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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
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Update forge_reference.py
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@@ -18,6 +18,8 @@ class PreprocessorReference(Preprocessor):
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self.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab = False
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self.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab = False
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self.do_not_need_model = True
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self.do_not_need_model = True
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self.is_recording_style = False
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def process_before_every_sampling(self, process, cond, *args, **kwargs):
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def process_before_every_sampling(self, process, cond, *args, **kwargs):
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unit = kwargs['unit']
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unit = kwargs['unit']
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weight = float(unit.weight)
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weight = float(unit.weight)
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@@ -25,6 +27,12 @@ class PreprocessorReference(Preprocessor):
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start_percent = float(unit.guidance_start)
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start_percent = float(unit.guidance_start)
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end_percent = float(unit.guidance_end)
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end_percent = float(unit.guidance_end)
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vae = process.sd_model.forge_objects.vae
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# This is a powerful VAE with integrated memory management, bf16, and tiled fallback.
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latent_image = vae.encode(cond.movedim(1, -1))
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latent_image = process.sd_model.forge_objects.unet.model.latent_format.process_in(latent_image)
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unet = process.sd_model.forge_objects.unet.clone()
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unet = process.sd_model.forge_objects.unet.clone()
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sigma_max = unet.model.model_sampling.percent_to_sigma(start_percent)
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sigma_max = unet.model.model_sampling.percent_to_sigma(start_percent)
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sigma_min = unet.model.model_sampling.percent_to_sigma(end_percent)
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sigma_min = unet.model.model_sampling.percent_to_sigma(end_percent)
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@@ -34,7 +42,9 @@ class PreprocessorReference(Preprocessor):
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if not (sigma_min <= sigma <= sigma_max):
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if not (sigma_min <= sigma <= sigma_max):
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return model, x, timestep, uncond, cond, cond_scale, model_options, seed
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return model, x, timestep, uncond, cond, cond_scale, model_options, seed
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a = 0
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self.is_recording_style = True
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self.is_recording_style = False
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return model, x, timestep, uncond, cond, cond_scale, model_options, seed
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return model, x, timestep, uncond, cond, cond_scale, model_options, seed
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