Create app.py

app.py

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+import torch
+from diffusers import StableDiffusionXLPipeline
+import gradio as gr
+import random
+import transformers
+import numpy as np
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import spaces
+
+
+if torch.cuda.is_available():
+    device = "cuda"
+    print("Using GPU")
+else:
+    device = "cpu"
+    print("Using CPU")
+
+
+MAX_SEED = np.iinfo(np.int32).max
+
+
+# Initialize the pipeline and download the sdxl flash model
+pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16)
+pipe.to(device)
+
+# Ensure sampler uses "trailing" timesteps.
+pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+# superprompt-v1
+tokenizer = T5Tokenizer.from_pretrained("roborovski/superprompt-v1")
+model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1", device_map="auto", torch_dtype="auto")
+model.to(device)
+
+# toggle visibility the enhanced prompt output
+def update_visibility(enhance_prompt):
+    return gr.update(visible=enhance_prompt)
+
+
+# Define the image generation function
+@spaces.GPU(duration=60)
+def generate_image(prompt, enhance_prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
+    if seed == 0:
+        seed = random.randint(1, 2**32-1)
+        
+    if enhance_prompt:
+        transformers.set_seed(seed)
+        
+        input_text = f"Expand the following prompt to add more detail: {prompt}"
+        input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
+        
+        outputs = model.generate(
+        input_ids,
+        max_new_tokens=512,
+        repetition_penalty=1.2,
+        do_sample=True,
+        temperature=0.7,
+        top_p=1,
+        top_k=50
+        )
+        prompt = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        height=height,
+        width=width,
+        guidance_scale=guidance_scale,
+        generator=generator,
+        num_images_per_prompt=num_images_per_prompt
+    ).images
+    
+    return output, prompt
+
+
+
+# Create the Gradio interface
+
+examples = [
+    ["A white car racing fast to the moon.", True],
+    ["A woman in a red dress singing on top of a building.", True],
+    ["An astrounat on mars in a futuristic cyborg suit.", True],
+]
+
+css = '''
+.gradio-container{max-width: 1000px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css) as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.HTML(
+            """
+            <h1 style='text-align: center'>
+            SDXL Flash
+            </h1>
+            """
+        )
+            gr.HTML(
+                """
+               Made by <a href='https://linktr.ee/Nick088' target='_blank'>Nick088</a>
+               <br> <a href="https://discord.gg/osai"> <img src="https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge" alt="Discord"> </a>
+                """
+        )
+    with gr.Group():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", info="Describe the image you want", placeholder="A cat...")
+            enhance_prompt = gr.Checkbox(label="Prompt Enhancement with SuperPrompt-v1", value=True)
+            run_button = gr.Button("Run")
+        result = gr.Gallery(label="Generated AI Images", elem_id="gallery")
+        better_prompt = gr.Textbox(label="Enhanced Prompt", info="The output of your enhanced prompt used for the Image Generation", visible=True)
+        enhance_prompt.change(fn=update_visibility, inputs=enhance_prompt, outputs=better_prompt)
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            negative_prompt = gr.Textbox(label="Negative Prompt", info="Describe what you don't want in the image", value="deformed, distorted, disfigured, poorly drawn, bad anatomy, incorrect anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", placeholder="Ugly, bad anatomy...")
+        with gr.Row():
+            num_inference_steps = gr.Slider(label="Number of Inference Steps", info="The number of denoising steps of the image. More denoising steps usually lead to a higher quality image at the cost of slower inference", minimum=1, maximum=50, value=25, step=1)
+            guidance_scale = gr.Slider(label="Guidance Scale", info="Controls how much the image generation process follows the text prompt. Higher values make the image stick more closely to the input text.", minimum=0.0, maximum=10.0, value=7.5, step=0.1)
+        with gr.Row():
+            width = gr.Slider(label="Width", info="Width of the Image", minimum=256, maximum=1344, step=32, value=1024)
+            height = gr.Slider(label="Height", info="Height of the Image", minimum=256, maximum=1344, step=32, value=1024)
+        with gr.Row():
+            seed = gr.Slider(value=42, minimum=0, maximum=MAX_SEED, step=1, label="Seed", info="A starting point to initiate the generation process, put 0 for a random one")
+            num_images_per_prompt = gr.Slider(label="Images Per Prompt", info="Number of Images to generate with the settings",minimum=1, maximum=4, step=1, value=2)
+
+    gr.Examples(
+        examples=examples,
+        inputs=[prompt, enhance_prompt],
+        outputs=[result, better_prompt],
+        fn=generate_image,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            run_button.click,
+        ],
+        fn=generate_image,
+        inputs=[
+            prompt,
+            enhance_prompt,
+            negative_prompt,
+            num_inference_steps,
+            width,
+            height,
+            guidance_scale,
+            seed,
+            num_images_per_prompt,
+        ],
+        outputs=[result, better_prompt],
+    )
+
+demo.queue().launch(share = False)