remove dependence on TQDM for sampler progress/interrupt functionality

modules/processing.py

@@ -402,12 +402,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted or state.skipped:
-
-                # if we are interrupted, sample returns just noise
-                # use the image collected previously in sampler loop
-                samples_ddim = shared.state.current_latent
-
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

modules/sd_samplers.py

@@ -98,25 +98,8 @@ def store_latent(decoded):
             shared.state.current_image = sample_to_image(decoded)
 
 
-
-def extended_tdqm(sequence, *args, desc=None, **kwargs):
-    state.sampling_steps = len(sequence)
-    state.sampling_step = 0
-
-    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
-ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
-ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
+class InterruptedException(BaseException):
+    pass
 
 
 class VanillaStableDiffusionSampler:
@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None
         self.sampler_noises = None
         self.step = 0
+        self.stop_at = None
         self.eta = None
         self.default_eta = 0.0
         self.config = None
+        self.last_latent = None
 
     def number_of_needed_noises(self, p):
         return 0
 
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
+
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise InterruptedException
+
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
-            store_latent(self.init_latent * self.mask + self.nmask * res[1])
+            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
         else:
-            store_latent(res[1])
+            self.last_latent = res[1]
+
+        store_latent(self.last_latent)
 
         self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
         return res
 
     def initialize(self, p):
@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
-        samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
+        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with certain step counts, like 9
         try:
-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
         return samples_ddim
 
@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
 
     def forward(self, x, sigma, uncond, cond, cond_scale):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
         return denoised
 
 
-def extended_trange(sampler, count, *args, **kwargs):
-    state.sampling_steps = count
-    state.sampling_step = 0
-
-    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        if sampler.stop_at is not None and x > sampler.stop_at:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
 class TorchHijack:
     def __init__(self, kdiff_sampler):
         self.kdiff_sampler = kdiff_sampler
@@ -314,9 +304,28 @@ class KDiffusionSampler:
         self.eta = None
         self.default_eta = 1.0
         self.config = None
+        self.last_latent = None
 
     def callback_state(self, d):
-        store_latent(d["denoised"])
+        step = d['i']
+        latent = d["denoised"]
+        store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
 
     def number_of_needed_noises(self, p):
         return p.steps
@@ -339,9 +348,6 @@ class KDiffusionSampler:
         self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if hasattr(k_diffusion.sampling, 'trange'):
-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
-
         if self.sampler_noises is not None:
             k_diffusion.sampling.torch = TorchHijack(self)
 
@@ -383,8 +389,9 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
@@ -406,6 +413,8 @@ class KDiffusionSampler:
                 extra_params_kwargs['n'] = steps
         else:
             extra_params_kwargs['sigmas'] = sigmas
-        samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
         return samples