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| 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 <head:id> <wing:id> bird"` where `id` ranges from 1~200 (200 classes corresponding to CUB-200-2011) | |
| For instance `"a photo of a <head:17> <wing:18> 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 <wing:17> cat"` | |
| Inspiring design: `"a photo of a <head:101> <wing:191> teddy bear"` | |
| (Experimental) You can also use two parts together such as: | |
| `"a photo of a <head:17> <head:18> 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 <nose:id> <ear:id> dog"` where `id` ranges from 0~119 (120 classes corresponding to Stanford Dogs) | |
| For instance `"a photo of a <nose:2> <ear:112> 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 <ear:112> cat"` | |
| Inspiring design: `"a photo of a <eye:38> <body:38> teddy bear"` | |
| (Experimental) You can also use two parts together such as: | |
| `"a photo of a <nose:1> <nose:112> 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" | |
| pipe = load_pipeline(args, torch.float16, 'cuda') | |
| pipe = pipe.to(torch.float16) | |
| 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 | |
| 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): | |
| generator = torch.Generator(device='cuda') | |
| generator = generator.manual_seed(int(seed)) | |
| try: | |
| pipe, MAPPING, ID2NAME = prepare_pipeline(model_name) | |
| 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() | |
| 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 <head:101> <wing:191> 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') | |