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Img2img sd2 attempt 2
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@ -808,13 +808,6 @@ def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code,
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def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask):
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# encode (scaled latent)
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z_enc = thread_data.model.stochastic_encode(
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init_latent,
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torch.tensor([t_enc] * batch_size).to(thread_data.device),
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opt_seed,
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opt_ddim_eta,
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opt_ddim_steps,
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)
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x_T = None if mask is None else init_latent
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if thread_data.test_sd2:
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@ -822,7 +815,13 @@ def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, o
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sampler = DDIMSampler(thread_data.model)
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samples_ddim = thread_data.model.sample(
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sampler.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
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z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(thread_data.device))
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#samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
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unconditional_conditioning=uc, )
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samples_ddim = sampler.sample(
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S=t_enc,
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batch_size=opt_n_samples,
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conditioning=c,
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@ -834,6 +833,14 @@ def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, o
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x_T=x_T
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)
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else:
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z_enc = thread_data.model.stochastic_encode(
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init_latent,
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torch.tensor([t_enc] * batch_size).to(thread_data.device),
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opt_seed,
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opt_ddim_eta,
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opt_ddim_steps,
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)
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# decode it
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samples_ddim = thread_data.model.sample(
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t_enc,
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