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Running
on
Zero
| # import gradio as gr | |
| # import torch | |
| # from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| # from PIL import Image | |
| # import base64 | |
| # from io import BytesIO | |
| # # You can change these: | |
| # BASE_MODEL = "runwayml/stable-diffusion-v1-5" | |
| # CONTROLNET_ID = "lllyasviel/sd-controlnet-canny" # placeholder; change to a QR-focused ControlNet if you have one | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # controlnet = ControlNetModel.from_pretrained( | |
| # CONTROLNET_ID, torch_dtype=torch.float16 if device=="cuda" else torch.float32 | |
| # ) | |
| # pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| # BASE_MODEL, | |
| # controlnet=controlnet, | |
| # torch_dtype=torch.float16 if device=="cuda" else torch.float32, | |
| # safety_checker=None | |
| # ) | |
| # pipe.to(device) | |
| # def generate(prompt, control_image, guidance_scale=7.5, steps=30, seed=0): | |
| # generator = torch.Generator(device=device).manual_seed(int(seed)) if seed else None | |
| # img = pipe( | |
| # prompt=prompt, | |
| # image=control_image, | |
| # num_inference_steps=int(steps), | |
| # guidance_scale=float(guidance_scale), | |
| # generator=generator | |
| # ).images[0] | |
| # return img | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("# ControlNet Image Generator") | |
| # with gr.Row(): | |
| # prompt = gr.Textbox(label="Prompt", value="A futuristic poster, high detail") | |
| # seed = gr.Number(label="Seed (0=random)", value=0) | |
| # with gr.Row(): | |
| # control = gr.Image(type="pil", label="Control image (e.g., QR or edge map)") | |
| # steps = gr.Slider(10, 50, 30, step=1, label="Steps") | |
| # guidance = gr.Slider(1.0, 12.0, 7.5, step=0.1, label="Guidance scale") | |
| # out = gr.Image(label="Result") | |
| # btn = gr.Button("Generate") | |
| # btn.click(generate, [prompt, control, guidance, steps, seed], out) | |
| # # Enable simple API use | |
| # gr.Examples([], inputs=[prompt, control, guidance, steps, seed], outputs=out) | |
| # demo.launch() | |
| import gradio as gr | |
| from PIL import Image | |
| def generate(prompt, control_image, guidance, steps, seed): | |
| # dummy return so Space builds | |
| return control_image | |
| demo = gr.Interface( | |
| fn=generate, | |
| inputs=[ | |
| gr.Textbox(label="Prompt"), | |
| gr.Image(type="pil", label="Control Image"), | |
| gr.Slider(1, 12, 7.5, label="Guidance scale"), | |
| gr.Slider(10, 50, 30, step=1, label="Steps"), | |
| gr.Number(0, label="Seed"), | |
| ], | |
| outputs=gr.Image(), | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |