Create app.py

app.py

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+#!/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 = """
+# OpenDalle
+
+## A demo of [OpenDalle](https://huggingface.co/dataautogpt3/OpenDalle) by @dataautogpt3
+
+**This demo is based on [@hysts's SD-XL demo.](https://huggingface.co/spaces/hysts/SD-XL).**
+"""
+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", "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"
+
+device = torch.device("cuda:0" 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/OpenDalle",
+        vae=vae,
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        variant="fp16",
+    )
+    if ENABLE_REFINER:
+        refiner = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-refiner-1.0",
+            vae=vae,
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+            variant="fp16",
+        )
+
+    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)
+
+
+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,
+    guidance_scale_refiner: float = 5.0,
+    num_inference_steps_base: int = 25,
+    num_inference_steps_refiner: int = 25,
+    apply_refiner: bool = False,
+) -> PIL.Image.Image:
+    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
+
+    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 = [
+    "A realistic photograph of an astronaut in a jungle, cold color palette, detailed, 8k",
+    "An astronaut riding a green horse",
+]
+
+theme = gr.themes.Base(
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
+)
+with gr.Blocks(css="footer{display:none !important}", theme=theme) 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():
+        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 options", open=False):
+        with gr.Row():
+            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,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+        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=25,
+            )
+        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=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,
+    )
+    apply_refiner.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=apply_refiner,
+        outputs=refiner_params,
+        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,
+            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).launch()