import gc import os import re import shutil import gradio as gr import requests import torch from dreamcreature.pipeline import create_args, load_pipeline CUB_DESCRIPTION = """ # DreamCreature (CUB-200-2011) To create your own creature, you can type: `"a photo of a bird"` where `id` ranges from 1~200 (200 classes corresponding to CUB-200-2011) For instance `"a photo of a bird"` using head of `cardinal (17)` and wing of `spotted catbird (18)` Please see `id` in https://github.com/kamwoh/dreamcreature/blob/master/src/data/cub200_2011/class_names.txt You can also try any prompt you like such as: Sub-concept transfer: `"a photo of a cat"` Inspiring design: `"a photo of a teddy bear"` (Experimental) You can also use two parts together such as: `"a photo of a bird"` mixing head of `cardinal (17)` and `spotted catbird (18)` The current available parts are: `head`, `body`, `wing`, `tail`, and `leg` """ DOG_DESCRIPTION = """ # DreamCreature (Stanford Dogs) To create your own creature, you can type: `"a photo of a dog"` where `id` ranges from 0~119 (120 classes corresponding to Stanford Dogs) For instance `"a photo of a dog"` using head of `maltese dog (2)` and wing of `cardigan (112)` Please see `id` in https://github.com/kamwoh/dreamcreature/blob/master/src/data/dogs/class_names.txt Sub-concept transfer: `"a photo of a cat"` Inspiring design: `"a photo of a teddy bear"` (Experimental) You can also use two parts together such as: `"a photo of a dog"` mixing head of `maltese dog (2)` and `spotted cardigan (112)` The current available parts are: `eye`, `neck`, `ear`, `body`, `leg`, `nose` and `forehead` """ def prepare_pipeline(model_name): is_cub = 'cub' in model_name checkpoint_name = { 'dreamcreature-sd1.5-cub200': 'checkpoint-74900', 'dreamcreature-sd1.5-dog': 'checkpoint-150000' } repo_url = f"https://huggingface.co/kamwoh/{model_name}/resolve/main" file_url = repo_url + f"/{checkpoint_name}/pytorch_model.bin" local_path = f"{model_name}/{checkpoint_name}/pytorch_model.bin" os.makedirs(f"{model_name}/{checkpoint_name}", exist_ok=True) download_file(file_url, local_path) file_url = repo_url + f"/{checkpoint_name}/pytorch_model_1.bin" local_path = f"{model_name}/{checkpoint_name}/pytorch_model_1.bin" download_file(file_url, local_path) OUTPUT_DIR = model_name args = create_args(OUTPUT_DIR) if 'dpo' in OUTPUT_DIR: args.unet_path = "mhdang/dpo-sd1.5-text2image-v1" device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') weight_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 pipe = load_pipeline(args, weight_dtype, device) pipe = pipe.to(weight_dtype) pipe.verbose = True pipe.v = 're' if is_cub: pipe.num_k_per_part = 200 MAPPING = { 'body': 0, 'tail': 1, 'head': 2, 'wing': 4, 'leg': 6 } ID2NAME = open('data/cub200_2011/class_names.txt').readlines() ID2NAME = [line.strip() for line in ID2NAME] else: pipe.num_k_per_part = 120 MAPPING = { 'eye': 0, 'neck': 2, 'ear': 3, 'body': 4, 'leg': 5, 'nose': 6, 'forehead': 7 } ID2NAME = open('data/dogs/class_names.txt').readlines() ID2NAME = [line.strip() for line in ID2NAME] return pipe, MAPPING, ID2NAME, device def download_file(url, local_path): if os.path.exists(local_path): return with requests.get(url, stream=True) as r: with open(local_path, 'wb') as f: shutil.copyfileobj(r.raw, f) def process_text(text, MAPPING, ID2NAME): pattern = r"<([^:>]+):(\d+)>" result = text offset = 0 part2id = [] for match in re.finditer(pattern, text): key = match.group(1) clsid = int(match.group(2)) clsid = min(max(clsid, 1), 200) # must be 1~200 replacement = f"<{MAPPING[key]}:{clsid - 1}>" start, end = match.span() # Adjust the start and end positions based on the offset from previous replacements start += offset end += offset # Replace the matched text with the replacement result = result[:start] + replacement + result[end:] # Update the offset for the next replacement offset += len(replacement) - (end - start) part2id.append(f'{key}: {ID2NAME[clsid - 1]}') return result, part2id def generate_images(model_name, prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, seed): try: pipe, MAPPING, ID2NAME, device = prepare_pipeline(model_name) generator = torch.Generator(device=device) generator = generator.manual_seed(int(seed)) prompt, part2id = process_text(prompt, MAPPING, ID2NAME) negative_prompt, _ = process_text(negative_prompt, MAPPING, ID2NAME) images = pipe(prompt, negative_prompt=negative_prompt, generator=generator, num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale, num_images_per_prompt=num_images).images del pipe except Exception as e: raise gr.Error(f"Probably due to the prompt have invalid input, please follow the instruction. " f"The error message: {e}") finally: gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() return images, '; '.join(part2id) with gr.Blocks(title="DreamCreature") as demo: with gr.Row(): main_desc = gr.Markdown(CUB_DESCRIPTION) with gr.Column(): with gr.Row(): with gr.Group(): dropdown = gr.Dropdown(choices=["dreamcreature-sd1.5-cub200", "dreamcreature-sd1.5-dog"], value="dreamcreature-sd1.5-cub200") prompt = gr.Textbox(label="Prompt", value="a photo of a teddy bear") negative_prompt = gr.Textbox(label="Negative Prompt", value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic") num_inference_steps = gr.Slider(minimum=10, maximum=100, step=1, value=30, label="Num Inference Steps") guidance_scale = gr.Slider(minimum=2, maximum=20, step=0.1, value=7.5, label="Guidance Scale") num_images = gr.Slider(minimum=1, maximum=4, step=1, value=4, label="Number of Images") seed = gr.Number(label="Seed", value=777881414) button = gr.Button() with gr.Column(): output_images = gr.Gallery(columns=4, label='Output') markdown_labels = gr.Markdown("") dropdown.change(fn=lambda x: {'dreamcreature-sd1.5-cub200': CUB_DESCRIPTION, 'dreamcreature-sd1.5-dog': DOG_DESCRIPTION}[x], inputs=dropdown, outputs=main_desc) button.click(fn=generate_images, inputs=[dropdown, prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, seed], outputs=[output_images, markdown_labels], show_progress=True) demo.queue().launch(inline=False, share=True, debug=True, server_name='0.0.0.0')