Initial commit

app.py

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+import hashlib
+import io
+import torch
+from pathlib import Path
+from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler
+from PIL import Image, ImageOps
+import gradio as gr
+
+# ---- Model loading ----
+CACHE_DIR = "./cache"
+CNET_MODEL = "MrPio/Texture-Anything_CNet-SD15"
+SD_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+
+controlnet = ControlNetModel.from_pretrained(
+    CNET_MODEL, cache_dir=CACHE_DIR, torch_dtype=torch.float16, local_files_only=True
+)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    SD_MODEL,
+    controlnet=controlnet,
+    cache_dir=CACHE_DIR,
+    torch_dtype=torch.float16,
+    safety_checker=None,
+)
+
+# speed & memory optimizations
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.enable_xformers_memory_efficient_attention()  # if xformers installed
+pipe.enable_model_cpu_offload()
+
+
+def pil2hash(image: Image.Image) -> str:
+    buffer = io.BytesIO()
+    image.save(buffer, format="PNG")
+    image_bytes = buffer.getvalue()
+    return hashlib.sha256(image_bytes).hexdigest()
+
+
+def caption2hash(caption: str) -> str:
+    return hashlib.sha256(caption.encode()).hexdigest()
+
+
+# ---- Inference function ----
+def infer(caption: str, condition_image: Image.Image, steps: int = 20, seed: int = 0, invert: bool = False):
+    img = condition_image.convert("RGB")
+    if invert:
+        img = ImageOps.invert(img)
+    cache_file = Path(f"inferences/{pil2hash(img)}_{caption2hash(caption)}.png")
+    if cache_file.exists():
+        return Image.open(cache_file)
+
+    generator = torch.manual_seed(seed)
+    output = pipe(prompt=caption, image=img, num_inference_steps=steps, generator=generator).images[0]
+    output.save(cache_file)
+    return output
+
+
+# ---- Gradio UI + API ----
+with gr.Blocks() as demo:
+    gr.Markdown("## ControlNet + Stable Diffusion 1.5")
+    with gr.Row():
+        txt = gr.Textbox(label="Prompt", placeholder="Describe the texture...")
+        cond = gr.Image(type="pil", label="Condition Image")
+    with gr.Row():
+        steps = gr.Slider(1, 50, value=20, label="Inference Steps")
+        seed = gr.Number(value=0, label="Seed (0 for random)")
+        inv = gr.Checkbox(label="Invert UV colors?")
+    btn = gr.Button("Generate")
+    out = gr.Image(label="Output")
+
+    btn.click(fn=infer, inputs=[txt, cond, steps, seed, inv], outputs=out)
+
+# enable the standard gradio REST API (/run/predict)
+demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+accelerate
+diffusers
+huggingface-hub
+numpy<2
+requests
+safetensors
+torch
+torchvision
+tqdm
+xformers
+gradio