102 lines
5.3 KiB
Python
102 lines
5.3 KiB
Python
import torch
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import gradio as gr
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import os
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import pathlib
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from modules import script_callbacks
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from modules.paths import models_path
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from modules.ui_common import ToolButton, refresh_symbol
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from modules.ui_components import ResizeHandleRow
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from modules import shared
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from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy
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from ldm_patched.modules.sd import load_checkpoint_guess_config
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from ldm_patched.contrib.external_stable3d import StableZero123_Conditioning
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from ldm_patched.contrib.external import KSampler, VAEDecode
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opStableZero123_Conditioning = StableZero123_Conditioning()
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opKSampler = KSampler()
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opVAEDecode = VAEDecode()
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model_root = os.path.join(models_path, 'z123')
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os.makedirs(model_root, exist_ok=True)
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model_filenames = []
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def update_model_filenames():
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global model_filenames
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model_filenames = [
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pathlib.Path(x).name for x in
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shared.walk_files(model_root, allowed_extensions=[".pt", ".ckpt", ".safetensors"])
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]
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return model_filenames
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@torch.inference_mode()
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@torch.no_grad()
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def predict(filename, width, height, batch_size, elevation, azimuth,
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sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler, sampling_denoise, input_image):
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filename = os.path.join(model_root, filename)
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model, _, vae, clip_vision = \
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load_checkpoint_guess_config(filename, output_vae=True, output_clip=False, output_clipvision=True)
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init_image = numpy_to_pytorch(input_image)
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positive, negative, latent_image = opStableZero123_Conditioning.encode(
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clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth)
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output_latent = opKSampler.sample(model, sampling_seed, sampling_steps, sampling_cfg,
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sampling_sampler_name, sampling_scheduler, positive,
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negative, latent_image, sampling_denoise)[0]
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output_pixels = opVAEDecode.decode(vae, output_latent)[0]
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outputs = pytorch_to_numpy(output_pixels)
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return outputs
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def on_ui_tabs():
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with gr.Blocks() as model_block:
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with ResizeHandleRow():
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with gr.Column():
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input_image = gr.Image(label='Input Image', source='upload', type='numpy', height=400)
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with gr.Row():
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filename = gr.Dropdown(label="Zero123 Checkpoint Filename",
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choices=model_filenames,
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value=model_filenames[0] if len(model_filenames) > 0 else None)
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refresh_button = ToolButton(value=refresh_symbol, tooltip="Refresh")
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refresh_button.click(
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fn=lambda: gr.update(choices=update_model_filenames),
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inputs=[], outputs=filename)
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width = gr.Slider(label='Width', minimum=16, maximum=8192, step=8, value=256)
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height = gr.Slider(label='Height', minimum=16, maximum=8192, step=8, value=256)
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batch_size = gr.Slider(label='Batch Size', minimum=1, maximum=4096, step=1, value=4)
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elevation = gr.Slider(label='Elevation', minimum=-180.0, maximum=180.0, step=0.001, value=10.0)
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azimuth = gr.Slider(label='Azimuth', minimum=-180.0, maximum=180.0, step=0.001, value=142.0)
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sampling_denoise = gr.Slider(label='Sampling Denoise', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
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sampling_steps = gr.Slider(label='Sampling Steps', minimum=1, maximum=10000, step=1, value=20)
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sampling_cfg = gr.Slider(label='CFG Scale', minimum=0.0, maximum=100.0, step=0.1, value=5.0)
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sampling_sampler_name = gr.Radio(label='Sampler Name',
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choices=['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2',
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'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive',
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'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu',
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'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu',
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'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim',
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'uni_pc', 'uni_pc_bh2'], value='euler')
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sampling_scheduler = gr.Radio(label='Sampling Scheduler',
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choices=['normal', 'karras', 'exponential', 'sgm_uniform', 'simple',
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'ddim_uniform'], value='sgm_uniform')
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sampling_seed = gr.Number(label='Seed', value=12345, precision=0)
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generate_button = gr.Button(value="Generate")
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ctrls = [filename, width, height, batch_size, elevation, azimuth, sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler, sampling_denoise, input_image]
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with gr.Column():
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output_gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain',
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visible=True, height=1024, columns=4)
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generate_button.click(predict, inputs=ctrls, outputs=[output_gallery])
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return [(model_block, "Z123", "z123")]
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update_model_filenames()
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script_callbacks.on_ui_tabs(on_ui_tabs)
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