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rework prompt paste

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Vladimir Mandic
2023-11-08 11:33:40 -05:00
parent 8bcb14d0ac
commit a68f0bcbe4
5 changed files with 38 additions and 57 deletions
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modules/processing.py
+9 -6
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@@ -588,7 +588,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
args["Denoising strength"] = p.denoising_strength
args["Latent sampler"] = p.latent_sampler
args["Image CFG scale"] = p.image_cfg_scale
args["CFG rescale"] = p.diffusers_guidance_rescale if shared.backend == shared.Backend.DIFFUSERS else None
args["CFG rescale"] = p.diffusers_guidance_rescale
if 'refine' in p.ops:
args["Second pass"] = p.enable_hr
args["Refiner"] = None if (not shared.opts.add_model_name_to_info) or (not shared.sd_refiner) or (not shared.sd_refiner.sd_checkpoint_info.model_name) else shared.sd_refiner.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
@@ -597,7 +597,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
args['Refiner start'] = p.refiner_start
args["Hires steps"] = p.hr_second_pass_steps
args["Latent sampler"] = p.latent_sampler
args["CFG rescale"] = p.diffusers_guidance_rescale if shared.backend == shared.Backend.DIFFUSERS else None
args["CFG rescale"] = p.diffusers_guidance_rescale
if 'img2img' in p.ops or 'inpaint' in p.ops:
args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}"
args["Init image hash"] = getattr(p, 'init_img_hash', None)
@@ -756,7 +756,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
return res
def validate_sample(sample):
def validate_sample(tensor):
if tensor.dtype == torch.bfloat16: # numpy does not support bf16
tensor = tensor.to(torch.float16)
if shared.backend == shared.Backend.ORIGINAL:
sample = 255.0 * np.moveaxis(tensor.cpu().numpy(), 0, 2)
else:
sample = 255. * tensor
with warnings.catch_warnings(record=True) as w:
cast = sample.astype(np.uint8)
if len(w) > 0:
@@ -914,7 +920,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image = x_sample
x_sample = np.array(x_sample)
else:
x_sample = 255. * (np.moveaxis(x_sample.cpu().numpy(), 0, 2) if shared.backend == shared.Backend.ORIGINAL else x_sample)
x_sample = validate_sample(x_sample)
image = Image.fromarray(x_sample)
if p.restore_faces:
@@ -1118,7 +1123,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
for i, x_sample in enumerate(decoded_samples):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = validate_sample(x_sample)
image = Image.fromarray(x_sample)
bak_extra_generation_params, bak_restore_faces = self.extra_generation_params, self.restore_faces
@@ -1134,7 +1138,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
batch_images = []
for _i, x_sample in enumerate(decoded_samples):
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = validate_sample(x_sample)
image = Image.fromarray(x_sample)
image = images.resize_image(1, image, target_width, target_height, upscaler_name=self.hr_upscaler)