Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-08-30 00:50:59 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix compel to full and add batch sizes

Browse Source
This commit is contained in:
Vladimir Mandic
2023-08-09 10:28:44 +00:00
parent e2b0d981ac
commit 16725ab38f
2 changed files with 58 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/processing_diffusers.py
+16 -10
View File
@@ -53,7 +53,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
return imgs
def set_pipeline_args(model, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str] =None, negative_prompt_2: typing.Optional[str] = None, is_refiner: bool = False, **kwargs):
def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: typing.Optional[list]=None, negative_prompts_2: typing.Optional[list]=None, is_refiner: bool=False, **kwargs):
args = {}
pipeline = model
signature = inspect.signature(type(pipeline).__call__)
@@ -65,7 +65,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
negative_embed = None
negative_pooled = None
if shared.opts.data['prompt_attention'] in {'Compel parser', 'Full parser'}:
prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompt(model, prompt, negative_prompt, prompt_2, negative_prompt_2, is_refiner, kwargs.pop("clip_skip", None))
prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompts(model,
prompts,
negative_prompts,
prompts_2,
negative_prompts_2,
is_refiner,
kwargs.pop("clip_skip", None))
if 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
args['prompt_embeds'] = prompt_embed
@@ -73,7 +79,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args['pooled_prompt_embeds'] = pooled
args['prompt_2'] = None #Cannot pass prompts when passing embeds
else:
args['prompt'] = prompt
args['prompt'] = prompts
if 'negative_prompt' in possible:
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and negative_embed is not None:
args['negative_prompt_embeds'] = negative_embed
@@ -81,7 +87,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
args['negative_pooled_prompt_embeds'] = negative_pooled
args['negative_prompt_2'] = None
else:
args['negative_prompt'] = negative_prompt
args['negative_prompt'] = negative_prompts
if 'num_inference_steps' in possible:
args['num_inference_steps'] = p.steps
if 'guidance_scale' in possible:
@@ -157,10 +163,10 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
refiner_enabled = shared.sd_refiner is not None and p.enable_hr
pipe_args = set_pipeline_args(
model=shared.sd_model,
prompt=prompts,
negative_prompt=negative_prompts,
prompt_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
negative_prompt_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
prompts=prompts,
negative_prompts=negative_prompts,
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
eta=shared.opts.eta_ddim,
guidance_rescale=p.diffusers_guidance_rescale,
denoising_start=0 if refiner_enabled and p.refiner_start > 0 and p.refiner_start < 1 else None,
@@ -211,8 +217,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
for i in range(len(output.images)):
pipe_args = set_pipeline_args(
model=shared.sd_refiner,
prompt=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts[i],
negative_prompt=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts[i],
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts[i],
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts[i],
num_inference_steps=p.hr_second_pass_steps,
eta=shared.opts.eta_ddim,
strength=p.denoising_strength,