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AI-Casanova
2023-12-16 20:16:01 -06:00
parent 2020d20bcb
commit 9e7757b7fe
3 changed files with 19 additions and 19 deletions
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modules/processing.py
+4
View File
@@ -231,6 +231,10 @@ class StableDiffusionProcessing:
self.hdr_maximize = hdr_maximize
self.hdr_max_center = hdr_max_center
self.hdr_max_boundry = hdr_max_boundry
self.prompt_embeds = []
self.positive_pooleds = []
self.negative_embeds = []
self.negative_pooleds = []
@property
modules/processing_diffusers.py
+10 -13
View File
@@ -89,8 +89,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if kwargs.get('latents', None) is None:
return kwargs
kwargs = correction_callback(p, timestep, kwargs)
kwargs["prompt_embeds"] = p.prompt_embeds[step-1]
kwargs["negative_prompt_embeds"] = p.negative_embeds[step-1]
try:
kwargs["prompt_embeds"] = p.prompt_embeds[step + 1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
kwargs["negative_prompt_embeds"] = p.negative_embeds[step + 1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
except:
pass
shared.state.current_latent = kwargs['latents']
if shared.cmd_opts.profile and shared.profiler is not None:
shared.profiler.step()
@@ -295,10 +300,6 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
possible = signature.parameters.keys()
generator_device = devices.cpu if shared.opts.diffusers_generator_device == "cpu" else shared.device
generator = [torch.Generator(generator_device).manual_seed(s) for s in seeds]
# prompt_embed = None
# pooled = None
# negative_embed = None
# negative_pooled = None
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
parser = 'Fixed attention'
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__:
@@ -312,20 +313,16 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
errors.display(e, 'Prompt parser encode')
if 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and p.prompt_embeds[0] is not None:
# if type(pooled) == list:
# pooled = pooled[0]
# if type(negative_pooled) == list:
# negative_pooled = p.negative_pooleds[0][0]
args['prompt_embeds'] = p.prompt_embeds[0]
if 'XL' in model.__class__.__name__:
args['pooled_prompt_embeds'] = p.positive_pooleds[0][0]
args['pooled_prompt_embeds'] = p.positive_pooleds[0]
else:
args['prompt'] = prompts
if 'negative_prompt' in possible:
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and p.negative_embeds[0] is not None:
args['negative_prompt_embeds'] = p.negative_embeds[0]
if 'XL' in model.__class__.__name__:
args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0][0]
args['negative_pooled_prompt_embeds'] = p.negative_pooleds[0]
else:
args['negative_prompt'] = negative_prompts
if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
@@ -345,7 +342,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args['callback'] = diffusers_callback_legacy
elif 'callback_on_step_end_tensor_inputs' in possible:
args['callback_on_step_end'] = diffusers_callback
args['callback_on_step_end_tensor_inputs'] = ['latents']
args['callback_on_step_end_tensor_inputs'] = ['latents', 'prompt_embeds', 'negative_prompt_embeds']
for arg in kwargs:
if arg in possible: # add kwargs
args[arg] = kwargs[arg]
modules/prompt_parser_diffusers.py
+5 -6
View File
@@ -58,22 +58,22 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
debug(f'Prompt: expand={prompt}')
return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
def get_prompt_schedule(prompt, steps, step):
def get_prompt_schedule(prompt, steps):
temp = []
schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
for chunk in schedule:
for s in range(steps):
if s + 1 <= chunk[0]:
if len(temp) < s + 1 <= chunk[0]:
temp.append(chunk[1])
return temp[step-1]
return temp
def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, step: int = 1, clip_skip: typing.Optional[int] = None):
if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion':
shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
return None, None, None, None
else:
positive_schedule = get_prompt_schedule(prompts[0])
negative_schedule = get_prompt_schedule(negative_prompts[0])
positive_schedule = get_prompt_schedule(prompts[0], steps)
negative_schedule = get_prompt_schedule(negative_prompts[0], steps)
p.prompt_embeds = []
p.positive_pooleds = []
@@ -85,7 +85,6 @@ def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int,
positive_schedule[i],
negative_schedule[i],
clip_skip)
if prompt_embed is not None:
p.prompt_embeds.append(torch.cat([prompt_embed]*len(prompts), dim=0))
if negative_embed is not None: