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downgrade diffusers

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This commit is contained in:
Vladimir Mandic
2023-09-15 13:41:56 -04:00
parent b67e986ec3
commit d833b853ff
5 changed files with 20 additions and 4 deletions
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modules/processing_diffusers.py
+10 -2
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@@ -187,8 +187,13 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
negative_embed = None
negative_pooled = None
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
if shared.opts.prompt_attention in {'Compel parser', 'Full parser'} and 'StableDiffusion' in model.__class__.__name__:
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))
parser = 'Fixed attention'
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__:
try:
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))
parser = shared.opts.prompt_attention
except Exception as e:
shared.log.error(f'Prompt parser: {e}')
if 'prompt' in possible:
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
if type(pooled) == list:
@@ -244,7 +249,10 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if 'negative_pooled_prompt_embeds' in clean:
clean['negative_pooled_prompt_embeds'] = clean['negative_pooled_prompt_embeds'].shape if torch.is_tensor(clean['negative_pooled_prompt_embeds']) else type(clean['negative_pooled_prompt_embeds'])
clean['generator'] = generator_device
clean['parser'] = parser
shared.log.debug(f'Diffuser pipeline: {model.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
# components = [{ k: getattr(v, 'device', None) } for k, v in model.components.items()]
# shared.log.debug(f'Diffuser pipeline components: {components}')
return args
def recompile_model(hires=False):