diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/img2img.py | 3 | ||||
| -rw-r--r-- | modules/processing.py | 7 | ||||
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 53 | ||||
| -rw-r--r-- | modules/ui.py | 12 |
4 files changed, 61 insertions, 14 deletions
diff --git a/modules/img2img.py b/modules/img2img.py index f813299c..bcc158dc 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -76,7 +76,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename))
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -142,6 +142,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s inpainting_fill=inpainting_fill,
resize_mode=resize_mode,
denoising_strength=denoising_strength,
+ image_cfg_scale=image_cfg_scale,
inpaint_full_res=inpaint_full_res,
inpaint_full_res_padding=inpaint_full_res_padding,
inpainting_mask_invert=inpainting_mask_invert,
diff --git a/modules/processing.py b/modules/processing.py index e544c2e1..e1b53ac0 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -186,7 +186,7 @@ class StableDiffusionProcessing: return conditioning
def edit_image_conditioning(self, source_image):
- conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+ conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
return conditioning_image
@@ -268,6 +268,7 @@ class Processed: self.height = p.height
self.sampler_name = p.sampler_name
self.cfg_scale = p.cfg_scale
+ self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
self.steps = p.steps
self.batch_size = p.batch_size
self.restore_faces = p.restore_faces
@@ -445,6 +446,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Steps": p.steps,
"Sampler": p.sampler_name,
"CFG scale": p.cfg_scale,
+ "Image CFG scale": getattr(p, 'image_cfg_scale', None),
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
@@ -901,12 +903,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None
- def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
+ def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
super().__init__(**kwargs)
self.init_images = init_images
self.resize_mode: int = resize_mode
self.denoising_strength: float = denoising_strength
+ self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
self.init_latent = None
self.image_mask = mask
self.latent_mask = None
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index aa7f106b..f076fc55 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -1,6 +1,7 @@ from collections import deque
import torch
import inspect
+import einops
import k_diffusion.sampling
from modules import prompt_parser, devices, sd_samplers_common
@@ -56,6 +57,7 @@ class CFGDenoiser(torch.nn.Module): self.nmask = None
self.init_latent = None
self.step = 0
+ self.image_cfg_scale = None
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
@@ -67,19 +69,36 @@ class CFGDenoiser(torch.nn.Module): return denoised
+ def combine_denoised_for_edit_model(self, x_out, cond_scale):
+ out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+ denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+ return denoised
+
def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
+ # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+ # so is_edit_model is set to False to support AND composition.
+ is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+ assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ if not is_edit_model:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ else:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
cfg_denoiser_callback(denoiser_params)
@@ -88,7 +107,10 @@ class CFGDenoiser(torch.nn.Module): sigma_in = denoiser_params.sigma
if tensor.shape[1] == uncond.shape[1]:
- cond_in = torch.cat([tensor, uncond])
+ if not is_edit_model:
+ cond_in = torch.cat([tensor, uncond])
+ else:
+ cond_in = torch.cat([tensor, uncond, uncond])
if shared.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
@@ -104,7 +126,13 @@ class CFGDenoiser(torch.nn.Module): for batch_offset in range(0, tensor.shape[0], batch_size):
a = batch_offset
b = min(a + batch_size, tensor.shape[0])
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
+
+ if not is_edit_model:
+ c_crossattn = [tensor[a:b]]
+ else:
+ c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
@@ -115,7 +143,10 @@ class CFGDenoiser(torch.nn.Module): elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ if not is_edit_model:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ else:
+ denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
@@ -198,6 +229,7 @@ class KDiffusionSampler: self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
self.model_wrap_cfg.step = 0
+ self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
self.eta = p.eta if p.eta is not None else opts.eta_ancestral
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
@@ -260,13 +292,14 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x
self.last_latent = x
-
- samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+ extra_args={
'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale
- }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ 'cond_scale': p.cfg_scale,
+ }
+
+ samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples
diff --git a/modules/ui.py b/modules/ui.py index 5e34fb07..f5df1ffe 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -765,7 +765,9 @@ def create_ui(): elif category == "cfg":
with FormGroup():
- cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ with FormRow():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
elif category == "seed":
@@ -861,6 +863,7 @@ def create_ui(): batch_count,
batch_size,
cfg_scale,
+ image_cfg_scale,
denoising_strength,
seed,
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
@@ -947,6 +950,7 @@ def create_ui(): (sampler_index, "Sampler"),
(restore_faces, "Face restoration"),
(cfg_scale, "CFG scale"),
+ (image_cfg_scale, "Image CFG scale"),
(seed, "Seed"),
(width, "Size-1"),
(height, "Size-2"),
@@ -1591,6 +1595,12 @@ def create_ui(): outputs=[component, text_settings],
)
+ text_settings.change(
+ fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
+ inputs=[],
+ outputs=[image_cfg_scale],
+ )
+
button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
button_set_checkpoint.click(
fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
|