Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into Base

1 files changed, 29 insertions, 4 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 71a9c6f5..6a99d383 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -119,8 +119,18 @@ def slerp(val, low, high):
     return res

 

 

-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):

+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):

     xs = []

+

+    # if we have multiple seeds, this means we are working with batch size>1; this then

+    # enables the generation of additional tensors with noise that the sampler will use during its processing.

+    # Using those pre-genrated tensors instead of siimple torch.randn allows a batch with seeds [100, 101] to

+    # produce the same images as with two batches [100], [101].

+    if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:

+        sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]

+    else:

+        sampler_noises = None

+

     for i, seed in enumerate(seeds):

         noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)

 

@@ -155,9 +165,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]

             noise = x

 

+        if sampler_noises is not None:

+            cnt = p.sampler.number_of_needed_noises(p)

 

+            for j in range(cnt):

+                sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))

 

         xs.append(noise)

+

+    if sampler_noises is not None:

+        p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]

+

     x = torch.stack(xs).to(shared.device)

     return x

 

@@ -170,7 +188,11 @@ def fix_seed(p):
 def process_images(p: StableDiffusionProcessing) -> Processed:

     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""

 

-    assert p.prompt is not None

+    if type(p.prompt) == list:

+        assert(len(p.prompt) > 0)

+    else:

+        assert p.prompt is not None

+        

     devices.torch_gc()

 

     fix_seed(p)

@@ -209,7 +231,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             "Seed": all_seeds[index],

             "Face restoration": (opts.face_restoration_model if p.restore_faces else None),

             "Size": f"{p.width}x{p.height}",

-            "Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model_hash else shared.sd_model_hash),

+            "Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),

             "Batch size": (None if p.batch_size < 2 else p.batch_size),

             "Batch pos": (None if p.batch_size < 2 else position_in_batch),

             "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),

@@ -247,6 +269,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

             subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

 

+            if (len(prompts) == 0):

+                break

+

             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])

             #c = p.sd_model.get_learned_conditioning(prompts)

             uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)

@@ -257,7 +282,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     comments[comment] = 1

 

             # we manually generate all input noises because each one should have a specific seed

-            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)

+            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)

 

             if p.n_iter > 1:

                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"