getting SD2.1 to run on SDXL repo
This commit is contained in:
AUTOMATIC1111
parent
7b833291b3
commit
af081211ee
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@ -235,11 +235,13 @@ def prepare_environment():
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openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
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stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
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stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
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k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
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codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
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blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
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stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "5c10deee76adad0032b412294130090932317a87")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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@ -297,6 +299,7 @@ def prepare_environment():
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os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
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git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
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git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
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git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
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git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
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git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
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@ -20,6 +20,7 @@ assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possibl
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path_dirs = [
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(sd_path, 'ldm', 'Stable Diffusion', []),
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(os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', []),
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(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
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(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
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(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
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@ -144,7 +144,12 @@ def get_learned_conditioning(model, prompts, steps):
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cond_schedule = []
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for i, (end_at_step, _) in enumerate(prompt_schedule):
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cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
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if isinstance(conds, dict):
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cond = {k: v[i] for k, v in conds.items()}
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else:
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cond = conds[i]
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cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond))
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cache[prompt] = cond_schedule
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res.append(cond_schedule)
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@ -214,20 +219,57 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
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return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
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class DictWithShape(dict):
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def __init__(self, x, shape):
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super().__init__()
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self.update(x)
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@property
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def shape(self):
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return self["crossattn"].shape
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def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
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param = c[0][0].cond
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is_dict = isinstance(param, dict)
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if is_dict:
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dict_cond = param
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res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()}
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res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape)
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else:
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res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
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for i, cond_schedule in enumerate(c):
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target_index = 0
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for current, entry in enumerate(cond_schedule):
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if current_step <= entry.end_at_step:
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target_index = current
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break
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if is_dict:
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for k, param in cond_schedule[target_index].cond.items():
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res[k][i] = param
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else:
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res[i] = cond_schedule[target_index].cond
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return res
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def stack_conds(tensors):
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# if prompts have wildly different lengths above the limit we'll get tensors of different shapes
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# and won't be able to torch.stack them. So this fixes that.
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token_count = max([x.shape[0] for x in tensors])
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for i in range(len(tensors)):
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if tensors[i].shape[0] != token_count:
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last_vector = tensors[i][-1:]
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last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
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tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
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return torch.stack(tensors)
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def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
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param = c.batch[0][0].schedules[0].cond
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@ -249,16 +291,14 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
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conds_list.append(conds_for_batch)
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# if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
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# and won't be able to torch.stack them. So this fixes that.
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token_count = max([x.shape[0] for x in tensors])
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for i in range(len(tensors)):
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if tensors[i].shape[0] != token_count:
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last_vector = tensors[i][-1:]
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last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
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tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
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if isinstance(tensors[0], dict):
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keys = list(tensors[0].keys())
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stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys}
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stacked = DictWithShape(stacked, stacked['crossattn'].shape)
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else:
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stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype)
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return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
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return conds_list, stacked
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re_attention = re.compile(r"""
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@ -166,6 +166,15 @@ class StableDiffusionModelHijack:
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undo_optimizations()
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def hijack(self, m):
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conditioner = getattr(m, 'conditioner', None)
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if conditioner:
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for i in range(len(conditioner.embedders)):
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embedder = conditioner.embedders[i]
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if type(embedder).__name__ == 'FrozenOpenCLIPEmbedder':
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embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self)
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m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(embedder, self)
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conditioner.embedders[i] = m.cond_stage_model
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if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
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model_embeddings = m.cond_stage_model.roberta.embeddings
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
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@ -16,6 +16,10 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
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self.id_end = tokenizer.encoder["<end_of_text>"]
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self.id_pad = 0
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self.is_trainable = getattr(wrapped, 'is_trainable', False)
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self.input_key = getattr(wrapped, 'input_key', 'txt')
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self.legacy_ucg_val = None
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def tokenize(self, texts):
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assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
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@ -14,7 +14,7 @@ import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
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from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl
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from modules.sd_hijack_inpainting import do_inpainting_hijack
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from modules.timer import Timer
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import tomesd
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@ -289,6 +289,9 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
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if state_dict is None:
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state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
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if hasattr(model, 'conditioner'):
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sd_models_xl.extend_sdxl(model)
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model.load_state_dict(state_dict, strict=False)
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del state_dict
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timer.record("apply weights to model")
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@ -334,6 +337,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
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model.sd_checkpoint_info = checkpoint_info
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shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
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if hasattr(model, 'logvar'):
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model.logvar = model.logvar.to(devices.device) # fix for training
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sd_vae.delete_base_vae()
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@ -6,11 +6,13 @@ from modules import shared, paths, sd_disable_initialization
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sd_configs_path = shared.sd_configs_path
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sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
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sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
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config_default = shared.sd_default_config
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config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
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config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
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config_sd2v = os.path.join(sd_xl_repo_configs_path, "sd_2_1_768.yaml")
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config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
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config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
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config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
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@ -0,0 +1,40 @@
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from __future__ import annotations
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import torch
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import sgm.models.diffusion
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import sgm.modules.diffusionmodules.denoiser_scaling
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import sgm.modules.diffusionmodules.discretizer
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from modules import devices
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def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: list[str]):
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for embedder in self.conditioner.embedders:
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embedder.ucg_rate = 0.0
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c = self.conditioner({'txt': batch})
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return c
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def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
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return self.model(x, t, cond)
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def extend_sdxl(model):
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dtype = next(model.model.diffusion_model.parameters()).dtype
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model.model.diffusion_model.dtype = dtype
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model.model.conditioning_key = 'crossattn'
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model.cond_stage_model = [x for x in model.conditioner.embedders if type(x).__name__ == 'FrozenOpenCLIPEmbedder'][0]
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model.cond_stage_key = model.cond_stage_model.input_key
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model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
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discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
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model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
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sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
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sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
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@ -53,6 +53,28 @@ k_diffusion_scheduler = {
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}
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def catenate_conds(conds):
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if not isinstance(conds[0], dict):
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return torch.cat(conds)
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return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
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def subscript_cond(cond, a, b):
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if not isinstance(cond, dict):
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return cond[a:b]
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return {key: vec[a:b] for key, vec in cond.items()}
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def pad_cond(tensor, repeats, empty):
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if not isinstance(tensor, dict):
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return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
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tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
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return tensor
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class CFGDenoiser(torch.nn.Module):
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"""
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Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
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@ -105,10 +127,13 @@ class CFGDenoiser(torch.nn.Module):
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if shared.sd_model.model.conditioning_key == "crossattn-adm":
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image_uncond = torch.zeros_like(image_cond)
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make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
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make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
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else:
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image_uncond = image_cond
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make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
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if isinstance(uncond, dict):
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make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
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else:
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make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
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if not is_edit_model:
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
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@ -140,28 +165,28 @@ class CFGDenoiser(torch.nn.Module):
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num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
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if num_repeats < 0:
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tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
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tensor = pad_cond(tensor, -num_repeats, empty)
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self.padded_cond_uncond = True
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elif num_repeats > 0:
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uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
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uncond = pad_cond(uncond, num_repeats, empty)
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self.padded_cond_uncond = True
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if tensor.shape[1] == uncond.shape[1] or skip_uncond:
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if is_edit_model:
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cond_in = torch.cat([tensor, uncond, uncond])
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cond_in = catenate_conds([tensor, uncond, uncond])
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elif skip_uncond:
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cond_in = tensor
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else:
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cond_in = torch.cat([tensor, uncond])
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cond_in = catenate_conds([tensor, uncond])
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if shared.batch_cond_uncond:
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x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
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x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
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else:
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x_out = torch.zeros_like(x_in)
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for batch_offset in range(0, x_out.shape[0], batch_size):
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a = batch_offset
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b = a + batch_size
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(cond_in[a:b], image_cond_in[a:b]))
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else:
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x_out = torch.zeros_like(x_in)
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batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
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@ -170,14 +195,14 @@ class CFGDenoiser(torch.nn.Module):
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b = min(a + batch_size, tensor.shape[0])
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if not is_edit_model:
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c_crossattn = [tensor[a:b]]
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c_crossattn = subscript_cond(tensor, a, b)
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else:
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c_crossattn = torch.cat([tensor[a:b]], uncond)
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
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if not skip_uncond:
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
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denoised_image_indexes = [x[0][0] for x in conds_list]
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if skip_uncond:
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