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| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| from peft import PeftModel, LoraConfig | |
| import os | |
| def get_lora_sd_pipeline( | |
| ckpt_dir='./lora_man_animestyle', | |
| base_model_name_or_path=None, | |
| dtype=torch.float16, | |
| adapter_name="default" | |
| ): | |
| unet_sub_dir = os.path.join(ckpt_dir, "unet") | |
| text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder") | |
| if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None: | |
| config = LoraConfig.from_pretrained(text_encoder_sub_dir) | |
| base_model_name_or_path = config.base_model_name_or_path | |
| if base_model_name_or_path is None: | |
| raise ValueError("Please specify the base model name or path") | |
| pipe = StableDiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype) | |
| before_params = pipe.unet.parameters() | |
| pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name) | |
| pipe.unet.set_adapter(adapter_name) | |
| after_params = pipe.unet.parameters() | |
| print("Parameters changed:", any(torch.any(b != a) for b, a in zip(before_params, after_params))) | |
| if os.path.exists(text_encoder_sub_dir): | |
| pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name) | |
| if dtype in (torch.float16, torch.bfloat16): | |
| pipe.unet.half() | |
| pipe.text_encoder.half() | |
| return pipe | |
| def process_prompt(prompt, tokenizer, text_encoder, max_length=77): | |
| tokens = tokenizer(prompt, truncation=False, return_tensors="pt")["input_ids"] | |
| chunks = [tokens[:, i:i + max_length] for i in range(0, tokens.shape[1], max_length)] | |
| with torch.no_grad(): | |
| embeds = [text_encoder(chunk.to(text_encoder.device))[0] for chunk in chunks] | |
| return torch.cat(embeds, dim=1) | |
| def align_embeddings(prompt_embeds, negative_prompt_embeds): | |
| max_length = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1]) | |
| return torch.nn.functional.pad(prompt_embeds, (0, 0, 0, max_length - prompt_embeds.shape[1])), \ | |
| torch.nn.functional.pad(negative_prompt_embeds, (0, 0, 0, max_length - negative_prompt_embeds.shape[1])) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model_id_default = "stable-diffusion-v1-5/stable-diffusion-v1-5" | |
| torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| pipe_default = get_lora_sd_pipeline(ckpt_dir='./lora_man_animestyle', base_model_name_or_path=model_id_default, dtype=torch_dtype).to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| def infer( | |
| prompt, | |
| negative_prompt, | |
| width=512, | |
| height=512, | |
| num_inference_steps=20, | |
| model_id='stable-diffusion-v1-5/stable-diffusion-v1-5', | |
| seed=4, | |
| guidance_scale=7.5, | |
| lora_scale=0.5, | |
| progress=gr.Progress(track_tqdm=True) | |
| ): | |
| generator = torch.Generator(device).manual_seed(seed) | |
| if model_id != model_id_default: | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device) | |
| prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder) | |
| negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder) | |
| prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds) | |
| else: | |
| pipe = pipe_default | |
| prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder) | |
| negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder) | |
| prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds) | |
| print(f"LoRA adapter loaded: {pipe.unet.active_adapters}") | |
| print(f"LoRA scale applied: {lora_scale}") | |
| pipe.fuse_lora(lora_scale=lora_scale) | |
| params = { | |
| 'prompt_embeds': prompt_embeds, | |
| 'negative_prompt_embeds': negative_prompt_embeds, | |
| 'guidance_scale': guidance_scale, | |
| 'num_inference_steps': num_inference_steps, | |
| 'width': width, | |
| 'height': height, | |
| 'generator': generator, | |
| } | |
| return pipe(**params).images[0] | |
| examples = [ | |
| "Young man in anime style. The image is of high sharpness and resolution. A handsome, thoughtful man. The man is depicted in the foreground, close-up or middle plan. The background is blurry, not sharp. The play of light and shadow is visible on the face and clothes." | |
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.", | |
| "An astronaut riding a green horse.", | |
| "A delicious ceviche cheesecake slice.", | |
| "A futuristic sports car is located on the surface of Mars. Stars, planets, mountains and craters are visible.", | |
| ] | |
| examples_negative = [ | |
| "blurred details, low resolution, poor image of a man's face, poor quality, artifacts, black and white image" | |
| "blurry details, low resolution, poorly defined edges", | |
| "bad face, bad quality, artifacts, low-res, black and white", | |
| ] | |
| css = """ | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 640px; | |
| } | |
| """ | |
| available_models = [ | |
| "stable-diffusion-v1-5/stable-diffusion-v1-5", | |
| "SG161222/Realistic_Vision_V3.0_VAE", | |
| "CompVis/stable-diffusion-v1-4", | |
| "stabilityai/sdxl-turbo", | |
| "runwayml/stable-diffusion-v1-5", | |
| "sd-legacy/stable-diffusion-v1-5", | |
| "prompthero/openjourney", | |
| "stabilityai/stable-diffusion-3-medium-diffusers", | |
| "stabilityai/stable-diffusion-3.5-large", | |
| "stabilityai/stable-diffusion-3.5-large-turbo", | |
| ] | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(" # Text-to-Image Gradio Template from V. Gorsky") | |
| with gr.Row(): | |
| model_id = gr.Textbox( | |
| label="Model", | |
| max_lines=1, | |
| placeholder="Enter model id like 'stable-diffusion-v1-5/stable-diffusion-v1-5'", | |
| value=model_id_default | |
| ) | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| ) | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| ) | |
| with gr.Row(): | |
| lora_scale = gr.Slider( | |
| label="LoRA scale", | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.5, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=7.5, | |
| ) | |
| with gr.Row(): | |
| seed = gr.Number( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=4, | |
| ) | |
| with gr.Row(): | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=30, | |
| ) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, | |
| ) | |
| with gr.Row(): | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=512, | |
| ) | |
| gr.Examples(examples=examples, inputs=[prompt]) | |
| gr.Examples(examples=examples_negative, inputs=[negative_prompt]) | |
| run_button = gr.Button("Run", scale=1, variant="primary") | |
| result = gr.Image(label="Result", show_label=False) | |
| gr.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn=infer, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| width, | |
| height, | |
| num_inference_steps, | |
| model_id, | |
| seed, | |
| guidance_scale, | |
| lora_scale, | |
| ], | |
| outputs=[result], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |