Delete LoRA

LoRA/LoRA.txt

@@ -1,193 +0,0 @@
-import gradio as gr
-import spaces
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-from PIL import Image
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo"
-
-torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-pipe.load_lora_weights("prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", weight_name="SD3.5-Turbo-Realism-2.0-LoRA.safetensors")
-trigger_word = "Turbo Realism"  
-pipe.fuse_lora(lora_scale=1.0)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-# Define styles
-style_list = [
-    {
-        "name": "3840 x 2160",
-        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
-    },
-    {
-        "name": "2560 x 1440",
-        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
-    },
-    {
-        "name": "HD+",
-        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
-    },
-    {
-        "name": "Style Zero",
-        "prompt": "{prompt}",
-        "negative_prompt": "",
-    },
-]
-
-STYLE_NAMES = [style["name"] for style in style_list]
-DEFAULT_STYLE_NAME = STYLE_NAMES[0]
-
-grid_sizes = {
-    "2x1": (2, 1),
-    "1x2": (1, 2),
-    "2x2": (2, 2),
-    "2x3": (2, 3),
-    "3x2": (3, 2),
-    "1x1": (1, 1)
-}
-
-@spaces.GPU(duration=60)
-def infer(
-    prompt,
-    negative_prompt="",
-    seed=42,
-    randomize_seed=False,
-    width=1024,
-    height=1024,
-    guidance_scale=7.5,
-    num_inference_steps=10,
-    style="Style Zero",
-    grid_size="1x1",
-    progress=gr.Progress(track_tqdm=True),
-):
-    selected_style = next(s for s in style_list if s["name"] == style)
-    styled_prompt = selected_style["prompt"].format(prompt=prompt)
-    styled_negative_prompt = selected_style["negative_prompt"]
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    grid_size_x, grid_size_y = grid_sizes.get(grid_size, (1, 1))
-    num_images = grid_size_x * grid_size_y
-
-    options = {
-        "prompt": styled_prompt,
-        "negative_prompt": styled_negative_prompt,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "width": width,
-        "height": height,
-        "generator": generator,
-        "num_images_per_prompt": num_images,
-    }
-
-    torch.cuda.empty_cache()  # Clear GPU memory
-    result = pipe(**options)
-
-    grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y))
-
-    for i, img in enumerate(result.images[:num_images]):
-        grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height))
-
-    return grid_img, seed
-
-examples = [
-    "A tiny astronaut hatching from an egg on the moon, 4k, planet theme",
-    "An anime-style illustration of a delicious, golden-brown wiener schnitzel on a plate, served with fresh lemon slices, parsley --style raw5",
-    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic",
-    "A cat holding a sign that says hello world --ar 85:128 --v 6.0 --style raw"
-]
-
-css = '''
-.gradio-container {
-    max-width: 585px !important;
-    margin: 0 auto !important;
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    justify-content: center;
-}
-h1 { text-align: center; }
-footer { visibility: hidden; }
-'''
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("## T2i Grid 6x")
-
-        with gr.Row():
-            prompt = gr.Text(
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(show_label=False)
-
-        with gr.Row():
-            grid_size_selection = gr.Dropdown(
-                choices=list(grid_sizes.keys()),
-                value="1x1",
-                label="Grid Size"
-            )
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                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",
-            )
-            seed = gr.Slider(0, MAX_SEED, value=0, label="Seed")
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(512, MAX_IMAGE_SIZE, step=32, value=1024, label="Width")
-                height = gr.Slider(512, MAX_IMAGE_SIZE, step=32, value=1024, label="Height")
-
-            with gr.Row():
-                guidance_scale = gr.Slider(0.0, 7.5, step=0.1, value=0.0, label="Guidance scale")
-                num_inference_steps = gr.Slider(1, 50, step=1, value=10, label="Number of inference steps")
-
-            style_selection = gr.Radio(
-                choices=STYLE_NAMES,
-                value=DEFAULT_STYLE_NAME,
-                label="Quality Style",
-            )
-
-        gr.Examples(
-            examples=examples,
-            inputs=[prompt],
-            outputs=[result, seed],
-            fn=infer,
-            cache_examples=False
-        )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt, negative_prompt, seed, randomize_seed,
-            width, height, guidance_scale, num_inference_steps,
-            style_selection, grid_size_selection
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()