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-rw-r--r--modules/processing.py64
-rw-r--r--modules/txt2img.py9
-rw-r--r--modules/ui.py19
3 files changed, 35 insertions, 57 deletions
diff --git a/modules/processing.py b/modules/processing.py
index d9b0e0e7..100a259f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
firstphase_width_truncated = 0
firstphase_height_truncated = 0
- def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
+ def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
- self.scale_latent = scale_latent
self.denoising_strength = denoising_strength
self.firstphase_width = firstphase_width
self.firstphase_height = firstphase_height
- self.crop_scale = crop_scale
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
@@ -530,6 +528,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples
+ self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
@@ -538,46 +538,36 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
width_ratio = self.width/self.firstphase_width
height_ratio = self.height/self.firstphase_height
- if self.crop_scale:
- if width_ratio > height_ratio:
- #Crop to landscape
- truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
+ if width_ratio > height_ratio:
+ truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
- elif width_ratio < height_ratio:
- #Crop to portrait
- truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
+ elif width_ratio < height_ratio:
+ truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
- samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
-
-
+ samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
-
+ decoded_samples = decode_first_stage(self.sd_model, samples)
- if self.scale_latent:
- samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+ if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
+ decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
else:
- decoded_samples = decode_first_stage(self.sd_model, samples)
+ lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
- if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
- decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
- else:
- lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
- batch_images = []
- for i, x_sample in enumerate(lowres_samples):
- x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
- x_sample = x_sample.astype(np.uint8)
- image = Image.fromarray(x_sample)
- image = images.resize_image(0, image, self.width, self.height)
- image = np.array(image).astype(np.float32) / 255.0
- image = np.moveaxis(image, 2, 0)
- batch_images.append(image)
-
- decoded_samples = torch.from_numpy(np.array(batch_images))
- decoded_samples = decoded_samples.to(shared.device)
- decoded_samples = 2. * decoded_samples - 1.
-
- samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+ batch_images = []
+ for i, x_sample in enumerate(lowres_samples):
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+ image = Image.fromarray(x_sample)
+ image = images.resize_image(0, image, self.width, self.height)
+ image = np.array(image).astype(np.float32) / 255.0
+ image = np.moveaxis(image, 2, 0)
+ batch_images.append(image)
+
+ decoded_samples = torch.from_numpy(np.array(batch_images))
+ decoded_samples = decoded_samples.to(shared.device)
+ decoded_samples = 2. * decoded_samples - 1.
+
+ samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
shared.state.nextjob()
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 447ec3d3..2381347f 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
from modules.ui import plaintext_to_html
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, 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, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, 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, firstphase_width: int, firstphase_height: int, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
restore_faces=restore_faces,
tiling=tiling,
enable_hr=enable_hr,
- scale_latent=scale_latent if enable_hr else None,
denoising_strength=denoising_strength if enable_hr else None,
- firstphase_width=firstphase_width if enable_hr else None,
- firstphase_height=firstphase_height if enable_hr else None,
- crop_scale=crop_scale if enable_hr else None,
-
+ firstphase_width=firstphase_width if enable_hr else None,
+ firstphase_height=firstphase_height if enable_hr else None,
)
if cmd_opts.enable_console_prompts:
diff --git a/modules/ui.py b/modules/ui.py
index f2d81f68..d66ddc14 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,16 +540,9 @@ def create_ui(wrap_gradio_gpu_call):
enable_hr = gr.Checkbox(label='Highres. fix', value=False)
with gr.Row(visible=False) as hr_options:
- with gr.Column(scale=1.0):
- firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
- firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
-
- with gr.Column(scale=1.0):
- with gr.Row():
- crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
- scale_latent = gr.Checkbox(label='Scale latent', value=False)
- with gr.Row():
- denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+ firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+ firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
with gr.Row(equal_height=True):
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
@@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call):
height,
width,
enable_hr,
- scale_latent,
denoising_strength,
firstphase_width,
firstphase_height,
- crop_scale,
] + custom_inputs,
outputs=[
txt2img_gallery,
@@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call):
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d),
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
- (firstphase_width, "First pass width"),
- (firstphase_height, "First pass height"),
+ (firstphase_width, "First pass size-1"),
+ (firstphase_height, "First pass size-2"),
]
modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-21 03:02:07 +0000