112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
import torch
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from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn
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from ldm_patched.modules.samplers import sampling_function
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from ldm_patched.modules import model_management
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def cond_from_a1111_to_patched_ldm(cond):
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if isinstance(cond, torch.Tensor):
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result = dict(
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cross_attn=cond,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cond),
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)
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)
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return [result, ]
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cross_attn = cond['crossattn']
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pooled_output = cond['vector']
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result = dict(
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cross_attn=cross_attn,
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pooled_output=pooled_output,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cross_attn),
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y=CONDRegular(pooled_output)
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)
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)
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return [result, ]
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def cond_from_a1111_to_patched_ldm_weighted(cond, weights):
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transposed = list(map(list, zip(*weights)))
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results = []
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for cond_pre in transposed:
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current_indices = []
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current_weight = 0
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for i, w in cond_pre:
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current_indices.append(i)
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current_weight = w
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if hasattr(cond, 'advanced_indexing'):
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feed = cond.advanced_indexing(current_indices)
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else:
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feed = cond[current_indices]
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h = cond_from_a1111_to_patched_ldm(feed)
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h[0]['strength'] = current_weight
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results += h
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return results
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def forge_sample(self, denoiser_params, cond_scale, cond_composition):
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model = self.inner_model.inner_model.forge_objects.unet.model
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control = self.inner_model.inner_model.forge_objects.unet.controlnet_linked_list
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x = denoiser_params.x
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timestep = denoiser_params.sigma
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uncond = cond_from_a1111_to_patched_ldm(denoiser_params.text_uncond)
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cond = cond_from_a1111_to_patched_ldm_weighted(denoiser_params.text_cond, cond_composition)
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model_options = self.inner_model.inner_model.forge_objects.unet.model_options
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seed = self.p.seeds[0]
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image_cond_in = denoiser_params.image_cond
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if isinstance(image_cond_in, torch.Tensor):
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if image_cond_in.shape[0] == x.shape[0] \
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and image_cond_in.shape[2] == x.shape[2] \
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and image_cond_in.shape[3] == x.shape[3]:
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for i in range(len(uncond)):
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uncond[i]['model_conds']['c_concat'] = CONDRegular(image_cond_in)
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for i in range(len(cond)):
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cond[i]['model_conds']['c_concat'] = CONDRegular(image_cond_in)
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if control is not None:
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for h in cond + uncond:
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h['control'] = control
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denoised = sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options, seed)
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return denoised
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def sampling_prepare(unet, x):
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B, C, H, W = x.shape
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unet_inference_memory = unet.memory_required([B * 2, C, H, W])
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additional_inference_memory = 0
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additional_model_patchers = []
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if unet.controlnet_linked_list is not None:
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additional_inference_memory += unet.controlnet_linked_list.inference_memory_requirements(unet.model_dtype())
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additional_model_patchers += unet.controlnet_linked_list.get_models()
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model_management.load_models_gpu(
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models=[unet] + additional_model_patchers,
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memory_required=unet_inference_memory + additional_inference_memory)
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real_model = unet.model
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percent_to_timestep_function = lambda p: real_model.model_sampling.percent_to_sigma(p)
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for cnet in unet.list_controlnets():
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cnet.pre_run(real_model, percent_to_timestep_function)
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return
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def sampling_cleanup(unet):
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for cnet in unet.list_controlnets():
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cnet.cleanup()
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return
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