Merge pull request #7481 from Klace/master

img2img instruct-pix2pix support

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,

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

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
 
@@ -40,6 +41,90 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+class CFGDenoiserEdit(torch.nn.Module):
+    """
+    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+    negative prompt.
+    """
+
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.step = 0
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale, image_cfg_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                out_cond, out_img_cond, out_uncond =  x_out.chunk(3)
+                denoised[i] =  out_uncond[cond_index] + cond_scale * (out_cond[cond_index] - out_img_cond[cond_index]) + image_cfg_scale * (out_img_cond[cond_index] - out_uncond[cond_index])
+
+        return denoised
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond, image_cfg_scale):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        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] + [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)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+
+        if tensor.shape[1] == uncond.shape[1]:
+            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]})
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            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": torch.cat([tensor[a:b]], uncond) , "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]:]]})
+
+        devices.test_for_nans(x_out, "unet")
+
+        if opts.live_preview_content == "Prompt":
+            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+        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, image_cfg_scale)
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        self.step += 1
+
+        return denoised
+
 
 class CFGDenoiser(torch.nn.Module):
     """
@@ -78,8 +163,8 @@ class CFGDenoiser(torch.nn.Module):
         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])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
 
         denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
         cfg_denoiser_callback(denoiser_params)
@@ -195,6 +280,9 @@ class KDiffusionSampler:
         return p.steps
 
     def initialize(self, p):
+        if shared.sd_model.cond_stage_key == "edit" and getattr(p, 'image_cfg_scale', None) != 1:
+            self.model_wrap_cfg = CFGDenoiserEdit(self.model_wrap)
+
         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
@@ -260,13 +348,17 @@ 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,
+        }
+
+        if hasattr(p, 'image_cfg_scale') and p.image_cfg_scale != 1 and p.image_cfg_scale != None:
+            extra_args['image_cfg_scale'] = p.image_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
 

modules/ui.py

@@ -766,6 +766,7 @@ 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")
+                            image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale (for instruct-pix2pix models only)', value=1.5, elem_id="img2img_image_cfg_scale")
                             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 +862,7 @@ 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 +949,7 @@ 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"),