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Running
on
Zero
| import torch | |
| import os | |
| import gradio as gr | |
| from PIL import Image | |
| from diffusers import ( | |
| DiffusionPipeline, | |
| StableDiffusionControlNetImg2ImgPipeline, | |
| ControlNetModel, | |
| DPMSolverMultistepScheduler, # <-- Added import | |
| EulerDiscreteScheduler # <-- Added import | |
| ) | |
| # Initialize both pipelines | |
| init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V2.0", torch_dtype=torch.float16).to("cuda") | |
| controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16) | |
| main_pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( | |
| "SG161222/Realistic_Vision_V2.0", | |
| controlnet=controlnet, | |
| safety_checker=None, | |
| torch_dtype=torch.float16, | |
| ).to("cuda") | |
| # Sampler map | |
| 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( | |
| control_image: Image.Image, | |
| prompt: str, | |
| negative_prompt: str, | |
| guidance_scale: float = 8.0, | |
| controlnet_conditioning_scale: float = 1, | |
| strength: float = 0.9, | |
| seed: int = -1, | |
| sampler = "DPM++ Karras SDE", | |
| ): | |
| if prompt is None or prompt == "": | |
| raise gr.Error("Prompt is required") | |
| # Generate the initial image | |
| init_image = init_pipe(prompt).images[0] | |
| # Rest of your existing code | |
| control_image = control_image.resize((512, 512)) | |
| main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config) | |
| generator = torch.manual_seed(seed) if seed != -1 else torch.Generator() | |
| out = main_pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=init_image, | |
| control_image=control_image, | |
| guidance_scale=guidance_scale, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| generator=generator, | |
| strength=strength, | |
| num_inference_steps=30, | |
| ) | |
| return out.images[0] | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| ''' | |
| <center> | |
| <span style="color:blue; font-size:24px;">Illusion Diffusion 🌀</span> | |
| <span style="color:black; font-size:16px;">Generate stunning illusion artwork with Stable Diffusion</span> | |
| <span style="color:black; font-size:10px;">A space by AP [Follow me on Twitter](https://twitter.com/angrypenguinPNG)</span> | |
| </center> | |
| <p style="text-align:center;"> | |
| <span style="color:black; font-size:10px;">This project works by using the QR Control Net by Monster Labs: [Monster Labs QR Control Net](https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster). | |
| Given a prompt, we generate an init image and pass that alongside the control illusion to create a stunning illusion! Credit to : MrUgleh (https://twitter.com/MrUgleh) for discovering the workflow :)</span> | |
| </p> | |
| ''' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| control_image = gr.Image(label="Input Illusion", type="pil") | |
| prompt = gr.Textbox(label="Prompt") | |
| 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=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, strength, seed, sampler], | |
| outputs=[result_image] | |
| ) | |
| app.queue(max_size=20) | |
| if __name__ == "__main__": | |
| app.launch(debug=True) |