Create LoRA/LoRA.txt

LoRA/LoRA.txt

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+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()