happyz
/
Fooocus
mirror of https://github.com/lllyasviel/Fooocus.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

fix all precision issues 

We fixed number precision issues again. Now 2.1.849 will give 100% exactly same results as 2.1.824.
This commit is contained in:
lllyasviel 2023-12-16 19:54:05 -08:00 committed by GitHub
parent efb312d495
commit 67808d5ee5
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 75 additions and 153 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
fooocus_version.py
View File

@ -1 +1 @@
version = '2.1.848'
version = '2.1.849'

5
modules/patch.py
View File

@ -271,12 +271,11 @@ def sdxl_encode_adm_patched(self, **kwargs):
height = float(height) * positive_adm_scale
def embedder(number_list):
h = [self.embedder(torch.tensor([x], dtype=torch.float32)) for x in number_list]
h = torch.cat(h)
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 = round_to_64(width), round_to_64(height)
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])

97
modules/patch_clip.py
View File

@ -63,32 +63,18 @@ def encode_token_weights_fooocus(self, token_weight_pairs):
return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
class SDClipModelFooocus(torch.nn.Module, ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = [
"last",
"pooled",
"hidden"
]
def __init__(self,
max_length=77,
freeze=True,
layer="last",
layer_idx=None,
textmodel_json_config=None,
dtype=None,
special_tokens=None,
layer_norm_hidden_state=True,
**kwargs):
super().__init__()
def patched_SDClipModel__init__(self, max_length=77, freeze=True, layer="last", layer_idx=None,
textmodel_json_config=None, dtype=None, special_tokens=None,
layer_norm_hidden_state=True, **kwargs):
torch.nn.Module.__init__(self)
assert layer in self.LAYERS
if special_tokens is None:
special_tokens = {"start": 49406, "end": 49407, "pad": 49407}
if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)), "sd1_clip_config.json")
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)),
"sd1_clip_config.json")
config = CLIPTextConfig.from_json_file(textmodel_json_config)
self.num_layers = config.num_hidden_layers
@ -117,66 +103,12 @@ class SDClipModelFooocus(torch.nn.Module, ldm_patched.modules.sd1_clip.ClipToken
self.layer_norm_hidden_state = layer_norm_hidden_state
if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) < self.num_layers
self.clip_layer(layer_idx)
self.layer_default = (self.layer, self.layer_idx)
def freeze(self):
self.transformer = self.transformer.eval()
# self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def clip_layer(self, layer_idx):
self.layer = "hidden"
self.layer_idx = layer_idx
def reset_clip_layer(self):
self.layer = self.layer_default[0]
self.layer_idx = self.layer_default[1]
def set_up_textual_embeddings(self, tokens, current_embeds):
out_tokens = []
next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
embedding_weights = []
for x in tokens:
tokens_temp = []
for y in x:
if isinstance(y, int):
if y == token_dict_size: # EOS token
y = -1
tokens_temp += [y]
else:
if y.shape[0] == current_embeds.weight.shape[1]:
embedding_weights += [y]
tokens_temp += [next_new_token]
next_new_token += 1
else:
print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored",
y.shape[0], current_embeds.weight.shape[1])
while len(tokens_temp) < len(x):
tokens_temp += [self.special_tokens["pad"]]
out_tokens += [tokens_temp]
n = token_dict_size
if len(embedding_weights) > 0:
new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1],
device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
for x in embedding_weights:
new_embedding.weight[n] = x
n += 1
new_embedding.weight[n] = current_embeds.weight[-1] # EOS embedding
self.transformer.set_input_embeddings(new_embedding)
processed_tokens = []
for x in out_tokens:
processed_tokens += [
list(map(lambda a: n if a == -1 else a, x))] # The EOS token should always be the largest one
return processed_tokens
def forward(self, tokens):
def patched_SDClipModel_forward(self, tokens):
backup_embeds = self.transformer.get_input_embeddings()
device = backup_embeds.weight.device
tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
@ -220,16 +152,6 @@ class SDClipModelFooocus(torch.nn.Module, ldm_patched.modules.sd1_clip.ClipToken
pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
return z.float(), pooled_output
def encode(self, tokens):
return self(tokens)
def load_sd(self, sd):
if "text_projection" in sd:
self.text_projection[:] = sd.pop("text_projection")
if "text_projection.weight" in sd:
self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1)
return self.transformer.load_state_dict(sd, strict=False)
class ClipVisionModelFooocus:
def __init__(self, json_config):
@ -262,6 +184,7 @@ class ClipVisionModelFooocus:
def patch_all_clip():
ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_fooocus
ldm_patched.modules.sd1_clip.SDClipModel = SDClipModelFooocus
ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__
ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward
ldm_patched.modules.clip_vision.ClipVisionModel = ClipVisionModelFooocus
return