diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/generation_parameters_copypaste.py | 6 | ||||
| -rw-r--r-- | modules/processing.py | 63 | ||||
| -rw-r--r-- | modules/txt2img.py | 5 | ||||
| -rw-r--r-- | modules/ui.py | 17 |
4 files changed, 84 insertions, 7 deletions
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 46e12dc6..98098cc8 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -252,7 +252,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if line.startswith("Negative prompt:"):
done_with_prompt = True
line = line[16:].strip()
-
if done_with_prompt:
negative_prompt += ("" if negative_prompt == "" else "\n") + line
else:
@@ -269,6 +268,11 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model else:
res[k] = v
+ if k.startswith("Hires prompt"):
+ res["Hires prompt"] = v[1:][:-1].replace(';', ',')
+ elif k.startswith("Hires negative prompt"):
+ res["Hires negative prompt"] = v[1:][:-1].replace(';', ',')
+
# Missing CLIP skip means it was set to 1 (the default)
if "Clip skip" not in res:
res["Clip skip"] = "1"
diff --git a/modules/processing.py b/modules/processing.py index bc541e2f..a1138491 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -236,7 +236,7 @@ class StableDiffusionProcessing: def init(self, all_prompts, all_seeds, all_subseeds):
pass
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts, hr_conditioning=None, hr_unconditional_conditioning=None):
raise NotImplementedError()
def close(self):
@@ -517,6 +517,18 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: else:
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 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:
+ p.all_hr_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
+
+ if type(p.negative_prompt) == list:
+ p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.hr_negative_prompt]
+ else:
+ p.all_hr_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
+
if type(seed) == list:
p.all_seeds = seed
else:
@@ -589,6 +601,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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]
+
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]
@@ -602,6 +620,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: 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 len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -611,7 +635,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}"
with devices.autocast():
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
+ 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)
+
+ else:
+ 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:
@@ -714,7 +753,7 @@ def old_hires_fix_first_pass_dimensions(width, height): class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
- def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
+ def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler: str = '---', hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
@@ -725,6 +764,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.hr_resize_y = hr_resize_y
self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y
+ self.hr_sampler = hr_sampler
+ self.hr_prompt = hr_prompt if hr_prompt != '' else ''
+ self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else ''
+ self.all_hr_prompts = None
+ self.all_hr_negative_prompts = None
if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width
@@ -749,6 +793,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.hr_resize_x == 0 and self.hr_resize_y == 0:
self.extra_generation_params["Hires upscale"] = self.hr_scale
+ self.extra_generation_params["Hires sampler"] = self.hr_sampler
+ self.extra_generation_params["Hires prompt"] = f'({self.hr_prompt.replace(",", ";")})'
+ self.extra_generation_params["Hires negative prompt"] = f'({self.hr_negative_prompt.replace(",", ";")})'
self.hr_upscale_to_x = int(self.width * self.hr_scale)
self.hr_upscale_to_y = int(self.height * self.hr_scale)
else:
@@ -798,7 +845,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.hr_upscaler is not None:
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts, hr_conditioning=None, hr_unconditional_conditioning=None):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
@@ -866,6 +913,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob()
img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM' # PLMS does not support img2img so we just silently switch ot DDIM
+
+ if self.hr_sampler == '---':
+ pass
+ else:
+ img2img_sampler_name = self.hr_sampler
+
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
@@ -876,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None
devices.torch_gc()
- samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+ samples = self.sampler.sample_img2img(self, samples, noise, hr_conditioning, hr_unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
return samples
diff --git a/modules/txt2img.py b/modules/txt2img.py index e945fd69..c06f9f9d 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -8,7 +8,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -38,6 +38,9 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
+ hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---',
+ hr_prompt=hr_prompt,
+ hr_negative_prompt=hr_negative_prompt
)
p.scripts = modules.scripts.scripts_txt2img
diff --git a/modules/ui.py b/modules/ui.py index 85ae62c7..e701393c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -495,6 +495,17 @@ def create_ui(): hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+ with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact"):
+ hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id=f"hr_sampler", choices=["---"] + [x.name for x in samplers_for_img2img], value="---", type="index")
+
+ with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact"):
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_prompt = gr.Textbox(label="Prompt", elem_id=f"hires_prompt", show_label=False, lines=3, placeholder="Prompt that will be used for hires fix pass (leave it blank to use the same prompt as in initial txt2img gen)")
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt that will be used for hires fix pass (leave it blank to use the same prompt as in initial txt2img gen)")
+
elif category == "batch":
if not opts.dimensions_and_batch_together:
with FormRow(elem_id="txt2img_column_batch"):
@@ -553,6 +564,9 @@ def create_ui(): hr_second_pass_steps,
hr_resize_x,
hr_resize_y,
+ hr_sampler_index,
+ hr_prompt,
+ hr_negative_prompt,
] + custom_inputs,
outputs=[
@@ -608,6 +622,9 @@ def create_ui(): (hr_second_pass_steps, "Hires steps"),
(hr_resize_x, "Hires resize-1"),
(hr_resize_y, "Hires resize-2"),
+ (hr_sampler_index, "Hires sampling method"),
+ (hr_prompt, "Hires prompt"),
+ (hr_negative_prompt, "Hires negative prompt"),
*modules.scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
|