Create app.py

app.py

@@ -0,0 +1,101 @@
+import torch
+import os
+import gradio as gr
+from PIL import Image
+from diffusers import (
+    StableDiffusionPipeline,
+    StableDiffusionControlNetImg2ImgPipeline,
+    ControlNetModel,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+    HeunDiscreteScheduler,
+    EulerDiscreteScheduler,
+)
+
+# Initialize ControlNet model
+controlnet = ControlNetModel.from_pretrained(
+    "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
+)
+
+# Initialize pipeline
+pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+    "XpucT/Deliberate",
+    controlnet=controlnet,
+    safety_checker=None,
+    torch_dtype=torch.float16,
+).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+
+# Sampler configurations
+SAMPLER_MAP = {
+    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
+    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
+}
+
+# Inference function
+def inference(
+    input_image: Image.Image,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: float = 10.0,
+    controlnet_conditioning_scale: float = 1.0,
+    strength: float = 0.8,
+    seed: int = -1,
+    sampler = "DPM++ Karras SDE",
+):
+    if prompt is None or prompt == "":
+        raise gr.Error("Prompt is required")
+    
+    pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
+    generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
+    
+    out = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=input_image,
+        control_image=input_image,  # type: ignore
+        width=512,  # type: ignore
+        height=512,  # type: ignore
+        guidance_scale=float(guidance_scale),
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
+        generator=generator,
+        strength=float(strength),
+        num_inference_steps=40,
+    )
+    return out.images[0]  # type: ignore
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown(
+        '''
+        # Illusion Diffusion
+        ## A simple UI for generating beatiful illusion art with Stable Diffusion 1.5
+        '''
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Illusion", type="pil")
+            prompt = gr.Textbox(label="Prompt", info="Prompt that guides the generation towards")
+            negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
+            with gr.Accordion(label="Advanced Options", open=False):
+                controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=1.1, label="Controlnet Conditioning Scale")
+                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.9, label="Strength")
+                guidance_scale = gr.Slider(minimum=0.0, maximum=50.0, step=0.25, value=7.5, label="Guidance Scale")
+                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE")
+                seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=2313123, label="Seed", randomize=True)
+            run_btn = gr.Button("Run")
+        with gr.Column():
+            result_image = gr.Image(label="Illusion Diffusion Output")
+            
+    run_btn.click(
+        inference,
+        inputs=[input_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, strength, seed, sampler],
+        outputs=[result_image]
+    )
+
+app.queue(concurrency_count=4, max_size=20)
+
+if __name__ == "__main__":
+    app.launch(debug=True)