Add option to experiment with results from other impl

Setting -> Compatibility -> Try to reproduce the results from external software

backend/nn/flux.py

@@ -48,7 +48,15 @@ def apply_rope(xq, xk, freqs_cis):
 def timestep_embedding(t, dim, max_period=10000, time_factor=1000.0):
     t = time_factor * t
     half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
+
+    # TODO: Once A trainer for flux get popular, make timestep_embedding consistent to that trainer
+
+    # Do not block CUDA steam, but having about 1e-4 differences with Flux official codes:
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
+
+    # Block CUDA steam, but consistent with official codes:
+    # freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
+
     args = t[:, None].float() * freqs[None]
     del freqs
     embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)

modules/processing.py

@@ -17,7 +17,7 @@ from typing import Any
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng, profiling
-from modules.rng import slerp # noqa: F401
+from modules.rng import slerp, get_noise_source_type  # noqa: F401
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -729,6 +729,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     if p.sd_model.use_distilled_cfg_scale:
         generation_params['Distilled CFG Scale'] = p.distilled_cfg_scale
 
+    noise_source_type = get_noise_source_type()
+
     generation_params.update({
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
         "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
@@ -750,7 +752,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
         "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
-        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "RNG": noise_source_type if noise_source_type != "GPU" else None,
         "Tiling": "True" if p.tiling else None,
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,

modules/rng.py

@@ -3,12 +3,20 @@ import torch
 from modules import devices, rng_philox, shared
 
 
+def get_noise_source_type():
+    if shared.opts.forge_try_reproduce in ['ComfyUI', 'DrawThings']:
+        return "CPU"
+
+    return shared.opts.randn_source
+
+
 def randn(seed, shape, generator=None):
     """Generate a tensor with random numbers from a normal distribution using seed.
 
     Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
 
     if generator is not None:
+        # Forge Note:
         # If generator is not none, we must use another seed to
         # avoid global torch.rand to get same noise again.
         # Note: removing this will make DDPM sampler broken.
@@ -16,10 +24,10 @@ def randn(seed, shape, generator=None):
     else:
         manual_seed(seed)
 
-    if shared.opts.randn_source == "NV":
+    if get_noise_source_type() == "NV":
         return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
 
-    if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
+    if get_noise_source_type() == "CPU" or devices.device.type == 'mps':
         return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
 
     return torch.randn(shape, device=devices.device, generator=generator)
@@ -30,11 +38,11 @@ def randn_local(seed, shape):
 
     Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
 
-    if shared.opts.randn_source == "NV":
+    if get_noise_source_type() == "NV":
         rng = rng_philox.Generator(seed)
         return torch.asarray(rng.randn(shape), device=devices.device)
 
-    local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
+    local_device = devices.cpu if get_noise_source_type() == "CPU" or devices.device.type == 'mps' else devices.device
     local_generator = torch.Generator(local_device).manual_seed(int(seed))
     return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
 
@@ -44,10 +52,10 @@ def randn_like(x):
 
     Use either randn() or manual_seed() to initialize the generator."""
 
-    if shared.opts.randn_source == "NV":
+    if get_noise_source_type() == "NV":
         return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
 
-    if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
+    if get_noise_source_type() == "CPU" or x.device.type == 'mps':
         return torch.randn_like(x, device=devices.cpu).to(x.device)
 
     return torch.randn_like(x)
@@ -58,10 +66,10 @@ def randn_without_seed(shape, generator=None):
 
     Use either randn() or manual_seed() to initialize the generator."""
 
-    if shared.opts.randn_source == "NV":
+    if get_noise_source_type() == "NV":
         return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
 
-    if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
+    if get_noise_source_type() == "CPU" or devices.device.type == 'mps':
         return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
 
     return torch.randn(shape, device=devices.device, generator=generator)
@@ -70,7 +78,7 @@ def randn_without_seed(shape, generator=None):
 def manual_seed(seed):
     """Set up a global random number generator using the specified seed."""
 
-    if shared.opts.randn_source == "NV":
+    if get_noise_source_type() == "NV":
         global nv_rng
         nv_rng = rng_philox.Generator(seed)
         return
@@ -79,10 +87,10 @@ def manual_seed(seed):
 
 
 def create_generator(seed):
-    if shared.opts.randn_source == "NV":
+    if get_noise_source_type() == "NV":
         return rng_philox.Generator(seed)
 
-    device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
+    device = devices.cpu if get_noise_source_type() == "CPU" or devices.device.type == 'mps' else devices.device
     generator = torch.Generator(device).manual_seed(int(seed))
     return generator
 

modules/shared_options.py

@@ -246,6 +246,7 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
+    "forge_try_reproduce": OptionInfo('None', "Try to reproduce the results from external software", gr.Radio, lambda: {"choices": ['None', 'Diffusers', 'ComfyUI', 'WebUI 1.5', 'InvokeAI', 'EasyDiffusion', 'DrawThings']}),
     "auto_backcompat": OptionInfo(True, "Automatic backward compatibility").info("automatically enable options for backwards compatibility when importing generation parameters from infotext that has program version."),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),