+    input_border_width='0px',
+    input_border_width_dark='0px',
+    slider_color='#2563eb',
+    slider_color_dark='#2563eb',
+    table_even_background_fill_dark='*neutral_950',
+    table_odd_background_fill_dark='*neutral_900',
+    button_border_width='*input_border_width',
+    button_shadow_active='none',
+    button_primary_background_fill='*primary_200',
+    button_primary_background_fill_dark='*primary_700',
+    button_primary_background_fill_hover='*button_primary_background_fill',
+    button_primary_background_fill_hover_dark='*button_primary_background_fill',
+    button_secondary_background_fill='*neutral_200',
+    button_secondary_background_fill_dark='*neutral_600',
+    button_secondary_background_fill_hover='*button_secondary_background_fill',
+    button_secondary_background_fill_hover_dark='*button_secondary_background_fill',
+    button_cancel_background_fill='*button_secondary_background_fill',
+    button_cancel_background_fill_dark='*button_secondary_background_fill',
+    button_cancel_background_fill_hover='*button_cancel_background_fill',
+    button_cancel_background_fill_hover_dark='*button_cancel_background_fill'
+)
 
-with gr.Blocks(css=css) as demo:
+
+with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
     
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
+        gr.Markdown(MARKDOWN)
+        gr.DuplicateButton()
         
+        with gr.Group():
+        prompt = gr.Text(
+            label="Prompt",
+            show_label=False,
+            max_lines=1,
+            container=False,
+            placeholder="Enter your prompt",
+        )
+        run_button = gr.Button("Generate")
+    result = gr.Image(label="Result", show_label=False)
+    with gr.Accordion("Advanced options", open=False):
         with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
+            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            visible=False,
+        )
+        prompt_2 = gr.Text(
+            label="Prompt 2",
+            max_lines=1,
+            placeholder="Enter your prompt",
+            visible=False,
+        )
+        negative_prompt_2 = gr.Text(
+            label="Negative prompt 2",
+            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=1024,
             )
-            
-            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,
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
             )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
+        apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
+        with gr.Row():
+            guidance_scale_base = gr.Slider(
+                label="Guidance scale for base",
+                minimum=1,
+                maximum=20,
+                step=0.1,
+                value=5.0,
+            )
+            num_inference_steps_base = gr.Slider(
+                label="Number of inference steps for base",
+                minimum=10,
+                maximum=100,
                 step=1,
-                value=0,
+                value=25,
             )
-            
-            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]
-        )
+        with gr.Row(visible=False) as refiner_params:
+            guidance_scale_refiner = gr.Slider(
+                label="Guidance scale for refiner",
+                minimum=1,
+                maximum=20,
+                step=0.1,
+                value=5.0,
+            )
+            num_inference_steps_refiner = gr.Slider(
+                label="Number of inference steps for refiner",
+                minimum=10,
+                maximum=100,
+                step=1,
+                value=25,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=result,
+        fn=infer,
+        cache_examples=CACHE_EXAMPLES,
+    )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        queue=False,
+        api_name=False,
+    )
+    use_prompt_2.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_prompt_2,
+        outputs=prompt_2,
+        queue=False,
+        api_name=False,
+    )
+    use_negative_prompt_2.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt_2,
+        outputs=negative_prompt_2,
+        queue=False,
+        api_name=False,
+    )
+    apply_refiner.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=apply_refiner,
+        outputs=refiner_params,
+        queue=False,
+        api_name=False,
     )
 
-demo.queue().launch()
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            prompt_2.submit,
+            negative_prompt_2.submit,
+            run_button.click,
+        ],
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            prompt_2,
+            negative_prompt_2,
+            use_negative_prompt,
+            use_prompt_2,
+            use_negative_prompt_2,
+            seed,
+            width,
+            height,
+            guidance_scale_base,
+            guidance_scale_refiner,
+            num_inference_steps_base,
+            num_inference_steps_refiner,
+            apply_refiner,
+        ],
+        outputs=result,
+        api_name="run",
+    )
+if __name__ == "__main__":
+    demo.queue(max_size=20, api_open=False).launch(show_api=False)