Update app.py

app.py

@@ -1,205 +1,184 @@
-
-import os
-import random
-import uuid
-import json
-
 import gradio as gr
-import numpy as np
-from PIL import Image
-import spaces
 import torch
-from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
-
-# Use environment variables for flexibility
-MODEL_ID = os.getenv("MODEL_ID", "sd-community/sdxl-flash")
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
-BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))  # Allow generating multiple images at once
-
-# Determine device and load model outside of function for efficiency
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-pipe = StableDiffusionXLPipeline.from_single_file(
-    "https://huggingface.co/kadirnar/Black-Hole/blob/main/tachyon.safetensors",
-    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    use_safetensors=True,
-    add_watermarker=False,
-).to(device)
-pipe.scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True).from_config(pipe.scheduler.config)
-
-# Torch compile for potential speedup (experimental)
-if USE_TORCH_COMPILE:
-    pipe.compile()
-
-# CPU offloading for larger RAM capacity (experimental)
-if ENABLE_CPU_OFFLOAD:
-    pipe.enable_model_cpu_offload()
-
-MAX_SEED = np.iinfo(np.int32).max
-
-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 = "",
-    use_negative_prompt: bool = False,
-    seed: int = 1,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    num_inference_steps: int = 30,
-    randomize_seed: bool = False,
-    use_resolution_binning: bool = True, 
-    num_images: int = 1,  # Number of images to generate
-    progress=gr.Progress(track_tqdm=True),
-):
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    # Improved options handling
-    options = {
-        "prompt": [prompt] * num_images,
-        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
-        "width": width,
-        "height": height,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "generator": generator,
-        "output_type": "pil",
-    }
-
-    output = pipe(prompt,negative_prompt, width, height,guidance_scale,num_inference_steps)
-
-    return output
-
-examples = [
-    "a cat eating a piece of cheese",
-    "a ROBOT riding a BLUE horse on Mars, photorealistic, 4k",
-    "Ironman VS Hulk, ultrarealistic",
-    "Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k",
-    "An alien holding a sign board containing the word 'Flash', futuristic, neonpunk",
-    "Kids going to school, Anime style"
-]
-
-css = '''
-.gradio-container{max-width: 700px !important}
-h1{text-align:center}
-footer {
-    visibility: hidden
-}
-'''
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("""# Black Hole SDXL-Lightning""")
-    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.Gallery(elem_id="gallery", label="Result", show_label=False) 
-        
-    with gr.Accordion("Advanced options", open=False):
-        num_images = gr.Slider(
-            label="Number of Images",
-            minimum=1,
-            maximum=4,
-            step=1,
-            value=1,
-        )
-        with gr.Row():
-            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=5,
-                lines=4,
-                placeholder="Enter a negative prompt",
-                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",
-                visible=True,
+from diffusers import StableDiffusionPipeline
+
+from diffusion_webui.utils.model_list import stable_model_list
+from diffusion_webui.utils.scheduler_list import (
+    SCHEDULER_MAPPING,
+    get_scheduler,
+)
+import spaces
+
+
+class StableDiffusionText2ImageGenerator:
+    def __init__(self):
+        self.pipe = None
+
+    def load_model(
+        self,
+        model_path,
+        scheduler,
+    ):
+        if self.pipe is None:
+            self.pipe = StableDiffusionPipeline.from_pretrained(
+                model_path, safety_checker=None, torch_dtype=torch.float16
             )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
+
+        self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
+        self.pipe.to("cuda")
+        self.pipe.enable_xformers_memory_efficient_attention()
+
+        return self.pipe
+
+    @spaces.GPU()
+    def generate_image(
+        self,
+        model_path: str,
+        prompt: str,
+        negative_prompt: str,
+        num_images_per_prompt: int,
+        scheduler: str,
+        guidance_scale: int,
+        num_inference_step: int,
+        height: int,
+        width: int,
+        seed_generator=0,
+    ):
+        pipe = self.load_model(
+            model_path=model_path,
+            scheduler=scheduler,
         )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row(visible=True):
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=MAX_IMAGE_SIZE,
-                step=64,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=MAX_IMAGE_SIZE,
-                step=64,
-                value=1024,
+        if seed_generator == 0:
+            random_seed = torch.randint(0, 1000000, (1,))
+            generator = torch.manual_seed(random_seed)
+        else:
+            generator = torch.manual_seed(seed_generator)
+
+        images = pipe(
+            prompt=prompt,
+            height=height,
+            width=width,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            num_inference_steps=num_inference_step,
+            guidance_scale=guidance_scale,
+            generator=generator,
+        ).images
+
+        return images
+
+    def app():
+        with gr.Blocks():
+            with gr.Row():
+                with gr.Column():
+                    text2image_prompt = gr.Textbox(
+                        lines=1,
+                        placeholder="Prompt",
+                        show_label=False,
+                    )
+
+                    text2image_negative_prompt = gr.Textbox(
+                        lines=1,
+                        placeholder="Negative Prompt",
+                        show_label=False,
+                    )
+                    with gr.Row():
+                        with gr.Column():
+                            text2image_model_path = gr.Dropdown(
+                                choices=stable_model_list,
+                                value=stable_model_list[0],
+                                label="Text-Image Model Id",
+                            )
+
+                            text2image_guidance_scale = gr.Slider(
+                                minimum=0.1,
+                                maximum=15,
+                                step=0.1,
+                                value=7.5,
+                                label="Guidance Scale",
+                            )
+
+                            text2image_num_inference_step = gr.Slider(
+                                minimum=1,
+                                maximum=100,
+                                step=1,
+                                value=50,
+                                label="Num Inference Step",
+                            )
+                            text2image_num_images_per_prompt = gr.Slider(
+                                minimum=1,
+                                maximum=30,
+                                step=1,
+                                value=1,
+                                label="Number Of Images",
+                            )
+                        with gr.Row():
+                            with gr.Column():
+                                text2image_scheduler = gr.Dropdown(
+                                    choices=list(SCHEDULER_MAPPING.keys()),
+                                    value=list(SCHEDULER_MAPPING.keys())[0],
+                                    label="Scheduler",
+                                )
+
+                                text2image_height = gr.Slider(
+                                    minimum=128,
+                                    maximum=1280,
+                                    step=32,
+                                    value=512,
+                                    label="Image Height",
+                                )
+
+                                text2image_width = gr.Slider(
+                                    minimum=128,
+                                    maximum=1280,
+                                    step=32,
+                                    value=512,
+                                    label="Image Width",
+                                )
+                                text2image_seed_generator = gr.Slider(
+                                    label="Seed(0 for random)",
+                                    minimum=0,
+                                    maximum=1000000,
+                                    value=0,
+                                )
+                    text2image_predict = gr.Button(value="Generator")
+
+                with gr.Column():
+                    output_image = gr.Gallery(
+                        label="Generated images",
+                        show_label=False,
+                        elem_id="gallery",
+                    ).style(grid=(1, 2), height=200)
+
+            text2image_predict.click(
+                fn=StableDiffusionText2ImageGenerator().generate_image,
+                inputs=[
+                    text2image_model_path,
+                    text2image_prompt,
+                    text2image_negative_prompt,
+                    text2image_num_images_per_prompt,
+                    text2image_scheduler,
+                    text2image_guidance_scale,
+                    text2image_num_inference_step,
+                    text2image_height,
+                    text2image_width,
+                    text2image_seed_generator,
+                ],
+                outputs=output_image,
             )
+
+import gradio as gr
+
+
+def diffusion_app():
+    app = gr.Blocks()
+    with app:
         with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                minimum=0.1,
-                maximum=6,
-                step=0.1,
-                value=3.0,
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=1,
-                maximum=15,
-                step=1,
-                value=4,
-            )
+            with gr.Column():
+                    StableDiffusionText2ImageGenerator.app()
+
+    app.launch(debug=True)
 
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        cache_examples=False
-    )
-
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-        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,
-            randomize_seed,
-            num_images
-        ],
-        outputs=[result, seed],
-        api_name="run",
-    )
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    diffusion_app()