diff options
Diffstat (limited to 'modules/prompt_parser.py')
| -rw-r--r-- | modules/prompt_parser.py | 114 |
1 files changed, 108 insertions, 6 deletions
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index a3b12421..f7420daf 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -97,10 +97,26 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
def get_learned_conditioning(model, prompts, steps):
+ """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
+ and the sampling step at which this condition is to be replaced by the next one.
+
+ Input:
+ (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
+
+ Output:
+ [
+ [
+ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0'))
+ ],
+ [
+ ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')),
+ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0'))
+ ]
+ ]
+ """
res = []
prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
@@ -123,13 +139,75 @@ def get_learned_conditioning(model, prompts, steps): cache[prompt] = cond_schedule
res.append(cond_schedule)
- return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
+ return res
+
+
+re_AND = re.compile(r"\bAND\b")
+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$")
+
+
+def get_multicond_prompt_list(prompts):
+ res_indexes = []
+
+ prompt_flat_list = []
+ prompt_indexes = {}
+
+ for prompt in prompts:
+ subprompts = re_AND.split(prompt)
+
+ indexes = []
+ for subprompt in subprompts:
+ text, weight = re_weight.search(subprompt).groups()
+
+ weight = float(weight) if weight is not None else 1.0
+
+ index = prompt_indexes.get(text, None)
+ if index is None:
+ index = len(prompt_flat_list)
+ prompt_flat_list.append(text)
+ prompt_indexes[text] = index
+
+ indexes.append((index, weight))
+
+ res_indexes.append(indexes)
+
+ return res_indexes, prompt_flat_list, prompt_indexes
+
+
+class ComposableScheduledPromptConditioning:
+ def __init__(self, schedules, weight=1.0):
+ self.schedules: list[ScheduledPromptConditioning] = schedules
+ self.weight: float = weight
+
+
+class MulticondLearnedConditioning:
+ def __init__(self, shape, batch):
+ self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
+ self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
-def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
- param = c.schedules[0][0].cond
- res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
- for i, cond_schedule in enumerate(c.schedules):
+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+ """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
+ For each prompt, the list is obtained by splitting the prompt using the AND separator.
+
+ https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
+ """
+
+ res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
+
+ learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+
+ res = []
+ for indexes in res_indexes:
+ res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
+
+ return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+
+
+def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+ param = c[0][0].cond
+ res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+ for i, cond_schedule in enumerate(c):
target_index = 0
for current, (end_at, cond) in enumerate(cond_schedule):
if current_step <= end_at:
@@ -140,6 +218,30 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step): return res
+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
+ param = c.batch[0][0].schedules[0].cond
+
+ tensors = []
+ conds_list = []
+
+ for batch_no, composable_prompts in enumerate(c.batch):
+ conds_for_batch = []
+
+ for cond_index, composable_prompt in enumerate(composable_prompts):
+ target_index = 0
+ for current, (end_at, cond) in enumerate(composable_prompt.schedules):
+ if current_step <= end_at:
+ target_index = current
+ break
+
+ conds_for_batch.append((len(tensors), composable_prompt.weight))
+ tensors.append(composable_prompt.schedules[target_index].cond)
+
+ conds_list.append(conds_for_batch)
+
+ return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+
+
re_attention = re.compile(r"""
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