mirror of https://github.com/Filexor/Clip_IO.git
Add ability to include tokens to on_save_embeddings_as_csv
File_xor
parent
605a0dfe55
commit
a425789a15
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@ -0,0 +1,2 @@
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*
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!.gitignore
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@ -1,4 +1,4 @@
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import os, csv, warnings
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import os, csv, warnings, datetime
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import gradio
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import torch
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@ -27,8 +27,9 @@ class Clip_IO(scripts.Script):
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positive_filenames = []
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negative_filenames = []
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conditioning_cache = {}
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negative_exist = False
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positive_exist = False
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negative_exist = False
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evacuate_get_learned_conditioning = None
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evacuate_get_multicond_learned_conditioning = None
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evacuate_get_conds_with_caching = None
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@ -445,7 +446,7 @@ class Clip_IO(scripts.Script):
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return batch_chunks
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pass
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def get_flat_embeddings(batch_chunks: PromptChunk, clip: FrozenCLIPEmbedderWithCustomWordsBase) -> torch.Tensor:
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def get_flat_embeddings(batch_chunks: PromptChunk, clip: FrozenCLIPEmbedderWithCustomWordsBase) -> tuple[torch.Tensor, list[str]]:
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input_ids = []
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fixes = []
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offset = 0
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@ -460,71 +461,92 @@ class Clip_IO(scripts.Script):
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pass
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clip.hijack.fixes = [fixes]
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input_ids_Tensor = torch.asarray([input_ids]).to(devices.device)
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return clip.wrapped.transformer.text_model.embeddings.token_embedding(input_ids_Tensor)
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tokens = [clip.wrapped.tokenizer.decoder.get(input_id) for input_id in input_ids]
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for fix in fixes:
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tokens[fix.offset + 1] = fix.embedding.name
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for i in range(1, fix.embedding.vec.shape[0]):
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tokens[fix.offset + 1 + i] = ""
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pass
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pass
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return clip.wrapped.transformer.text_model.embeddings.token_embedding(input_ids_Tensor), tokens
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pass
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def on_save_embeddings_as_pt(prompt: str, filename: str, transpose: bool):
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clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
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batch_chunks = Clip_IO.get_chunks(prompt, clip)
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embeddings: torch.Tensor = Clip_IO.get_flat_embeddings(batch_chunks, clip)
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filename = os.path.realpath(filename)
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dir = os.path.dirname(filename)
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if not os.path.exists(dir): os.makedirs(dir)
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if not filename.endswith(".pt"): filename += ".pt"
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torch.save(embeddings.t() if transpose else embeddings, filename)
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pass
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def on_save_embeddings_as_csv(prompt: str, filename: str, transpose: bool):
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clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
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batch_chunks = Clip_IO.get_chunks(prompt, clip)
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embeddings: torch.Tensor = Clip_IO.get_flat_embeddings(batch_chunks, clip)
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filename = os.path.realpath(filename)
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dir = os.path.dirname(filename)
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if not os.path.exists(dir): os.makedirs(dir)
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if not filename.endswith(".csv"): filename += ".csv"
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embeddings_numpy = embeddings[0].t().to("cpu").numpy() if transpose else embeddings[0].to("cpu").numpy()
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embeddings_dataframe = pandas.DataFrame(embeddings_numpy)
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embeddings_dataframe.to_csv(filename, float_format = "%.8e")
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pass
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def on_save_conditioning_as_pt(prompt: str, filename: str, transpose: bool, no_emphasis: bool, no_norm: bool):
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with devices.autocast():
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def on_save_embeddings_as_pt(prompt: str, filename: str, overwrite: bool):
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try:
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clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
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batch_chunks = Clip_IO.get_chunks(prompt, clip)
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chunk_count = max([len(x) for x in batch_chunks])
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zs = []
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for i in range(chunk_count):
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batch_chunk = [chunks[i] if i < len(chunks) else clip.empty_chunk() for chunks in batch_chunks]
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remade_batch_tokens = [x.tokens for x in batch_chunk]
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tokens = torch.asarray([x.tokens for x in batch_chunk]).to(devices.device)
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clip.hijack.fixes = [x.fixes for x in batch_chunk]
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if clip.id_end != clip.id_pad:
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for batch_pos in range(len(remade_batch_tokens)):
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index = remade_batch_tokens[batch_pos].index(clip.id_end)
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tokens[batch_pos, index+1:tokens.shape[1]] = clip.id_pad
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z = clip.encode_with_transformers(tokens)
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if not no_emphasis:
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batch_multipliers = torch.asarray([x.multipliers for x in batch_chunk]).to(devices.device)
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original_mean = z.mean()
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z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
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new_mean = z.mean()
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z = z * (original_mean / new_mean) if not no_norm else z
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zs.append(z[0])
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conditioning = torch.hstack(zs)
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embeddings: torch.Tensor = Clip_IO.get_flat_embeddings(batch_chunks, clip)
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filename = os.path.join(os.path.dirname(__file__), "../conditioning", filename)
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filename = os.path.realpath(filename)
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dir = os.path.dirname(filename)
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if not os.path.exists(dir): os.makedirs(dir)
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if not filename.endswith(".pt"): filename += ".pt"
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torch.save(conditioning.t() if transpose else conditioning, filename)
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if os.path.exists(filename):
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raise FileExistsError()
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pass
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torch.save(embeddings, filename)
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pass
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except FileExistsError as e:
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print(repr(e))
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return f'<span style="color: red">Saving failed. File "{filename}" already exists. {datetime.datetime.now().isoformat()}</span>'
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pass
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except Exception as e:
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print(repr(e))
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return f'<span style="color: red">Saving failed. {datetime.datetime.now().isoformat()}</span>'
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pass
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return f'File {filename} is successfully saved. {datetime.datetime.now().isoformat()}'
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pass
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def on_save_conditioning_as_csv(prompt: str, filename: str, transpose: bool, no_emphasis: bool, no_norm: bool):
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def on_save_embeddings_as_csv(prompt: str, filename: str, transpose: bool, add_token: bool, overwrite: bool):
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try:
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clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
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batch_chunks = Clip_IO.get_chunks(prompt, clip)
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embeddings, tokens = Clip_IO.get_flat_embeddings(batch_chunks, clip)
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embeddings: list[list[str]] = embeddings[0].tolist()
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width = len(embeddings[0])
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for i, row in enumerate(embeddings):
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row.insert(0, str(i))
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if add_token:
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row.insert(0, tokens[i])
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pass
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pass
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row_first = list(range(width))
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row_first.insert(0, "embeddingsT" if transpose else "embeddings")
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if add_token:
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row_first.insert(0, "")
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pass
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embeddings.insert(0, row_first)
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if transpose:
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embeddings = [list(x) for x in zip(*embeddings)]
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pass
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filename = os.path.join(os.path.dirname(__file__), "../conditioning", filename)
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filename = os.path.realpath(filename)
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dir = os.path.dirname(filename)
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if not os.path.exists(dir): os.makedirs(dir)
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if not filename.endswith(".csv"): filename += ".csv"
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with open(filename, "wt" if overwrite else "xt") as file:
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writer = csv.writer(file, lineterminator = "\n")
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writer.writerows(embeddings)
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pass
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pass
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except FileExistsError as e:
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print(repr(e))
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return f'<span style="color: red">Saving failed. File "{filename}" already exists. {datetime.datetime.now().isoformat()}</span>'
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pass
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except Exception as e:
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print(repr(e))
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return f'<span style="color: red">Saving failed. {datetime.datetime.now().isoformat()}</span>'
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pass
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return f'File {filename} is successfully saved. {datetime.datetime.now().isoformat()}'
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pass
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def on_save_conditioning_as_pt(prompt: str, filename: str, no_emphasis: bool, no_norm: bool, overwrite: bool):
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with devices.autocast():
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clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
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batch_chunks = Clip_IO.get_chunks(prompt, clip)
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@ -551,6 +573,43 @@ class Clip_IO(scripts.Script):
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zs.append(z[0])
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conditioning = torch.hstack(zs)
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filename = os.path.join(os.path.dirname(__file__), "../conditioning", filename)
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filename = os.path.realpath(filename)
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dir = os.path.dirname(filename)
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if not os.path.exists(dir): os.makedirs(dir)
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if not filename.endswith(".pt"): filename += ".pt"
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torch.save(conditioning, filename)
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pass
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pass
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def on_save_conditioning_as_csv(prompt: str, filename: str, transpose: bool, no_emphasis: bool, no_norm: bool, add_token: bool, overwrite: bool):
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with devices.autocast():
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clip: FrozenCLIPEmbedderWithCustomWordsBase = shared.sd_model.cond_stage_model
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batch_chunks = Clip_IO.get_chunks(prompt, clip)
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chunk_count = max([len(x) for x in batch_chunks])
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zs = []
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for i in range(chunk_count):
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batch_chunk = [chunks[i] if i < len(chunks) else clip.empty_chunk() for chunks in batch_chunks]
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remade_batch_tokens = [x.tokens for x in batch_chunk]
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tokens = torch.asarray([x.tokens for x in batch_chunk]).to(devices.device)
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clip.hijack.fixes = [x.fixes for x in batch_chunk]
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if clip.id_end != clip.id_pad:
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for batch_pos in range(len(remade_batch_tokens)):
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index = remade_batch_tokens[batch_pos].index(clip.id_end)
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tokens[batch_pos, index+1:tokens.shape[1]] = clip.id_pad
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z = clip.encode_with_transformers(tokens)
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if not no_emphasis:
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batch_multipliers = torch.asarray([x.multipliers for x in batch_chunk]).to(devices.device)
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original_mean = z.mean()
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z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
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new_mean = z.mean()
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z = z * (original_mean / new_mean) if not no_norm else z
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zs.append(z[0])
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conditioning = torch.hstack(zs)
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filename = os.path.join(os.path.dirname(__file__), "../conditioning", filename)
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filename = os.path.realpath(filename)
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dir = os.path.dirname(filename)
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if not os.path.exists(dir): os.makedirs(dir)
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@ -565,23 +624,24 @@ class Clip_IO(scripts.Script):
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with gradio.Blocks() as tab:
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prompt = gradio.TextArea(max_lines = 256, label = "Prompt")
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with gradio.Row():
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output_embeddings_name = gradio.Textbox(value = r"outputs\embeddings\out_emb", label = "Output embeddings name")
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output_embeddings_transpose = gradio.Checkbox(value = False, label = "Transpose matrix")
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output_embeddings_as_pt_button = gradio.Button("Save embeddings as .pt")
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output_embeddings_as_csv_button = gradio.Button("Save embeddings as .csv")
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output_transpose = gradio.Checkbox(value = True, label = "Transpose matrix")
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output_ignore_emphasis = gradio.Checkbox(value = False, label = "Ignore emphasis")
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output_bypass_conditioning_normalization = gradio.Checkbox(value = False, label = "Bypass conditioning normalization")
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output_token_string = gradio.Checkbox(value = True, label = "Add token strings")
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output_overwrite = gradio.Checkbox(value = False, label = "Overwrite")
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pass
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with gradio.Row():
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output_conditioning_name = gradio.Textbox(value = r"outputs\embeddings\out_cond", label = "Output conditioning name")
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output_conditioning_transpose = gradio.Checkbox(value = False, label = "Transpose matrix")
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output_conditioning_ignore_emphasis = gradio.Checkbox(value = False, label = "Ignore emphasis")
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output_conditioning_bypass_conditioning_normalization = gradio.Checkbox(value = False, label = "Bypass conditioning normalization")
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output_conditioning_button_as_pt = gradio.Button("Save conditioning as .pt")
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output_conditioning_button_as_csv = gradio.Button("Save conditioning as .csv")
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output_name = gradio.Textbox(value = r"output", label = "Output name")
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output_embeddings_as_pt = gradio.Button("Save embeddings as .pt")
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output_embeddings_as_csv = gradio.Button("Save embeddings as .csv")
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output_conditioning_as_pt = gradio.Button("Save conditioning as .pt")
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output_conditioning_as_csv = gradio.Button("Save conditioning as .csv")
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pass
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output_embeddings_as_pt_button.click(Clip_IO.on_save_embeddings_as_pt, [prompt, output_embeddings_name, output_embeddings_transpose])
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output_embeddings_as_csv_button.click(Clip_IO.on_save_embeddings_as_csv, [prompt, output_embeddings_name, output_embeddings_transpose])
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output_conditioning_button_as_pt.click(Clip_IO.on_save_conditioning_as_pt, [prompt, output_conditioning_name, output_conditioning_transpose, output_conditioning_ignore_emphasis, output_conditioning_bypass_conditioning_normalization])
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output_conditioning_button_as_csv.click(Clip_IO.on_save_conditioning_as_csv, [prompt, output_conditioning_name, output_conditioning_transpose, output_conditioning_ignore_emphasis, output_conditioning_bypass_conditioning_normalization])
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output_notification = gradio.HTML()
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output_embeddings_as_pt.click(Clip_IO.on_save_embeddings_as_pt, [prompt, output_name, output_overwrite], [output_notification])
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output_embeddings_as_csv.click(Clip_IO.on_save_embeddings_as_csv, [prompt, output_name, output_transpose, output_token_string, output_overwrite], [output_notification])
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output_conditioning_as_pt.click(Clip_IO.on_save_conditioning_as_pt, [prompt, output_name, output_ignore_emphasis, output_bypass_conditioning_normalization, output_overwrite], [output_notification])
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output_conditioning_as_csv.click(Clip_IO.on_save_conditioning_as_csv, [prompt, output_name, output_transpose, output_ignore_emphasis, output_bypass_conditioning_normalization, output_token_string, output_overwrite], [output_notification])
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pass
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return [(tab, "Clip Output", "Clip_Output")]
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pass
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