Merge branch 'release_candidate'

1 files changed, 42 insertions, 36 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index aa79dc09..04dda585 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -181,40 +181,7 @@ class EmbeddingDatabase:
         else:

             return

 

-

-        # textual inversion embeddings

-        if 'string_to_param' in data:

-            param_dict = data['string_to_param']

-            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11

-            assert len(param_dict) == 1, 'embedding file has multiple terms in it'

-            emb = next(iter(param_dict.items()))[1]

-            vec = emb.detach().to(devices.device, dtype=torch.float32)

-            shape = vec.shape[-1]

-            vectors = vec.shape[0]

-        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding

-            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}

-            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]

-            vectors = data['clip_g'].shape[0]

-        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts

-            assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

-

-            emb = next(iter(data.values()))

-            if len(emb.shape) == 1:

-                emb = emb.unsqueeze(0)

-            vec = emb.detach().to(devices.device, dtype=torch.float32)

-            shape = vec.shape[-1]

-            vectors = vec.shape[0]

-        else:

-            raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")

-

-        embedding = Embedding(vec, name)

-        embedding.step = data.get('step', None)

-        embedding.sd_checkpoint = data.get('sd_checkpoint', None)

-        embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)

-        embedding.vectors = vectors

-        embedding.shape = shape

-        embedding.filename = path

-        embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')

+        embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)

 

         if self.expected_shape == -1 or self.expected_shape == embedding.shape:

             self.register_embedding(embedding, shared.sd_model)

@@ -313,6 +280,45 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     return fn

 

 

+def create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):

+    if 'string_to_param' in data:  # textual inversion embeddings

+        param_dict = data['string_to_param']

+        param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11

+        assert len(param_dict) == 1, 'embedding file has multiple terms in it'

+        emb = next(iter(param_dict.items()))[1]

+        vec = emb.detach().to(devices.device, dtype=torch.float32)

+        shape = vec.shape[-1]

+        vectors = vec.shape[0]

+    elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding

+        vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}

+        shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]

+        vectors = data['clip_g'].shape[0]

+    elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:  # diffuser concepts

+        assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

+

+        emb = next(iter(data.values()))

+        if len(emb.shape) == 1:

+            emb = emb.unsqueeze(0)

+        vec = emb.detach().to(devices.device, dtype=torch.float32)

+        shape = vec.shape[-1]

+        vectors = vec.shape[0]

+    else:

+        raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")

+

+    embedding = Embedding(vec, name)

+    embedding.step = data.get('step', None)

+    embedding.sd_checkpoint = data.get('sd_checkpoint', None)

+    embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)

+    embedding.vectors = vectors

+    embedding.shape = shape

+

+    if filepath:

+        embedding.filename = filepath

+        embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')

+

+    return embedding

+

+

 def write_loss(log_directory, filename, step, epoch_len, values):

     if shared.opts.training_write_csv_every == 0:

         return

@@ -386,7 +392,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"

 

 

-def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_name, preview_cfg_scale, preview_seed, preview_width, preview_height):

     from modules import processing

 

     save_embedding_every = save_embedding_every or 0

@@ -590,7 +596,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         p.prompt = preview_prompt

                         p.negative_prompt = preview_negative_prompt

                         p.steps = preview_steps

-                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name

+                        p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]

                         p.cfg_scale = preview_cfg_scale

                         p.seed = preview_seed

                         p.width = preview_width