mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-04 11:12:35 +00:00
Merge branch 'master' into stable
This commit is contained in:
Jairo Correa
@@ -5,238 +5,44 @@ import traceback
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import torch
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import numpy as np
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from torch import einsum
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from modules import prompt_parser
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from modules.shared import opts, device, cmd_opts
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from ldm.util import default
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from einops import rearrange
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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from torch.nn.functional import silu
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import modules.textual_inversion.textual_inversion
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from modules import prompt_parser, devices, sd_hijack_optimizations, shared
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from modules.shared import opts, device, cmd_opts
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# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
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def split_cross_attention_forward_v1(self, x, context=None, mask=None):
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h = self.heads
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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del context, x
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
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for i in range(0, q.shape[0], 2):
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end = i + 2
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s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
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s1 *= self.scale
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s2 = s1.softmax(dim=-1)
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del s1
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r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
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del s2
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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return self.to_out(r2)
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attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
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diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
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diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
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# taken from https://github.com/Doggettx/stable-diffusion
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def split_cross_attention_forward(self, x, context=None, mask=None):
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h = self.heads
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def apply_optimizations():
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ldm.modules.diffusionmodules.model.nonlinearity = silu
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q_in = self.to_q(x)
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context = default(context, x)
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k_in = self.to_k(context) * self.scale
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v_in = self.to_v(context)
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del context, x
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if cmd_opts.opt_split_attention_v1:
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
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elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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def undo_optimizations():
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ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
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ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
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ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
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s2 = s1.softmax(dim=-1, dtype=q.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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del q, k, v
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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return self.to_out(r2)
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def cross_attention_attnblock_forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q1 = self.q(h_)
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k1 = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q1.shape
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q2 = q1.reshape(b, c, h*w)
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del q1
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q = q2.permute(0, 2, 1) # b,hw,c
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del q2
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k = k1.reshape(b, c, h*w) # b,c,hw
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del k1
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h_ = torch.zeros_like(k, device=q.device)
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stats = torch.cuda.memory_stats(q.device)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
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mem_required = tensor_size * 2.5
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w2 = w1 * (int(c)**(-0.5))
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del w1
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w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
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del w2
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# attend to values
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v1 = v.reshape(b, c, h*w)
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w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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del w3
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h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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del v1, w4
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h2 = h_.reshape(b, c, h, w)
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del h_
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h3 = self.proj_out(h2)
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del h2
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h3 += x
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return h3
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class StableDiffusionModelHijack:
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ids_lookup = {}
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word_embeddings = {}
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word_embeddings_checksums = {}
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fixes = None
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comments = []
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dir_mtime = None
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layers = None
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circular_enabled = False
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clip = None
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def load_textual_inversion_embeddings(self, dirname, model):
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mt = os.path.getmtime(dirname)
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if self.dir_mtime is not None and mt <= self.dir_mtime:
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return
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self.dir_mtime = mt
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self.ids_lookup.clear()
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self.word_embeddings.clear()
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tokenizer = model.cond_stage_model.tokenizer
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def const_hash(a):
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r = 0
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for v in a:
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r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
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return r
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def process_file(path, filename):
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name = os.path.splitext(filename)[0]
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data = torch.load(path, map_location="cpu")
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# textual inversion embeddings
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if 'string_to_param' in data:
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param_dict = data['string_to_param']
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if hasattr(param_dict, '_parameters'):
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param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
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assert len(param_dict) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(param_dict.items()))[1]
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# diffuser concepts
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elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
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assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(data.values()))
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if len(emb.shape) == 1:
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emb = emb.unsqueeze(0)
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self.word_embeddings[name] = emb.detach().to(device)
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self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'
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ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
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first_id = ids[0]
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if first_id not in self.ids_lookup:
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self.ids_lookup[first_id] = []
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self.ids_lookup[first_id].append((ids, name))
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for fn in os.listdir(dirname):
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try:
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fullfn = os.path.join(dirname, fn)
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if os.stat(fullfn).st_size == 0:
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continue
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process_file(fullfn, fn)
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except Exception:
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print(f"Error loading emedding {fn}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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continue
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print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
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embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
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def hijack(self, m):
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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@@ -246,13 +52,7 @@ class StableDiffusionModelHijack:
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self.clip = m.cond_stage_model
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ldm.modules.diffusionmodules.model.nonlinearity = silu
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if cmd_opts.opt_split_attention_v1:
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ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
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elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
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ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
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apply_optimizations()
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def flatten(el):
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flattened = [flatten(children) for children in el.children()]
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@@ -290,7 +90,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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def __init__(self, wrapped, hijack):
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super().__init__()
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self.wrapped = wrapped
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self.hijack = hijack
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self.hijack: StableDiffusionModelHijack = hijack
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self.tokenizer = wrapped.tokenizer
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self.max_length = wrapped.max_length
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self.token_mults = {}
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@@ -311,7 +111,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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if mult != 1.0:
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self.token_mults[ident] = mult
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def tokenize_line(self, line, used_custom_terms, hijack_comments):
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id_start = self.wrapped.tokenizer.bos_token_id
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id_end = self.wrapped.tokenizer.eos_token_id
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@@ -333,28 +132,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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while i < len(tokens):
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token = tokens[i]
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possible_matches = self.hijack.ids_lookup.get(token, None)
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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if possible_matches is None:
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if embedding is None:
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remade_tokens.append(token)
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multipliers.append(weight)
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i += 1
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else:
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found = False
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for ids, word in possible_matches:
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if tokens[i:i + len(ids)] == ids:
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emb_len = int(self.hijack.word_embeddings[word].shape[0])
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fixes.append((len(remade_tokens), word))
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remade_tokens += [0] * emb_len
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multipliers += [weight] * emb_len
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i += len(ids) - 1
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found = True
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used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
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break
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if not found:
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remade_tokens.append(token)
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multipliers.append(weight)
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i += 1
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emb_len = int(embedding.vec.shape[0])
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fixes.append((len(remade_tokens), embedding))
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remade_tokens += [0] * emb_len
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multipliers += [weight] * emb_len
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used_custom_terms.append((embedding.name, embedding.checksum()))
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i += embedding_length_in_tokens
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if len(remade_tokens) > maxlen - 2:
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vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
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@@ -425,32 +215,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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while i < len(tokens):
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token = tokens[i]
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possible_matches = self.hijack.ids_lookup.get(token, None)
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
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if mult_change is not None:
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mult *= mult_change
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elif possible_matches is None:
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i += 1
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elif embedding is None:
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remade_tokens.append(token)
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multipliers.append(mult)
|
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i += 1
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else:
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found = False
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for ids, word in possible_matches:
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if tokens[i:i+len(ids)] == ids:
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emb_len = int(self.hijack.word_embeddings[word].shape[0])
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fixes.append((len(remade_tokens), word))
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remade_tokens += [0] * emb_len
|
||||
multipliers += [mult] * emb_len
|
||||
i += len(ids) - 1
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found = True
|
||||
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
|
||||
break
|
||||
|
||||
if not found:
|
||||
remade_tokens.append(token)
|
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multipliers.append(mult)
|
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|
||||
i += 1
|
||||
emb_len = int(embedding.vec.shape[0])
|
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fixes.append((len(remade_tokens), embedding))
|
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remade_tokens += [0] * emb_len
|
||||
multipliers += [mult] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
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i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
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@@ -458,6 +239,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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overflowing_words = [vocab.get(int(x), "") for x in ovf]
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overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
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hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
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|
||||
token_count = len(remade_tokens)
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remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
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remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
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@@ -478,7 +260,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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else:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
|
||||
|
||||
self.hijack.fixes = hijack_fixes
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self.hijack.comments = hijack_comments
|
||||
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||||
@@ -511,14 +292,19 @@ class EmbeddingsWithFixes(torch.nn.Module):
|
||||
|
||||
inputs_embeds = self.wrapped(input_ids)
|
||||
|
||||
if batch_fixes is not None:
|
||||
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
||||
for offset, word in fixes:
|
||||
emb = self.embeddings.word_embeddings[word]
|
||||
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
|
||||
tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
|
||||
if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
|
||||
return inputs_embeds
|
||||
|
||||
return inputs_embeds
|
||||
vecs = []
|
||||
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
||||
for offset, embedding in fixes:
|
||||
emb = embedding.vec
|
||||
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
|
||||
tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
|
||||
|
||||
vecs.append(tensor)
|
||||
|
||||
return torch.stack(vecs)
|
||||
|
||||
|
||||
def add_circular_option_to_conv_2d():
|
||||
|
||||
Reference in New Issue
Block a user