Update Adv_app.py

Adv_app.py

@@ -0,0 +1,299 @@
+import os
+import random
+import uuid
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import load_dynamic_module
+
+DESCRIPTION = """
+# ImagesXL
+"""
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may work on CPU.</p>"
+
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
+
+MODEL_CHOICES = {
+    "Fluently-XL-v2": "fluently/Fluently-XL-v2",
+    "Stable Diffusion v1-5": "runwayml/stable-diffusion-v1-5",
+    "Stable Diffusion v2-1": "stabilityai/stable-diffusion-2-1",
+}
+
+SCHEDULER_CHOICES = {
+    "Euler Ancestral Discrete": EulerAncestralDiscreteScheduler,
+    "DDIM": DDIMScheduler,
+    "LMS Discrete": LMSDiscreteScheduler,
+    "PNDM": PNDMScheduler,
+}
+
+UPSCALER_CHOICES = {
+    "None": None,
+    "Real-ESRGAN": "stabilityai/real-esrgan",
+    "Latent Diffusion": "stabilityai/latent-diffusion-upscaler",
+}
+
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 512,
+    height: int = 512,
+    guidance_scale: float = 3,
+    num_inference_steps: int = 25,
+    scheduler: str = "Euler Ancestral Discrete",
+    model_name: str = "Fluently-XL-v2",
+    randomize_seed: bool = False,
+    num_images_per_prompt: int = 1,
+    use_lora: bool = False,
+    lora_model_name: str = "",
+    use_upscaler: bool = False,
+    upscaler_model_name: str = "None",
+    progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    if not use_negative_prompt:
+        negative_prompt = ""  # type: ignore
+
+    if torch.cuda.is_available():
+        pipe = StableDiffusionPipeline.from_pretrained(
+            MODEL_CHOICES[model_name],
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+    else:
+        pipe = StableDiffusionPipeline.from_pretrained(
+            MODEL_CHOICES[model_name],
+            torch_dtype=torch.float32,
+            use_safetensors=True,
+        )
+
+    pipe.scheduler = SCHEDULER_CHOICES[scheduler].from_config(pipe.scheduler.config)
+
+    if use_lora and lora_model_name:
+        pipe = load_dynamic_module("diffusers.loaders", "Lora", "lora")(pipe, lora_model_name, device_map="auto")
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=num_images_per_prompt,
+        output_type="pil",
+    ).images
+
+    if use_upscaler and upscaler_model_name:
+        upscaler = StableDiffusionPipeline.from_pretrained(
+            UPSCALER_CHOICES[upscaler_model_name],
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+        )
+        images = [upscaler(image).images[0] for image in images]
+
+    image_paths = [save_image(img) for img in images]
+    print(image_paths)
+    return image_paths, seed
+
+examples = [
+    "neon holography crystal cat",
+    "a cat eating a piece of cheese",
+    "an astronaut riding a horse in space",
+    "a cartoon of a boy playing with a tiger",
+    "a cute robot artist painting on an easel, concept art",
+    "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
+]
+
+css = '''
+.gradio-container{max-width: 800px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+'''
+with gr.Blocks(css=css, theme="pseudolab/huggingface-korea-theme") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=False,
+    )
+
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=2,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                lines=4,
+                max_lines=6,
+                value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
+                placeholder="Enter a negative prompt",
+                visible=True,
+            )
+        with gr.Row():
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+                visible=True
+            )
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=1024,
+                step=8,
+                value=512,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=1024,
+                step=8,
+                value=512,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=20.0,
+                step=0.1,
+                value=6,
+            )
+            num_inference_steps = gr.Slider(
+                label="Inference Steps",
+                minimum=1,
+                maximum=100,
+                step=1,
+                value=25,
+            )
+        with gr.Row():
+            scheduler = gr.Dropdown(
+                label="Scheduler",
+                choices=list(SCHEDULER_CHOICES.keys()),
+                value="Euler Ancestral Discrete",
+            )
+            model_name = gr.Dropdown(
+                label="Model",
+                choices=list(MODEL_CHOICES.keys()),
+                value="Fluently-XL-v2",
+            )
+        with gr.Row():
+            num_images_per_prompt = gr.Slider(
+                label="Images per Prompt",
+                minimum=1,
+                maximum=8,
+                step=1,
+                value=1,
+            )
+        with gr.Row():
+            use_lora = gr.Checkbox(label="Use LoRA Model", value=False)
+            lora_model_name = gr.Text(
+                label="LoRA Model Name",
+                placeholder="Enter a LoRA model name (e.g., 'runwayml/stable-diffusion-v1-5-lora')",
+                visible=False,
+            )
+        with gr.Row():
+            use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
+            upscaler_model_name = gr.Dropdown(
+                label="Upscaler Model",
+                choices=list(UPSCALER_CHOICES.keys()),
+                value="None",
+                visible=False,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate,
+        cache_examples=False,
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
+    use_lora.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_lora,
+        outputs=lora_model_name,
+        api_name=False,
+    )
+
+    use_upscaler.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_upscaler,
+        outputs=upscaler_model_name,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            scheduler,
+            model_name,
+            randomize_seed,
+            num_images_per_prompt,
+            use_lora,
+            lora_model_name,
+            use_upscaler,
+            upscaler_model_name,
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
+    
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(show_api=False, debug=False)