Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import numpy as np | |
| import random | |
| import torch | |
| from PIL import Image | |
| from diffusers import ( | |
| DiffusionPipeline, | |
| StableDiffusionControlNetPipeline, | |
| ControlNetModel | |
| ) | |
| from peft import PeftModel | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| LORA_MODEL = "akaUNik/braindo-poster-v6" | |
| LORA_BASE_MODEL = "runwayml/stable-diffusion-v1-5" | |
| # Model list including LoRA model | |
| MODEL_LIST = [ | |
| "runwayml/stable-diffusion-v1-5", | |
| "stabilityai/sdxl-turbo", | |
| "stabilityai/stable-diffusion-2-1", | |
| LORA_MODEL, # LoRA model option | |
| ] | |
| # ControlNet modes list with aliases | |
| CONTROLNET_MODES = { | |
| "Canny Edge Detection": "lllyasviel/control_v11p_sd15_canny", | |
| "Pixel to Pixel": "lllyasviel/control_v11e_sd15_ip2p", | |
| "Inpainting": "lllyasviel/control_v11p_sd15_inpaint", | |
| "Multi-Level Line Segments": "lllyasviel/control_v11p_sd15_mlsd", | |
| "Depth Estimation": "lllyasviel/control_v11f1p_sd15_depth", | |
| "Surface Normal Estimation": "lllyasviel/control_v11p_sd15_normalbae", | |
| "Image Segmentation": "lllyasviel/control_v11p_sd15_seg", | |
| "Line Art Generation": "lllyasviel/control_v11p_sd15_lineart", | |
| "Anime Line Art": "lllyasviel/control_v11p_sd15_lineart_anime", | |
| "Human Pose Estimation": "lllyasviel/control_v11p_sd15_openpose", | |
| "Scribble-Based Generation": "lllyasviel/control_v11p_sd15_scribble", | |
| "Soft Edge Generation": "lllyasviel/control_v11p_sd15_softedge", | |
| "Image Shuffling": "lllyasviel/control_v11e_sd15_shuffle", | |
| "Image Tiling": "lllyasviel/control_v11f1e_sd15_tile", | |
| } | |
| if torch.cuda.is_available(): | |
| torch_dtype = torch.float16 | |
| else: | |
| torch_dtype = torch.float32 | |
| # Cache to avoid re-initializing pipelines repeatedly | |
| model_cache = {} | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 512 | |
| def infer( | |
| model_id, | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| randomize_seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| num_inference_steps, | |
| lora_scale, | |
| controlnet_enable, | |
| controlnet_mode, | |
| controlnet_strength, | |
| controlnet_image, | |
| ip_adapter_enable, | |
| ip_adapter_scale, | |
| ip_adapter_image, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| # Cache | |
| # if (model_id, controlnet_enable, controlnet_image, controlnet_mode) in model_cache: | |
| # pipe = model_cache[(model_id, controlnet_enable, controlnet_image, controlnet_mode)] | |
| # else: | |
| pipe = None | |
| if controlnet_enable and controlnet_image: | |
| controlnet_model = ControlNetModel.from_pretrained( | |
| CONTROLNET_MODES.get(controlnet_mode), | |
| torch_dtype=torch_dtype | |
| ) | |
| if model_id == LORA_MODEL: | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| LORA_BASE_MODEL, | |
| controlnet=controlnet_model, | |
| torch_dtype=torch_dtype | |
| ) | |
| else: | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| model_id, | |
| controlnet=controlnet_model, | |
| torch_dtype=torch_dtype | |
| ) | |
| else: | |
| if model_id == LORA_MODEL: | |
| # Use the specified base model for your LoRA adapter. | |
| pipe = DiffusionPipeline.from_pretrained( | |
| LORA_BASE_MODEL, | |
| torch_dtype=torch_dtype | |
| ) | |
| # Load the LoRA weights | |
| pipe.unet = PeftModel.from_pretrained( | |
| pipe.unet, | |
| model_id, | |
| subfolder="unet", | |
| torch_dtype=torch_dtype | |
| ) | |
| pipe.text_encoder = PeftModel.from_pretrained( | |
| pipe.text_encoder, | |
| model_id, | |
| subfolder="text_encoder", | |
| torch_dtype=torch_dtype | |
| ) | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch_dtype | |
| ) | |
| if ip_adapter_enable: | |
| pipe.load_ip_adapter( | |
| "h94/IP-Adapter", | |
| subfolder="models", | |
| weight_name="ip-adapter-plus_sd15.bin" | |
| ) | |
| pipe.set_ip_adapter_scale(ip_adapter_scale) | |
| pipe.safety_checker = None | |
| pipe.to(device) | |
| # model_cache[(model_id, controlnet_enable, controlnet_image, controlnet_mode)] = pipe | |
| image = pipe( | |
| prompt=prompt, | |
| image=controlnet_image if controlnet_enable else None, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| width=width, | |
| height=height, | |
| generator=generator, | |
| cross_attention_kwargs={"scale": lora_scale}, | |
| controlnet_conditioning_scale=controlnet_strength, | |
| ip_adapter_image=ip_adapter_image if ip_adapter_enable else None | |
| ).images[0] | |
| return image, seed | |
| # @title Gradio | |
| examples = [ | |
| "braindo_poster, minimalistic unicorn illustration, elegant flat vector style, smooth red-white gradient colors, clean white background, bold sans-serif typography, red lines, highly detailed, professional graphic design, trending on Behance", | |
| "braindo_poster, minimalistic car illustration, elegant flat vector style, smooth red-white gradient colors, clean white background, bold sans-serif typography, red lines, highly detailed, professional graphic design, trending on Behance", | |
| "braindo_poster, minimalistic frog stone illustration, elegant flat vector style, smooth red-white gradient colors, clean white background, bold sans-serif typography, red lines, highly detailed, professional graphic design, trending on Behance", | |
| ] | |
| css = """ | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 640px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(" # Text-to-Image Gradio Template") | |
| with gr.Row(): | |
| # Dropdown to select the model from Hugging Face | |
| model_id = gr.Dropdown( | |
| label="Model", | |
| choices=MODEL_LIST, | |
| value=MODEL_LIST[0], # Default model | |
| ) | |
| 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, variant="primary") | |
| 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", | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=42, # Default seed | |
| ) | |
| 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, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=20.0, | |
| step=0.5, | |
| value=7.0, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=20, | |
| ) | |
| # New slider for LoRA scale. | |
| lora_scale = gr.Slider( | |
| label="LoRA Scale", | |
| minimum=0.0, | |
| maximum=2.0, | |
| step=0.1, | |
| value=1.0, | |
| info="Adjust the influence of the LoRA weights", | |
| ) | |
| # --- ControlNet Settings --- | |
| with gr.Accordion("ControlNet Settings", open=False): | |
| controlnet_enable = gr.Checkbox( | |
| label="Enable ControlNet", | |
| value=False | |
| ) | |
| with gr.Group(visible=False) as controlnet_group: | |
| controlnet_mode = gr.Dropdown( | |
| label="ControlNet Mode", | |
| choices=list(CONTROLNET_MODES.keys()), | |
| value=list(CONTROLNET_MODES.keys())[0], | |
| ) | |
| controlnet_strength = gr.Slider( | |
| label="ControlNet Conditioning Scale", | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.7, | |
| ) | |
| controlnet_image = gr.Image( | |
| label="ControlNet Image", | |
| type="pil" | |
| ) | |
| def show_controlnet_options(enable): | |
| return {controlnet_group: gr.update(visible=enable)} | |
| controlnet_enable.change( | |
| fn=show_controlnet_options, | |
| inputs=controlnet_enable, | |
| outputs=controlnet_group, | |
| ) | |
| # --- IP-adapter Settings --- | |
| with gr.Accordion("IP-adapter Settings", open=False): | |
| ip_adapter_enable = gr.Checkbox( | |
| label="Enable IP-adapter", | |
| value=False | |
| ) | |
| with gr.Group(visible=False) as ip_adapter_group: | |
| ip_adapter_scale = gr.Slider( | |
| label="IP-adapter Scale", | |
| minimum=0.0, | |
| maximum=2.0, | |
| step=0.1, | |
| value=1.0 | |
| ) | |
| ip_adapter_image = gr.Image( | |
| label="IP-adapter Image", | |
| type="pil" | |
| ) | |
| # Show/hide IP-adapter parameters when checkbox is toggled | |
| def show_ip_adapter_options(enable): | |
| return {ip_adapter_group: gr.update(visible=enable)} | |
| ip_adapter_enable.change( | |
| fn=show_ip_adapter_options, | |
| inputs=ip_adapter_enable, | |
| outputs=ip_adapter_group, | |
| ) | |
| gr.Examples(examples=examples, inputs=[prompt]) | |
| gr.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn=infer, | |
| inputs=[ | |
| model_id, | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| randomize_seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| num_inference_steps, | |
| lora_scale, | |
| controlnet_enable, | |
| controlnet_mode, | |
| controlnet_strength, | |
| controlnet_image, | |
| ip_adapter_enable, | |
| ip_adapter_scale, | |
| ip_adapter_image, | |
| ], | |
| outputs=[result, seed], | |
| ) | |
| # @title Run | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) # show errors in colab notebook |