Update app.py

app.py

@@ -1,12 +1,7 @@
 #!/usr/bin/env python
-
 from __future__ import annotations
-
-import os
-import random
-import glob
+import os, random, glob, re
 from datetime import datetime
-
 import gradio as gr
 import numpy as np
 import PIL.Image
@@ -16,19 +11,12 @@ from diffusers import AutoencoderKL, DiffusionPipeline
 
 DESCRIPTION = """
 # OpenDalle 1.1 with Gallery
-
 **Demo by [mrfakename](https://mrfake.name/) - [Twitter](https://twitter.com/realmrfakename) - [GitHub](https://github.com/fakerybakery/) - [Hugging Face](https://huggingface.co/mrfakename)**
-
 This is a demo of <a href="https://huggingface.co/dataautogpt3/OpenDalleV1.1">OpenDalle V1.1</a> by @dataautogpt3.
-
 It's a merge of several different models and is supposed to provide excellent performance. Try it out!
-
 [Not Working?](https://huggingface.co/spaces/mrfakename/OpenDalleV1.1-GPU-Demo/discussions/4)
-
 **The code for this demo is based on [@hysts's SD-XL demo](https://huggingface.co/spaces/hysts/SD-XL) running on a A10G GPU.**
-
 **NOTE: The model is licensed under a non-commercial license**
-
 Also see [OpenDalle Original Demo](https://huggingface.co/spaces/mrfakename/OpenDalle-GPU-Demo/)
 """
 if not torch.cuda.is_available():
@@ -44,123 +32,48 @@ 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/OpenDalleV1.1",
-        vae=vae,
-        torch_dtype=torch.float16,
-        use_safetensors=True,
-        variant="fp16",
-    )
+    pipe = DiffusionPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", 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",
-        )
-
+        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()
+        if ENABLE_REFINER: refiner.enable_model_cpu_offload()
     else:
         pipe.to(device)
-        if ENABLE_REFINER:
-            refiner.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)
+        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
+    return random.randint(0, MAX_SEED) if randomize_seed else seed
 
 def save_image(image: PIL.Image.Image, prompt: str) -> str:
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"{timestamp}_{prompt[:50]}.png"  # Limit filename length
+    clean_prompt = re.sub(r'[^\w\-_\. ]', '_', prompt)[:50]
+    filename = f"{timestamp}_{clean_prompt}.png"
     image.save(filename)
     return filename
 
 def get_image_gallery():
-    image_files = glob.glob("*.png")
-    image_files.sort(key=os.path.getmtime, reverse=True)
-    return image_files
+    return sorted(glob.glob("*.png"), key=os.path.getmtime, reverse=True)
 
 @spaces.GPU(enable_queue=True)
-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,
-    progress=gr.Progress(track_tqdm=True),
-) -> PIL.Image.Image:
+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, progress=gr.Progress(track_tqdm=True)) -> PIL.Image.Image:
     print(f"** Generating image for: \"{prompt}\" **")
     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 use_negative_prompt: negative_prompt = None
+    if not use_prompt_2: prompt_2 = None
+    if not use_negative_prompt_2: negative_prompt_2 = None
     if not apply_refiner:
-        image = 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]
+        image = 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]
-    
+        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]
     save_image(image, prompt)
     return image, get_image_gallery()
 
-examples = [
-    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} painting of a majestic lighthouse on a rocky coast. Use bold brushstrokes and a vibrant color palette to capture the interplay of light and shadow as the lighthouse beam cuts through a stormy night sky.",
+examples = [f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} painting of a majestic lighthouse on a rocky coast. Use bold brushstrokes and a vibrant color palette to capture the interplay of light and shadow as the lighthouse beam cuts through a stormy night sky.",
     f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} still life featuring a pair of vintage eyeglasses. Focus on the intricate details of the frames and lenses, using a warm color scheme to evoke a sense of nostalgia and wisdom.",
     f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} depiction of a rustic wooden stool in a sunlit artist's studio. Emphasize the texture of the wood and the interplay of light and shadow, using a mix of earthy tones and highlights.",
     f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} scene viewed through an ornate window frame. Contrast the intricate details of the window with a dreamy, soft-focus landscape beyond, using a palette that transitions from cool interior tones to warm exterior hues.",
@@ -169,27 +82,14 @@ examples = [
     f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} interpretation of heaven. Create an ethereal atmosphere with soft, billowing clouds and radiant light, using a palette of celestial blues, golds, and whites.",
     f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} portrayal of an ancient, mystical gate. Combine architectural details with elements of fantasy, using a rich, jewel-toned palette to create an air of mystery and magic.",
     f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} portrait of a curious cat. Focus on capturing the feline's expressive eyes and sleek form, using a mix of bold and subtle colors to bring out the cat's personality.",
-    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} abstract representation of toes in sand. Use textured brushstrokes to convey the feeling of warm sand, with a palette inspired by a sun-drenched beach.",
-]
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} abstract representation of toes in sand. Use textured brushstrokes to convey the feeling of warm sand, with a palette inspired by a sun-drenched beach."]
 
-theme = gr.themes.Base(
-    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
-)
+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",
-    )
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1")
     with gr.Group():
-        prompt = gr.Text(
-            label="Prompt",
-            show_label=False,
-            max_lines=1,
-            container=False,
-            placeholder="Enter your prompt",
-        )
+        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)
     gallery = gr.Gallery(label="Generated Images", show_label=True, columns=4, height="auto")
@@ -198,152 +98,34 @@ with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
             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,
-        )
+        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,
-            )
+            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,
-            )
+            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,
-            )
+            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, gallery],
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
+    gr.Examples(examples=examples, inputs=prompt, outputs=[result, gallery], 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,
-    )
+    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, gallery],
-        api_name="run",
-    )
+    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, gallery], api_name="run")
 
     demo.load(fn=get_image_gallery, outputs=gallery)