diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 76 |
1 files changed, 38 insertions, 38 deletions
diff --git a/modules/processing.py b/modules/processing.py index 43fac21c..72dd3f6f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -528,7 +528,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
if type(p) == StableDiffusionProcessingTxt2Img:
- if p.enable_hr and p.hr_prompt != '':
+ if p.enable_hr and p.hr_prompt == '':
+ p.all_hr_prompts, p.all_hr_negative_prompts = p.all_prompts, p.all_negative_prompts
+ elif p.enable_hr and p.hr_prompt != '':
if type(p.prompt) == list:
p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
else:
@@ -555,14 +557,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
- _, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
- if type(p) == StableDiffusionProcessingTxt2Img:
- if p.enable_hr and p.hr_prompt != '':
- _, hr_extra_network_data = extra_networks.parse_prompts(p.all_hr_prompts[0:1])
- if p.all_hr_prompts != p.all_prompts:
- extra_network_data.update(hr_extra_network_data)
-
-
if p.scripts is not None:
p.scripts.process(p)
@@ -600,13 +594,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
sd_vae_approx.model()
- if not p.disable_extra_networks:
- extra_networks.activate(p, extra_network_data)
-
- with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
- processed = Processed(p, [], p.seed, "")
- file.write(processed.infotext(p, 0))
-
if state.job_count == -1:
state.job_count = p.n_iter
@@ -623,9 +610,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
if type(p) == StableDiffusionProcessingTxt2Img:
- if p.enable_hr and p.hr_prompt != '':
- hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ if p.enable_hr:
+ if p.hr_prompt == '':
+ hr_prompts, hr_negative_prompts = prompts, negative_prompts
+ else:
+ hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
@@ -633,19 +623,40 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if len(prompts) == 0:
break
- prompts, _ = extra_networks.parse_prompts(prompts)
+ prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+ if type(p) == StableDiffusionProcessingTxt2Img:
+ if p.enable_hr and hr_prompts != prompts:
+ _, hr_extra_network_data = extra_networks.parse_prompts(hr_prompts)
+ extra_network_data.update(hr_extra_network_data)
+
+
+
+ if not p.disable_extra_networks:
+ with devices.autocast():
+ extra_networks.activate(p, extra_network_data)
if p.scripts is not None:
p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+ # params.txt should be saved after scripts.process_batch, since the
+ # infotext could be modified by that callback
+ # Example: a wildcard processed by process_batch sets an extra model
+ # strength, which is saved as "Model Strength: 1.0" in the infotext
+ if n == 0:
+ with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+ processed = Processed(p, [], p.seed, "")
+ file.write(processed.infotext(p, 0))
+
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+
if type(p) == StableDiffusionProcessingTxt2Img:
- if p.enable_hr and p.hr_prompt != '':
- hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps,
- cached_uc)
- hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps,
- cached_c)
+ if p.enable_hr:
+ if prompts != hr_prompts:
+ hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps, cached_uc)
+ hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps, cached_c)
+ else:
+ hr_uc, hr_c = uc, c
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -658,20 +669,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr:
- if p.hr_prompt != '':
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
- else:
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=c,
- hr_unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
- subseed_strength=p.subseed_strength, prompts=prompts)
- else:
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
- subseed_strength=p.subseed_strength, prompts=prompts)
-
+ samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
else:
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
- subseeds=subseeds,
- subseed_strength=p.subseed_strength, prompts=prompts)
+ samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
for x in x_samples_ddim:
|