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| import spaces | |
| import gradio as gr | |
| from hft2is import load_models, warm_models, generate_image_hf | |
| MODELS = ["kayfahaarukku/UrangDiffusion-2.0", "kayfahaarukku/irAsu-1.0"] | |
| load_models(MODELS) | |
| warm_models(MODELS) | |
| # Define Gradio interface | |
| def interface_fn(prompt, negative_prompt="", use_defaults=True, resolution="832x1216", guidance_scale=7, num_inference_steps=28, seed=0, randomize_seed=True, model_name=MODELS[0], progress=gr.Progress()): | |
| image, seed, metadata_text = generate_image_hf(model_name, prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress) | |
| return image, seed, gr.update(value=metadata_text) | |
| def reset_inputs(): | |
| return gr.update(value=''), gr.update(value=''), gr.update(value=True), gr.update(value='832x1216'), gr.update(value=7), gr.update(value=28), gr.update(value=0), gr.update(value=True), gr.update(value=''), gr.update(value=MODELS[0]) | |
| with gr.Blocks(title="UrangDiffusion 2.0 Demo", theme="NoCrypt/miku@1.2.1") as demo: | |
| gr.HTML( | |
| "<h1>UrangDiffusion 2.0 Demo</h1>" | |
| "This demo is intended to showcase what the model is capable of and is not intended to be the main generation platform. Results produced with Diffusers are not the best, and it's highly recommended for you to get the model running inside Stable Diffusion WebUI or ComfyUI." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt") | |
| negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt") | |
| use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True) | |
| resolution_input = gr.Radio( | |
| choices=[ | |
| "1024x1024", "1152x896", "896x1152", "1216x832", "832x1216", | |
| "1344x768", "768x1344", "1536x640", "640x1536" | |
| ], | |
| label="Resolution", | |
| value="832x1216" | |
| ) | |
| guidance_scale_input = gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7) | |
| num_inference_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=28) | |
| seed_input = gr.Slider(minimum=0, maximum=999999999, step=1, label="Seed", value=0, interactive=True) | |
| randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True) | |
| model_name = gr.Dropdown(label="Model", choices=MODELS, value=MODELS[0]) | |
| generate_button = gr.Button("Generate") | |
| reset_button = gr.Button("Reset") | |
| with gr.Column(): | |
| output_image = gr.Image(type="pil", label="Generated Image") | |
| with gr.Accordion("Parameters", open=False): | |
| gr.Markdown( | |
| """ | |
| This parameter is compatible with Stable Diffusion WebUI's parameter importer. | |
| """ | |
| ) | |
| metadata_textbox = gr.Textbox(lines=6, label="Image Parameters", interactive=False, max_lines=6) | |
| gr.Markdown( | |
| """ | |
| ### Recommended prompt formatting: | |
| `1girl/1boy, character name, from what series, everything else in any order, best quality, amazing quality, very aesthetic,` | |
| **PS:** `best quality, amazing quality, very aesthetic,` is automatically added when "Use Default Quality Tags and Negative Prompt" is enabled | |
| ### Recommended settings: | |
| - Steps: 25-30 | |
| - CFG: 5-7 | |
| """ | |
| ) | |
| examples = gr.Examples( | |
| examples = [ | |
| ["1girl"], | |
| ["beautiful sunset"], | |
| ], | |
| inputs=[prompt_input], | |
| cache_examples=False, | |
| ) | |
| generate_button.click( | |
| interface_fn, | |
| inputs=[ | |
| prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input, model_name, | |
| ], | |
| outputs=[output_image, seed_input, metadata_textbox] | |
| ) | |
| reset_button.click( | |
| reset_inputs, | |
| inputs=[], | |
| outputs=[ | |
| prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input, metadata_textbox, model_name, | |
| ] | |
| ) | |
| demo.launch(ssr_mode=False) |