| import os
|
| import torch
|
| import time
|
| import math
|
| import ldm_patched.modules.model_base
|
| import ldm_patched.ldm.modules.diffusionmodules.openaimodel
|
| import ldm_patched.modules.model_management
|
| import modules.anisotropic as anisotropic
|
| import ldm_patched.ldm.modules.attention
|
| import ldm_patched.k_diffusion.sampling
|
| import ldm_patched.modules.sd1_clip
|
| import modules.inpaint_worker as inpaint_worker
|
| import ldm_patched.ldm.modules.diffusionmodules.openaimodel
|
| import ldm_patched.ldm.modules.diffusionmodules.model
|
| import ldm_patched.modules.sd
|
| import ldm_patched.controlnet.cldm
|
| import ldm_patched.modules.model_patcher
|
| import ldm_patched.modules.samplers
|
| import ldm_patched.modules.args_parser
|
| import warnings
|
| import safetensors.torch
|
| import modules.constants as constants
|
|
|
| from ldm_patched.modules.samplers import calc_cond_uncond_batch
|
| from ldm_patched.k_diffusion.sampling import BatchedBrownianTree
|
| from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control
|
| from modules.patch_precision import patch_all_precision
|
| from modules.patch_clip import patch_all_clip
|
|
|
|
|
| class PatchSettings:
|
| def __init__(self,
|
| sharpness=2.0,
|
| adm_scaler_end=0.3,
|
| positive_adm_scale=1.5,
|
| negative_adm_scale=0.8,
|
| controlnet_softness=0.25,
|
| adaptive_cfg=7.0):
|
| self.sharpness = sharpness
|
| self.adm_scaler_end = adm_scaler_end
|
| self.positive_adm_scale = positive_adm_scale
|
| self.negative_adm_scale = negative_adm_scale
|
| self.controlnet_softness = controlnet_softness
|
| self.adaptive_cfg = adaptive_cfg
|
| self.global_diffusion_progress = 0
|
| self.eps_record = None
|
|
|
|
|
| patch_settings = {}
|
|
|
|
|
| def calculate_weight_patched(self, patches, weight, key):
|
| for p in patches:
|
| alpha = p[0]
|
| v = p[1]
|
| strength_model = p[2]
|
|
|
| if strength_model != 1.0:
|
| weight *= strength_model
|
|
|
| if isinstance(v, list):
|
| v = (self.calculate_weight(v[1:], v[0].clone(), key),)
|
|
|
| if len(v) == 1:
|
| patch_type = "diff"
|
| elif len(v) == 2:
|
| patch_type = v[0]
|
| v = v[1]
|
|
|
| if patch_type == "diff":
|
| w1 = v[0]
|
| if alpha != 0.0:
|
| if w1.shape != weight.shape:
|
| print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
|
| else:
|
| weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype)
|
| elif patch_type == "lora":
|
| mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32)
|
| mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32)
|
| if v[2] is not None:
|
| alpha *= v[2] / mat2.shape[0]
|
| if v[3] is not None:
|
| mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32)
|
| final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
|
| mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1),
|
| mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
|
| try:
|
| weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(
|
| weight.shape).type(weight.dtype)
|
| except Exception as e:
|
| print("ERROR", key, e)
|
| elif patch_type == "fooocus":
|
| w1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32)
|
| w_min = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32)
|
| w_max = ldm_patched.modules.model_management.cast_to_device(v[2], weight.device, torch.float32)
|
| w1 = (w1 / 255.0) * (w_max - w_min) + w_min
|
| if alpha != 0.0:
|
| if w1.shape != weight.shape:
|
| print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
|
| else:
|
| weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype)
|
| elif patch_type == "lokr":
|
| w1 = v[0]
|
| w2 = v[1]
|
| w1_a = v[3]
|
| w1_b = v[4]
|
| w2_a = v[5]
|
| w2_b = v[6]
|
| t2 = v[7]
|
| dim = None
|
|
|
| if w1 is None:
|
| dim = w1_b.shape[0]
|
| w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32))
|
| else:
|
| w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32)
|
|
|
| if w2 is None:
|
| dim = w2_b.shape[0]
|
| if t2 is None:
|
| w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32))
|
| else:
|
| w2 = torch.einsum('i j k l, j r, i p -> p r k l',
|
| ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32))
|
| else:
|
| w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32)
|
|
|
| if len(w2.shape) == 4:
|
| w1 = w1.unsqueeze(2).unsqueeze(2)
|
| if v[2] is not None and dim is not None:
|
| alpha *= v[2] / dim
|
|
|
| try:
|
| weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
|
| except Exception as e:
|
| print("ERROR", key, e)
|
| elif patch_type == "loha":
|
| w1a = v[0]
|
| w1b = v[1]
|
| if v[2] is not None:
|
| alpha *= v[2] / w1b.shape[0]
|
| w2a = v[3]
|
| w2b = v[4]
|
| if v[5] is not None:
|
| t1 = v[5]
|
| t2 = v[6]
|
| m1 = torch.einsum('i j k l, j r, i p -> p r k l',
|
| ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32))
|
|
|
| m2 = torch.einsum('i j k l, j r, i p -> p r k l',
|
| ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32))
|
| else:
|
| m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32))
|
| m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32),
|
| ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32))
|
|
|
| try:
|
| weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
|
| except Exception as e:
|
| print("ERROR", key, e)
|
| elif patch_type == "glora":
|
| if v[4] is not None:
|
| alpha *= v[4] / v[0].shape[0]
|
|
|
| a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
|
| a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
|
| b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
|
| b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
|
|
|
| weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
|
| else:
|
| print("patch type not recognized", patch_type, key)
|
|
|
| return weight
|
|
|
|
|
| class BrownianTreeNoiseSamplerPatched:
|
| transform = None
|
| tree = None
|
|
|
| @staticmethod
|
| def global_init(x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
|
| if ldm_patched.modules.model_management.directml_enabled:
|
| cpu = True
|
|
|
| t0, t1 = transform(torch.as_tensor(sigma_min)), transform(torch.as_tensor(sigma_max))
|
|
|
| BrownianTreeNoiseSamplerPatched.transform = transform
|
| BrownianTreeNoiseSamplerPatched.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
|
|
|
| def __init__(self, *args, **kwargs):
|
| pass
|
|
|
| @staticmethod
|
| def __call__(sigma, sigma_next):
|
| transform = BrownianTreeNoiseSamplerPatched.transform
|
| tree = BrownianTreeNoiseSamplerPatched.tree
|
|
|
| t0, t1 = transform(torch.as_tensor(sigma)), transform(torch.as_tensor(sigma_next))
|
| return tree(t0, t1) / (t1 - t0).abs().sqrt()
|
|
|
|
|
| def compute_cfg(uncond, cond, cfg_scale, t):
|
| pid = os.getpid()
|
| mimic_cfg = float(patch_settings[pid].adaptive_cfg)
|
| real_cfg = float(cfg_scale)
|
|
|
| real_eps = uncond + real_cfg * (cond - uncond)
|
|
|
| if cfg_scale > patch_settings[pid].adaptive_cfg:
|
| mimicked_eps = uncond + mimic_cfg * (cond - uncond)
|
| return real_eps * t + mimicked_eps * (1 - t)
|
| else:
|
| return real_eps
|
|
|
|
|
| def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
|
| pid = os.getpid()
|
|
|
| if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
|
| final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
|
|
|
| if patch_settings[pid].eps_record is not None:
|
| patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu()
|
|
|
| return final_x0
|
|
|
| positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
|
|
|
| positive_eps = x - positive_x0
|
| negative_eps = x - negative_x0
|
|
|
| alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress
|
|
|
| positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
|
| positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
|
|
|
| final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
|
| cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress)
|
|
|
| if patch_settings[pid].eps_record is not None:
|
| patch_settings[pid].eps_record = (final_eps / timestep).cpu()
|
|
|
| return x - final_eps
|
|
|
|
|
| def round_to_64(x):
|
| h = float(x)
|
| h = h / 64.0
|
| h = round(h)
|
| h = int(h)
|
| h = h * 64
|
| return h
|
|
|
|
|
| def sdxl_encode_adm_patched(self, **kwargs):
|
| clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
|
| width = kwargs.get("width", 1024)
|
| height = kwargs.get("height", 1024)
|
| target_width = width
|
| target_height = height
|
| pid = os.getpid()
|
|
|
| if kwargs.get("prompt_type", "") == "negative":
|
| width = float(width) * patch_settings[pid].negative_adm_scale
|
| height = float(height) * patch_settings[pid].negative_adm_scale
|
| elif kwargs.get("prompt_type", "") == "positive":
|
| width = float(width) * patch_settings[pid].positive_adm_scale
|
| height = float(height) * patch_settings[pid].positive_adm_scale
|
|
|
| def embedder(number_list):
|
| h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
|
| h = torch.flatten(h).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
|
| return h
|
|
|
| width, height = int(width), int(height)
|
| target_width, target_height = round_to_64(target_width), round_to_64(target_height)
|
|
|
| adm_emphasized = embedder([height, width, 0, 0, target_height, target_width])
|
| adm_consistent = embedder([target_height, target_width, 0, 0, target_height, target_width])
|
|
|
| clip_pooled = clip_pooled.to(adm_emphasized)
|
| final_adm = torch.cat((clip_pooled, adm_emphasized, clip_pooled, adm_consistent), dim=1)
|
|
|
| return final_adm
|
|
|
|
|
| def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
|
| if inpaint_worker.current_task is not None:
|
| latent_processor = self.inner_model.inner_model.process_latent_in
|
| inpaint_latent = latent_processor(inpaint_worker.current_task.latent).to(x)
|
| inpaint_mask = inpaint_worker.current_task.latent_mask.to(x)
|
|
|
| if getattr(self, 'energy_generator', None) is None:
|
|
|
| self.energy_generator = torch.Generator(device='cpu').manual_seed((seed + 1) % constants.MAX_SEED)
|
|
|
| energy_sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(x.shape) - 1))
|
| current_energy = torch.randn(
|
| x.size(), dtype=x.dtype, generator=self.energy_generator, device="cpu").to(x) * energy_sigma
|
| x = x * inpaint_mask + (inpaint_latent + current_energy) * (1.0 - inpaint_mask)
|
|
|
| out = self.inner_model(x, sigma,
|
| cond=cond,
|
| uncond=uncond,
|
| cond_scale=cond_scale,
|
| model_options=model_options,
|
| seed=seed)
|
|
|
| out = out * inpaint_mask + inpaint_latent * (1.0 - inpaint_mask)
|
| else:
|
| out = self.inner_model(x, sigma,
|
| cond=cond,
|
| uncond=uncond,
|
| cond_scale=cond_scale,
|
| model_options=model_options,
|
| seed=seed)
|
| return out
|
|
|
|
|
| def timed_adm(y, timesteps):
|
| if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
|
| y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
|
| y_with_adm = y[..., :2816].clone()
|
| y_without_adm = y[..., 2816:].clone()
|
| return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
|
| return y
|
|
|
|
|
| def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
|
| t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
|
| emb = self.time_embed(t_emb)
|
| pid = os.getpid()
|
|
|
| guided_hint = self.input_hint_block(hint, emb, context)
|
|
|
| y = timed_adm(y, timesteps)
|
|
|
| outs = []
|
|
|
| hs = []
|
| if self.num_classes is not None:
|
| assert y.shape[0] == x.shape[0]
|
| emb = emb + self.label_emb(y)
|
|
|
| h = x
|
| 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))
|
|
|
| if patch_settings[pid].controlnet_softness > 0:
|
| for i in range(10):
|
| k = 1.0 - float(i) / 9.0
|
| outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k)
|
|
|
| return outs
|
|
|
|
|
| def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
| self.current_step = 1.0 - timesteps.to(x) / 999.0
|
| patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
|
|
|
| y = timed_adm(y, timesteps)
|
|
|
| transformer_options["original_shape"] = list(x.shape)
|
| transformer_options["transformer_index"] = 0
|
| transformer_patches = transformer_options.get("patches", {})
|
|
|
| num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames)
|
| image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator)
|
| time_context = kwargs.get("time_context", None)
|
|
|
| assert (y is not None) == (
|
| self.num_classes is not None
|
| ), "must specify y if and only if the model is class-conditional"
|
| hs = []
|
| t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
|
| emb = self.time_embed(t_emb)
|
|
|
| if self.num_classes is not None:
|
| assert y.shape[0] == x.shape[0]
|
| emb = emb + self.label_emb(y)
|
|
|
| h = x
|
| for id, module in enumerate(self.input_blocks):
|
| transformer_options["block"] = ("input", id)
|
| h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
|
| h = apply_control(h, control, 'input')
|
| if "input_block_patch" in transformer_patches:
|
| patch = transformer_patches["input_block_patch"]
|
| for p in patch:
|
| h = p(h, transformer_options)
|
|
|
| hs.append(h)
|
| if "input_block_patch_after_skip" in transformer_patches:
|
| patch = transformer_patches["input_block_patch_after_skip"]
|
| for p in patch:
|
| h = p(h, transformer_options)
|
|
|
| transformer_options["block"] = ("middle", 0)
|
| h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
|
| h = apply_control(h, control, 'middle')
|
|
|
| for id, module in enumerate(self.output_blocks):
|
| transformer_options["block"] = ("output", id)
|
| hsp = hs.pop()
|
| hsp = apply_control(hsp, control, 'output')
|
|
|
| if "output_block_patch" in transformer_patches:
|
| patch = transformer_patches["output_block_patch"]
|
| for p in patch:
|
| h, hsp = p(h, hsp, transformer_options)
|
|
|
| h = torch.cat([h, hsp], dim=1)
|
| del hsp
|
| if len(hs) > 0:
|
| output_shape = hs[-1].shape
|
| else:
|
| output_shape = None
|
| h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
|
| h = h.type(x.dtype)
|
| if self.predict_codebook_ids:
|
| return self.id_predictor(h)
|
| else:
|
| return self.out(h)
|
|
|
|
|
| def patched_load_models_gpu(*args, **kwargs):
|
| execution_start_time = time.perf_counter()
|
| y = ldm_patched.modules.model_management.load_models_gpu_origin(*args, **kwargs)
|
| moving_time = time.perf_counter() - execution_start_time
|
| if moving_time > 0.1:
|
| print(f'[Fooocus Model Management] Moving model(s) has taken {moving_time:.2f} seconds')
|
| return y
|
|
|
|
|
| def build_loaded(module, loader_name):
|
| original_loader_name = loader_name + '_origin'
|
|
|
| if not hasattr(module, original_loader_name):
|
| setattr(module, original_loader_name, getattr(module, loader_name))
|
|
|
| original_loader = getattr(module, original_loader_name)
|
|
|
| def loader(*args, **kwargs):
|
| result = None
|
| try:
|
| result = original_loader(*args, **kwargs)
|
| except Exception as e:
|
| result = None
|
| exp = str(e) + '\n'
|
| for path in list(args) + list(kwargs.values()):
|
| if isinstance(path, str):
|
| if os.path.exists(path):
|
| exp += f'File corrupted: {path} \n'
|
| corrupted_backup_file = path + '.corrupted'
|
| if os.path.exists(corrupted_backup_file):
|
| os.remove(corrupted_backup_file)
|
| os.replace(path, corrupted_backup_file)
|
| if os.path.exists(path):
|
| os.remove(path)
|
| exp += f'Fooocus has tried to move the corrupted file to {corrupted_backup_file} \n'
|
| exp += f'You may try again now and Fooocus will download models again. \n'
|
| raise ValueError(exp)
|
| return result
|
|
|
| setattr(module, loader_name, loader)
|
| return
|
|
|
|
|
| def patch_all():
|
| if ldm_patched.modules.model_management.directml_enabled:
|
| ldm_patched.modules.model_management.lowvram_available = True
|
| ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
|
|
|
| patch_all_precision()
|
| patch_all_clip()
|
|
|
| if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'):
|
| ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu
|
|
|
| ldm_patched.modules.model_management.load_models_gpu = patched_load_models_gpu
|
| ldm_patched.modules.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched
|
| ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward
|
| ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward
|
| ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
|
| ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward
|
| ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched
|
| ldm_patched.modules.samplers.sampling_function = patched_sampling_function
|
|
|
| warnings.filterwarnings(action='ignore', module='torchsde')
|
|
|
| build_loaded(safetensors.torch, 'load_file')
|
| build_loaded(torch, 'load')
|
|
|
| return
|
|
|
