Spaces:
Runtime error
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 einops | |
| import torch | |
| import torch as th | |
| import torch.nn as nn | |
| from ldm.modules.diffusionmodules.util import ( | |
| conv_nd, | |
| linear, | |
| zero_module, | |
| timestep_embedding, | |
| ) | |
| from einops import rearrange, repeat | |
| from torchvision.utils import make_grid | |
| from ldm.modules.attention import SpatialTransformer | |
| from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock | |
| from ldm.models.diffusion.ddpm import LatentDiffusion | |
| from ldm.util import log_txt_as_img, exists, instantiate_from_config | |
| from ldm.models.diffusion.ddim import DDIMSampler | |
| class ControlledUnetModel(UNetModel): | |
| def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs): | |
| hs = [] | |
| with torch.no_grad(): | |
| t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) | |
| emb = self.time_embed(t_emb) | |
| h = x.type(self.dtype) | |
| for module in self.input_blocks: | |
| h = module(h, emb, context) | |
| hs.append(h) | |
| h = self.middle_block(h, emb, context) | |
| if control is not None: | |
| h += control.pop() | |
| for i, module in enumerate(self.output_blocks): | |
| if only_mid_control or control is None: | |
| h = torch.cat([h, hs.pop()], dim=1) | |
| else: | |
| h = torch.cat([h, hs.pop() + control.pop()], dim=1) | |
| h = module(h, emb, context) | |
| h = h.type(x.dtype) | |
| return self.out(h) | |
| class ControlNet(nn.Module): | |
| def __init__( | |
| self, | |
| image_size, | |
| in_channels, | |
| model_channels, | |
| hint_channels, | |
| num_res_blocks, | |
| attention_resolutions, | |
| dropout=0, | |
| channel_mult=(1, 2, 4, 8), | |
| conv_resample=True, | |
| dims=2, | |
| use_checkpoint=False, | |
| use_fp16=False, | |
| num_heads=-1, | |
| num_head_channels=-1, | |
| num_heads_upsample=-1, | |
| use_scale_shift_norm=False, | |
| resblock_updown=False, | |
| use_new_attention_order=False, | |
| use_spatial_transformer=False, # custom transformer support | |
| transformer_depth=1, # custom transformer support | |
| context_dim=None, # custom transformer support | |
| n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model | |
| legacy=True, | |
| disable_self_attentions=None, | |
| num_attention_blocks=None, | |
| disable_middle_self_attn=False, | |
| use_linear_in_transformer=False, | |
| ): | |
| super().__init__() | |
| if use_spatial_transformer: | |
| assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' | |
| if context_dim is not None: | |
| assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' | |
| from omegaconf.listconfig import ListConfig | |
| if type(context_dim) == ListConfig: | |
| context_dim = list(context_dim) | |
| if num_heads_upsample == -1: | |
| num_heads_upsample = num_heads | |
| if num_heads == -1: | |
| assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' | |
| if num_head_channels == -1: | |
| assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' | |
| self.dims = dims | |
| self.image_size = image_size | |
| self.in_channels = in_channels | |
| self.model_channels = model_channels | |
| if isinstance(num_res_blocks, int): | |
| self.num_res_blocks = len(channel_mult) * [num_res_blocks] | |
| else: | |
| if len(num_res_blocks) != len(channel_mult): | |
| raise ValueError("provide num_res_blocks either as an int (globally constant) or " | |
| "as a list/tuple (per-level) with the same length as channel_mult") | |
| self.num_res_blocks = num_res_blocks | |
| if disable_self_attentions is not None: | |
| # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not | |
| assert len(disable_self_attentions) == len(channel_mult) | |
| if num_attention_blocks is not None: | |
| assert len(num_attention_blocks) == len(self.num_res_blocks) | |
| assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) | |
| print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " | |
| f"This option has LESS priority than attention_resolutions {attention_resolutions}, " | |
| f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " | |
| f"attention will still not be set.") | |
| self.attention_resolutions = attention_resolutions | |
| self.dropout = dropout | |
| self.channel_mult = channel_mult | |
| self.conv_resample = conv_resample | |
| self.use_checkpoint = use_checkpoint | |
| self.dtype = th.float16 if use_fp16 else th.float32 | |
| self.num_heads = num_heads | |
| self.num_head_channels = num_head_channels | |
| self.num_heads_upsample = num_heads_upsample | |
| self.predict_codebook_ids = n_embed is not None | |
| time_embed_dim = model_channels * 4 | |
| self.time_embed = nn.Sequential( | |
| linear(model_channels, time_embed_dim), | |
| nn.SiLU(), | |
| linear(time_embed_dim, time_embed_dim), | |
| ) | |
| self.input_blocks = nn.ModuleList( | |
| [ | |
| TimestepEmbedSequential( | |
| conv_nd(dims, in_channels, model_channels, 3, padding=1) | |
| ) | |
| ] | |
| ) | |
| self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)]) | |
| self.input_hint_block = TimestepEmbedSequential( | |
| conv_nd(dims, hint_channels, 16, 3, padding=1), | |
| nn.SiLU(), | |
| conv_nd(dims, 16, 16, 3, padding=1), | |
| nn.SiLU(), | |
| conv_nd(dims, 16, 32, 3, padding=1, stride=2), | |
| nn.SiLU(), | |
| conv_nd(dims, 32, 32, 3, padding=1), | |
| nn.SiLU(), | |
| conv_nd(dims, 32, 96, 3, padding=1, stride=2), | |
| nn.SiLU(), | |
| conv_nd(dims, 96, 96, 3, padding=1), | |
| nn.SiLU(), | |
| conv_nd(dims, 96, 256, 3, padding=1, stride=2), | |
| nn.SiLU(), | |
| zero_module(conv_nd(dims, 256, model_channels, 3, padding=1)) | |
| ) | |
| self._feature_size = model_channels | |
| input_block_chans = [model_channels] | |
| ch = model_channels | |
| ds = 1 | |
| for level, mult in enumerate(channel_mult): | |
| for nr in range(self.num_res_blocks[level]): | |
| layers = [ | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| out_channels=mult * model_channels, | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ) | |
| ] | |
| ch = mult * model_channels | |
| if ds in attention_resolutions: | |
| if num_head_channels == -1: | |
| dim_head = ch // num_heads | |
| else: | |
| num_heads = ch // num_head_channels | |
| dim_head = num_head_channels | |
| if legacy: | |
| # num_heads = 1 | |
| dim_head = ch // num_heads if use_spatial_transformer else num_head_channels | |
| if exists(disable_self_attentions): | |
| disabled_sa = disable_self_attentions[level] | |
| else: | |
| disabled_sa = False | |
| if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: | |
| layers.append( | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads, | |
| num_head_channels=dim_head, | |
| use_new_attention_order=use_new_attention_order, | |
| ) if not use_spatial_transformer else SpatialTransformer( | |
| ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, | |
| disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, | |
| use_checkpoint=use_checkpoint | |
| ) | |
| ) | |
| self.input_blocks.append(TimestepEmbedSequential(*layers)) | |
| self.zero_convs.append(self.make_zero_conv(ch)) | |
| self._feature_size += ch | |
| input_block_chans.append(ch) | |
| if level != len(channel_mult) - 1: | |
| out_ch = ch | |
| self.input_blocks.append( | |
| TimestepEmbedSequential( | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| out_channels=out_ch, | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| down=True, | |
| ) | |
| if resblock_updown | |
| else Downsample( | |
| ch, conv_resample, dims=dims, out_channels=out_ch | |
| ) | |
| ) | |
| ) | |
| ch = out_ch | |
| input_block_chans.append(ch) | |
| self.zero_convs.append(self.make_zero_conv(ch)) | |
| ds *= 2 | |
| self._feature_size += ch | |
| if num_head_channels == -1: | |
| dim_head = ch // num_heads | |
| else: | |
| num_heads = ch // num_head_channels | |
| dim_head = num_head_channels | |
| if legacy: | |
| # num_heads = 1 | |
| dim_head = ch // num_heads if use_spatial_transformer else num_head_channels | |
| self.middle_block = TimestepEmbedSequential( | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ), | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads, | |
| num_head_channels=dim_head, | |
| use_new_attention_order=use_new_attention_order, | |
| ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn | |
| ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, | |
| disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, | |
| use_checkpoint=use_checkpoint | |
| ), | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ), | |
| ) | |
| self.middle_block_out = self.make_zero_conv(ch) | |
| self._feature_size += ch | |
| def make_zero_conv(self, channels): | |
| return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0))) | |
| def forward(self, x, hint, timesteps, context, **kwargs): | |
| t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) | |
| emb = self.time_embed(t_emb) | |
| guided_hint = self.input_hint_block(hint, emb, context) | |
| outs = [] | |
| h = x.type(self.dtype) | |
| for module, zero_conv in zip(self.input_blocks, self.zero_convs): | |
| if guided_hint is not None: | |
| h = module(h, emb, context) | |
| h += guided_hint | |
| guided_hint = None | |
| else: | |
| h = module(h, emb, context) | |
| outs.append(zero_conv(h, emb, context)) | |
| h = self.middle_block(h, emb, context) | |
| outs.append(self.middle_block_out(h, emb, context)) | |
| return outs | |
| class ControlLDM(LatentDiffusion): | |
| def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self.control_model = instantiate_from_config(control_stage_config) | |
| self.control_key = control_key | |
| self.only_mid_control = only_mid_control | |
| self.control_scales = [1.0] * 13 | |
| def get_input(self, batch, k, bs=None, *args, **kwargs): | |
| x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs) | |
| control = batch[self.control_key] | |
| if bs is not None: | |
| control = control[:bs] | |
| control = control.to(self.device) | |
| control = einops.rearrange(control, 'b h w c -> b c h w') | |
| control = control.to(memory_format=torch.contiguous_format).float() | |
| return x, dict(c_crossattn=[c], c_concat=[control]) | |
| def apply_model(self, x_noisy, t, cond, *args, **kwargs): | |
| assert isinstance(cond, dict) | |
| diffusion_model = self.model.diffusion_model | |
| cond_txt = torch.cat(cond['c_crossattn'], 1) | |
| if cond['c_concat'] is None: | |
| eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control) | |
| else: | |
| control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt) | |
| control = [c * scale for c, scale in zip(control, self.control_scales)] | |
| eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control) | |
| return eps | |
| def get_unconditional_conditioning(self, N): | |
| return self.get_learned_conditioning([""] * N) | |
| def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None, | |
| quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, | |
| plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None, | |
| use_ema_scope=True, | |
| **kwargs): | |
| use_ddim = ddim_steps is not None | |
| log = dict() | |
| z, c = self.get_input(batch, self.first_stage_key, bs=N) | |
| c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N] | |
| N = min(z.shape[0], N) | |
| n_row = min(z.shape[0], n_row) | |
| log["reconstruction"] = self.decode_first_stage(z) | |
| log["control"] = c_cat * 2.0 - 1.0 | |
| log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16) | |
| if plot_diffusion_rows: | |
| # get diffusion row | |
| diffusion_row = list() | |
| z_start = z[:n_row] | |
| for t in range(self.num_timesteps): | |
| if t % self.log_every_t == 0 or t == self.num_timesteps - 1: | |
| t = repeat(torch.tensor([t]), '1 -> b', b=n_row) | |
| t = t.to(self.device).long() | |
| noise = torch.randn_like(z_start) | |
| z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) | |
| diffusion_row.append(self.decode_first_stage(z_noisy)) | |
| diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W | |
| diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') | |
| diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') | |
| diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) | |
| log["diffusion_row"] = diffusion_grid | |
| if sample: | |
| # get denoise row | |
| samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, | |
| batch_size=N, ddim=use_ddim, | |
| ddim_steps=ddim_steps, eta=ddim_eta) | |
| x_samples = self.decode_first_stage(samples) | |
| log["samples"] = x_samples | |
| if plot_denoise_rows: | |
| denoise_grid = self._get_denoise_row_from_list(z_denoise_row) | |
| log["denoise_row"] = denoise_grid | |
| if unconditional_guidance_scale > 1.0: | |
| uc_cross = self.get_unconditional_conditioning(N) | |
| uc_cat = c_cat # torch.zeros_like(c_cat) | |
| uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} | |
| samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, | |
| batch_size=N, ddim=use_ddim, | |
| ddim_steps=ddim_steps, eta=ddim_eta, | |
| unconditional_guidance_scale=unconditional_guidance_scale, | |
| unconditional_conditioning=uc_full, | |
| ) | |
| x_samples_cfg = self.decode_first_stage(samples_cfg) | |
| log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg | |
| return log | |
| def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): | |
| ddim_sampler = DDIMSampler(self) | |
| b, c, h, w = cond["c_concat"][0].shape | |
| shape = (self.channels, h // 8, w // 8) | |
| samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs) | |
| return samples, intermediates | |
| def configure_optimizers(self): | |
| lr = self.learning_rate | |
| params = list(self.control_model.parameters()) | |
| if not self.sd_locked: | |
| params += list(self.model.diffusion_model.output_blocks.parameters()) | |
| params += list(self.model.diffusion_model.out.parameters()) | |
| opt = torch.optim.AdamW(params, lr=lr) | |
| return opt | |
| def low_vram_shift(self, is_diffusing): | |
| if is_diffusing: | |
| self.model = self.model.cuda() | |
| self.control_model = self.control_model.cuda() | |
| self.first_stage_model = self.first_stage_model.cpu() | |
| self.cond_stage_model = self.cond_stage_model.cpu() | |
| else: | |
| self.model = self.model.cpu() | |
| self.control_model = self.control_model.cpu() | |
| self.first_stage_model = self.first_stage_model.cuda() | |
| self.cond_stage_model = self.cond_stage_model.cuda() | |