Create app.py

app.py

@@ -0,0 +1,288 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import os
+import random
+
+import gradio as gr
+import numpy as np
+import PIL.Image
+import spaces
+import torch
+from diffusers import AutoencoderKL, DiffusionPipeline
+
+DESCRIPTION = "# Run any LoRA or SD Model"
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1824"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
+ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1"
+ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+
+@spaces.GPU
+def generate(
+    prompt: str,
+    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,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale_base: float = 5.0,
+    num_inference_steps_base: int = 25,
+    use_vae: bool = False,
+    use_lora: bool = False,
+    model = 'stabilityai/stable-diffusion-xl-base-1.0',
+    vaecall = 'madebyollin/sdxl-vae-fp16-fix',
+    lora = '',
+    lora_scale: float = 0.7,
+) -> PIL.Image.Image:
+    if torch.cuda.is_available():
+
+        if not use_vae:  
+            pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
+            
+        if use_vae:
+            vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
+            pipe = DiffusionPipeline.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
+            
+        if use_lora:
+            pipe.load_lora_weights(lora)
+            pipe.fuse_lora(lora_scale)
+                                   
+        if ENABLE_CPU_OFFLOAD:
+            pipe.enable_model_cpu_offload()
+            
+        else:
+            pipe.to(device)
+
+        if USE_TORCH_COMPILE:
+            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+        
+    generator = torch.Generator().manual_seed(seed)
+
+    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
+
+        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]
+        return image
+
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+]
+
+with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
+    with gr.Group():
+        model = gr.Text(label='Model', placeholder='e.g. stabilityai/stable-diffusion-xl-base-1.0')
+        vaecall = gr.Text(label='VAE', placeholder='e.g. madebyollin/sdxl-vae-fp16-fix')
+        lora = gr.Text(label='LoRA', placeholder='e.g. nerijs/pixel-art-xl')
+        lora_scale = gr.Slider(
+                info="The closer to 1, the more it will resemble LoRA, but errors may be visible.",
+                label="Lora Scale",
+                minimum=0.01,
+                maximum=1,
+                step=0.01,
+                value=0.7,
+            )
+        with gr.Row():
+            prompt = gr.Text(
+                placeholder="Input prompt",
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Image(label="Result", show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            use_vae = gr.Checkbox(label='Use VAE', value=False, visible=ENABLE_USE_VAE)
+            use_lora = gr.Checkbox(label='Use Lora', value=False, visible=ENABLE_USE_LORA)
+            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(
+            placeholder="Input Negative Prompt",
+            label="Negative prompt",
+            max_lines=1,
+            visible=False,
+        )
+        prompt_2 = gr.Text(
+            placeholder="Input Prompt 2",
+            label="Prompt 2",
+            max_lines=1,
+            visible=False,
+        )
+        negative_prompt_2 = gr.Text(
+            placeholder="Input Negative Prompt 2",
+            label="Negative prompt 2",
+            max_lines=1,
+            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,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+            
+        with gr.Row():
+            guidance_scale_base = gr.Slider(
+                info="Scale for classifier-free guidance",
+                label="Guidance scale",
+                minimum=1,
+                maximum=20,
+                step=0.1,
+                value=5.0,
+            )
+            num_inference_steps_base = gr.Slider(
+                info="Number of denoising steps",
+                label="Number of inference steps",
+                minimum=10,
+                maximum=100,
+                step=1,
+                value=25,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=result,
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    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,
+    )
+    use_vae.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_vae,
+        outputs=vaecall,
+        queue=False,
+        api_name=False,
+    )
+    use_lora.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_lora,
+        outputs=lora,
+        queue=False,
+        api_name=False,
+    )
+
+    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=generate,
+        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,
+            num_inference_steps_base,
+            use_vae,
+            use_lora,
+            model,
+            vaecall,
+            lora,
+            lora_scale,
+        ],
+        outputs=result,
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=100).launch()