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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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50 lines
1.7 KiB
Python
50 lines
1.7 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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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 forge_sample(self, denoiser_params, cond_scale):
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model = self.inner_model.inner_model.forge_objects.unet.model
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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(denoiser_params.text_cond)
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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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uncond[0]['model_conds']['c_concat'] = CONDRegular(image_cond_in)
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cond[0]['model_conds']['c_concat'] = CONDRegular(image_cond_in)
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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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