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Runtime error
Joseph Catrambone
First import. Move gradio example from ControlNet branch to a standalone for use in HF Space. Add loading from HF hub.
2a6b1af
| import os | |
| import random | |
| from typing import Mapping | |
| import gradio as gr | |
| import numpy | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from cldm.model import create_model, load_state_dict | |
| from cldm.ddim_hacked import DDIMSampler | |
| from mediapipe_face_common import generate_annotation | |
| # Download the SD 1.5 model from HF | |
| model_path = hf_hub_download(repo_id="CrucibleAI/ControlNetMediaPipeFace", filename="models/controlnet_sd21_laion_face_v2_full.ckpt", repo_type="model") | |
| config_path = hf_hub_download(repo_id="CrucibleAI/ControlNetMediaPipeFace", filename="models/cldm_v21.yaml", repo_type="model") | |
| model = create_model(config_path).cpu() | |
| model.load_state_dict(load_state_dict(model_path, location='cuda')) | |
| model = model.cuda() | |
| ddim_sampler = DDIMSampler(model) # ControlNet _only_ works with DDIM. | |
| def process(input_image: Image.Image, prompt, a_prompt, n_prompt, max_faces, min_confidence, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta): | |
| with torch.no_grad(): | |
| empty = generate_annotation(input_image, max_faces, min_confidence) | |
| visualization = Image.fromarray(empty) # Save to help debug. | |
| empty = numpy.moveaxis(empty, 2, 0) # h, w, c -> c, h, w | |
| control = torch.from_numpy(empty.copy()).float().cuda() / 255.0 | |
| control = torch.stack([control for _ in range(num_samples)], dim=0) | |
| # control = einops.rearrange(control, 'b h w c -> b c h w').clone() | |
| # Sanity check the dimensions. | |
| B, C, H, W = control.shape | |
| assert C == 3 | |
| assert B == num_samples | |
| if seed != -1: | |
| random.seed(seed) | |
| os.environ['PYTHONHASHSEED'] = str(seed) | |
| numpy.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.backends.cudnn.deterministic = True | |
| # model.low_vram_shift(is_diffusing=False) | |
| cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} | |
| un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} | |
| shape = (4, H // 8, W // 8) | |
| # model.low_vram_shift(is_diffusing=True) | |
| model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 | |
| samples, intermediates = ddim_sampler.sample( | |
| ddim_steps, | |
| num_samples, | |
| shape, | |
| cond, | |
| verbose=False, | |
| eta=eta, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=un_cond | |
| ) | |
| # model.low_vram_shift(is_diffusing=False) | |
| x_samples = model.decode_first_stage(samples) | |
| # x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(numpy.uint8) | |
| x_samples = numpy.moveaxis((x_samples * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(numpy.uint8), 1, -1) # b, c, h, w -> b, h, w, c | |
| results = [visualization] + [x_samples[i] for i in range(num_samples)] | |
| return results | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown("## Control Stable Diffusion with a Facial Pose") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| prompt = gr.Textbox(label="Prompt") | |
| run_button = gr.Button(label="Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) | |
| max_faces = gr.Slider(label="Max Faces", minimum=1, maximum=5, value=1, step=1) | |
| min_confidence = gr.Slider(label="Min Confidence", minimum=0.01, maximum=1.0, value=0.5, step=0.01) | |
| strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) | |
| guess_mode = gr.Checkbox(label='Guess Mode', value=False) | |
| ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) | |
| scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox(label="Negative Prompt", | |
| value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') | |
| with gr.Column(): | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| ips = [input_image, prompt, a_prompt, n_prompt, max_faces, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta] | |
| run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) | |
| block.launch(server_name='0.0.0.0') | |