mirror of https://github.com/Filexor/Clip_IO.git
bugfix
File_xor
parent
b66dc07f3a
commit
b2e3a39d27
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@ -20,6 +20,7 @@ class Clip_IO(scripts.Script):
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mode_positive = "Disabled"
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mode_negative = "Disabled"
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conditioning_cache = {}
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global_carry = {}
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evacuate_get_learned_conditioning = None
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evacuate_get_multicond_learned_conditioning = None
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@ -43,12 +44,14 @@ class Clip_IO(scripts.Script):
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mode_positive = gradio.Dropdown(["Disabled", "Simple", "Directive"], value = "Disabled", max_choices = 1, label = "Positive prompt mode")
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mode_negative = gradio.Dropdown(["Disabled", "Simple", "Directive"], value = "Disabled", max_choices = 1, label = "Negative prompt mode")
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pass
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pre_batch_process = gradio.TextArea(max_lines=1024, label="Pre-batch-process")
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post_batch_process = gradio.TextArea(max_lines=1024, label="Post-batch-process-process")
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pass
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if not is_img2img:
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return [enabled, mode_positive, mode_negative]
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return [enabled, mode_positive, mode_negative, post_batch_process, pre_batch_process]
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pass
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else:
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return [enabled, mode_positive, mode_negative]
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return [enabled, mode_positive, mode_negative, post_batch_process, pre_batch_process]
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pass
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return []
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pass
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@ -282,7 +285,7 @@ class Clip_IO(scripts.Script):
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Process().transform(lark.Lark(Clip_IO.syntax_directive).parse(input))
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if len(conds) == 0:
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tmp = Clip_IO.FrozenCLIPEmbedderWithCustomWordsBase_forword(("a",), manual_chunk=True)
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tmp = Clip_IO.FrozenCLIPEmbedderWithCustomWordsBase_forword(("a",), manual_chunk=False)
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conds.append(torch.zeros(0, tmp.shape[1]).to(devices.device))
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pass
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i = torch.vstack(conds)
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@ -297,25 +300,25 @@ class Clip_IO(scripts.Script):
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try:
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for t in range(i.shape[0]):
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for d in range(i.shape[1]):
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local = {"i": i, "o": o, "c": c, "p": p, "t": t, "d": d, "torch": torch.__dict__} | math.__dict__
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local = {"i": i, "o": o, "g": Clip_IO.global_carry, "c": c, "p": p, "t": t, "d": d, "sd_model": shared.sd_model, "torch": torch.__dict__} | math.__dict__
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o[t, d] = eval(dir.inner, None, local)
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pass
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pass
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pass
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except Exception as e:
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print(repr(e))
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o = i
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raise e
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pass
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finally:
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i = o.clone()
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pass
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elif dir.name == "exec":
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try:
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local = {"i": i, "o": o, "c": c, "p": p, "torch": torch.__dict__} | math.__dict__
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local = {"i": i, "o": o, "g": Clip_IO.global_carry, "c": c, "p": p, "sd_model": shared.sd_model, "torch": torch.__dict__} | math.__dict__
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exec(dir.inner, None, local)
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except Exception as e:
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print(repr(e))
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o = i
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raise e
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pass
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finally:
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i = local["o"].clone()
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@ -323,7 +326,7 @@ class Clip_IO(scripts.Script):
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elif dir.name == "prompt":
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# prompt(prompt: str, clip_skip: int|None=None, no_padding=True)
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prompt: tuple[str]
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keyword_arguments: dict = {"clip_skip": None, "no_padding": False}
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keyword_arguments: dict = {"clip_skip": [None], "no_padding": False}
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class prompt_transformer(lark.visitors.Transformer):
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keyword_position = 0
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@ -355,7 +358,7 @@ class Clip_IO(scripts.Script):
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match self.keyword_position:
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case 0:
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if token.strip(" ").lower() == "none":
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keyword_arguments["clip_skip"] = [None]
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keyword_arguments["clip_skip"] = [[None]]
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pass
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else:
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try:
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@ -432,6 +435,27 @@ class Clip_IO(scripts.Script):
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o = torch.vstack([o, Clip_IO.FrozenCLIPEmbedderWithCustomWordsBase_forword(prompt, manual_chunk= keyword_arguments["no_padding"], clip_skips=[keyword_arguments["clip_skip"]])])
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i = o.clone()
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pass
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elif dir.name == "execfile":
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try:
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if dir.inner.startswith('"') and dir.inner.endswith('"') or dir.inner.startswith("'") and dir.inner.endswith("'"):
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dir.inner = dir.inner[1:-1]
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pass
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if not os.path.exists(dir.inner):
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dir.inner = os.path.join(os.path.dirname(__file__), "../program", dir.inner)
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if not os.path.exists(dir.inner):
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dir.inner = dir.inner + ".py"
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pass
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pass
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with open(dir.inner) as program:
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local = {"i": i, "o": o, "g": Clip_IO.global_carry, "c": c, "p": p, "sd_model": shared.sd_model, "torch": torch.__dict__} | math.__dict__
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exec(program, None, local)
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pass
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pass
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except Exception as e:
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o = i
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raise e
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pass
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pass
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else:
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warnings.warn(f'Directive "{dir.name}" does not exist.')
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pass
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@ -727,7 +751,6 @@ class Clip_IO(scripts.Script):
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prompt_parser.get_learned_conditioning = Clip_IO.my_get_learned_conditioning
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prompt_parser.get_multicond_learned_conditioning = Clip_IO.my_get_multicond_learned_conditioning
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#Clip_IO.replace_inner_function(processing.process_images_inner, Clip_IO.get_my_get_conds_with_caching())
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pass
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else:
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Clip_IO.enabled = False
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pass
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@ -751,15 +774,30 @@ class Clip_IO(scripts.Script):
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Clip_IO.evacuate_get_conds_with_caching = p.get_conds_with_caching
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p.get_conds_with_caching = Clip_IO.get_my_get_conds_with_caching(p)
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pass
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try:
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local = {"g": Clip_IO.global_carry, "p": p, "sd_model": shared.sd_model, "torch": torch.__dict__} | math.__dict__
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exec(args[4], None, local)
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except Exception as e:
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raise e
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pass
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pass
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pass
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pass
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def postprocess_batch(self, p: processing.StableDiffusionProcessing, *args, **kwargs):
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if Clip_IO.enabled:
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Clip_IO.mode_positive = "Disabled"
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Clip_IO.mode_negative = "Disabled"
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if getattr(p, "get_conds_with_caching", None) is not None:
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p.get_conds_with_caching = Clip_IO.evacuate_get_conds_with_caching
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try:
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local = {"g": Clip_IO.global_carry, "p": p, "sd_model": shared.sd_model, "torch": torch.__dict__} | math.__dict__
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exec(args[3], None, local)
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except Exception as e:
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raise e
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pass
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finally:
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Clip_IO.mode_positive = "Disabled"
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Clip_IO.mode_negative = "Disabled"
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if getattr(p, "get_conds_with_caching", None) is not None:
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p.get_conds_with_caching = Clip_IO.evacuate_get_conds_with_caching
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pass
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pass
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pass
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pass
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@ -936,7 +974,7 @@ class Clip_IO(scripts.Script):
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def get_chunks(prompt: str | tuple[str, ...], clip: FrozenCLIPEmbedderWithCustomWords | FrozenOpenCLIPEmbedderWithCustomWords, manual_chunk: bool) -> tuple[list[list[PromptChunk]],list[list[tuple[int,int]]]]:
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"Return: PromptChunks, token separation position"
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if isinstance(prompt, str):
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prompt = [prompt]
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prompt = (prompt,)
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pass
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if opts.use_old_emphasis_implementation:
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raise NotImplementedError
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@ -1120,6 +1158,7 @@ class Clip_IO(scripts.Script):
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pass
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def encode_with_transformers(clip, tokens, chunk_count: int, clip_skips: list[list[int | None]], separation_starts_list:list[list[tuple[int,int]]]):
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clip.wrapped.transformer.to(shared.device)
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outputs = clip.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
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zs = []
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@ -1130,7 +1169,7 @@ class Clip_IO(scripts.Script):
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if opts.CLIP_stop_at_last_layers > 1:
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zo = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
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zo = clip.wrapped.transformer.text_model.final_layer_norm(z)
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zo = clip.wrapped.transformer.text_model.final_layer_norm(zo)
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else:
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zo = outputs.last_hidden_state
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@ -1184,7 +1223,7 @@ class Clip_IO(scripts.Script):
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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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if clip.id_end != clip.id_pad and not manual_chunk:
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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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