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Running
on
Zero
| import gradio as gr | |
| import numpy as np | |
| import random | |
| import spaces | |
| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline | |
| import torch | |
| from src.linfusion import LinFusion | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| all_model_id = { | |
| "DreamShaper-8": "Lykon/dreamshaper-8", | |
| "RealisticVision-v4.0": "SG161222/Realistic_Vision_V4.0_noVAE" | |
| } | |
| if torch.cuda.is_available(): | |
| torch_dtype = torch.float16 | |
| else: | |
| torch_dtype = torch.float32 | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer_t2i(model, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| pipe = StableDiffusionPipeline.from_pretrained(all_model_id[model], torch_dtype=torch_dtype) | |
| pipe = pipe.to(device) | |
| linfusion = LinFusion.construct_for(pipe) | |
| image = pipe( | |
| prompt = prompt, | |
| negative_prompt = negative_prompt, | |
| guidance_scale = guidance_scale, | |
| num_inference_steps = num_inference_steps, | |
| width = width, | |
| height = height, | |
| generator = generator | |
| ).images[0] | |
| return image, seed | |
| def infer_i2i(model, prompt, image, strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained(all_model_id[model], torch_dtype=torch_dtype) | |
| pipe = pipe.to(device) | |
| linfusion = LinFusion.construct_for(pipe) | |
| image = pipe( | |
| prompt = prompt, | |
| image = image.resize((width, height)), | |
| strength = strength, | |
| negative_prompt = negative_prompt, | |
| guidance_scale = guidance_scale, | |
| num_inference_steps = num_inference_steps, | |
| width = width, | |
| height = height, | |
| generator = generator | |
| ).images[0] | |
| return image, seed | |
| def infer_ip_adapter(model, prompt, image, scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| pipe = StableDiffusionPipeline.from_pretrained(all_model_id[model], torch_dtype=torch_dtype) | |
| pipe = pipe.to(device) | |
| pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-plus_sd15.bin") | |
| pipe.set_ip_adapter_scale(scale) | |
| linfusion = LinFusion.construct_for(pipe) | |
| image = pipe( | |
| prompt = prompt, | |
| image = image.resize((width, height)), | |
| negative_prompt = negative_prompt, | |
| guidance_scale = guidance_scale, | |
| num_inference_steps = num_inference_steps, | |
| ip_adapter_image = image, | |
| width = width, | |
| height = height, | |
| generator = generator | |
| ).images[0] | |
| return image, seed | |
| examples = [ | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| "An astronaut riding a green horse", | |
| "A delicious ceviche cheesecake slice", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 640px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Tab("Text-to-Image"): | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # Text-to-Image Gradio Template | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=False, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.keys()), value=list(all_model_id.keys())[0]) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, #Replace with defaults that work for your model | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, #Replace with defaults that work for your model | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=7.5, #Replace with defaults that work for your model | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=25, #Replace with defaults that work for your model | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [prompt] | |
| ) | |
| run_button.click( | |
| fn=infer_t2i, | |
| inputs = [model_choice, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs = [result, seed] | |
| ) | |
| with gr.Tab("Image-to-Image"): | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # Text-to-Image Gradio Template | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| image_upload_input = gr.Image(label="Upload an Image", type="pil") | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=False, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.keys()), value=list(all_model_id.keys())[0]) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, #Replace with defaults that work for your model | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, #Replace with defaults that work for your model | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=7.5, #Replace with defaults that work for your model | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=25, #Replace with defaults that work for your model | |
| ) | |
| editing_strength = gr.Slider( | |
| label="Strength of editing", | |
| minimum=0, | |
| maximum=1, | |
| step=0.01, | |
| value=0.5, #Replace with defaults that work for your model | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [prompt] | |
| ) | |
| run_button.click( | |
| fn=infer_i2i, | |
| inputs = [model_choice, prompt, image_upload_input, editing_strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs = [result, seed] | |
| ) | |
| with gr.Tab("IP-Adapter"): | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # Text-to-Image Gradio Template | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| image_upload_input = gr.Image(label="Upload an Image", type="pil") | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=False, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.keys()), value=list(all_model_id.keys())[0]) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, #Replace with defaults that work for your model | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, #Replace with defaults that work for your model | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=7.5, #Replace with defaults that work for your model | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=25, #Replace with defaults that work for your model | |
| ) | |
| ip_adapter_scale = gr.Slider( | |
| label="Strength of image condition", | |
| minimum=0, | |
| maximum=1, | |
| step=0.01, | |
| value=0.4, #Replace with defaults that work for your model | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| inputs = [prompt] | |
| ) | |
| run_button.click( | |
| fn=infer_ip_adapter, | |
| inputs = [model_choice, prompt, image_upload_input, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
| outputs = [result, seed] | |
| ) | |
| demo.queue().launch() |